Saturday, September 6, 2014

How to Create an Enterprise Data Strategy

I work as an IT Architect. One of the more interesting things I get asked to do on occasion is to create Strategies in particular technology areas. This represents the "high-level" side of the typical IT Architecture duties one tends to run into (if you're interested in more IT Architecture related topics check my new blog here - The IT Architecture Journal). A very popular strategic focus across industry lately is Data Strategy. In this post, I will try to explain why it has gotten so popular and some of the fundamental aspects of actually producing one.

Data Strategy, Defined
Data Strategy is the collection of principles, decisions, expectations as well as specific goals and objectives in regards to how enterprise data and related data systems and services will be managed or enhanced over a specified future time period. As an actual artifact, Data Strategy is usually manifested as a document, but is not limited to that format.

A Data Strategy is usually conducted in conjunction with some IT Portfolio planning process. In optimal situations, portfolio decisions and follow-on data-related projects can be mapped directly back to goals and objectives in the Data Strategy.

Why Data Strategy is so popular
Organizations across the world have become more aware of the need for greater attention to data related issues in recent years. Some of this has been driven by collaborative industry initiatives through groups like DAMA (the Data Management Association) and the resulting Data Management Book of Practice (DMBOK). Other drivers include the near-flood of new data technologies released over the past decade as well as the exponentially growing quantity of data out there.

So, what does having a Strategy give actually give you?

What it often provides, if used properly, is both a set of shared expectations as well as a clear path for actualization of those expectations. The Data Strategy allows organizations to deliberately decide how best to exploit their data and to commit to the major investments which might be necessary to support that. This, when contrasted with an hoc and decentralized technology evolution scenario presents a much easier picture to grasp. And it also at least implies a situation that will be easier to predict or otherwise manage. It is that promise of manageability that makes creating a Data Strategy so attractive.

Elements of a Typical Data Strategy
The following mindmap illustrates some of the common elements that you'll find in many data strategies. One noteworthy item in this diagram is the idea of sub-strategies (which can be split off into separate documents / artifacts) ...

The Top 7 Considerations for Data Strategy
While there are many more things to keep in mind, I've tried to distill some of the most important considerations for this post...

  1. The strategy should take into account all data associated with the enterprise. This may sound obvious but in fact it isn't really that obvious. Many organizations explicitly separate management of dedicated data systems from other systems which may have data in them but aren't strictly just DBMSs or reports, etc. For example, there may be state data in small data stores associated with a web-based application that supports an online form / application - the data structures supporting the completion of the form may be different than the ones which collect the completed form data. However, all data, in all applications regardless of where it may be located or how or it is used must be considered.  
  2. There generally needs to be an attempt to define an organizational 'lingua franca' - or a common semantic understanding of data. There are many ways this might be achieved, but it is important that this included within the strategic plan.
  3. The Strategy cannot be entirely generic, even if one of the most vital objectives is some type of industry-driven standardization. Wholly generic plans are usually less than helpful.
  4. The Data Strategy must be presented within a larger context. What this means is that there needs to be an expectation that the Strategy will indeed be the precursor to other activities which ought to be able to map back to it for traceability purposes. 
  5. The Data Strategy needs to have sufficient detail to be meaningful. If it is too high-level it becomes merely an elaborate Mission Statement. The expectation behind any Strategy or plan is that it be actionable. 
  6. The Data Strategy ought to be need or capability based - not product or Hype focused. 
  7. There ought to be a way to measure success 'built into' the Data Strategy. This can come in the form of basic service level expectations or business outcomes or both. 

What goes into one Data Strategy versus another can be radically different from group to group. If you have a Social Media company your needs will be quite different than the US Coast Guard for example - but both will likely need their own Data Strategy.

Copyright 2014, Stephen Lahanas



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