The Changing Face of Data Governance

Data is everything. It’s a mantra you’ve likely heard a dozen times. And while it might seem overused, in the age of artificial intelligence, those words ring truer than ever. Data is the lifeblood of AI.

Without it, the most sophisticated tools offer little more than guesswork. Why then is data often so hard to track down or convert for AI usage? If AI is the future standard for the modern enterprise, why isn’t its fuel more readily available?

The answer involves a struggle between businesses and software vendors over data ownership, access and usage.

Welcome to the complex (and often messy) world of data governance. Let’s explore what the current situation looks like and how new tools are helping to free captive data and make it AI-ready.

What Is Data Governance?

Simply put, data governance refers to how an enterprise manages its data. It is a set of internal policies that define how it gathers, uses, stores and secures the various types of data it uses.

Because much enterprise data involves customer information, there is also a compliance element to data governance.

Enterprises must ensure their data governance policies adhere to current security standards and privacy regulations.

These can be particularly strict for those in finance, healthcare and other heavily regulated industries.

Businesses that fail to meet compliance standards risk accruing huge fines or, worse, jeopardizing the trust and loyalty of their customers.

What Makes Data Governance So Complex?

Enterprise data comes in multiple formats from a variety of sources. There’s data from sales calls, marketing campaigns and customer service interactions, just to name a few.

Enterprises that leverage AI can synthesize this data to paint a hyper-detailed picture of each customer—their preferences, inclinations and behaviors—but only if the data is accessible and formatted for AI engines. Until recently, this has been a challenge for data governance teams for several reasons:

Data Silos

Many enterprises struggle with data silos—pockets of data isolated within the org structure. Marketing might not have a clear line of sight into Sales data, for instance.

Customer Service data might live on a “compliance and quality assurance” island. AI can connect these datapoints and render rich, actionable customer insights—under the right circumstances.

Poor Data Quality

One of the biggest obstacles to creating an AI-friendly ecosystem is the availability of high-quality data.

As discussed earlier, many legacy enterprise systems capture data in low-quality format. This is particularly true of call recording software, which was originally designed for human playback and never intended for AI usage.

As a result, many call recordings remain unstructured—and unready for most (but not all) AI engines.

Data Ownership Barriers

Arguably, an even bigger challenge to effective data governance is the question of data ownership.

Do enterprises own their customer data, or does it belong to the software vendors who furnish enterprise platforms and act as “data stewards”?

Vendors that fall in the second camp often restrict how enterprises can access and use their recording data—charging for access, compressing the files and/or only making them available in batch—which limits their ability to fully leverage its value.

Achieving Complete Data Governance

Effective data governance is an enterprise-wide effort. It requires executive buy-in and participation from all departments.

It’s also become remarkably easier thanks to recent innovations in data capturing and AI-ready conversion.

These advancements, which form the core of Uniphore’s U-Capture enterprise recording platform, enable businesses to overcome the biggest challenges to call recording data governance.

U-Capture layers over existing recording software to capture all recording data (structured and unstructured) and convert it to a non-proprietary format that can be used by any enterprise AI application.

It also provides built-in tools to address data quality concerns and ensure compliance with privacy and security standards.

As a result, enterprises gain 100% access and control of their data. No third-party gatekeepers. No complicated file reformatting. No application integration barriers.

That’s the power of data sovereignty.

From removing data restrictions to enabling regional compliance filtering (i.e. keeping UK-based calls within the UK for GDPR compliance), our architecture gives enterprises total control over their data, regardless of the source or intended use case.

By allowing enterprises to access, manage and use their data however they deem fit, enterprise leaders can explore new AI initiatives and craft strategies that were previously unthinkable.

They can finally achieve complete data governance—with the freedom to choose where and how to use their data for the biggest impact.

Author: Guest Author