Having a foundational strategy to a Data Dictionary (1 of 5)

This is the first of a five-blog series, based on an engagement this past year. We were approached by a private organization to provide a Data Dictionary, which is one of those terms we sometimes have a bit of skepticism.  The immediate questions were:

• ·      What is your definition of a Data Dictionary?
• ·      How many systems are involved that would require the Data Dictionary?
• ·      Are you ready to maintain a Data Dictionary?
• ·      What kinds of changes are going on at the company that may be causing this request?
• ·      What kinds of pain points are being experienced that may be causing this request?
• ·      What is the overall knowledge of the data management?

After several meetings, it became clear that there were some misinterpretations of definitions and some key foundational requirements that were not aligned between what we would consider a successful approach and one that may not be best for their long-term needs.

So, our proposal was not just to give them the Data Dictionary, but to get them “to” the Data Dictionary.  (Think about the old proverb: “Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime.”)

There was reluctance from the organization, as they wanted to have the final product but at the expense of not having to do the work up front to make sure the final product was going to succeed.

This brings up an interesting paradigm that we preach at Liaison.  The mantra is “Right Up Front” (RUF).  So, in order to have the best Data Dictionary and process to maintain the Data Dictionary, we were compelled to propose several RUF initiatives to make sure it was successful.  We’re not just going to turn over a Data Dictionary without engaging with the prospect on the foundation to make it work. So, we stood firm on several additional exercises to lay that foundation:

• ·      Data Governance – This included workflow processes, resource capacity, resource responsibilities and setting up of a governing council.
• ·      Cultural Change Management – This included finding out more about how well the company gets along internally.  If you can’t play well together, you may not be able to handle data well together, either.
• ·      Strategic Thinking – This included a common goal, how those goals will be achieved and an asynchronous attitude to bring those strategies to fruition.

It is Liaison’s model to make sure these are in place (or at least understood) and with a plan to have the right tools in order to proceed down the data management path. Without this, data management will be data mismanagement – then both the customer and Liaison reputation could be at risk.