Generative AI, Digitalization Combined Drive True Data DemocratizationGenerative AI, Digitalization Combined Drive True Data Democratization

Despite the hype, many organizations have been hesitant to adopt a business-focused generative AI solution for any number of reasons. Some aren’t quite sure whether it will actually make an impact or have a concrete return on investment. In fact, Gartner noted that GenAI has slid into the “trough of disillusionment” in its 2024 Hype Cycle for Emerging Technologies report. That said, McKinsey estimates generative AI could add up to $7.9 trillion to the global economy, annually, much of this due to potential productivity gains.

Most companies today rely on data to drive business processes and operations, but they are still “on the fence” about generative AI. However, when it comes to employees making business decisions, studies show that the majority of those decisions are made “on the cuff” or based on a gut feeling. A study by BARC found that 58% of respondents base at least half of their decisions on “gut feel” or experience as opposed to using data and information.  For businesses to succeed, they need to use a combination of experience and data to make important decisions. This is one important reason why organizations should consider leveraging generative AI’s ability to make data and business insights more accessible to frontline workers – regardless of their technical expertise.

Related:Understanding the Data Governance Divide

The quality and effectiveness of a business-focused generative AI solution is based not only on the specific technology used but also on the amount and quality of the data in the large language model database source. Companies should implement data hygiene practices before implementing a generative AI solution. This includes metadata tagging of key information properties, including security and access rights. It also includes digitizing paper assets to ensure that legacy customer and business information can be utilized in the new system.  These practices are essential to a successful implementation that can promote informed decision-making across the organization and foster a data-driven company culture.

First, let’s take a look at why generative AI is the ideal technology to level the data playing field.

Making Data and Insights More Accessible

True data democratization requires equipping teams with the tools and knowledge they need to analyze and interpret data independently. It also requires transparency, which means providing employees with a clear understanding of where the data is coming from and the best practices for using it. In turn, this transparency helps to build trust and encourage the responsible use of data across the organization.

Related:Unlocking AI’s Value in Infrastructure: A Business Leader’s Guide

While the experience may look different from organization to organization, generative AI can be applied in five ways to make data democratization initiatives successful:

Digitalization as a Data Democratization Driver

Like generative AI, digitalization is a key component to data democratization. Digitalization plays a critical role in making data more accessible, understandable, and usable without requiring advanced technical skills so that it is suitable for everyone from frontline workers up to the C-suite. There are three attributes of digitalization that creates an environment where data is a shared resource, promoting informed decision-making and fostering a data-driven culture:

The Transformative Power of Data Democratization

Generative AI and digitalization are powerful tools driving broader access to data across organizations. As AI tools become more user-friendly, individuals across various skill levels will be able to access and utilize data without requiring extensive technical training. generative AI-powered applications will increasingly facilitate real-time collaboration, allowing teams to work together on data projects seamlessly, regardless of where they’re located.

While a greater emphasis on data governance will be required to accommodate the principles of data democratization, these efforts will support privacy, security policies, and ethical considerations. Overall, one of the biggest benefits of data democratization in the AI era will be more inclusive environments where data-driven decision-making becomes the norm, driving innovation and new levels of efficiency.