Question 1: Do you believe that automated or manual loading and/or cleaning can best remedy data quality issues?

Difficulties in measuring the benefits of automation can prevent investment in systems. Benefits attributed to automation used to be easily quantifiable. However, more recently automation has been used to develop business insight to support managerial decisions. The benefits here have been much less quantifiable using simple cost benefit analysis methods. Today’s methods of justification have focused too strongly on quantifiable benefits and have not necessarily justified expenditure on automation. While it is understandable that organisations have needed to be able to justify investments in financial terms, these methods need to evolve to access benefits at both a quantitative and qualitative level which will provide a more credible measure of the value of investments. I have attempted to look at whether the benefits in quality that can be leveraged from the application of automated solutions in projects where large-scale marketing efforts have been co-ordinated from often heterogeneous sources.

31.25% of respondents suggested that automation should be employed to detect errors (26.32% of responses). Respondent 13 asserts that automation can be employed to “alert system users to manually inspect certain records if they aren’t clean. The goal is not 100% data quality, but a level of quality that you can comfortably use the system. Companies with the right resources try to address these issues in the source systems.” Respondent 16 asserts automation “doesn’t tend to solve the problems but… can be tremendously powerful for raising flags for things you should be looking at.”

A quarter of respondents believed that neither automated nor manual solutions alone can remedy the data quality problems in most marketing databases, and that a hybrid solution is required to some degree (21.05% of responses to the question). Respondent 12 suggested that “the data that people use now is often so sophisticated, so complex and interrelated, that the tools for cleaning are just not sophisticated enough, and so relying on automated cleaning is certainly helpful but it’s not an ultimate solution.”

25% of respondents believed that automated solutions can be very effective in remedying the data quality problems in marketing databases, but only when placed in the framework of an enterprise-wide, ongoing and iterative approach to data quality (21.05% of responses). Respondent 10 asserted that automation is “one important component, but not the silver bullet; ultimately process is the most important piece to get right.”

18.75% of respondents asserted that neither automated nor manual solutions for data quality problems were ideal, as errors should be identified and eliminated or remedied as close to source as possible by employing techniques to identify errors at point of entry (17.65% of responses).

12.5% of respondents went on to say that automated solutions for understanding, transforming and repurposing complex and unpredictable product information relied upon in an organisation or supply chain can be hugely beneficial and worth the upfront cost (10.53% of responses). Respondent 15 claimed that “to operate more effectively and trust data and ship it around throughout your supply chain, the goal is automation.”

One respondent (5.26% of the total responses) claimed that automation may be the best solution to manipulate values such as date and time values from disparate systems into a consistent format and leverage the potential for reporting. Respondent 1 suggests the use of “mass concatenation and conversion to common format once you’ve imported data into a single view. Text manipulation can be a painstaking job, so better to try and find automated ways of doing repeatable tasks such as this.” This should be utilised in other areas too, such as text string manipulation.

Question 2: How possible do you think it is to achieve fully automated processes to improve data quality?

Through this question I was attempting to delineate the extent to which automation can be deployed to improve data quality. Automating processes to improve data quality would present a challenge for most organisations, not least because they may not be fully aware of the data quality levels within their enterprise (something I have attempted to establish earlier in the interview). Now that we have determined the business impact of poor quality data, I am seeking to ascertain whether the corrective or preventative work can be entirely automated (and if it is desirable to do so).

37.5% of respondents (in 35.29% of responses) suggested that the process of eliminating data quality problems can never be fully automated, asserting that at some point a user interface is required for any system (such as checking success of loading and screening for errors). Respondent 7 advised that “it is possible to implement batch processing on poor quality data, but bear in mind that it isn’t possible to produce as good quality data from automated bulk cleaning processes after data entry than properly sort out validated entry. Also, it costs more to clean data after than to collect data well at source. Companies often prefer to spend more when they can see the poor quality data which needs cleaning than to spend money on prevention of inadequate data getting into the system, but this has poor yields and is a myopic approach.”

A quarter of respondents suggested that fully automated processes to improve data quality were possible to achieve, but required a great resource commitment from any enterprise wishing to implement them (23.53% of responses). Respondent 5 suggests, “the biggest obstacles to automation are time and cost. With enough money and commitment thrown at an automation project, it is possible to achieve automation.” Respondent 15 suggests that automating data quality improvements (such as “automatic detection of missing or suspect values, automated consolidation of data content”) is a prerequisite for successfully automating business processes.

18.75% of respondents asserted that a balance of manual and automated solutions is the correct approach (17.65% of responses). An enthusiasm for some automation on the part of these respondents was tempered by an awareness of its apparent limitations. Respondent 4 assesses that “current technologies don’t really lend themselves to automating such things so most often the data or information is being manually shaped at each step along the way. Respondent 7 proposes that “it is certainly possible to correct and standardise data as it comes in from disparate sources, that’s probably the realistic extent of automation for improving quality of data.”

12.5% of respondents advise further that automation isn’t the panacea for data quality problems (11.76% of responses). Respondent 6 suggests that “the major barriers to improving data quality are… a lack of understanding of the importance of quality by organisations (whether managers or data producers), lack of rigorous standards and data documentation, turnover of, or lack of, personnel trained in data management and collection of data. I am not sure these issues can fully be solved by automated processes. By correcting them in an automated way you can entrench the behaviour that created the problems.” Respondent 5 advises that “in order to achieve data quality, good internal procedures must exist so that staff can be trained and supported in their work. Careful monitoring and error correction can support good quality data, but it is more effective and efficient for data to be entered correctly first time.”

Finally, 12.5% respondents maintain that it is impossible to use automation to improve poor quality data in marketing databases (11.76% of total responses). Due partly to poor user inputs, “there will always have to be manual intervention for exceptions,” as Respondent 10 argues.

Question 3: Are there any major impediments to being able to improve data quality using automated processes?

Impediments to automation are often cost, lack of equipment, and lack of time. I looked to establish how profoundly these issues have impacted decisions to not automate data quality improvement processes, and, beyond that, delineate the technical barriers to automation particular to this area.

18.75% of respondents suggested that due to exceptions and the intricacy of some inputs and visual checks, systems will continue to need significant human interface (17.65% of total responses to the question).

18.75% of respondents suggested a significant impediment to improving data quality using automated processes would be the lack of data standards and data documentation that exists in most enterprises (17.65% of responses).

18.75% of respondents expanded on this, suggesting a universal lack of data standards and naming conventions (or the issue of different or competing global standards) would seriously hinder automation in this area (17.65% of responses). Respondent 15 emphasised the “three to four thousand data elements that financial institutions track [that] all have important functions in terms of business processing,” and cited the example of ‘closing price’ – “the closing price of a stock or the closing price of a bond – and that’s used for valuation, it’s used for trading, it’s used for position keeping, it’s used for a lot of different things. There is not one thing called closing price, there’s probably ten things called closing price, the official close, the last trading price, it could be the last quoted price, it could be an average price, it could be a valuated price, all of those things in essence are used for the function of closing price. They’re not the same thing! If you tag it correctly and everything is identified with precision, then you can compare it, then you can automate more processes.”

12.5% of respondents highlighted the lack of understanding of the importance of quality by potential sponsors and internal stakeholders in many organisations as a significant impediment (11.76% of responses).

Respondent 1 emphasised the cost of undertaking automation and suggested that there was insufficient funding available for data quality improvement in many organisations as data quality isn’t a priority. The same respondent also suggested that a lack of trained personnel in the collection of data and the maintenance of databases might hinder automation programmes in this area.

Respondent 3 was sceptical about the possibility for effecting long-term improvements to data quality using this method, as “users will continue to bypass controls to enter junk, no matter what.”

Respondent 4 suggested that a significant barrier to automation is that few people know how to fully leverage the potential of technology for this purpose.

Respondent 7 said automation would be difficult in this sphere as the “tools used to validate data, such as addressing information, global postal codes, are continually updated and in a state of flux,” meaning there are few validation processes that can be relied on over the long-term.

Respondent 8 suggested that because of the sheer amount of information that is captured in a modern organisation, it will be difficult to know where to focus efforts.

Respondent 9 highlighted the problem of disparate systems, and the difficulties involved in integrating data from such divergent sources, as the major barrier to using automated processes to improve data quality.

Question 4: What change do you think automated data loading and cleaning solutions have on an organisation?

High quality data supports smoother operations and enables effective decision-making. Conversely, poor quality data causes organisational inefficiency and capital losses (Redman, 1996). Traditional data management primarily focuses on functionality and technical efficiency (storage, retrieval, delivery, and presentation). However, with steadily increasing investments in data management (Wixom & Watson, 2001), there is a growing concern about its economic aspects, namely, its contribution to business value and its effect on costs. Successes in automating data quality improvement processes should therefore have an effect in terms of cost savings, revenue generation, and/or profitability. I hoped to outline these and any other advantages brought to an organisation in this sphere and, to some degree, quantify them.

43.75% of respondents professed that a significant benefit to the enterprise would be improved management information (16.67% of responses) as automated data loading and cleaning solutions “make it easier for the organisation to generate and build reports.” (Respondent 2) As a result, revenues can be improved through effective “analytics forecasting and a proper engagement model with customers.” (Respondent 14)

A positive result of automation is that it can save time and money, if done correctly, bringing productivity gains to the enterprise, as stated by 37.5% of respondents (in 14.29% of total responses). Respondent 8 declares that “automation ultimately brings fast, accurate and repeatable production,” resulting in “small amounts of required manual intervention” (Respondent 9) and ultimately “significant time and resource savings for an organisation.” (Respondent 9)

31.25% of respondents asserted that customer satisfaction could be significantly improved as a result of automation, if it is implemented correctly (11.9% of responses). One example of where the customer experience could be enhanced would be through using automated processes to create a single customer view within a database used by a call centre.

25% of respondents argued that a programme of automation is beneficial to an organisation as it entrenches a beneficial focus on data quality and institutionalises important processes (9.25% of responses).

18.75% of respondents advocated automation as a solution to the issue of information overload (7.14% of responses). Automated processes can increase the amount of organisational knowledge captured. As Respondent 5 pointed out, electronic records are more easily stored and archived and locatable than the paper files kept with manual systems.

18.75 of respondents declared that successful automation would allow an organisation to devote more time to understanding and reaching customers and prospects (7.14% of total responses). Automation leads to a change of roles – staff can find themselves freed from making large numbers of simple decisions to spending their time analysing overall patterns of decisions.

Similarly, a benefit of automation could be significantly reduced labour costs for the organisation. This was proposed by 12.5% of respondents (4.76% of responses).

12.5% of respondents highlighted an immediate correctional impact that automation could have on data quality as a potential positive effect (4.76% of responses).

Also related to the benefits automation could offer in creating a single view of the customers, 12.5% of respondents proposed that automation would result in less wasted marketing spend (4.76% of responses).

12.5% of respondents suggested that an organisation that aims to automate the absorption, normalisation and integration of incoming data so that it can be flexible enough to be used whenever and wherever an organisation needs it would reap significant rewards (4.76% of responses). Respondent 4 suggested that if an organisation “can automate solutions for understanding, transforming and repurposing the complex and unpredictable product information relied upon in an organisation or supply chain, that can be hugely beneficial, and worth the upfront cost.”

Other positive impacts of automation were considered to be the elimination of human error and repetition (Respondent 5), the facility to easily flag rule violations and statistical deviations (Respondent 9) and less stress for employees (Respondent 2). Respondent 8 believes that if an automation programme is successfully implemented in data quality it can help in rolling automation out to other business areas.

However, Respondent 3 suggested that automation would actually lead to a significant deterioration in data quality and Respondent 6 thought that automation would be an “expensive way of eradicating issues that needn’t crop up in the first place with some forethought and training.” These can be seen as possible negative impacts of automation.

