Alice in “Qualityland”

Alice: Would you tell me, please, which way I ought to go from here?
The Cheshire Cat: That depends a good deal on where you want to get to
Alice: I don’t much care where.
The Cheshire Cat: Then it doesn’t much matter which way you go
– Lewis Carroll, Alice’s Adventures in Wonderland

When confronted with the problem of how to address their data quality issues many organisations are faced with a similar dilemma to that which confronted Alice during her travels in Wonderland; “I know that I need to do something, but I don’t know where to start”. Knowing where to start and, equally importantly, the size of the problem as well as where an organisation needs to go are critical factors in ensuring that their data quality journey takes them where they need to be at the price they are prepared to pay.

When planning their “journey” organisations need to address the issue of data quality holistically by considering each of the three DQ pillars in turn; firstly “People”, then “Ideas” and finally “Technology”. Many DQ initiatives have failed as the primary focus has been on delivering a technical solution. However without the right framework in place and operated by the right people this approach will never deliver the results that organisations need. Time and time again within the IT industry it has been proved that the pure application of technology will never solve business issues, as technology in itself will never win the “war”, it is always the right people with the right ideas who use the technology in the right way.
The “People” pillar represents the data quality function within an organisation and will typically involve, amongst other activities, the identification of data owners, the appointment of data stewards and the establishment of appropriate data governance on a scale that is appropriate to an organisation. A successful “People” pillar will encompass both the empowerment of the right people in the right places as well as address the cultural shift required to accommodate the concepts of data ownership and accountability; i.e. moving the business perception of the role that IT plays, from one of owner to that of custodian while communicating a clear vision on where it wants to be.

Once the “People” are in place then appropriate “ideas” can be developed. By “ideas” we mean the data quality initiative, program or strategy that encapsulates the key quality principles that are relevant to a particular organisation, i.e. their critical success factors, as well as defining how they will implemented and by whom. The “People” component cannot operate in a vacuum and therefore it is crucial that they function inside of a defined framework which is understood by them and conversely, can be easily communicated to others.

A critical step in this process is defining exactly what constitutes data quality for an organisation for as John Guaspari’s discusses in his book, “The Customer Connection”, quality is not just the absence of defects as determined by the producer but also the presence of value as determined by customers. Integral to this process is the identification of the key metrics that will be used to underpin this definition and thus enable objective rather than subjective measurement of the problem.

However whatever the final outcome of this exercise it must be recognised that not every problem in every instance can or for that matter should be addressed. The “People” driving the process should review the work being performed from a perspective of both appropriate risk mitigation and overall pragmatism.

Once the first two pillars are in place then the appropriate “technology” can be identified which can both deliver the “Ideas” and be simply and cost-effectively used by the “People” to enable, and not drive, the DQ process. When reviewing which particular “technology” an organisation should adopt it is important to consider the following:
• Its capacity to deliver the generic functionality of contact efficiency and relationship identification but also it’s extensibility to perform across all business data types and it’s ability to deliver sophisticated data analysis
• Its ability to empower business users so the focus of the work can be moved from the IT custodians to the data owners with its related shift of accountability and cultural mindset
• Its ability to be deployed across the full spectrum of business data and not be constrained to only name and address data, as are many of today’s products.
• Its ability to support vertical solutions which address the specific requirements of many industries be it Sarbanes-Oxley reporting for Financial Services, Global Data Synchronisation for CPG or Product Cataloguing for Pharmaceuticals
• Its support of open data repositories and “out-of-the-box” functionality that can be quickly deployed and integrated easily into an existing IT environment

Finally, central to an organisation’s successful adoption of data quality principles is the ability to communicate to all stakeholders not only what work is being performed but also how it is progressing be it a discrete vertical solution such as Basel II compliance project or a horizontal data integration project, e.g. data migration . Increasingly the use of benchmarking and score carding is being adopted to achieve this and organisations should take note of a product’s capability to generate such reports and how easily they can be integrated into their normal business intelligence portfolio.

Alice: Would you tell me, please, which way I ought to go from here?
The Cheshire Cat: That depends a good deal on where you want to get to
Alice: I don’t much care where.
The Cheshire Cat: Then it doesn’t much matter which way you go
Alice: `–so long as I get SOMEWHERE,’ Alice added as an explanation.

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