Active Data Governance vs. Passive
Passive vs. Active Data Governance
There are countless ways to oversee and manage your corporate data. But the approach you take will likely determine the success or failure of your active data governance initiative.
Some companies take a “passive” approach. They give users the ability to freely interact with information in Back-end systems. Afterwards, they monitor the results of those interactions using various reporting and data assessment tools. Others are more proactive. They monitor the way data is created, modified, and handled in real-time as these tasks are performed. In this way, problems can be detected and corrected before “bad” data is introduced into the operating system environment.
In this post, we will discuss the problems that arise when companies take a passive approach to data governance. We will also highlight the reasons why active data governance is the more effective route to take.
Drawbacks of Passive Data Governance
The key issue with passive data governance is the fact that, once invalid or incorrect information enters a database, it immediately begins creating potential problems. In the time between when a user adds the bad data, and a tool identifies its existence and “cleanses” it, an incorrect bill may be sent to a customer. An inaccurate work order may be transmitted to a field service technician. Or even worse, an invalid report may be sent to a regulatory body. Therefore, catching corrupt information beforehand is critical. Particularly in heavily regulated sectors such as financial services and healthcare.
Bad data within a corporate environment can also lead to interruptions in core business processes. Downtime can be particularly significant if poor-quality information in question triggers or is consumed by automated workflows. Firstly, these disruptions drain worker productivity. Secondly, they can also negatively affect revenue generation.
And, most importantly, the validation and correction of bad data after the fact will require more resources than the proactive monitoring of data activities. The amount of time and cost associated with scanning a massive database for integrity issues, then cleansing or deleting corrupt information, far surpasses the resources needed to flag suspicious data interactions as they occur and validate them on the fly.
While passive data governance can serve as a highly effective secondary measure, catching the one or two errors or inconsistencies that may mistakenly bypass more active monitoring measures, the truth is that active data governance is the only true way to promote optimum information accuracy, timeliness, and completeness across an entire business.
The value of data is simply immeasurable to today’s corporations, and the speed at which information flows throughout and beyond an organization leaves little room for error. Even the slightest problem with data integrity, even if for a short period of time, could be detrimental to operational efficiency, profitability, and regulatory compliance.