An interesting trend has started in Data Quality. Data Quality has lost its cachet (if it ever had it), and is quickly being overshadowed by the concept of “Data Control”. Data Quality is often thought of as a backroom activity, relegated to IT, and just another part of the plumbing of an overly complicated technology stack. Data Control, however, is seen as exciting. It connotes authority, which the business has long wanted to have over the data it so desperately needs for every tactical and strategic decision coming down the pike. But what is Data Control?
I break down the concept of Data Control into three distinct areas: access, accuracy and agility. From the perspective of the business, you really need to have all three if you want to claim that you’ve got data control.
Access is your ability to get at the data you need. It doesn’t have to be access to all data, as the odds are high that there is a mountain (or a lake) of data that doesn’t impact your specific business problem. However, you will need to navigate IT to determine the data you need, what you can have access to, and what data needs additional data controls like data masking. There are several key questions you will need to ask: Is the data you need accessible on your network? Do you have authority rights to access this data? Are the systems built with access points for tools (ODBC, JDBC, DB Drivers, APIs), or are they set up for automated, scheduled batch file extract? Based on the answers to these questions, you can determine if you’ve been able to get over the first hurdle to Data Control.
You know how I said people like to talk in terms of Data Control rather than Data Quality? Like it or not, they are not getting away from Data Quality that easily. You can’t have Data Control without data accuracy (if I have control of my car, and the speedometer says I’m going 30mph, but I’m really going 80mph, do I really have control of my car?). Data Quality is still, and always will be, a fundamental necessity when dealing with data. While some business users think IT just takes care of that, in the best cases, you may be accessing data from various systems with various levels of Data Quality, and varying standardization methods. Even “good” data, taken from two systems in two different formats, can quickly turn into “bad” data. Data Quality is a part of many data conversations, and will continue to be an integral component to Data Control.
Agility speaks to the tools you use to gain Data Control. To make use of the data, you need to have a tool that can be used by the business members of your team. These may be “data” people (business analysts, data analysts, statisticians), but likely not “technical” people (programmers and system engineers). With that in mind, the tools used need to consider that different audience and the different needs they require around ease-of-use. Ideally they are intuitive and can help you understand your information, fix discrepancies and consolidate records across systems.
Data Control really is about access, accuracy and agility. It will take some work for the business to gain data control, and stakeholders really need to think about the skills they have available and what they can leverage internally and externally from third parties to make that data control a reality. But remember, once the business has control of that data, they are on the hook. IT shifts to become the “safe deposit box” of data, rather than the rulers of data (it’s not the banks job to build a collage of the old photos in your safe deposit box – they just keep them safe – you’re responsible for the result).
It’s a lot of responsibility, but the opportunities that having Data Control provide for the business are limitless.
Author: Kevin W McCarthy