The Value of Data Quality

The Value of Data Quality

Robert Hillard (Deloitte, Australia)
Copyright: © 2014 |Pages: 7
DOI: 10.4018/978-1-4666-4892-0.ch020
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Abstract

Almost everyone acknowledges the importance to business of data quality, however most organisations treat it as a technology issue. Increasingly, organisations are moving from cost recovery models to ones where they reward behaviours that develop their data as an asset. At their core, these models recognise that businesses live in an information economy that needs to be reflected at the micro level within the enterprise with individuals being rewarded as “information traders.” Introducing a trading approach to information changes, the mindset of the originators and managers of information who become motivated to maximise the value of the content they are responsible for. Just like any other resource, poor quality directly impacts its value. The approach also creates the opportunity to move the focus of data governance away from what is often perceived to be compliance activities to helping to establish pricing and the market as a whole.
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Changing Behaviours

Increasingly, business and government is realising that treating repositories of data as part of a computer system encourages all of the wrong behaviours when it comes to managing information. This situation is not helped by current accounting treatments which don’t allow for information to be explicitly recorded on the balance sheet as a standalone item, but rather require it to be wrapped-up in the capitalisation of the system that created it as well as the goodwill of transactions that brought the system into the organisation.

The treatment of a database as a system first, and a resource second, allows stakeholders across the organisation to abdicate their involvement. This abdication quickly turns into hostility under the regime of cost recovery which most large information technology departments operate under.

Cost recovery is the process by which information technology pays for itself by assuming that work is either operational and funded as part of the base load of the department or it is project based in which case it must be clearly funded with a beginning, middle and end. Only activities that are funded under either of these two scenarios are undertaken.

The implication of the cost recovery model is that the information technology team take less and less interest in the prioritisation or decision making around what gets done. They have to make the assumption that the team with the most budget will also correspond to the greatest business value.

The problem is that projects are large and the political activities required getting them funded means that value and budget aren’t always aligned. Even when they are, much that is valuable gets left behind. Data quality with it.

Confusing the situation more are the architectural requirements that are imposed by large information technology teams. The most common, relating to information, is the mandate that information is an enterprise resource and so reporting must be consolidated in the form of an enterprise data warehouse.

Consider the situation where the marketing team develop a useful campaign management tool. They decide that they would like to do some reporting from the database but are quickly told by the information technology team that they will need to submit a project request, together with a budget, to load their data into the enterprise data warehouse. Then, after spending a considerable amount of money for which they see no value, they will be allowed to submit another project request with budget to develop a business intelligence report against their own data.

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