Respondent 9 held that an organisation’s main goal should be to “entrench change… to ensure data quality is preserved in the long term. This involves incentivising people who are responsible for data production to provide clean, quality data for systems that business intelligence relies on.”

Respondent 4 outlined that there are “two methods of automating things like this – top-down and bottom-up – and the kind of change and its success will probably depend to some extent on how the automation process is steered and handled. The top-down method is quick, with the decision to change made by those with the best overall view of the organisational environment and resources. The implementation will succeed to the extent that users follow the behaviours prescribed for them. The bottom-up method utilises the knowledge of those employees who are doing the work, and the intricacies of the work. The employees have a detailed knowledge of their tasks, but have to be given the overview of organisational aims in order to participate effectively in the change process, in implementing automation. This method takes more time but because it is more democratic produces greater commitment to the change, therefore needing less management control and less effort to foster buy-in.”

References

Redman, T. (1996). Data Quality For The Information Age. Boston, Massachusetts: Artech House.
Watson, H. J., & Wixom, B. H. (2001). An Empirical Investigation Of The Factors Affecting Data Warehousing Success. MIS Quarterly , 25 (1), 17-41.

• Accuracy – The extent to which the data is free from significant error. Does the data accurately represent reality or a verifiable source?
• Completeness – The extent to which enough of the required data elements are collected from a sufficient portion of the target population or sample. Is all necessary data present?
• Consistency – The extent to which data is collected using the same procedures and definitions across collectors and times. Do any data values give conflicting information?
• Conformity – Is any data is stored in a non-standard format?
• Duplication – Are records repeated?
• Ease of use – Is data readily accessible by the users who need it, aided by clear data definitions, user-friendly software and easily used access procedures?
• Integrity – Is data missing important relationship linkages?
• Timeliness – Is data available when needed? Does the age of the data meet user requirements?
• Validity – Are the data items stored in the systems valid entries? Are there any aberrant values?

Individuals inevitably have different definitions or terminology, however, and give higher salience to some attributes over others. For example, Bouzeghoub and Peralta (2004) suggest data ‘freshness’ as the most important attribute for data consumers. Traditionally, the measure of freshness is related to view consistency when materialising source data at the integration level or the user level. This is also known as ‘currency’ and describes how stale is data with respect to the sources. Bouzeghoub and Peralta (2004) give eminence to an aggregation of this and the ‘timeliness’ metric and call the attribute ‘freshness’.

Even and Shankaranarayanan (2007) suggest metrics should be driven by data utility, a conceptual measure of the business value that is associated with the data within a specific usage context. I had hoped to capture which of these attributes were important in different business contexts and, where possible, additional metrics and attributes I hadn’t identified. The metrics identified are useful when quantifying the success of programmes to improve data quality.

62.5% of respondents suggested an important metric of data quality is comprehensiveness (15.87% of responses). Respondent 7 suggests two broad sets of tests could be employed here. Firstly, in a test of ‘population’, data should be examined in a columnar fashion, in an attempt to identify missing values or where a high proportion of default values have been entered. Secondly, a test of ‘completeness’ can be performed, in which full records would be examined to see the number of data fields that have been filled in.

Half of the respondents suggested an important metric of data quality is accuracy (12.7% of responses). Respondent 9 defined accuracy as the measure to which data “is correct and precisely reflects the entity or transaction it describes.” This could be measured, for instance, by checking in addresses that the town correctly matches the postcode entered.

43.75% of respondents also identified validity as a salient test of the quality of data (11.11% of responses). Respondent 5 affirms, “All data items held on systems must be valid entries. Wherever possible, systems should be programmed to only accept valid entries.” This is not always practical or desirable, however, so other respondents suggest that “data inspections [are performed] for aberrant values.” (Respondent 4) Such audits “should test non-default data against known acceptable or known unacceptable values with frequency statistics.” (Respondent 7) Respondent 6 expands on this, suggesting “statistical tests might include things like peaks in distribution, extreme values, sudden changes and bifurcations.”

37.5% of respondents suggested consistency as a measure of data quality (9.52% of responses for the question). Data consistency is a contentious term that could refer to a number of quantifications in different contexts within computing. Here, as elsewhere, ‘consistency’ is multifaceted. The respondents have broadly referred to consistency being determined along two lines. Firstly, that data is consistently tagged (the metadata is consistent) – an example of this is that all country data is entered only in the country field, and the country field is used only for the storage of country information. Secondly, that entities and transaction data is represented in a uniform way in the database – for instance, applying naming standards for products or address details (insisting on the fully declared town name Newcastle-upon-Tyne being used instead of Newcastle, for instance), so that this information be properly aggregated when needed.

25% of respondents suggested duplication is an important measure of data quality (6.35% of responses). This is defined by Respondent 7 as “rate of record redundancy [as identified] using a number of merge keys” (that determine a commonality between records).

A quarter of respondents (6.35% of responses) also suggested the occurrence with which logic checks/business rules were violated as an important data quality measure, for example, an individual’s birth date being entered as a date after their programme enrolment date on a college admissions system, or an eight year old credit card prospect on a marketing database, as suggested by Respondent 5.

A quarter of respondents also suggested timeliness as a key metric (6.35% of responses). Timeliness refers to the speed of dissemination of the data from the point of collection. It is important that the appropriate data is available to the data consumer (whether internal or external) when it is needed, for reasons such as advantageous decision making or helping staff to manage the relationship with their clients and see potential sales opportunities.

18.75% of respondents suggested reputation or reliability of source as an important measure of data that is fit for purpose (4.76% of responses). As Respondent 9 asserts, it is imperative that “users trust the data. If data isn’t ‘credible’, people will be seriously reluctant to base big decisions on it.”

18.75% of respondents (4.76% of responses) suggested cross-validation as a useful indicator of error rate within databases. Cross-validation is the statistical practice of partitioning a sample of the data into subsets such that the analysis is initially performed on a single subset, while the other subsets are retained for subsequent use in confirming and validating the initial analysis. It is useful where further samples may be costly or difficult to collect.

18.75% of respondents raised the issue of appropriateness of data (4.76% of responses). This is an important measure, but one that is difficult to evaluate. In order to determine whether data should be used in a process or system, one needs to establish the original purpose of the collection of that data, and test whether the data collected satisfies that need. Respondent 9 broadens this measure, suggesting that ‘appropriateness’ is when “the data is relevant to the needs of consumers and stakeholders.”

This leads on to another important measure, identified by the same number of respondents – that of the value added to the organisation by that data. This may be measured, to some degree, by an aggregate of some of the metrics outlined above, though by applying stricter financial (value-added) evaluation as opposed to qualitative measurements.

12.5% respondents suggest that user feedback should also be measured in order to determine data quality (3.17% of responses). Again, establishing the needs of consumers and how far they are satisfied may require an aggregation of many of the metrics detailed above. Because it can be difficult to define objective measures of data quality, this is a useful approach. Certainly, user feedback can help establish whether the data collected satisfies the need of stakeholders and data consumers.

Interpretability of data was raised by one respondent (forming 1.59% of responses to this question) as a significant measure of data quality. This is certainly prevalent in fields such as the medical profession, where jargon and specialist coding is prevalent, which can result in the “expertise required to interpret codes [becoming] a barrier to accessibility.” (Strong et al., 1997)

One respondent suggested conciseness of data as a significant quality dimension. Systems should be designed to record data succinctly and reporting designed to eliminate irrelevant or unnecessary information. Conciseness is important in order to alleviate the problems of information overload detailed previously.

Considerable value is added to data by its ease of manipulation (which I would term ‘malleability’), a measure that is suggested by one respondent. This is heavily informed by the degree to which appropriate formatting and tagging of data is achieved within the enterprise. This allows the organisation to easily integrate data across environments, facilitating analysis of the same data in often divergent systems.

The next two measures (accessibility and confidentiality) were each identified by one respondent, and need to be balanced against one another. The benefit of increasing data accessibility can be better informed internal and external stakeholders who have the information they need when it is needed. The risk of expanded access to potentially sensitive data is an increased probability of breaching data confidentiality and, in turn, eroding the confidence of contributors or customers in the data collection enterprise, and opening the enterprise.

Age of data (‘currency’) is considered an important measure by one respondent. Again, this may be about finding a balance most amenable to the organisation. If marketing databases and data warehouses are updated or synchronised infrequently, this can result in costly errors in decision making arising from out-of-date information. However, if updating and synchronisation is performed very frequently, the associated costs (including resources needed to perform and check updates and hours lost due to systems downtime) might be quite high.

Data quality and integrity need also be assured by testing referential integrity, it is asserted by one respondent. Referential integrity in a relational database is consistency between coupled tables. Referential integrity in database systems is usually enforced by the combination of a primary key and foreign keys, however this referential integrity can be at risk or bypassed even in robust database systems. For instance, these constraints may exist in vendor-delivered software but may have been purposely disabled to allow an automated update (for example). Therefore, measuring the number of disabled constraints in the database system may be a useful metric for assessing the integrity of a data system.

References

Bouzeghoub, M., & Peralta, V. (2004). A Framework For Analysis Of Data Freshness. IQIS.
Even, A., & Shankaranarayanan, G. (2007). Utility-Driven Assessment Of Data Quality. ACM SIGMIS Database , 38 (2).
Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data Quality In Context. Communications Of The ACM , 40 (5).

Poor data quality is a costly issue. According to a PricewaterhouseCoopers Global Data Management Survey, 75 per cent of those surveyed reported significant problems as a result of defective and fragmented data, more than 50 percent had incurred extra costs due to the need for internal reconciliations, 33 percent had been forced to delay or scrap new systems, 33 percent had failed to bill or collect receivables, and 20 percent had failed to meet a contractual or service-level agreement (Friedman et al., 2004). I interviewed 16 industry experts, in roles ranging from Database Administrator to Data Operations Vice President, in locations as disparate as the United States, the United Kingdom and Singapore, in order to elicit examples of the tangible costs of poor quality data.

Half of respondents said incorrect billing, often caused by failures in creating a single customer view, is a major cost of poor quality data (15.69% of responses to the question). Customer relationships are damaged when companies produce incorrect or untimely bills. Customers may feel that a business that does not present timely and accurate bills is disinterested in producing the quality services and products they expect, with an obvious effect on customer retention. Beyond customer alienation, costs to handle billing complaints increase operational overhead and expend non-value-added customer care time. An employee resolving a billing problem could be better employed time cross- or up-selling, or engaging in other value-added work. In addition, billing systems are normally not biased to over-bill. When they under-bill even conscientious customers are less likely to notice or report this. This incorrect billing directly impacts the timely collection of revenue. The greater number and frequency of billing errors, the longer it takes to collect revenue.

Further to this, inaccurate billing creates difficulties in knowing the genuine financial picture. ‘Billed’ revenues may be false indicators that will be unmasked only when customers complain, and lost revenues from under-billing may never be unmasked. In summary, billing errors cause customer dissatisfaction, increase operating costs, prevents staff from engaging in value-added work, decreases or delays revenue, and masks real financial performance. A further associated problem is that if a customer is incorrectly identified, that customer may be extended credit where they otherwise should not have been. As Respondent 16 states, “if [the enterprise] doesn’t know who the customer is it is a lot harder to establish… risk parameters.”

Half of all respondents also raised the issue of significant business decisions being formulated based on poor quality information (15.69% of responses). Business intelligence and performance management deployments are being relied upon by more and more users with greater business impact. A broad range of transactional, financial and operational data drives critical business decisions in the modern enterprise. Therefore, the impact of poor quality data is a growing concern as organisations more are more reliant on technology. Information intensive applications such as Enterprise Resource Planning and Customer Relationship Management systems deliver value only if the data they use is reliable, complete and accurate. Process automation increasingly means that data is the foundation of critical business operations, and poor data quality can therefore lead to breakdowns in the supply chain and poor business decisions. From a marketing perspective, poor quality data can result in flawed segmentation resulting in lost business, poor targeting, inadequate budgeting and unreliable financial projections. As Respondent 15 suggested, “the ability to use the data to develop your process products, understand your customer requirements, do asset servicing correctly, track your internal performance, your profitability, all of those things,” relies on data that is fit for purpose.

