When handling large amounts of data, errors are bound to result from time to time. The ways mistakes are rectified are often mishandled though. Writing for Harvard Business Review, Thomas C. Redman believes that the standard process of discovering and fixing errors on an as-necessary basis is inefficient, and data quality should become the job of everyone so that fewer mistakes happen in the first place.
Quality Gaps Are Myriad
Redman’s fundamental point to make about data is this: “The quality of data is fixed at the moment of creation.” It is either useful in the first place or it is junk, but nobody finds out until somebody actually tries to use the data for something. Only then is a decision made that the data is accurate or must be fixed, and if it must be fixed, it is very possible the people who fix it will never inform anyone else who could benefit from the corrected data. Indeed, often one department might produce data for its own purposes without even realizing other departments are trying or could try to benefit from the data too.
At present, IT is asked to become the hero when quality problems become serious, but Redman sees a flaw in this: IT can only correct other groups’ mistakes, and the business would do better to teach these other groups to improve their measurement quality in the first place. Redman also adds this:
The incentives for IT are weak as well. Business departments benefit tremendously from having access to good data to improve products, services, and decision making. IT reaps little reward, and it doesn’t feel the pain when the data are wrong. It is the business units and managers who must face angry customers, make good on promises, or explain poor results to shareholders.
Redman refers to employees who make data quality a priority as “data revolutionaries,” in that they disrupt the organizational status quo of data collection, though he acknowledges that such people are usually just motivated by a desire to find ways to do their jobs better. A natural result of trying to do one’s job better is to seek out clearer and more accurate data. The people who get proactive, even in the face of the business’s concerns, about collecting better data can be thought of as “provocateurs.” Redman recommends such people start small with their projects and maintain open communication channels so that the risks are nominal. Ultimately, executives rely on forward-thinking employees to push the company forward. Data collection could be an area in which to create critical improvements.
For further discussion of data’s birth defects, you can view “Data’s Credibility Problem” here: https://hbr.org/2013/12/datas-credibility-problem
For further discussion of data revolutionaries and provocateurs, you can view “Data Quality Should Be Everyone’s Job” here: https://hbr.org/2016/05/data-quality-should-be-everyones-job