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Strategy Components

Data Governance Strategy

This is my first column on data governance strategy. In this column, I’ll attempt to lay out the primary components of a strategy and give you a starter kit for each one. This will allow you to tailor each component to your organization and then cobble together your own data governance strategy. Writers, lecturers and consultants in this field have different definitions of what constitutes data governance, and some will disagree with me on one point or another. That’s OK. I would expect you to take what is relevant for you and your organization and discard the rest. I’ll attempt to define any terminology that might be misinterpreted. This doesn’t mean that I have the only and correct definition, but at least you will know how I’m using the term. I strongly suggest you create your own glossary that you will share with your team, your stakeholders and your organization and put it up on your internal Web site. If you don’t like my definitions, change them, but try to use them consistently within your organization.

 

Here we go. In my concept of data governance I include:

 

  • Data quality - including conformance to valid values; uniqueness; nonredundant, complete, accurate, understood, timely, referential integrity;
  • Metadata creation and maintenance - information about data, both technical and business;
  • Master data management;
  • Data integration;
  • Data categorization for performance, availability and security;
  • Business intelligence;
  • Organization to support data governance - data ownership, data stewardship, data administration, database administration, security officers and sponsorship for the data governance initiative;
  • Security and privacy;
  • Rogue data that is not under any controls or standards;
  • Treating data as an asset;
  • Traditional database care and feeding, including database design, performance, backup, recovery and archiving;
  • Tools and products to support data governance; and
  • Measurement - usage, performance, benefits, data quality measures.

Sponsorship

 

Before much else happens, a data governance initiative needs executive sponsorship, and it needs this sponsorship for three reasons:

 

  1. The initiative takes resources and effort with the right people and the right skills with the proper level of authority.
  2. Without the right sponsorship, a wonderful strategy might be developed, but there is little chance that it would ever be implemented.
  3. Without the right sponsorship, the heads of each fiefdom will resist change - they may resist anyway - and it would be impossible to get the requisite cooperation and commitment to change.

Data governance takes an ongoing commitment. It cannot be “this year’s initiative.” It needs sustained support.

 

Find a Sponsor

 

In attempting to determine who would sponsor the data governance initiative, you should look for the person who has the most to gain or the most to lose. The CIO should be a co-sponsor, but the primary sponsor should come from the business. It needs to be an executive with power and money. It could be the CFO, who has much to lose if he signs off on a grossly inaccurate representation of the company’s finances; he could be doing time in the Big House. He cares about the accuracy of the organization’s books.

 

The sponsor needs a realistic assessment of the effort involved, especially the effort required from his or her organization as well as the expected time frame and the anticipated benefits. The sponsor would want to review the data governance strategy, possibly at a high level, but some of these people will want to delve into the details, so you will want two versions of the strategy.

 

The Message

 

The message has to resonate with the sponsor and with all the data governance stakeholders. The message about data governance needs to be consistent and needs to support the strategic goals of the organization. For example, if a primary goal is customer service, it is obvious that without a high quality customer database - you will hear customer data integration (CDI) as the current buzzword and acronym - an organization will not be able to provide a high level of customer service. The high quality customer database is a necessity but is not a sufficient requirement for great customer service. This is the message. Another example is the goal to not miss deadlines for consolidated reporting of quarterly results. An integrated financial database should shorten the process of consolidating the organization’s financial picture. This is the message. The message is always a business message, never an IT message.

 

The message will morph into how the project is progressing and the tangible and intangible results, such as customer satisfaction increased from 58 percent to 62 percent and we are now able to report to the Securities & Exchange Commission one week earlier than before we instituted the data governance program.

 


Sid Adelman is a principal in Sid Adelman & Associates, an organization specializing in planning and implementing data warehouses, in data warehouse and BI assessments, and in establishing effective data architectures and strategies. He is a regular speaker at DW conferences. Adelman chairs the "Ask the Experts" column on www.dmreview.com. He is a frequent contributor to journals that focus on data warehousing. He co-authored Data Warehouse Project Management and is the principal author on Impossible Data Warehouse Situations with Solutions from the Experts and Data Strategy. He can be reached at (818) 783-9634 or visit his Web site at www.sidadelman.com.

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