A major issue again identified by half of respondents was time and resource cost of re-entry and/or verification of incorrect or incomplete data (15.69% of coded responses). There is an associated opportunity cost of hours spent resolving issues caused by data quality problems (as stated previously, staff who are tasked with resolving such issues could be employed elsewhere).

43.75% of respondents asserted that alienation of customers as a result of poor addressing was a primary cost of poor quality data (13.73% of responses). Customers will lose faith in the ability of the company to provide high quality products and services if they fail to address them correctly. Failure to properly capture address data also inevitably results in lost dispatches and increased sending costs.

A significant indirect cost of poor quality data is that opportunities for cross- or up-selling are lost, partly as a result of failure to establish a single view of the customer and their order history. Accurate capture of enquiries and transactions facilitates a better understanding of buying patterns that allows sales representatives to better manage the sales process and ensure high conversion ratios. Business intelligence applications armed with more accurate data can accurately indicate customer profiles that are historically receptive to similar sales efforts. This was highlighted by 31.25% of respondents (9.8% of responses).

Another issue that arises as a result of poor quality data is that of lost production through supply chain problems. This was raised by 25% of participants (7.84% of responses). The flow of data is at the heart of modern businesses’ extended supply chains. Efficient flow of accurate data has helped to fuel impressive productivity gains and advances in operational planning. However, there are numerous points in the supply chain where data quality potentially becomes a major issue and can cause logistical collapses; particularly where data is arrives from external sources, as there are often no universal standards for either the format or content of data. Respondent 8 in particular cited an inability to match and integrate data from disparate internal or external sources as a major and catastrophic result of poor quality data. Different systems and users need to see the data in very different ways within very different contexts; product categories can be divergent (with different schemas, validation and business rules, and definitions). Different systems (for instance, an inventory system and a customer relationship management system) utilise the data differently and repurposing the data can be a complex task. This is certainly one area in which automated solutions can make an impact as the more manual methods are utilised to repurpose complex data throughout the supply chain, the more expensive and unreliable the solution is likely to be. An associated cost can be damaged supplier relationships.

Regulatory non-compliance was cited as a key potential cost of poor quality data by 12.5% of respondents (3.92% of responses). Noticeably, these respondents were based in the United States, and one worked in the financial services industry. Data quality problems can result in difficulty in compliance with current and future regulations such as Basel II, Sarbanes-Oxley, The US Patriot Act and International Accounting Standards.

The remaining costs of poor quality data are to a degree interrelated and can impact one another. Firstly, a significant cost of poor quality data can be an inability to deliver data to decision makers in a timely manner, often as inaccuracies have to be reviewed or corrected. Timely data can mitigate the risks involved in making strategic decisions about competitive strategy, pricing policy and launching products and services. Specifically, accurate data delivered in a timely way can give advanced notice of competitor moves, warn key decision makers about issues in production capacity and facilitate rapid response to market opportunities. This was outlined by 12.5% of respondents (3.92% of responses).

From a marketing perspective, problems in campaign formulation, resulting in poor campaign responses can be a significant cost of poor data quality. This was underlined by 12.5% of respondents (3.92% of responses). Typically, marketing campaigns are formulated by segmenting a database on several dimensions – for example, account types and deposit levels in financial services or usage and service categories within the telecommunications industry. Segmentation based on erroneous data will result in people being targeted with inappropriate offers or products. This leads to wasted spend on marketing literature production and postage, as well as potentially irritated customers (for instance, a forty-five year old woman being targeted with products aimed at the recently retired demographic).

An issue in part resulting from those costs of poor data quality mentioned above is a lack of confidence from end users in the reports being generated by the systems. This was a concern for 18.75% of respondents (in 5.88% of responses). This in turn can be one of the contributory factors in low rates of user adoption of systems, which was outlined by 12.5% of respondents (3.92% of coded responses). Poor data quality is only one factor contributing to low user adoption of systems (sales people can be naturally hesitant to share their information with others in the organisation, for instance), but can undermine incentives for people within the organisation to use such systems.

References

Friedman, T., Nelson, S. D., & Radcliffe, J. (2004). CRM Demands Data Cleansing. Gartner.

• Human Errors – A major source of incorrect data in systems. A large portion of these errors can be attributed to a system’s inability to validate data, but some errors are logical in nature. For instance, if a telephone operator is an inputter (s)he may mishear a respondent’s town and input a town that may be valid but can be wrong from a business perspective.
• Deliberate Manipulations – A user may be forced by the legacy system to enter data that is prima-facie incorrect but is inevitable because the legacy system would reject the data otherwise.
• Conformance to a target system model definition – This can be a factor when a legacy system data conversion and migration is taking place. The target system model may dictate the data to be in a certain format, and the need for conversion and migration may make it compulsive for changes to be made to the data.

but wanted to triangulate my experience with the thoughts of experts within industry sectors as disparate as investment management, call centre management and market research. I asked sixteen experts what they thought the main causes of data quality problems were. Semi-structured interviews were conducted with each participant.

English (1999) suggests the main data quality problems arise from poor data architecture, inconsistently defined departmental data, inability to relate data from different data sources, missing and inaccurate data values, inconsistent use of data fields, unacceptable query performance (timeliness of information) and lack of business sponsor, and it is specific examples such as these I was looking to elicit from the answers, while acknowledging that over time, even in the best maintained systems, deficiencies in stored data will develop (Ballou & Tayi, 1989).

43.75% of respondents attributed major data quality issues to organisations having distributed systems (15.56% of responses). Often there exist disparate systems within an enterprise that use different business rules, and store data with different definitions and possibly conflicting values. Customer data exists in silos – there may be a marketing database, a transactions or operational database and a customer service database, all holding different information on or ‘views’ of the same customers. As Respondent 6 recognised that “database design recommendations say you should not store and update the same data in multiple places because it prevents consistency, but this is not always enforced, especially when systems have been unified in a chaotic way, or phased in.” Without a proper integration model to eliminate inconsistent definitions, formats and values, the organisation often finds it is unable to aggregate and harmonise new data as it is introduced from various sources.

37.5% of respondents cited a lack of naming conventions and standards causing a diminution in data quality (13.33% of responses). The symptoms of this are also discussed in the research paper Data Quality Issues In Marketing Databases.

A quarter of respondents suggested a failure of organisations to harness the maximum potential of technological advances to ensure erroneous or duplicated address data isn’t stored in the marketing database (8.89% of responses). Specifically, 12.5% respondents suggested a lack of address verification at point of entry was a significant contributor to poor data quality (4.44% of responses). Address verification modules are an increasingly common feature of systems, their presence allowing for instant address verification at point of entry, immediate identification of a potential duplicate entry and significantly reducing the number of keystrokes at point-of-entry. Where systems are used in countries where street-level post coding exists, such as the United Kingdom, these modules can facilitate the collecting of a full address simply by entering a house number and postcode. Even in a more reactive manner, software advances can aid in improving data quality through address hygiene software.

Poor stewardship of data is cited as a major contributory factor to poor data quality by 25% of respondents (8.89% of coded responses). Respondent 3 suggests that “databases full of junk data happen most often when there is no business owner of the data.” Also, failure to implement processes that properly capture the information needed by the enterprise can be seen as another prominent example of poor data stewardship that tangibly affects the enterprise’s marketing data quality, and this aspect was underlined by two of those respondents.

Input rules that are too restrictive and bypassed completely are a significant contributor to poor quality data according to a quarter of respondents (8.89% of responses for this question). This is a major cause of too little data being captured – “Imposing superfluous controls on data input and editing might cause important data to be lost entirely,” as Respondent 1 notes. It is also closely related to inaccuracy in data stored, or a lack of granularity in the data stored, as Respondent 8 noted – “data entry forms often have a single structure, causing users to force complex inputs into a one size fits all form.”

12.5% of respondents identified inappropriate access levels as having a detrimental effect on the quality of data in systems (4.44% of responses). This can take the form of easy access to data, which may conflict with good security practice or privacy requirements. Conversely, as Respondent 1 suggests, “if the balance is too great in favour of barriers to access… it will obviously have an impact on the quality of data held in the system.”

12.5% of respondents suggested an increasingly overwhelming amount of data (‘information overload’) is adversely affecting quality of data held in information systems, not least marketing databases and customer relationship management systems (4.44% of responses). As Respondent 6 noted, “In larger organisations there is often simply too much data, duplicated across the organisation.” This can make it “difficult to access required data in a reasonable time, negatively impacting on information systems quality. Customers on a telephone call, for instance, expect an operator to retrieve their account details in a reasonable amount of time, and may be put off using the services if it takes an eternity to retrieve a billing history,” as Respondent 1 points out.

Overburdened or time-strapped staff was cited as a key contributor to data quality issues by 12.5% of respondents (4.44% of responses). Respondent 9 commented that “organisational focus on speed and productivity alone is often a fairly significant cause of data quality problems. For example, something like a record store or bookshop that takes orders by phone might make it a priority to reduce time for each phone call to get greater volume of orders with fewer staff, but this will guarantee a lot of duplicate customer records and unverified addresses being captured. If pay incentives or bonuses are based on number of orders taken… this causes problems like this to happen.”

An inability to identify duplicates or instance of customer records already stored on system at the point of entry is a key contributor to poor data quality, specifically the issue of duplicate data, and this was raised as a key issue by 12.5% of respondents (4.44% of responses).

12.5% of respondents underlined their concerns regarding turnover of information (churn) and the impact this can have on an enterprise’s data and decision-making if they are unable to keep up with it (4.44% of responses). Respondent 16 cited figures from their previous organisation, Dun & Bradstreet, who suggest that in a single year, 20.7% of the business postal addresses in an enterprise’s data store will have changed (of new business,  the rate of change is higher, at 27.3%). Telephone numbers change at the rate of 18%. Company names are unstable, changing at the rate of 12.4% a year, making it difficult to sustain effective customer relationships if data isn’t constantly updated. Data quality inherently degrades at a rapid rate, and steps must be taken to ensure this is counteracted.

12.5% of respondents were concerned that many organisations treat information as a by-product rather than a product and as a result they often address data quality issues in a reactive manner (4.44% of responses). They assert that information is not a by-product but a direct product of processes that capture knowledge about the actors, locations and events discovered while conducting business transactions. A corollary of this approach is that there is often no plan for information lifecycle management. Information captured by an enterprise goes through various stages of significance. Initially, it may have to be available for several days then over time will become needed less frequently. Later, it may be needed in order for litigation or regulatory reasons. Therefore, as information has a lifecycle it must be managed appropriately, using a combination of products and services used to manage it based on criteria such as reducing storage costs, ensuring easy access when needed and speeding up business critical applications. Failure to manage it appropriately can have a degradative impact on data quality.

A related issue is that of limited resources or infrastructure. If there doesn’t exist the will or budget to computerise areas of the business, not all communications or transactions may be captured. That this was raised by only one respondent (forming 6.25% of respondents, 2.22% of responses) is perhaps indicative of the strides many enterprises have made in ensuring a great deal of business information is properly captured, facilitated in part by decreasing storage costs and effective off-the-shelf database management software.

One respondent suggested selective judgment in data production, resulting in biased or skewed information, can have an adverse effect on data quality, particularly where that is used for decisioning. Although this is more prevalent in softer systems, such as medical systems, it can still arise in marketing databases, particularly where descriptive text fields are used to store notes on suppliers or customers.

