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| Time-Series Data |
DAMA defines Data management as development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.
On a colloquial note, Right Data at the Right time to the Right person defines good Data Management practice. But what is the Right place for the Data management organization within the Business.
From the definition, it is reasonably clear to assume that data management is a culmination of activities that run across both IT and Business. Within the E&P upstream industry, there has always been an ambiguity around where the Data management organization be - within IT, within Business, across both IT and Business, a siloed organization on its own. Every approach has its pros and cons.
Business owns the data, so should the business have a team to manage their data. The advantage of this approach is (a) Business is a profit center. This will help in funding and sustenance of Data management (b) Good understanding of business helps in effective and efficient Data management.
On the other hand, this promotes technology and solution created within the business which may not be healthy on a longer run. The emergence of Shadow IT and Siloed / pointed solutions are a result of doing DM within Business
Data Management within IT can help provide a overarching enterprise wide Data management Platform and can help in sustainable solutions. However IT is a cost center and the IT mantra year on year is how to reduce cost. Building and sustaining DM needs requires consistent funding and the IT mantra will degrade the value of Data management within the Organization. Further more the translation of Business requirements to IT requirements adds to the gaps in entirety of the solution.
Having data management organization split across IT and Business will ensure that the activities belong where they should belong. It is however a well known secret on how Business and IT work with each other. Processes, Documentation, Communication gap, Friction and preconceived thoughts will become the routine that will hinder the progress of Data management. A typical light hearted example is shown.
Data management as its own organization is healthy in many ways. It provides a clear focus on Information and Data management. The end to end accountability is very clear and not barred by the disadvantages of Business or IT rules of engagement. There might be occasional rifts with IT but with strong relations this can be subsided. When the climate is good, this model provides the best of both worlds and becomes a value add to the enterprise. However it also comes with its disadvantages. Delivering a initial business case and make the leader of the organization buy in to this model is a uphill task. When the chips are down and the company / industry is not doing well, this will an easy target to kill / outsource and could be demotivating factor for the employee.
In conclusion there is no one size fits all. With the world becoming digital and everything getting driven by data, it is time for industries to consider a Chief Data office to run a Data Management Organization.
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