Setting Your Group Up For Data Governance Success

November 5, 2020

By Allison Proffitt 

November 5, 2020 | Data governance is a somewhat fluid term, encompassing all aspects of making sure our data are accurate, consistent, current, and secure. As policy, data governance oversees where data are stored, how they are analyzed, and how long they are kept. Data governance relies on input from scientists, IT teams, and executives, and it must remain flexible as stakeholders change, business interests evolve, and collaborations mature.   

It’s quite a tangle to consider, and Bio-IT World was happy to listen in as Michael Riener, president of RCH Solutions and Alok Tayi, vice president of life sciences at Egnyte teased apart some of the strands in a recent webinar.  

Scientists often view data governance as an imposition, Reiner explained. He oversees the executive management of RCH, a global provider of computational science expertise, and personally brings more than 25 years of experience to these questions. Data governance is just another roadblock IT is putting in the way of science—at least according to some scientists, Reiner said. 

The first step in a successful data governance plan is to bring all stakeholders to the table—science, IT, and leadership—and commit together to identifying sensible practices that will improve the business. Compromise will be needed, Reiner predicted, to deliver value, scale, and growth, but adopting a “one team, one dream” mentality can bring everyone together.  

The goal, Reiner said, might be termed “total data domination”: quality data, managed properly so that scientists are self-sufficient.  

Egnyte’s Tayi outlined challenges facing life sciences specifically: there are myriad applications in use, and valuable IP are fragmented, he said. Ransomware attacks and data breaches are constant threats, and data privacy laws are “creating a thicket of highly-dynamic compliance requirements,” including GDPR, CCPA, and more. “I think data privacy is going to be one of those sneaky issues that’s going to come up and challenge our conventional approaches to data governance,” he predicted.    

Collaboration is, “the mainstay of the new operating model of the life sciences industry”, Tayi said. Yet collaboration is also a major point of weakness in a security plan. In life sciences and med device companies today, for every internal scientist, three to four are working outside the company as consultants, CROs, service providers, or other partners, he said. As data moves between those team members, “it’s really difficult to manage that diaspora.”   

“Culture is at the core of many of these challenges,” Tayi said. Companies are making investments in training and gathering varied team members from science, IT, and executive leadership to have conversations about risk and security. Talk together about which data are mission-critical, and which don’t present quite the same level of risk. Bring scientists to the table to talk about how data could be managed independently, and what new data types and streams are coming, Reiner added. “Having a mandate is never successful,” he said. The researchers need to be part of the conversation, and they need to feel that their work is protected.   

Reiner warns against layering new regulations or processes on top of legacy systems. Create a new strategy based on the guidelines of the evolving governance model, he suggested. Any model needs to be flexible enough to expand and respond to changes.  

A common blind spot, Reiner said, is in new technologies. Scientists are always way ahead of IT when looking at the shiny new tools, he said. Tools like artificial intelligence, machine learning, and even the cloud can be “world changers,” he said. So data governance plans, must remain open to new technologies. Tayi, too, emphasized the need for multi-platform, multi-vendor approaches for flexibility.   

He recommended using machine learning, in particular, as a tool in your data arsenal to detect and monitor data governance issues. It’s pre-competitive, he said, and lets you socialize and learn from larger datasets.  

Finally, both speakers emphasized setting a starting point that is attainable. Start a conversation about data governance, set an attainable goal, and bring the team an early win. Rewriting the whole data governance landscape for your organization will take time, but creating a culture of security starts with the first collaboration.    

You can watch the full conversation and hear Tayi’s and Reiner’s top five data governance strategies at the Bio-IT World Webinar.