The cost of deciphering and inputting coded or jargonised information can have an adverse effect on the quality and retrievability of an enterprise’s data. Similarly, this issue will be most prevalent in systems such as those in specialised areas such as the medical profession. This was contributed by a single respondent.

Another data quality issue is caused by difficulties in representing non-numeric and non-textual information, for instances images. There is a large storage overhead for storage of such information, and although when stored in systems such data may be retrievable, systems have so far struggled with indexing, aggregating, manipulating such data and identifying trends. This issue was raised by one respondent.

One respondent cited changing data needs in an organisation as a primary source of data quality problems. Data consumers’ needs and the organisation environment are often in a state of flux, meaning systems will need to be redesigned often to maintain the quality and reliability of the data. If the organisation’s processes and systems fail to keep pace with the changing environment and needs of data consumers, the corollary can be lost data or analysis and decisioning based on incomplete or inaccurate information.

Lack of training for staff employed with entering data can have a profound effect on data quality. The accurate and efficient capture of data requires attention to detail and typing speed. Spelling, punctuation and numeracy skills of staff are also important if the enterprise is to accurately and efficiently handle information. However, students increasingly acquire word processing, spreadsheet, and database management skills using computer software in secondary schools, colleges, universities, meaning organisations can rely on a pool of workers who have already acquired many of the skills necessary to capture data accurately and efficiently. That only one respondent underlined this as a salient issue may reflect that it is no longer such a concern for the reasons explained above.

Lack of effective cross checks and validation beyond the point of entry to assure the validity and veracity of collected data was cited as a source of data quality issues by one respondent. Often entry-point validation will be implemented but this will not necessarily eliminate the need for further validation and cross checks against existing data sources, as the user interface is often bypassed by operators performing bulk inserts of records or mass updates to the database for valid business reasons. Errors that can occur in these instances also need to be identified and protected against or corrected in order to maintain data quality and integrity. Further applications that handle validation logic can be introduced (through triggers, or stored procedures that search for business rule violations or anomalies in expected trends), or, less reactively, it can be made a requirement that all transactional updates are run through the component to check validity.

References

Ballou, D. P., & Tayi, G. K. (1989). Methodology For Allocating Resources For Data Quality Enhancement. Communications Of The ACM, 32 (3).
English, L. (1999). Improving Data Warehouse And Business Information Quality: Methods For Reducing Costs And Increasing Profits. Indianapolis: John Wiley & Sons.

However, more sophisticated attributes of data quality have been developed Strong and Wang (1996) to categorise these issues from the perspective of the data user, and I hoped that data quality issues might be discussed with reference to this framework. To expand on this framework briefly, the attributes of data commonly cited in the research literature are as follows:

Accuracy – The extent to which the data is free from significant error. Does the data accurately represent reality or a verifiable source?
Completeness – The extent to which enough of the required data elements are collected from a sufficient portion of the target population or sample. Is all necessary data present?
Consistency – The extent to which data is collected using the same procedures and definitions across collectors and times. Do any data values give conflicting information?
Conformity – Is any data is stored in a non-standard format?
Duplication – Are records repeated?
Ease of use – Is data readily accessible by the users who need it, aided by clear data definitions, user-friendly software and easily used access procedures?
Integrity – Is data missing important relationship linkages?
Timeliness – Is data available when needed? Does the age of the data meet user requirements?
Validity – Are the data items stored in the systems valid entries? Are there any aberrant values?
Respondents from different backgrounds will inevitably have different definitions and requirements for data quality, and this is also what I had hoped to capture.

I asked sixteen experts, in roles that included database administration, statistics and systems architecture, what they thought the primary areas of concern were. Semi-structured interviews were conducted with each participant.

The data quality issue in marketing databases identified by most respondents (50% of sources, 17.02% of total responses) was that an inappropriate amount of data (specifically, too little) had been captured. This issue was highlighted from respondents from backgrounds as diverse as database administrators to statisticians. Respondent 11 outlined ways in which missing data may be dealt with, either through case-wise or variable-wise deletion, or by populating missing fields with some default value in order to conduct analysis on the whole set. However, it was underlined that these techniques are not ideal and it is better to ensure the data is captured in order that robust analysis and modeling can be conducted on the database. Ultimately, as noted by Respondent 8, “Incomplete data becomes incomplete information, and business decisions are formulated based on this incomplete information.”

43.75% of the respondents highlighted inaccuracy of data as being one of the main data quality issues afflicting marketing databases (14.89% of total responses). One example of this would be an invalid address or an instance where the postcode does not corroborate other address details. This has significant implications not least in delivery of marketing literature, as suggested by Respondent 9 – “Poor address capture resulting in lost dispatches and increased sending costs. Incorrect titles, misspelling of names and poor addressing alienates customers.” These themes are expanded upon further below. Another example of inaccuracy might be individuals being marked as active customers when they have been lapsed for some time, which would be a very significant issue for a publisher, as magazines may continue to be dispatched to individuals who are no longer paying their subscription fees. Another significant example, underlined by Respondent 13, was the problem of recording transactional information accurately contributing to data quality issues. This is a foremost concern of this respondent as he has significant experience working with retail transactional systems, and he cited the issue retailers have with counting returns, exchanges, or voided transactions correctly. This leads to them over counting or inaccurately estimating sales.

Perhaps unsurprisingly, duplication of data was identified as a major data quality issue for marketing databases (by 43.75% of respondents, 14.89% of responses). The effects of duplicated data are far-reaching and are explored further in Question 2. Despite the progress that many data centres have now made in creating single customer views and generating effective merge algorithms to eliminate duplicate entries within their databases, it appears that duplication within databases remains a foremost consideration.

37.5% of respondents (12.77% of coded responses) identified inconsistency between sources as a prominent contributor to poor data quality in marketing databases. When multiple data sources produce different values or values in different formats, this can impact an organisation’s ability to leverage key decision-making information from the database. This problem is exacerbated by the divergence in standards across national and cultural boundaries, as noted by Respondent 6, who gives the example of the American date format in one system “[which], if not handled, correctly, may be transposed into another system as 3rd September rather than 9th March.”

An associated issue is that of a lack of standardisation. This can take the form of inconsistent spelling of names or addresses (identified as significant by a quarter of respondents, in 9.3% of total responses) – for example, using Beijing or Peiching depending upon the method of transliteration favoured by the inputter. As Respondent 9 underlines, “Often there are inconsistencies in referencing organisation names – this can be a big problem that is difficult for merge keys to eradicate in deduplication processing, for example, National Security Agency, Central Security Services, NSA – all names that could be entered on a database for the same entity without appropriate guidance for input.” Similarly, inconsistency can affect product buying history information, as more than one name can be used for a single product, if an enterprise-wide product naming convention has not been established.

A quarter of respondents noted time-degraded data as a major data quality issue (9.3% of responses). Respondent 16, who has significant experience of dealing with this issue for a number of risk management companies, provides examples collated while with Dun & Bradstreet – “about 20% of all businesses will move in a year, about 15% or 16% will change their name, about 18% will get a new phone number. Those tend to be smaller businesses, so if you do business on a business-to-business basis with small businesses, that rate of change is a huge problem to overcome. And the typical way that companies try to overcome it is by using a timestamp in the database, but that presumes that the person putting the data in, and thus, attaching the timestamp, has the current name and address. In my experience, that’s often not true.” Inevitably, this causes issues in terms of being sure of the reliability of your data as well as the enterprise’s ability to integrate data and generate a single view of the customer.

A significant issue identified by 18.75% of respondents (6.38% of responses) was that incorrect data can often be entered due to inappropriate input controls. Incorrect data can be entered intentionally in order to evade validation rules.

Spelling or typographical errors were cited as a main data quality issue by 18.75% respondents, in 6.38% of responses. A typographical error can change an address completely, for instance, if Hull Road is entered erroneously as Hill Road (both of these are fairly common street-level addresses in the United Kingdom, Hull Road having 39 observations and Hill Road having 121 observations nationally, some of which are relatively close to one another). Respondent 11 cited examples of “the 800-year old man or the 80-year old lady that just had a baby. They’re just data entry errors [and] there are lots of ways to deal with that, you can have a computer go back, you can write programs that do quality checks, but another way to deal with it which is again quite expensive is if you’re dealing with hand-entered data, you can actually have two people enter the data and then cross-check. The survey places (for example, Gallup) actually do that… taking telephone interviews. Anybody that sets up a database is going to put in allowable and non-allowable values, so there’s simple, inexpensive checks that can be done computationally just like that, but people do this thing where they have two people sitting there typing. I don’t know how much that’s done but I have actually seen it done.” Admittedly, this is likely to be very expensive.

Inappropriate metadata was cited as a salient issue in 4.26% of total responses, by 12.5% of respondents. This issue is closely related to, and overlaps to some degree, with the lack of standardisation issue outlined earlier. As Respondent 15 suggests, “data comes from multiple places, it’s stored in multiple databases, all of which use different terminology, different approaches, different processes for not only tagging what it is, but for measuring its quality.” This is a particularly significant issue in Respondent 15’s industry sector, investment banking, and the respondent cited the varying definitions for closing price in global financial markets as a major source of misunderstanding. ‘Closing price’ generally refers to the last price at which a stock trades during a regular trading session. For many markets (including the New York Stock Exchange and the Nasdaq), regular trading sessions run from 9:30 a.m. to 4:00 p.m. Eastern Time. But a number of markets offer after-hours trading, and some market data vendors use the last trade in these after-hours markets as the closing price for the day. Others publish the 4:00 p.m. price as the closing price and display prices for after-hours trading separately.

12.5% of respondents identified type mismatches as a data quality issue (4.26% of total responses). Type mismatches occur when data fields that are being used as variables or being used as keys to join data sets are of different types, for example, a numeric field that is stored as an integer in one system might be stored as a text string in another. Issues might arise because a variable in a program that requires an integer value can’t accept a text string value unless the whole string can be recognised as an integer. Similarly, a field that stores numbers as an integer data type cannot be properly bound to a text field, so relational joins cannot be performed using the field.

The respondent from a statistical background underlined another significant data quality issue, which was overlooked by respondents from other backgrounds, namely the propensity of individuals to willfully provide false data. This issue is prominent where data is being collected about drugs, prostitution, gambling, alcohol intake or similarly taboo subjects.

References

Chaudhuri, S., Ganjam, K., Ganti, V., Kapoor, R., Narasayya, V., & Vassilakis, T. (2005). Data Cleaning In Microsoft SQL Server 2005. SIGMOD.
Wand, Y., & Wang, R. Y. (1996). Anchoring Data Quality Dimensions In Ontological Foundations. Communications Of The ACM , 39 (11).

DeLone and McLean (1992) suggest data quality is a key antecedent of information systems success. Orr (1998) noted that data quality should always be considered in terms of ‘use-based data quality’. Explicitly, the means of information presentation and delivery that enables information use should not be separated from the information itself (content) when talking about data quality. This is considered particularly significant in e-business systems because ignoring information presentation and delivery aspects will create problems in terms of irrelevant information, disorientation, and cognitive overhead (Nielsen, 2000). The quality of a product depends on the process used to design and produce the product. Similarly, the quality of data depends on the design and production processes involved in generating the data. In order to design for better quality, it is necessary first to understand what quality means and how it can be measured.

An additional factor that influences data quality is who affected by errors and to what degree. If, as was the case for the criminal justice systems examined by Laudon (1986), the consequences of errors fall most heavily on those outside the system, the motivation to maintain data integrity can often be diminished.

The notion of data quality traditionally refers to the degree to which data satisfy user requirements or are suitable for a specific process. Both theoretical and experimental results assert that data quality is a ‘multi-dimensional’ concept (Ballou et al., 1998; Redman, 1996; Strong & Wang, 1996; Wand & Wang, 1996). These dimensions are often based on intuitive understanding (Ballou & Pazer, 1985) or industrial experience (Firth & Wang, 1996). Ballou and Pazer (1985) identified and discussed four dimensions of data quality: accuracy, completeness, consistency, and timeliness.

