People, Process & Politics: Data Governance

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People, Process & Politics: Data Governance

For those of us in high tech, it’s easy to fall into the trap of concentrating too hard on the products, technology and architectural aspects of solutions. I’ll admit I’m a bit of a nerd and many people in IT feel comfortable with technology too. It also goes beyond IT; in today’s society people often assume technology is going to solve their problems.

There is a tendency, especially in the US and Europe, to believe that technology can solve the difficult problems that we face. Technology, especially in corporate settings, is viewed as the cure-all or silver bullet that overcomes all the obstacles that have been encountered with data in the past.

But the problem with our comfort level and faith in technology is that often the critical success factors of a solution lie with people and policies. And often there’s a little bit of politics thrown in to keep things interesting. Very interesting…

So, if you are embarking on or trying to revitalize enterprise information, you’ve got to put the people and the policies on your front burner. There are several areas that you need to consider:

•    Data Governance
•    Program & Portfolio Management
•    Integration Competency Center (CC)
•    BI Competency Center (CC)
•    Skills Assessment & Training

Let’s start with data governance.

I see it with my clients and you surely see it in your business: you’re striving to better use data to improve the top line by increasing customers, revenue, and profit.  Or, increasingly important in today’s economy, you’re examining operational performance to impact the bottom line by reducing costs. Add in compliance and privacy drivers and there is a compelling business case for enterprises to manage their data as a corporate asset.

Many of us have heard the “single version of the truth” pitch from enterprise application vendors or from people trying to justify business intelligence (or performance management or MDM or SOA or…) initiatives. These pitches all involve a massive dose of technology and products to solve the data problems you have never been able to in the past. Do not get lulled into the product hype or the silver bullet solutions.  CapEx (capital expenditures) give people the feeling that progress has been made, but the investment will be wasted without the proper amount of people, process and politics.

Regardless of what tools you use, you have to define, transform, measure, monitor, manage and then act upon it. Despite their best intentions, IT does not own data;  rather, the business does. Business responsibility for defining the data is essential to obtain trusted and actionable information.
Data is created and then transformed as it flows through various business processes throughout an enterprise. Ownership and the responsibility to define the data and its transformations are political. IT often is wary of the politics, but there is no way around it to be successful. Business ownership can get IT out of data politics and into their proper roles of enablers and stewards for corporate information. 

If you get over the political hurdle then IT has to avoid the “Goldilocks and the three bears” syndrome. The governance processes cannot be either too hot (complex) or too cold (not comprehensive enough). They need to be just right. It will likely take a few iterations to get the processes to be constructive rather than obstructive.

Getting the right people involved in data governance is another stumbling block people encounter. Too often the people involved are too senior and too removed to guide the processes. The other extreme is that the business people involved are at too low a level in the organization to have enough authority to make decisions that others will follow. This, like processes, may also take a few iterations to get right.

Data governance is neither simple nor a one-time event. Do not underestimate time commitments by both business and IT people. Too often governance is thrown on top of workers’ existing workloads. Failing to allocate sufficient time people to work on data governance is recipe for project failure.

It’s people, process and politics that make the shiny tools succeed.

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