AI Meets Data Access Governance
Data is the viral sensation crashing the data governance capacity. Use of data is disrupting industries, economies, even some government elections. Unlocking the secrets data holds is the number one challenge in every single company regardless of the size or industry.
However, organizations are facing a challenge: having the framework is key. And yet, execution, as related to the individual silos, has become more and more difficult. Why?
Data’s Increasing Value
For one thing, if proper controls are not in place, the organization may incur significant fines from one of the regulators involved with ensuring and enforcing data privacy. Under Gramm Leach Bliley Act (GLBA), penalties for non-compliance can include fines of up to $100,000 per violation, with fines for officers and directors of up to $10,000 per violation. As if these fines are not significant enough, provisions include criminal penalties of up to five years in prison, and the revocation of licenses. Beyond the risk of fines and therefore reputation, the consolidation of data governance tools still poses a great challenge to most organizations today.
This report created and conducted by Precisely and Drexel University’s LeBow Center for Business Analytics (LeBow) found that only 64% of organizations surveyed had an ongoing data governance program in place. Only 43% said they had software in place, meaning that the majority of companies operate on ‘tribal knowledge’ at best.
On the flip side, 83% of the organizations with mature data governance programs saw value increase because they had federated access to their data, which improved business outcomes. It is clear that data governance is critical to the success of companies in today’s economic climate. However, the value is not derived from the data governance itself, but because these data-driven companies are able to provide their data analysts controlled and compliant access to more data. They can travel further ‘past the gate’ so to speak.
The desired outcome for data governance is to know instantly who has access to what data, for what purpose, according to which internal policy or external regulation, during what time, what was done with the data, where is the data now that it’s been transformed into something else, and then repeating again…but the execution falls short here. Why?
Today’s Data Governance Challenges
Here are the top reasons data governance cannot be maintained:
Tools & Policies: Evaluation of tools, selection of tools, purchase of tools, onboarding of tools, and if not tools, the documentation explaining everything and the continual education of those policies takes time, and effort, not to mention that each silo may require its own tool.
People: Employment, especially with the mass migration these past few years, is extremely fluid. Most enterprise companies hiring in data governance have 15% or more openings (per current LinkedIn job openings). With constant internal changes, there is a lot of effort with removing and changing permissions. Couple that with new software being added which will require more manpower for administrative setup and maintenance.
Manual Effort: The majority of the process today in requesting and then gaining approval to access data in a controlled, compliant manner is currently manual (and unsustainable).
Some data analysts do not have access to the applications they want the data from because it may take significant time and approval is not guaranteed. Most software is licensed by User so there may be additional cost involved from a software license perspective.
“Data Policy” for the application may not exist on paper, it might be very general, or it may be too strict to provide access to the data sets that contain sensitive information.