An example of the role of these dimensions can again be found in Laudon’s study of data problems in the criminal justice system (1986). Accuracy could refer to recording correctly facts regarding the disposition of a criminal case, completeness to having all relevant information recorded, consistency to a uniform format for recording the relevant information, and timeliness to recording the information shortly after the disposition. Understandably, poor quality in any of these dimensions could be very damaging to individuals or the state.

Strong and Wang (1996) analysed the various attributes of data quality from the perspective of data users. Their analysis solicited information from users regarding various quality descriptors attributable to data that resulted in over 100 items that were taxonomically grouped into about twenty categories. These were further clustered into four broad data quality classes: intrinsic quality, contextual quality, representational quality, and accessibility. Accuracy is a facet of intrinsic quality; completeness, and timeliness are components of contextual quality; consistency belongs to the representational data quality class. Problems with data quality cannot be addressed effectively without an understanding of the data quality dimensions.

Wang (1998) combines various research with the experiences of practitioners to produce a framework for dealing systematically with data and information quality issues. The methodology presents a cycle resembling that found in manufacturing and contains concepts and procedures that assist in defining, measuring, analysing, and improving data quality.

However, the relative scarcity of research on what constitutes a data quality policy and a data quality system contrasts sharply with the growing anecdotal evidence that organisations are increasingly aware of the need to develop a corporate policy for data quality management (Bulkeley, 1992; Cronin, 1993; Liepens, 1989). The articles detailed above, while not contemporary, provide the framework within which much significant current research (Goldman, 2007; Stephens, 2007) continues to be conducted.

There is no commonly accepted definition of what accuracy means in terms of data quality. For example, Kriebel (1979) characterises accuracy as “the correctness of the output information.” Ballou and Pazer (2003) describe accuracy as “the recorded value is in conformity with the actual value.” It appears the term is viewed as equivalent to correctness.

Data freshness has been identified as one of the most important attributes of data quality for data consumers (Shin, 2003). Some surveys and empirical studies have proved that data freshness is linked to information system success (Mannino & Walter, 2004).

Maintaining essential data freshness is a challenge for the development of a large variety of applications. In addition, the increasing need to access to information that is available in several data sources introduces the problem of choosing between alternative data providers and of combining data having different freshness values. The traditional freshness definition is related to view consistency when materialising source data at the integration level or the user level and is usually called currency (Segev & Weiping, 1990). Currency describes how outdated data is with respect to the sources. Other proposals incorporate another notion of freshness, called timeliness (Strong & Wang, 1996), which describes how old data is. Freshness represents a family of quality factors, or a quality dimension, with different associated metrics. Each factor may have a different impact on a particular type of system.

With the exception of a few instances (Laudon, 1986; Miller & Strong, 1995), research has rarely examined data quality management from the economic perspective. Cost savings, revenue generation, and/or profitability associated with data quality are rarely quantified. This gap in data quality management research is evident in data quality metrics described in the literature. Traditional data quality metrics are often driven by the physical characteristics of the data (e.g. item counts or failure rates), assuming an absolute and objective quality standard. The alternative explored here is to derive quality metrics from data content and evaluate them within specific usage contexts. The former approach has been termed as structure-based (or structural), and the latter as content-based (Ballou & Pazer, 2003). It may be asserted that business value is actually reflected more by the content and business use of data and less by its physical characteristics. Hence, content-based assessment of quality is more appropriate for examining the economics of data quality management.

Kaplan et al. (1998) assess the role of analytical modeling in information systems in general and data quality in particular. Such approaches facilitate the systematic exploration of data quality issues. They present a decision support system to assist auditors to carry out data quality assessments of accounting information systems. The focus of the system they propose is to enable the auditors to decide the extent of testing and to select the minimum set of control procedures needed to ensure data reliability.

With the growing volumes and complexity of data resources, managing data quality is becoming an important success factor for firms (Firth et al., 1995; Watson & Wixom, 2001). High quality data supports smoother operations and expedites effective decision-making. Equally, poor quality data causes organisational inefficiency and capital losses (Redman, 1996). Traditional data management primarily focuses on functionality (having the right data, in the right format, at the right time, to the right place) and technical efficiency (storage, retrieval, delivery, and presentation). However, with steadily increasing investments in data management (Watson & Wixom, 2001), there is a growing concern about its economic aspects, namely, its contribution to business value and its effect on costs. This shift in focus has important implications for managing data and must be reflected in methods for measuring its quality.

Many research studies have presented procedures designed to prevent the storage of erroneous information in computer systems. Maintaining a satisfactory level of integrity for the stored data resource, however, is an ongoing responsibility that requires a continual infusion of resources (Davis & Olson, 1985).

Orr (1998) presents concepts that are critical for ensuring an information system will generate outputs over time that are trusted and hence used. Further, he identifies six data quality rules and explores the implications of these rules. A recurring theme in his work is the need for continual feedback from users to ensure that the data’s quality is maintained.

Data exploration based on techniques such as exploratory data analysis (Tukey, 1977), missing value imputation (Little & Rubin, 1987) and outlier detection (Knorr & Ng, 1998; Breuning et al., 2000) can be used to detect and repair damaged data. Alternately, the data to be validated can be compared to a gold standard using set comparison methods (Johnson & Dasu, 1998).

References

Ballou, D. P., & Pazer, H. L. (1985). Modeling Data And Process Quality In Multi-Input, Multi-Output Information Systems. Management Science, 31 (2), 150-162.
Ballou, D. P., Pazer, H. L., Tayi, G. K., & Wang, R. (1998). Modeling Information Manufacturing Systems To Determine Information Product Quality. Management Science, 44 (4).
Ballou, D. P., & Pazer, H. L. (2003). Modeling Completeness Versus Consistency Tradeoffs In Information Decision Systems. IEEE Transactions On Knowledge And Data Engineering, 15 (1), 240-243.
Breunig, M. M., Kriegel, H. P., & Sander, J. (2000). Identifying Density-based Local Outliers. ACM SIGMOD Conference, (pp. 93-104).
Bulkeley, W. (1992, May 26). Databases Are Plagued By Reign Of Error. Wall Street Journal, p. 86.
Cronin, P. (1993, June). Close The Data Quality Gap Through Total Data Quality Management. MIT Management .
Cushing, B. E. (1974). A Mathematical Approach To The Analysis And Design Of Internal Control Systems. Accountancy Review, 49 (1), 24-41.
Davis, G., & Olson, M. (1985). Management Information Systems: Conceptual Foundations, Structure And Development (2 ed.). New York: McGraw-Hill.
DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest For The Dependent Variable. Information Systems Research, 3 (1), 60-95.
Firth, C. P., & Wang, R. Y. (1996). Data Quality Systems: Evaluation And Implementation. London: Cambridge Market Intelligence.
Firth, C. P., Storey, V. C., & Wang, R. Y. (1995). A Framework For Analysis Of Data Quality Research. IEEE Transactions On Knowledge And Data Engineering, 7 (4).
Goldman, L. (2007, August). Data Warehouse Quality Assurance Best Practices. DM Review Magazine.
Johnson, T., & Dasu, T. (1998). Comparing massive high-dimensional data sets. Proceedingsof the Fourth International Conference on Knowledge Discovery and Data Mining (pp. 229-233). New York, NY: ACM.
Kaplan, D., Krishnan, R., Padman, R., & Peters, J. (1998). Assessing Data Quality In Accounting Information Systems. Communications Of The ACM, 41 (2).
Knorr, E., & Ng, R. (1998). Algorithms For Mining Distance-based Outliers In Large Database. Proceedings Of The International Conference On Very Large Databases, (pp. 392-403).
Kriebel, C. H. (1979). Evaluating the quality of information systems. In N. Szysperski, & E. Grochla, Design and Implementation of Computer Based Information Systems. Germantown, PA: Sijthtoff & Noordhoff.
Laudon, K. C. (1986). Data Quality And Due Process In Large Interorganizational Record Systems. Communications Of The ACM, 29 (1), 4-18.
Liepens, G. E. (1989). Sound Data Are A Sound Investment. Quality Progress, 22 (9), 61-64.
Little, R. J., & Rubin, D. B. (1987). Statistical Analysis With Missing Data. New York: Wiley.
Mannino, M., & Walter, Z. (2004). A Framework For Data Warehouse Refresh Policies. Retrieved May 31, 2008, from Technical Report CSIS-2004-001: http://business.cudenver.edu/Disciplines/InfoSystems/PhD/WhitePapers/CSIS-2004-001.pdf
Miller, S., & Strong, D. M. (1995). Exceptions And Exception Handling In Computerized Information Processes. ACM Transactions On Information Systems, 13 (2), 206-233.
Nielsen, J. (2000). Designing Web Usability: The Practice Of Simplicity. Indianapolis: New Riders.
Orr, K. (1998). Data Quality And Systems Theory. Communications Of The ACM, 41 (2).
Redman, T. (1992). Data Quality: Management And Technology. New York: Bantam.
Redman, T. (1996). Data Quality For The Information Age. Boston, Massachusetts: Artech House.
Redman, T. (1998). The Impact Of Poor Quality Data On The Typical Enterprise. Communications Of The ACM , 41 (2).
Segev, A., & Weiping, F. (1990). Currency-based Updates To Distributed Materialized Views. Proceedings Of The 6th International Conference On Data Engineering.
Shin, B. (2003). An Exploratory Investigation Of System Success Factors In Data Warehousing. Journal Of The Association For Information Systems, 4.
Stephens, R. T. (2007, April). Data Quality: The Price Of Entry. DM Review Magazine.
Strong, D. M., & Wang, R. Y. (1996). Beyond Accuracy: What Data Quality Means To Data Consumers. Journal Of Management Information Systems, 12 (4).
Tukey, J. (1977). Exploratory Data Analysis. Reading: Addison-Wesley.
Wand, Y., & Wang, R. Y. (1996). Anchoring Data Quality Dimensions In Ontological Foundations. Communications Of The ACM, 39 (11).
Wang, R. Y. (1998). A Product Perspective On Total Data Quality Management. Communications Of The ACM, 41 (2).
Watson, H. J., & Wixom, B. H. (2001). An Empirical Investigation Of The Factors Affecting Data Warehousing Success. MIS Quarterly, 25 (1), 17-41.

Experiential learning is one theory of learning, stating that learning occurs when people reflect on their experiences in order to understand and apply what they have learnt. Learning is therefore a personal ‘construction’ of meaning through experience. Experiential learning is enhanced through creation of an environment in which people can be stimulated to think and act in ways that help them make good use of their experiences. Kolb proposed a learning cycle consisting of four stages – concrete experience (planned or accidental); reflective observation; abstract conceptualisation (theorising); active experimentation (Kolb, Rubin, & McIntyre, 1974). To learn effectively, individuals must shift from being observers to participants, from direct involvement to objective analytical detachment.

Honey & Mumford identified four learning style preferences – activists, who involve themselves fully without bias in new experiences and revel in new challenges; reflectors, who stand back and observe new experiences from different angles, collecting data and forming conclusions; theorists, who adapt and apply their observations in the form of logical theories, tending towards perfectionism; pragmatists, who look to try out new ideas, approaches and concepts to gauge their effectiveness. These different learning styles can be loosely associated to the different stages of the learning cycle proposed above (Honey & Mumford, The Manual of Learning Styles, 1996), although there are some salient differences – for instance, those identified as pragmatists by Honey & Mumford plan next steps rather than undertaking the ‘active experimentation’ suggested by Kolb.

