When Profiling Is A Good Thing

We all know the kind of profiling that is completely unacceptable and that’s not what I’m talking about here. I neither condone nor practice any kind of socially unacceptable profiling. But there IS one type of profiling that I strongly recommend: Data Profiling. Especially before you migrate your data.

If you think that sounds like a luxury you don’t have the time to fit into your project’s schedule, consider this: Bloor Research conducted a study and found that the data migration projects that used data profiling best practices were significantly more likely (72% compared to 52%) to came in on time and on budget. That’s big difference and there are a lot more benefits organizations realize when they use data profiling in their projects.

Data Profiling enables better regulatory compliance, more efficient master data management and better data governance. It all goes back to the old adage that “You have to measure what you want to manage.” Profiling data is the first step in measuring how good the quality of your data is before you migrate or integrate it. It allows monitoring the quality of the data throughout the life of the data. Data deteriorates at around 1.25-1.5% per month. That adds up to a lot of bad data over the course of a year or two. The lower your data quality is, the lower your process and project efficiencies will be. No one wants that. Download the Bloor Research “Data Profiling – The Business Case” white paper and learn more about the results of this study.

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Three Most Likely Culprits for Data Quality Problems

Few things get better with time.  Without careful attention your data certainly won’t be one of them.

For most organizations, a love/hate relationship exists with their data.  We love that we can draw together information from various systems and use it to see a picture of how effective we’re being. We hate how difficult it is to move and maintain that information.

Recently, I’ve been working with a large organization that is making changes to its data integration infrastructure.  As part of the project, we’re reviewing how data moves into the organization and through multiple core business systems.  It’s been remarkable how many times the data is touched and the potential negative impact this whole cycle has on data quality.

Through identifying actual problem areas we’ve come across some now familiar culprits:

  1. Intake of information.  Often all the data isn’t loaded. The risk is that we don’t get everything, and it reduces the quality of what we have.
  2. Cyclic miscommunication between systems.  Dependencies and the system strain associated with moving large amounts of data in and out result in periodically missing a transfer.  One process gets backed up or breaks and the delays snowball.
  3. Complexity of processes.  At some point in every process, business rules get inserted to make decisions on how and where the data belongs.  Knowledgeable IT staffers are asked to create complex processes that are very difficult to test.

We see these same problem areas to varying degrees with most of our larger clients.  Data is certainly difficult to handle – that’s not a new idea.  But what is the collected result of this difficulty?

The quality of the information you use to run your business depreciates steadily over time. Given time and complexity the quality of your data will decrease.

External factors can add fuel to the fire.  If some of this data is about people (and some of it surely is), then there’s a silent but significant change going on external to your organization.  People are constantly in flux – moving, changing jobs, getting married, etc. – all of these activities are bad for the information you house about them.  Even in a short amount of time you know less than you did initially.

What’s to be done? How do you earn top marks for clean data?

A+-Grade

Ideally, the solution is to examine your data handling processes and look for problem areas.  Is your organization using the best tools to do the job?  While this is the best approach, it can be overwhelming.  At some point this just has to be done and the longer you wait the more difficult the mess will be to unravel.

At the other extreme you can ignore the problem and treat the symptoms.  While this seems like a bad idea for the long haul (it is), it can be very cost efficient and give the organization a significant lift.  Taking a look at the data where it’s being used and identifying missing or bad data is the first step.   Once you see the troubles then solutions become possible.

Data is a corporate asset.  It requires maintenance and it depreciates over time.  Like everything else you do, recognizing the problem is the first step to a solution.