As I explained in Part 1 of this series, creating a strategy for the data in an organization is not a straightforward task. Two of the most important issues you'll want to address in your data strategy are data quality and big data.
There can be no data that is perfect. There, I finally said it out loud! So, is 99% OK for a specific set of attributes or data stores? What about 99.9%? The higher data quality expectations or standards, the higher the cost of that data quality. Consider data quality part of your data governance strategy and start small.
First, decide where the data quality monitoring should take place. I like to get as close to the source (if not directly at the source) as possible. Then decide what to monitor, how to store the results and – the most important part – decide on the process for correcting the data in the sources. A big risk is the fact that sometimes we have no influence on maintenance of those source systems. So, know how to escalate to get the results you need.
Everyone keeps asking me how to include big data as part of the data strategy. This is one of those changing data stores again! It seems that big data technologies and toolsets change every week. That said, consider the following factors (including any risks they involve):
- Is the data unstructured? If so, this is a great solution.
- Do we need to integrate with structured data? Still could be great!
- What security do I need on this data? (Who uses it, etc.) Is there privacy information included in this data?
- How complex are the updates for the server? Complexity does not always mean SPEED.
- Who is using the big data?
- What is the use case for the big data? Please don’t tell me that you want to use it because it is new.
Just a few things to consider.
Find out what the EIU has to say about big data strategy.