Completion of Honey & Mumford’s Learning Styles Questionnaire revealed my preferred learning style to be pragmatic,  suggesting I learn the most from activities where there is an obvious link between subject matter and a problem or opportunity on the job, techniques with obvious practical advantages are demonstrated to me, I have the chance to try out techniques with feedback from a credible expert and I am given immediate opportunities to implement what I have learned and I am able to concentrate on practical issues such as drawing up action plans with an obvious end goal or product or finding time- and/or resource- saving solutions.

During my time studying for my MBA, I have seen that a significant schism exists between management theory and management practice. Awareness of this ‘relevance gap’, however, is helping me to produce and impart more relevant and effective management knowledge. It’s asserted that recognition of the idiosyncratic nature of organizations presents a problem of predictability and generalisation for management scholars and students, and theoretical work can offer tentative conclusions that were tested in limited empirical contexts, ignoring the complexity of intricate, multi-causal reality of managers (McKelvey, 2003).

This knowledge has been supplemented by a realisation that many managers are not prepared to abdicate decision-making responsibilities and effective heuristics to scientific models and theories tested in divergent or limited environments. Increasing experience in management practice and readings in the management literature have therefore led to an increasing agreement with the assessment espoused by the pioneer of systems dynamics, Forrester, that the human mind is not yet “adapted to interpreting how social systems behave” and there is currently “no way to estimate the behaviour of social systems except by contemplation, discussion, argument and guesswork.” (Weizenbaum, 1976) I am optimistic that there is a place for the pragmatic mind in a business academic environment that has increasingly moved away from practice.

I have learned by constructing meaning and developing knowledge through experience triangulated with an increasingly familiarity of the body of literature of management theory. Experiential learning theory suggests that learning through experience can be enhanced by encouraging learners to reflect on and make better use of what they have learnt through their own work and from other people. Involvement in the MBA cohort has strengthened this reflective practice and increased exposure to people whose ideas and experiences I can learn from. Pragmatists benefit from experiential learning as it focuses on interfacing science and practice, and defines the notion of truth in a manner that renders it imminently practical.

Pragmatism is a philosophy of science that emphasises the link between action and truth, arguing that the ultimate test of a belief is the willingness to act on it. To pragmatists, knowledge is useful when it helps people to better cope with the social environment or create more effective organisations. Usefulness is assessed across two dimensions: epistemological (is this information credible and reliable?) and normative (does this help advance my work?).

My learning, predominantly qualitative and heuristic in nature, has been supplemented by enhanced reflective practices. Confrontation with reality through action, for pragmatists, is the principal source of doubt, which in turn feeds scientific curiosity and becomes the motivating force to inquire in order to settle that doubt. It is only exposure to the rigorous testing ground of the working environment that reveal the flaws inherent in universal models, and pertinently (in the current economic conditions) that some are ineffective in a negative growth climate. Therefore, action and the interrogations stemming from it are what have driven my learning. To a pragmatist, models that identify several co-producers of a product (the kind produced by most management research) are generally assumed to be incomplete, even if it accounts for most of the observed variance in dependent variables.

Pragmatism is therefore linked to chaos, as well as a rejection of grand theory or grand narrative. In common with pragmatists, I also see my action and learning driven by the Hegelian notion of dialectics, a process of arriving at truth through confrontation of different points of view. My learning has therefore been created from a deep commitment to practice, because in for dialectics to work most effectively there must be at least two actors in some disagreement, each with divergent points of view. The process can come to an end either by the actors developing jointly a new point of view or by there being an arbiter who does this.

I have acquired the knowledge that there is a significant theory-practice gap as a result of witnessing the opposition I have sometimes met on the occasions where I have attempted wholesale implementation of theoretical models to the workplace. However, truth emerges as a synthesis of the opposing views, and becomes the thesis for the next cycle of dialectical progression. Therefore, these contradictory processes and pluralism are very effective at producing truth in certain environments and situations.

References

Honey, P., & Mumford, A. (1996). The Manual of Learning Styles (3 ed.). Maidenhead: Honey Publications.
Kolb, D. A., Rubin, I. M., & McIntyre, J. M. (1974). Organizational Psychology: An experimental approach. Englewood Cliffs, NJ: Prentice-Hall.
McKelvey, B. (2003). From fields to science: can organization studies make the transition? In R. Westwood, & S. R. Clegg, Debating Organization: Point-Counterpoint in Organization Studies (pp. 47-73). Oxford: Blackwell.
Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation. San Francisco, CA: Freeman.

My own experience and perceptions of the culture of the public sector, triangulated with appropriate readings, suggested there is an increase in prominence of the managerial function within the public sector, associated with the evolution of the new public management movement in the United Kingdom. As the NHS is one such organisation that has been affected by these developments, I considered an exchange with a manager from an NHS Trust to be useful in terms of broadening my understanding. Prior to the advent of managerialism within the NHS, decision making was influenced through ‘consensus management’ by the various professional groups, a system criticised for being wasteful of resources and lacking professional management and direction.

The extent to which public sector organisations have been positively transformed since is difficult to assess. Normative control strategies in the public sector, of which management development is an example, may exist largely as symbolic and rhetorical artefacts towards which the majority of staff are cynical (Hoggett, 1996). Often, reforms which don’t correspond to local managers’ ideas of good management will be rejected or reshaped in ways which fit existing values and assumptions (Ranade, 1995). I felt it would be interesting to see the extent to which these issues remained in public sector management and if I could prescribe methods for change.

The delivery unit in which I undertook my manager exchange was established to deliver a National Health Service cancer screening programme in a UK region. Screening operations are divided into geographical divisions with centres in various major towns in the region. Mobile units work across the region to support these centres, visiting over 100 sites in every three year round of screening.

As a Radiography Manager, the observed manager was directly responsible for all Radiographers in the region. Her duties included managing, planning and organising the screening programme throughout the region from a central base in a major regional centre and a satellite base in another major town. The role includes maintaining  high levels of staff morale and motivation, promoting active teamwork, guiding new staff, being an exemplar in setting and maintaining excellent standards of clinical care, responsibility for ensuring risk management is carried out and safe systems of work are adhered to, and maintaining high standards of care through the appropriate use of interpersonal skills and knowledge. Her remit included daily travel to any site on which mobile units may be located and overnight stays on a regular basis. Due to the extensive travelling involved in my exchange partner’s work and the sensitive nature of the work, I was unable to follow her in her duties. Therefore, it was agreed that the observation process would take place through email correspondence, provision of job specification and noteworthy documentation pertaining to the Trust and the manager’s role and a morning of interviews and discussion.

From background reading and initial conversations, I immediately perceived the culture of my exchange partner’s organisation to be markedly different from that of my own organisation. Organisational culture has been defined in numerous ways. One definition is “a pattern of shared basic assumptions that the group learned as it solved its problems of external adaptation and internal integration, that had worked well enough to be considered valid, and therefore, to be taught to new members as the correct way to perceive, think and feel in relation to those problems. (Schien, 1992)

There is no clear agreement on methods for measuring organisational culture. It has been suggested only certain cultural dimensions may be appropriately studied using quantitative methods (Rousseau, 1990), and that researchers and practitioners currently have inadequate means to measure organisational culture (Reigle & Westbrook, 2000). It is also asserted that many survey instruments help identify cultural artefacts and espoused values, but do not reveal important tacit shared assumptions in an organisation (Schein, 1999).

Nevertheless, given the constraints on time spent with my exchange partner, I felt it worthwhile to conduct a survey based on the competing values framework (Cameron & Quinn, 1999) in order to corroborate my observations and demonstrate this difference. It is suggested that organisations do not necessarily share common values but will often have cultures that are inconsistent and ambiguous, and disparate groups will espouse values that are unique to them (Meyerson & Martin, 1987). This has been found to be true within NHS Trusts (Mackenzie, 1995).

I therefore concentrated on assessing the organisational unit within which my exchange partner manages. In all areas assessed in the framework (dominant characteristics, organisational leadership, management of employees, organisation glue, strategic emphases, criteria of success), it was demonstrated that the delivery unit was positioned equally as a ‘clan’ and a ‘hierarchy’ in the competing values framework quadrant. In the clan culture, the leaders are mentors and even parent figures, the organisation is held together by loyalty and tradition, commitment is high, the organisation emphasises long-term benefit of human resources development and attaches great commitment to cohesion and morale. Success is defined in terms of concern for people and participation and consensus are encouraged. The hierarchy culture is a formalised place to work with procedures governing much activity. Success is defined in terms of dependable delivery. The management of employees is concerned with secure employment and predictability.

Overall, the department in which I currently work fits the hierarchy model, with tendencies towards ‘adhocracy’ in some areas, as would be expected in a smaller, private-sector technology organisation. An adhocracy is an entrepreneurial and dynamic organisation where success means gaining new products and services.

The organisational culture in the delivery unit represented an equal mix of the ‘clan’ and ‘adhocracy’ cultures. The clan culture is perceived to have benefits such as equipping the organisation to meet employees’ needs, promoting teamwork and participation, increasing sensitivity in service delivery, generating morale and creating high levels of trust. These were seen as requisite qualities for an NHS Trust both by my exchange partner and most other NHS employees, who believe the shared core values of the NHS should be altruistic.

However, people within the health service often have these values early in their careers and they retain them as they continue up the management chain, and the inflexibility of such values may create barriers to positive change within the health service and reinforce a deeper ideological conflict with managerialism. The clan culture also encourages self-management which may add to this rejection of managerialism. It is clear that within many NHS Trusts, the implementation of the Griffiths Report has not fully succeeding in its tacit aim of challenging “the hegemony of professional groups within the NHS.” (Ferlie, Ashburner, Fitzgerald, & Pettigrew, 1996)

There is some divergence between the delivery unit in which I undertook my manager exchange and other NHS Trusts, however, where quality was sometimes felt to be compromised because volume was considered to be more important by managers and innovation in practice is encouraged. Even in those Trusts, however, staff felt the common norms were to co-operate with others, support colleagues and share information and the organisational values were to provide quality care and develop – qualities common to a clan culture such as that identified in the workplace of my exchange partner. These are positive characteristics and the “uniqueness of a hospital trust lies in the history and professional elaboration of groups in the health service.” (Currie, 1997)

The assessment revealed a great deal of congruence between the desired organisational culture of my exchange partner and her desired organisational culture. This is not to say that change should not be prescribed. Greater innovation in practice, as occurs in other NHS Trusts, was one recommendation I made. In order to facilitate this, an adoption of certain characteristics of the ‘adhocracy’ quadrant should be adopted. A critical analysis of the mission statement could be undertaken to see if it promotes creative initiative and encourages focus on managing future challenges, such as those posed by demographic changes. New technology should be explored to create, test and adopt alternative solutions more quickly. Employees could be offered a training programme that includes the practical application of creative thinking, the strategic reasons for increased responsiveness and the principles of organisational innovation. Such a shift may be difficult because of the hegemony of professionals within the health service. Culture is often the most difficult organisational attribute to change, outlasting organisational products, services, founders and leadership and all other physical attributes of the organisation.

As a result of my manager exchange experience, I was able to see that the department in which I work could benefit from adopting more of a ‘clan’ culture in its management of employees and its strategic emphases. In terms of managing teams, a clear, overarching vision for the team should be agreed on, as exists in public service delivery teams. Team meetings should be held more frequently, during which members are reminded of team objectives and agreements reached, and this is being acted upon with impressive results in terms of hugely improved ability to hit project deadlines. When managing interpersonal relationships, I now seek to clarify expectations for team members’ performance, in order that they will be less frustrated by uncertainty, and ambiguity of goals will be reduced.

