TRILLIUM SOFTWARE INSIGHTS

Data Governance – What do we call it and where is it headed?

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

While the data governance industry is rapidly maturing, there are times when it seems to be like shifting sand from an ocean breeze. In the last 60 days alone I’ve seen data governance actually defined in a number of ways. In other words, companies are talking about implementing data governance practices around initiatives, but they call it many different names. In actuality, they’re really trying to solve similar data governance challenges. 

As I’ve said in a past blog post, “no two solutions are ever the same,” but many times, companies are in fact trying to solve the same general dilemmas – aligning people, process, policy, standards, technology, and business units in order to drive the desired business value and outcomes from their data assets. Though the challenge may be the same across organizations though, it’s the scope of these projects that differ, which in turn, accounts for the various labels companies apply to common initiatives.

Among the most common terms that I am hearing these days include:

• IT Governance
• BI Governance
• MDM Governance
• Stewardship
• Risk & Compliance
• Data Management
• Data Asset Management
• Information Management

For example, when people speak with me about their IT/BI/MDM governance, they are simply trying to apply data governance practices at a project layer. In many cases the projects just happen to be large.

When people talk about stewardship, risk and compliance they are often talking about sub-domains that typically fall under the umbrella of data governance. It just so happens that some of these sub-domains play a significant role in the business such as risk and compliance within the financial industry.

Terms and practices I expect will evolve, survive and eventually converge are data management, data asset management, information management, and data governance itself. In reality, each of these has striking similarities. In addition, many people are asking the question, “why should we restrict data governance to structured data only? Can’t the same principles and practices be applied to all our company information? The answer is most certainly yes.

Today data governance is the predominant term and practice used to align people and processes and standards. However, I foresee that this too will continue to evolve and may at some point down the road incorporate unstructured data assets which in turn may lead to the next generation of data governance.

Data governance is complex, but don’t make it too hard. Many times, you’re simply solving the same issues on a larger or smaller scale. But keep in mind it takes a lot of planning. Just realize that many of the same data governance best practices can be applied to all of these types of projects or definitions, and number one is creating the business case or you won’t get budget, resources or buy-in to realize your data governance goals.

Is Data Quality just a Checkbox on an RFP? Well, it better not be.

By Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte Hanks Trillium Software

When your company plans to spend $5 million for a strategic data management, warehousing or BI solution, you better be taking an extremely close look at the information and data that feeds that solution.  Take data quality lightly at your peril.

Data is one of the most important assets in your company, and its quality needs to be clearly understood at all times as it flows through your organization. Yet, some companies see data quality as simply a commodity to be “checked off” as part of some RFP, instead of a strategic, unique part of your business.  Data quality doesn’t lend itself to being a commodity, especially when evaluating the impact of data quality on your company’s operational performance.

Industry analyst firm Gartner says in research from August that companies are losing an average of $8 million annually because of data quality problems.  Do you think data quality is just a check box now?

Former customers of the “mega-vendors” tell me these software providers say they “understand data quality” and “we do data quality.”  But, chances are, they will not meet your expectations if you ask them to explain how their data quality solution will solve your unique business challenge.  They may think they do, but they’re simply treating data quality as a component of an ETL process and will fall negligently short of goal in an operational environment.  Their perception will hurt your business, so get under the hood, or better yet, make them get under the hood to define their data quality solution. You will be surprised by their lack of knowledge.

Our advice; be vigilant and advocate on your own behalf for the most comprehensive solution that meets your business needs. Demand a solution that:

• Solves the challenges inherent in multi-domain applications…. companies have data touching virtually every part of the business,
• Maps, both methodologically and physically, to your existing and future business processes,
• Demonstrates the ability to grow as your company changes and grows – it can handle 50 million records today, can it handle 150 million tomorrow?
• Is application-agnostic, and integrates with existing and future applications and infrastructure,
• Will be your data quality partner….one who is knowledgeable and understands all your inherent challenges and how they relate to your business.

If your data quality partner can’t explain to you how data and information changes at every stage of the data lifecycle – from the moment it enters your systems until it is archived – than you will have made the wrong choice.  Data changes and manifests itself in many different ways as it flows from point-of-entry through sales, marketing, billing, manufacturing and shipping departments.

Your data quality partner needs to be able to clearly tell you what data gets added, what gets taken away, what needs to be verified and how, and what must be reported on. 

So, just because you think you checked off that box on the initial RFP, does not mean you really solved your data quality problems?