Opinion: Alice in “Quality” land

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Opinion: Alice in “Quality” land

When confronted with the problem of how to address issues of data quality (DQ) many organisations face a similar dilemma to 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, the size of the problem, and where an organisation needs to go are critical factors in ensuring that a DQ ‘journey’ takes the organisation where it needs to be at the price it is prepared to pay.

Plan for the journey
When planning a journey organisations need to address the issue 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. 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 it has been proved that the pure application of technology will never solve a business issue 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.

It’s all about the people
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 in turn appropriate to the organisation.

A successful People pillar will encompass both the empowerment of the right people in the right places and the implementation of 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.

The ideology
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 be 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 data quality constitutes for an organisation. In his book “The Customer Connection”, John Guaspari's argues that 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.

Whatever the final outcome of this exercise, it must be recognised that not
every problem in every instance can, or 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.

Here comes the technology
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 its extensibility to perform across all business data types and its 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 integrated easily into an existing IT environment.

And on a final note, 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.

Neil Gow is Informatica Australia’s Senior Solutions Consultant and resident DQ Guru.

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