References

Cameron, K. S., & Quinn, R. E. (1999). Diagnosing and Changing Organizational Culture. Reading, MA: Addison-Wesley.
Currie, G. (1997). Management development and a mismatch of objectives: the culture change process in the NHS. Leadership & Organization Development Journal, 18 (6), 304-311.
Ferlie, E., Ashburner, L., Fitzgerald, L., & Pettigrew, A. (1996). The New Public Management in Action. Oxford: Oxford University Press.
Hoggett, P. (1996). New modes of control in public services. Public Administration, 74 (1), 9-32.
Mackenzie, S. (1995). Surveying the organizational culture in an NHS trust. Journal of Management in Medicine, 9 (6), 69-77.
Meyerson, D., & Martin, J. (1987). Cultural change: an integeration of three different views. Journal of Management Studies, 24, 623-647.
Pedler, M., Burgoyne, J., & Boydell, T. (2007). A Manager’s Guide to Self-development. London: McGraw-Hill.
Ranade, W. (1995). The theory and practice of managed competition in the NHS. Public Administration, 73 (Summer), 241-262.
Reigle, R., & Westbrook, J. D. (2000). Organizational Culture Assessment. National Conference of the American Society for Engineering Management. Washington, DC.
Rousseau, D. M. (1990). Assessing organizational culture: The case for multiple methods. In B. Schneider, Organizational climate and culture (pp. 153-192). San Francisco: Jossey-Bass.
Schein, E. H. (1999). The corporate culture survival guide: Sense and nonsense about culture change. San Francisco: Jossey-Bass.
Schien, E. H. (1992). Organizational Culture and Leadership (2 ed.). San Francisco: Jossey-Bass.

Tesco’s management places an emphasis on customer needs through the ‘Tesco Values’ philosophy, expressed as two values – “no one tries harder for customers; treat people how we like to be treated.” The values are disseminated through an internal marketing strategy, which includes distribution of company newspaper to employees. However, these values emanate from the UK and recognition of a need for country-specific practices and local can be secondary to corporate unity.

Tesco has a reputation for innovative information solutions, and its ‘Clubcard’ loyalty scheme and web sites are central to this. Tesco is the UK’s largest retailer and therefore has a significant customer base on which analysis can be performed. Because many of those customers return at weekly or similar intervals, Clubcard data and relationships are both deep and wide. Accordingly, Clubcard has significant potential to influence consumer behaviour in the UK. Staff are briefed on the importance of Clubcard, and the initial launch was preceded by fervent internal marketing. Clubcard is not only closely integrated with business processes, but aligns with the brand and brand strategy as the active expression of the brand’s personality and its values (Humby, Hunt, & Phillips, 2004).

There are different dimensions of Tesco’s online customer experience, including ease of use, speed of site, relevance, value, service, and product development. Tesco is continually developing more online products to meet the needs of the customers. For instance, Tesco has recently started offering music downloads as well as a grocery delivery service that includes wine and white goods.

Tesco uses this product range to create a strong customer experience as a customer can do a one stop shop instead of purchasing products from multiple vendors. For the online customer, ease of operations is an essential player in their purchasing decisions. Efforts have been made to decrease the amount of time it takes a customer to complete their first order. The time has dropped significantly from one hour to 35 minutes. This provides a better customer experience.

There are three dimensions to Tesco’s use of technology in marketing information gathering and planning. The first aspect is data collection, of which the Clubcard that could be scanned at the till is an important foundation. Later, ‘EPOS’ tills were installed that could collect every transaction. Clubcard engages with a large number of corporate partners in relation to the earning and delivery of rewards. Tesco allows customers to gain rewards from activities as disparate as travel, dry cleaning, and car maintenance. In addition, many of these activities will be performed in the locality of the Tesco store that the customer normally uses, and from a customer experience perspective extends their engagement with the local retailing community. From a customer data perspective, the opportunity to earn Clubcard points through partner organisations means that Tesco is able to expand its customer profiles relating to their consumption activities to arenas beyond supermarket shopping. For example, a customer who earns points though the use of Autocentre is providing the data that makes it possible for Tesco to collect data concerning the type of car that their customers drive.

The second aspect is customer interactions – customers can collect points through the scheme through transactions with various partners, and through their online purchases. Tesco manage a portfolio of interactions that the customer has with the brand, and seek to reward every interaction. Clubcard forges a strong link between the ‘click and brick’ (online and in-store) sides of the business. By collecting data through both channels, Tesco can easily see the similarities and differences between online and offline customers, in terms of what they buy, how they respond to the service and how they mix channels.

Furthermore, data collected through customer interactions with one channel, such as in store can offer valuable insights for potential new customers, and approaches to optimising operations associated with the development of other channels or services. In addition to standard direct marketing procedures, Tesco also provides special offers to its most loyal customers. These personalised offers help to bind the customer to the company. A further focus has been how to increase the frequency of customer visits. Tesco addresses this issue by sending incentives to all customers deemed dormant. The company also provides more incentives to the customer ‘after the first shop after a break’ (Chaffey, Ellis-Chadwick, Johnston, & Mayer, 2008). Tesco has thus utilised detailed customer information to go further than others whose loyalty schemes simply offer the customer a reduction of the price paid at the checkout.

The third aspect is data analysis. Tesco have paid careful attention to the design of data analysis. They have worked to ensure an adequate database, data currency, data quality and tight control of data analysis costs. A further refinement was to seek to understand why customers behaved in certain ways. This process began with profiling the attributes of products, then creating clusters on the basis of customer’s product purchase profile and using these product attributes to profile and segment customers defined in terms of the contents of Tesco shopping baskets. Each of these segments had broad similarity but with a size that made them worth addressing (Humby, Hunt, & Phillips, 2004).

Analysis of the UK database has enabled Tesco to leverage benefits in innovative ways. For instance, when analysis showed that customers were not buying nappies, investigation revealed the products were being purchased at Boots pharmacies, despite a 20% higher price. As a result, Tesco created a baby club offering advice on pregnancy and motherhood. Within two years the company had cornered almost a quarter of the mother and baby market (Strategic Direction, 2008).

Tesco’s trading philosophy is based around the ‘Steering Wheel’. This comprises a Customer Plan, Operations Plan, People Plan and Finance Plan. Marketing information systems play an integral role in the formation of these plans as they are based on information gained from customers through the ‘Brand Review’. Information gained from the Brand Review is presented to the board of each country and the central board. Each country carries out separate Brand Reviews and the results are amalgamated for the central board. This results in a Customer Plan aiming to serve customer needs associated with price, service and quality. The Operations Plan looks to cut costs by obtaining better margins from suppliers through operational efficiencies. Examples of this include the deployment of new ordering or stock replenishment systems.

From 1999 onwards, Customer Panels have been operating in most countries where Tesco has a presence to enable intelligence to be communicated to Head Office. Tesco also analyse brand share and have a weekly sales report. Image research is carried out across each country and a brand image analysis is completed every six months. Questions focus on perceptions of price image, quality image, service, range and promotions. These large-scale questionnaires are distributed to catchment areas of their own and competitors’ stores. Since 2002, detailed research has been carried out focusing on the market’s biggest competitors in each country. Local research is focused on stores in specific areas of each country and examines variance. Cross-sections of customers sit with store management to describe their experiences at Tesco, in terms of price, quality, products, departments and customer services. Every year for one week each store asks every tenth customer about their postcode at the checkout. The amount spent and time/date of purchase is then analysed. Cross border research in landlocked countries such as Hungary and Slovakia looks at how more trade can be drawn from over borders. Customer complaints are recorded with the numbers of problems in each area analysed. Seasonal and pre/post advertisement research is typically undertaken through qualitative methods to aid the marketing planning process.

Tesco has embarked on a programme of development pan-regional information systems infrastructures since 1998, communicating operational capabilities for each country. However, most knowledge in developing markets continues to be shared through informal networks such as meetings and telephone calls. Tesco has a UK-based International Commercial team that works with individual country teams and in 2004 the International Support Office Marketing Team was established. This ensures that all markets Tesco operates in utilise similar strategies regarding how marketing research is used and managed, facilitating comparable results and actionable planning recommendations. The Process team works with the Support Offices in each country to develop their own capabilities and solve their own retail issues, giving a localised aspect to the utilisation of marketing information. These offices help central operations to communicate with stores, and they accumulate market research information from disparate parts of the business.

Tesco maintains a focus on innovation in products, placing an emphasis on added value for the consumer. For example, in 2006 Tesco introduced a new line of dairy products manufactured with Reducol, a phytosterol-based ingredient proved to reduce cholesterol. The product range has since expanded, helping the retailer to become a thought leader to help raise its image above those of the competition.

The strategy of developing market share for goods outside the usual supermarket arena has led to Tesco surpassing Sainsbury’s to become the biggest supermarket in the UK, and it currently holds over a quarter of the market (Strategic Direction, 2005). In 2008, Tesco launched its first in-store order-and-collect service as it attempts to take on Argos, the current leader the catalogue sector. The new service was tested at Tesco’s Cribbs Causeway store (which is used as a laboratory store for testing new products and services) and has since been rolled out to other locations. Tesco has also launched of a suite of software products for home or office use that competes directly with products from Microsoft. Tesco Complete Office includes two security/antivirus products, a personal finance application, a DVD-writing tool, and a photo-editing feature and is priced at under £20 in contrast to Microsoft Office 2007 which retails at £45.

Tesco has recently launched the ‘Discounter’ product range in order to combat the perceived threat of Aldi and Lidl. The discount supermarket sector has profited from recent economic turmoil as rising food costs have steered more people to their stores. 80% of Tesco’s sales come from areas in which Aldi and Lidl are not represented. Commercial director Richard Brasher acknowledges the “centre of gravity in the marketplace” has moved (Ritson, 2008). The forthcoming challenge is to avoid the predicament of becoming caught in the middle of the market when consumers are trading down. However, it might be contended that fighter brands created to target a specific competitor invariably fail as they cannibalise the owner’s higher-priced products rather than driving share from their intended target. Indeed it remains unclear how successful this product range has been at preventing customer defection. In the last quarter, £22m of spending moved from Tesco to Asda, with another £10m moving to Morrisons (Wearden, 2008).

Tesco has also recently undertaken significant social marketing initiatives, including the opening of a flagship environmentally friendly supermarket in Wick, featuring wind turbines, a system to gather and use rainwater, energy-saving cooling and cooking equipment and low-energy lighting. The store is designed to have half the ‘carbon footprint’ of comparable sized conventional supermarket. Exploring the value in environmentally conscious initiatives can reduce costs and please customers. However, as Tesco attempts to consolidate a position as a solidly price-conscious retailer the choice to pursue green initiatives may send out perplexing messages and this strategy should be questioned when the majority of its customers are lower middle to middle class and are changing their priorities in the current economic turbulence.

There are three dimensions that inform Tesco’s strategic behaviour regarding internationalisation. Firstly, buying successful companies is central to their strategy of overseas expansion, with greater movement into growing markets from the 1990s onwards. Tesco moved into Asia in 1998 with the purchase of a majority stake in Lotus hypermarkets in Thailand, and further developed operations in the region when they entered Malaysia in 2002, Japan in 2003 and China in 2004. The Malaysian operation was established as a joint venture with a local company Sime Darby Behad. Tesco owns 70% of the equity, but the operation is under local control. In China, Tesco signed a similar agreement with Shanghai Hymall. Tesco bought into successful companies with local operational knowledge and established market share and its expansion strategy aims at eventual market domination.

The second dimension concerns market selection. Tesco chose to enter into markets (Eastern Europe and South East Asia) where local competition was soft, away from other expanding giants such as Wal-Mart. Tesco also adapted to opportunistic events, and decided on different entry modes in order to develop knowledge.

The third dimension was that recognition that learning wouldn’t be enabled until some kind of store was opened, regardless of ultimate success of the venture. Tesco were comparatively weak internationally compared to more experienced rivals, but nevertheless decided on an aggressive expansion strategy in its target markets with a vulnerable period seen as a necessity for long-term growth internationally. Small experiential or pilot stores were therefore an integral part of the initial learning phase of expansion. These stores later might be seen as surplus to requirements and consequently divested. International retailers frequently emphasise the cognitive aspects of the retail internationalisation process. Tesco took an innovative approach to accruing marketing knowledge to aid its expansion and one example of this was the utilisation of embedded research teams within Japanese families to monitor consumption behaviour prior to their acquisition of the Japanese C2 chain.

