Data Governance the Key to Increasing Efficiency: EncompaaS CPO

Generative AI adoption has disrupted virtually every market, but it has also renewed interest in strong data governance protocols that comply with various regulatory and efficiency standards. Tech vendor EncompaaS was founded to simplify the often complex processes involved in keeping organizations compliant with data regulations worldwide.

Chief Product Officer Jaimie Tilbrook spoke with Channel Insider about the long course towards data governance and how the conversations businesses have around data have changed so much so quickly.

Data governance applies to a variety of business needs, not just AI

Tilbrook says EncompaaS was founded not to help companies leverage AI tech but rather to solve the complex, often manual approaches organizations historically have taken to properly file, store, and retain data protected by privacy regulations. Now, though, the company finds itself providing value outside the bounds of its initial goal, thanks to the increasing demand for structured data that GenAI tools have brought.

“This has always been necessary from a compliance perspective, but the switch that’s occurred with AI adoption is that now organizations see data governance as not just a checkbox activity but also something that can deliver value,” said Tilbrook. “Now companies go, ‘oh, I can use my data for things,’ and they understand more how to leverage all of this.”

The recent wave of interest in data has also shifted how companies are approaching their data in regards to tech adoption, Tilbrook argues, as leadership more frequently now is understanding of the need to take on data classification and management projects in tandem with leveraging new solutions.

“Now, we’re seeing more organizations shift towards saying, ‘we still want to use that tech, but now we also need to solve the data problem to make it work for us,’ and that’s a definite shift in the overall approach,” Tilbrook said. 

EncompaaS builds its technical offerings on four basic actions, accounting for the full life cycle of data, to best support its clients needs and goals across governance and AI implementation.

Those four areas are:

• Discover: determine all data sources within the business and any important regulations or protocols applicable to those sources;
• Understand: analyze, classify, and sort the data accordingly to best know what your organization holds in its data sources;
• Govern: apply the relevant regulatory and compliance standards to follow rules concerning data retention, privacy, and security;
• Use: take this information, now properly stored and classified, to train AI models and perform business tasks with a more informed approach.

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How technology and people can work together for better outcomes

Tilbrook also says generative AI tools have broken the mold when it comes to non-technical roles seeking out emerging technology with more understanding than was found previously. Products like ChatGPT and Microsoft’s Copilot are commonly referenced and talked about in a variety of ways. To Tilbrook, this gives businesses an opportunity to bring new tech to the workplace with transparency and potentially even excitement.

“Organizations now have larger communities of people demanding new technology,” Tilbrook noted. “It isn’t just the CIO or the IT team talking about tech before, these are now tools that average workers now know about and have opinions on.”

Tilbrook cites four key focus pillars as the foundation for how successful customers implement data governance in their organizations.

The focus areas include:

• Awareness: Your organization needs to keep up with emerging technologies and ongoing product updates not to overwhelm the decision process, but to best align strategy as you implement new tools. Consider all options on the market and weigh use cases for GenAI against more traditional rules-based approaches or other possibilities.
• Cost: As Tilbrook says, AI costs can vary greatly depending on usage fees and scale. Leaders need to think carefully about when to scale programs run on paid tools, when free or lower-cost alternatives might make more sense, and how these costs will factor into technology budgets before beginning complex adoption processes.
• Security: There are very real, very serious questions you need to ask (and answer) about data security and privacy. Proper governance protocols will help with these decisions, and properly vetting the various tools on the market will also provide clarity.
• Trust: Tilbrook places trust above all else in terms of importance to the success of any kind of technology program a business wants to implement. You should prioritize open communication and feedback from employees as they build trust in AI and other new tools’ ability to correctly perform the tasks they are assigned.

“Compliance used to be a task dumped on employees, and many people wouldn’t see the need to add ‘extra’ work outside of their task,” Tilbrook said. “Automation and AI-enabled processes can actually help workers get back to things they want to do, but they need to understand how and why tech is used in their business so they trust those tools to actually do the work.”

Some service providers find value for their clients and themselves in offering compliance services. Learn how centrexIT has built successful capabilities around complicated regulations.