Perfect Data Quality?

Can a Data Manager ever raise a hand in front of his CTO or DGO and say he has achieved perfect data quality? The answer unfortunately is a big ‘NO’.

Why? Because there is nothing like perfect data; it’s a mirage which, if you start chasing, will get you stranded in the middle of a desert. So, is it worth trying and remediating bad data? Yes of course, but we have to take a selective approach. The Pareto principle, the 80-20 rule, will come to the rescue here. Identify 20% of the issues that fix 80% of the data. Easier said than done but at-least it is something achievable and can be aspired for.

Even more important is to realize that fixing the process is far more important than fixing the data. I am not trying to imply that data remediation is not important. It is and needs to be done. But it should be more of a correction or fix in case of exceptions or a one-time effort when you laying down the foundations at the onset of your Data Quality program.

Data Management, Governance And Stewardship - II

Continuing the discussion from the last post, let’s understand the relationship between Data management, governance and stewardship.

You have just built a world class analytics platform that will cater to the data needs of your analysts and business managers so that they can slice and dice it and bring you insights that will improve operational efficiency and give you a cutting edge over your competitors. But what happens when after spending millions of dollars your business managers are not confident about the data they are using? Can your organization rely on the decisions they make? How do you ensure that the system maintains the same level of quality with which you built the system and it is not deteriorating over time? How do you ensure that your data and its handling is compliant and in agreement with various internal and external regulations?

If you are asking these questions you are probably questioning the quality of your data or information. Some people argue that data and information are two separate entities with data representing the ‘numbers’ and information providing the ‘context around those numbers’. Since both are incomplete without each other, for the purpose of this blog we will treat them as the same.

Data Governance, Stewardship and Management

Data governance, stewardship and management are more often than not mentioned in the same breath but are all three the same? If not, what is the difference between the three? How to determine what function belongs to which area?

All three are closely related often with blurring boundaries and sometimes have overlapping functions and at the very least need to work closely with each other to reach their end goals. But still there is a need to understand the basic difference between the three to be able to perform whatever role you have, more effectively. Based on my understanding I will layout what each means and how they are related to each other at a very high level.

Data Management, I believe, encompasses the broadest scope of the three - governance and stewardship being the sub-functions. It encompasses everything related to the data that an organization has – right from definition, delivery, quality to security and access. As per DAMA Data Management Body of Knowledge (DAMA-DMBOK):

"Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.”