Tesco also initiated a vigorous public relations exercise to get shareholders on board with a risky internationalisation strategy. Finally, Tesco ensured it had the best human resource to drive its expansion, with financial capital and marketing expertise considered imperative to formation of correct strategies in foreign markets. This was salient as expansion can be seen as an invasion by those in the targeted country. The adoption of an intensive PR campaign once business success started to develop overseas highlighted the need for an evolutionary marketing strategy.

Expansion into the US under the Fresh & Easy banner, however, has followed a different model. The US market is typified by extremes with luxury goods at one end and cheap products at the other. Pressure from both sides has resulted in the near elimination of the mid-market specialist. Aiming directly at this sector would therefore appear reckless. However, economist Frank Badillo asserts underlines the intelligence of their strategy, suggesting “It would be hard for Tesco to take on Wal-Mart head-on in the U.S. Instead they’re targeting a format that Wal-Mart isn’t strong in, but one that Wal-Mart is looking to get more into – smaller, convenience-driven stores.” Tesco is “beating Wal-Mart to the punch,” because the British retailer will have the capability to expand faster than Wal-Mart (McTaggart, Industry awaits Tesco’s invasion, 2006). Tesco’s entry strategy is to focus on a segment of cash-rich, time-poor customers, offering fresh products. The name ‘Fresh & Easy’ underlines the attempt to appeal to this segment.

The concept is a convenience format that emphasises simplicity from merchandise displays to the checkout (customers at Fresh & Easy can take use ‘assisted self checkouts’ or do it themselves). Most people in the US live close to a large supermarket. However, Tesco believes many will prefer the convenience of a smaller store providing the store caters for their needs. Although relatively small, the stores are large enough to stock a much wider product range than is the norm for similar US outlets, many of which don’t look beyond convenience store mainstays such as alcohol, frozen foods and snacks.

Tesco has also capitalised on concerns about obesity levels and the growing interest in healthy eating. Fresh & Easy offers a range of additive-free ready meals common in the UK but relatively unheard of in many parts of the US. Despite this, Tesco has been able to keep costs relatively low, at around 20% less than the major supermarkets. Tesco started its expansion into the market in California, as marketing research indicated a large population of health-conscious shoppers. In terms of experiential marketing, Tesco has aimed to hire local people so customers feel comfortable in the new environment.

Most retailers aim for a certain demographic segment in the market, particularly in the UK. For instance, Sainsbury’s targets middle-to-upper-income customers, while ASDA aims for lower-to-middle-income customers. Tesco’s targeting, however, is more inclusive and they remain committed to a broader reach. This has been possible through inventive segmentation and targeting. The most recent example of this has been the introduction of the ‘Discounter’ range of products to appeal to customers currently shopping at Aldi and Lidl. Since Wal-Mart purchased ASDA in 1999, Tesco has widened its market share advantage over ASDA by 2.5%. This is attributed to a strategy negating ASDA’s perceived price advantage. At the same time, Tesco increased its food advantage in terms of depth of range, quality, perception, and other attributes.

Tesco is also the world’s leading internet grocer, and it runs a highly successful financial services arm through a joint venture with the Royal Bank of Scotland. In contrast, Wal-Mart has been unsuccessful at entering the banking business in the US. Outside of the UK, Tesco’s most market presence is powerful among developed countries is in Thailand, South Korea, Hungary, Czech Republic, Slovakia and Poland. A strategy of entering markets where competition is weak or fragmented and there is no presence of established global retail giants such as Wal-Mart has contributed to the success of Tesco’s internationalisation. Tesco has shown great adaptability. For example, it’s highly successful Clubcard scheme was not introduced in the US because research showed consumers there to be cynical about the concept and already felt they had too many loyalty cards.

Tesco has also demonstrated astute response to perceived opportunities and threats. For instance, Tesco entered the UK convenience market with the Express format in 1995. In 1999, after refining the format, Tesco began opening a new generation of Express units, which included some prefabricated facilities (prebuilt and simply lowered onto the site). In 2003 Tesco bought the T&S chain of around 2,000 convenience outlets. Although it later sold some, it converted many others to the Express banner. Although investors were initially sceptical about Tesco entering this market, Express now has a higher return on investment than other Tesco formats, driven by sales densities as high as £30 per square foot per week. Tesco’s speedy realisation of ideas such as this, driven by the fear of copycat ventures, gives it first mover advantage.

However, attempting to introduce its UK model in other markets (particularly the US) is not without risks. Many Tesco convenience stores in the UK benefit from close proximity to public transport. This is thought to encourage more frequent shopping trips and a preference for perishable products over the meals typically sold in US supermarkets. An insurmountable challenge may be to persuade US consumers to behave likewise.

References

Chaffey, D., Ellis-Chadwick, F., Johnston, K., & Mayer, R. (2008). Internet Marketing: Strategy, Implementation and Practice. Harlow: Financial Times/Prentice Hall.
Humby, C., Hunt, T., & Phillips, T. (2004). Scoring points: how Tesco is winning customer loyalty. Sterling, VA: Kogan Page.
McTaggart, J. (2006). Industry awaits Tesco’s invasion. Progressive Grocer , 85 (4), 8-10.
Narver, J. C., & Slater, S. F. (1990). The effect of a market orientation on business profitability. Journal of Marketing, 54 , 20-35.
Ritson, M. (2008, October 29). Tesco takes fight to Aldi. Marketing, p. 20.
Strategic Direction. (2008). Tesco’s American dream. Strategic Direction, 24 (2), 11-15.
Strategic Direction. (2005). The secrets of Tesco’s expansion success. Strategic Direction, 21 (11), 5-7.
Wearden, G. (2008, December 2). Discount drive hits Tesco sales. Retrieved December 2, 2008, from Guardian.co.uk: http://www.guardian.co.uk/business/2008/dec/02/tesco-discount-sales

The marketing concept’s central premise is that when organisational culture is comprised of shared values and beliefs that place the customer at the centre of the decision-making process, the attainment of long-term corporate goals is stimulated. This is enshrined in Starbucks’ mission statement, to “develop enthusiastically satisfied customers all of the time.” This indicates intent to not only satisfy customers but also train them to appreciate an experience that Starbucks and few others could exclusively offer, as elaborated by CEO Howard Schultz, “We set out to educate our customers about the romance of coffee drinking. We wanted to introduce them to fine coffees the way wine stewards bring forward fine wines.” Thus, Starbucks aim to develop their consumers as they implement the marketing concept. Stakeholders play a significant role in influencing organisations’ implementation of marketing, yet  stakeholder theory’s application to marketing has proved problematic as there is no universally accepted definition of what constitutes a stakeholder, there is little research into the relative attention that companies give to their stakeholders  and the marketing literature tends toward bias in its orientation to one specific stakeholder – the consumer.

Starbucks’ key stakeholders can be grouped into consumers, employees and partners/suppliers. Basic consumer needs centre on having coffee variations made up from high quality beans, and fast service. Starbucks satisfies this by sourcing quality coffees from disparate locations and packaging them to reflect this, with products such as ‘Colombia Nariño Supremo’. In November 2001, the ‘Starbucks Card’ (a stored-value card that can be loaded in denominations up to $500) was introduced in the US. It is a convenience tool that can serve as a gift, and reduces time spent at the till. This contributes to the enhancement of the customer’s experience. Starbuck’s decision to forego high advertising spending, particular in new media channels, in favour of more traditional, understated means of attracting business is an example of how the profile of one group of stakeholders (customers) influences how marketing is implemented.

Starbucks advertises heavily through print media, as Starbucks’ target market tends to be educated city-dwellers who read more than the average person. In 2005, Starbucks spent $87.7 million (1.4% of revenue) on advertising, compared to Coca-Cola’s $2.5 billion (11% of revenue), preferring to reach likely consumers through events targeted at the educated metropolitan demographic. One example was an outdoor samba party celebrating a Miami Art Museum exhibition called ‘Big, Juicy Paintings’ for the launch of Starbucks’ juice-based Frappuccino drinks. Starbucks hosts a nationwide coffee break in the US, giving free coffee to customers who visit any branch between 10am and noon that day. This received national TV coverage and was therefore more valuable than paid advertising. This approach has proved effective and is a throwback to the way businesses appealed to customers before the mass media came along. Starbucks went back to basics, and they’ve approached the basics with a science and intensity that no one has ever done before.

Sensory branding also plays a large part in Starbuck’s engagement with customers. Recently, the firm has returned to grinding coffee in its stores for the sole purpose of improving the coffee aroma, despite it being cheaper to ship the coffee pre-ground in sealed packages. Any productivity loss at the stores is felt to be offset by improved customer loyalty and higher sales. Starbucks earlier removed its egg breakfast toasted sandwiches from its menu because the smell conflicted with the desirable coffee smell.

Starbucks’ noted consideration for its employees also influences their marketing implementation. It is an unusual company in that it strives to mix capitalism with social responsibility. It gives all employees who work more than 20 hours a week stock options and thousands of part-time Starbucks workers have full medical benefits. Providing part-time workers with benefits is not widely applied in contemporary corporate culture. This represents a position of ‘enlightened self-interest’ on the part of the organisation that appeals to its customers’ values, underpinned by the rationale espoused by Jim Donald, Starbucks’ former president, that “customers tend to patronise a business that is like them.” This consideration of such key stakeholders facilitates the more effective implementation of the marketing concept, as asserted by one employee who suggests, “We all have this common belief in the product we sell.”

In terms of suppliers, Starbucks approach has again been dictated by social conscience and the firm has gained prominence for its supportive approach toward coffee growers, including in East Timor, where coffee provides the livelihood of 25% of the population (Butler, 2006). By paying beyond the market price (at least $1.20 per lb), Starbucks tries to ensure a better quality of life for its suppliers and their workforces, which automatically gets translated in greater enthusiasm about the business, and elevated attempts toward performance excellence.

Nevertheless, coffee is, famously, the world’s second largest traded commodity and the supply is affected by weather conditions and the health of coffee trees. The price of the coffee bean is rising due to fluctuating supply and heightened demand. The quality of coffee sought by Starbucks is traditionally very high but its ability to continue to deliver such quality product to its customers may be influenced by suppliers’ increased bargaining power. It also remains to be seen how pervasive this approach will be as the economic environment becomes more challenging and their competitors increase.

Starbucks has utilised its stakeholders as effective partners in the delivery of high quality product. This interconnectedness, not only within the corporation through embracing workers of various backgrounds and capacities, but also between the organisation and its customers and suppliers, through supporting fair-trade pricing for independent coffee growers have proved an effective way to establish a connection with stakeholders at all levels. This has resulted in greater motivation and organisational excellence. Starbucks rapid growth and recent reports – record quarterly retail store openings of 728 stores; net revenues of $2.4 billion, an increase of 22%; comparable store sales growth of 6%; net earnings of $205 million, an increase of 18%; earnings per share of $0.26, compared to $0.22 per share, an increase of 18 % and record quarterly Starbucks Card activations of $287 million (Marques, 2008) – attest to the success of this stakeholder-centric approach.

References

Baker, M. J. (1984). Macmillan Dictionary of Marketing & Advertising. London: Macmillan.
Butler, K. M. (2006, May 1). Examining the benefits of corporate social responsibility. Employee Benefit News, p. 1.
Kotler, P. (1980). Marketing Management: Analysis, Planning and Control . Englewood Cliffs, NJ: Prentice-Hall.
Marques, J. F. (2008). Spiritual performance from an organizational perspective: the Starbucks way. Corporate Governance, 8 (3), 248-257.