By Contributor,Dr. Jonathan Reichental

Copyright forbes

Leaders increasingly understand that building a strong data culture is an essential function in today’s competitive economy.

Leaders are now recognizing that to be a data-driven organization and foster a culture where data is a driver of success requires more than just data management. At the heart of delivering the optimum value of data while also managing its risks requires rigor in the governance of data.

But data governance, despite its obvious advantages, can struggle to get traction and deliver results. Part of the challenge is that it requires new processes that too often levy unwelcome bureaucracy. Fortunately, there are now a whole new set of AI-powered data tools and approaches available for leaders to finally unlock the benefits of data governance.

Data Governance Acquires New AI Capabilities

Data-related processes, policies, and compliance requirements, some of the components necessary for better data quality, decision-making, and the enablement of a high-performing data-driven culture are too frequently poorly executed.

A big part of the solution can be found in process automation. In fact, a new, promising branch of data governance has emerged called DataGovOps. It’s modeled on the success of DevOps and DataOps in that it integrates high degrees of automation, collaboration, and continuous feedback to achieve elevated operational efficiencies.

As a historically manual and repetitive discipline, data governance provides abundant opportunities for automation.

Through new tools and approaches, DataGovOps also creates a more closely integrated, collaborative and communicative environment for teams and stakeholders involved in data management.

MORE FOR YOU

It won’t come as a surprise too that AI is beginning to play an increasingly important role in driving the positive outcomes of DataGovOps.

AI Brings Boosters To Data Governance

It’s not an exaggeration to suggest that when data is managed well, it can transform an organization. Right now, demand for new tools and technologies remains high in data operations.

Fortunately, AI is increasingly being adopted by enterprises to drive improvements, augment human efforts, and get faster and more accurate results across the entire data management and governance landscape.

The vendor community is stepping up with providers quickly and comprehensively incorporating AI capabilities into their data governance solutions.

Today’s AI is increasingly well suited for accurately completing complex and repetitive tasks that can utilize machine-learned judgments.

Here are three data governance areas where AI techniques can immediately add notable value.

1. Data Classification And Cataloging

In data governance, classifying and cataloging data and datasets is essential. After all, without knowing the details about data—the metadata–and how to easily find datasets, the opportunities for value creation are immediately reduced.

Achieving a viable and well-maintained data catalog has historically been a tediously manual and error-prone process.

AI is making the classification and cataloging of data a whole lot easier, including identification of datasets and their respective metadata from across an enterprise.

Rather than a data catalog being manually updated periodically and with the risk of content quickly becoming out of date, AI tools can now continuously monitor metadata currency and update it automatically.

2. Data Policy Creation

A sizable part of data governance is the creation of policies and procedural documentation in support of data requirements in an organization. For example, these can be driven by business needs or from external sources such as federal, state, and industry regulations.

Traditionally, this documentation was created manually—an intensive exercise requiring legal input, great writing skills, and many rounds of approvals.

Today, new regulation, for example, can be processed by AI and a first draft of a policy can be created that considers all internal and external requirements to ensure compliance. It doesn’t completely eliminate manual work, but it certainly reduces it.

Perhaps even more importantly, AI can help to determine whether new data policies and regulations impact existing ones and can highlight and suggest modifications. In this way, organizations can avoid becoming non-compliant and have confidence that their documentation is current.

3. Data Availability

Finally, data governance is concerned with ensuring that data is available and useful when it is needed by those that need it. Simply capturing and storing data is far from sufficient.

A whole suite of AI capabilities can now, for example, continuously monitor for data availability and identify accessibility risks.

It can extract and structure data from unstructured sources.

Algorithms can detect and correct errors in data sets, find inconsistencies, and duplicates, resulting in significantly improved data quality and reliability.

AI can also predict potential data failure scenarios and propose measures to minimize downtime and data loss.

AI Is A Welcome Addition To Data Governance

Consider these three areas a tease at the possibilities of AI use in driving greater adoption and better results from data governance efforts. The list is long and worth investigating.

All evidence points to an acceleration of AI capability and deployment in the enterprise. Its impact on data governance through approaches such as AI-powered DataGovOps will be consequential.

While AI will reduce and even eliminate a high number of manual touchpoints, success in the medium term will come through the right balance of human and AI collaboration. People will still play an important role in resolving corner case issues, participating in judgments and problem solving, and lead in determining data goals and objectives.

The evidence is loud and clear: data governance can unleash the value of data in your business and deliver superior results across the enterprise.

With AI-powered DataGovOps, leaders now have a new set of powerful capabilities at their fingertips.

Editorial StandardsReprints & Permissions