MIT research shows data governance is the precursor to effective AI implementation.

It’s no secret that artificial intelligence (AI) is revolutionizing the way companies operate with its ability to sift through mountains of data and make accurate predictions at record speed. But with great power comes great responsibility. As AI systems are more regularly incorporated into business, it’s critical that data sources are both accurate and secure to prevent error.

Mergers and acquisitions (M&A) have become a stepping stone to corporate growth strategies. Companies worldwide are actively turning to these deals to expand market reach and drive financial performance. The latest data from EY-Parthenon confirms this trend, with M&A activity projected to surge by 12% in 2024. While the idea of combining companies is undeniably exciting, a critical yet often overlooked factor that can either make or break a deal is data governance.

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

The way that companies govern data has evolved over the years. Previously, data governance processes focused on rigid procedures and strict controls over data assets. But now, with the data-driven culture, modern enterprises are adopting an agile approach toward data governance that primarily centers around data accessibility and empowering business users to take responsibility for governing and managing data.

Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline. But, as organizations increasingly adopt automation in their governance processes, this perception is changing.

Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions.

Data governance covers processes, roles, policies, standards, and metrics that help an organization achieve its goals by ensuring the effective and efficient use of information. It sets up the processes and responsibilities necessary to maintain the data’s quality and security across the business. Data governance manages the formal data assets of an organization.