TRILLIUM SOFTWARE INSIGHTS

Changing Wrong to Right – Making the Case for Data Governance

By Nigel Turner, Vice President, Information Management Strategy, Harte-Hanks Trillium Software

In my last blog, our data quality (DQ) protégés of LA’s most famous fictional detective Philip Marlowe had been investigating a number of key suspects which they believed had been hurting the business. They had pounded the mean streets, interrogating witnesses to the crime. Evidence had been gathered. It was now time to act. The next step was to make the case to the Business to prosecute the data and try to right the wrongs that had been perpetrated.  

No data quality improvement project should ever be started without a business case.   DQ improvement is never an end in itself. Before deciding to invest in DQ, it’s vital to demonstrate to the business that the problem to be addressed is worth tackling, and if successful, will deliver real benefits to the organisation. 

From my time in British Telecom, where many cases were assembled and presented for business endorsement, a number of general lessons emerged.

First, before preparing a case based on the analysis and evidence gathered earlier, ensure that the case has a business sponsor. This is a clear sign that someone in the business cares about it and feels it worthwhile to pursue. The sponsor should have sufficient influence and authority to champion the case, gather support and break down inevitable barriers that others will put up. 

It also needs, at least, the initial support of key stakeholders who will be affected by the proposed improvements, and / or who might be needed to make the changes required, especially if those changes demand alterations to business processes, people’s objectives and so on.

Second, always try to present more than one option for action. For example, your investigations might have uncovered inconsistencies between billing and sales data which had been causing loss of revenue. You might lay out two improvement strategies. One might be a relatively inexpensive option with tentative or reduced risk and benefit, such as introducing an additional manual check of data before transferring data from Sales to Billing. 

The second option might be a full blown automated DQ tool-based validation and remediation solution, which would initially be more costly but which would deliver more enduring longer term benefits. Make sure you include a recommended option, but give the business some leeway. In any and all cases always include a ‘Do Nothing’ option. Here doing nothing might mean that billing and sales sources become increasingly out of step, with potential revenue losses increasing gradually over time. It also shows that doing nothing is not a viable choice, now or into the future. 

Third, whatever the core rationale of the business case (whether it’s enhancing business performance, customer perceptions, conformance to regulation etc.) always try to include quantitative financial benefits. DQ improvement almost always requires material investment and costs, so your case will be more compelling if you can show it will positively impact the bottom line. Even regulatory drivers usually have some underpinning or tangential financial benefits, e.g. avoiding the risks of hefty fines from a regulator if you get it wrong. 

Fourth, remember that each business case is unique. Every DQ problem I have come across is caused by a unique combination of people, process and technology shortcomings. The benefits of improving it, and the costs involved in doing so, are therefore always specific to that case. If you can, enlist the help of a finance expert who has worked on business cases before to help you assemble the case and to validate any financial benefits. He or she will know what works and what doesn’t work in your organisation. 

Once you’ve gone through these steps you are ready to present the case to the business and IT people who you need to make it become a reality. In the next blog, I’ll highlight some techniques for winning support within your stakeholder community.  Putting a strong case together is a necessary but not sufficient step. You now need strong advocacy to convince the judge and jury.

Philip Marlowe has done his job and is working on his next investigation. It’s now time to call in Perry Mason.

 

Data Quality Becomes More Crucial for Business Intelligence

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

Boris Evelson of Forrester Research provided his Top 10 Business Intelligence Predictions for 2012. 

There are two predictions that really stood out as key data quality pitfall areas:

“…No management or control is not acceptable, but too much control does not work. Finding the right win-win combination that combines the flexibility and agility that self-service brings with behind-the-scenes monitoring and adjusting will become the name of the game.

“Exploration (without preconceived notions, prebuilt fixed data models, or up-front specific questions in mind) will be the new bread and butter of BI suites in addition to reporting, querying, OLAP, and dashboards/data visualization.”

In each of these instances, analysts and consumers of data may not have the quality controls in place to gather and get the insight necessary to make informed decisions on what the data tells them. Data quality programs still have not reached an enterprise-wide state to bridge gaps across silos of data. Even when paired with master data management initiatives, quality is sporadic to highly-specific business requirements and business units. Open up the doors to data exploration across multiple and possible un-regulated data stores and you can see where consumers will come up against data reconciliation issues and poor data quality.

However, all is not lost. Data quality solution capabilities have come a long way from back office middleware for data warehouse integration. Trillium Software has specifically focused on quality controls at the point of exploration and consumption in environments that analysts and business consumers live in. Whether controls are introduced through a SOA deployment, or assessment and analysis are instituted within business intelligence activities, poor quality data introduced into self-service intelligence and data exploration don’t have to impede insight.