Generative AI is the Future of Data Management

Data management principles can ensure the data used for AI is holistic, accurate, up-to-date, accessible and protected

Sidd Rajagopal, Chief architect, Informatica

July 5, 2024

4 Min Read
A man analyzing data across multiple devices
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Even though businesses are flooded with data, they are still thirsty for trusted insights. Informatica research shows that leaders face serious challenges accessing reliable, trusted data, with one-third (34%) of chief data officers in the UK lacking a complete view and holistic understanding of their organization’s information.

However, this situation is changing rapidly. Generative AI is emerging as a seminal innovation in technology and a catalyst for a new era in data. The rise of generative AI is reshaping how organizations explore, manage and analyze their data, enabling non-technical users to access and interpret datasets using analytical capabilities that were once only available to highly trained experts.

Data is a primary enabler of transformation. But generative AI is the key that can unlock that data more easily and effectively than ever before.

Bridging the Data Divide 

One of the biggest challenges that companies often face is in the “final mile£ of data delivery. Data often isn’t made available to the right person, at the right time and in the right context. Or it is inherently fragmented, of poor quality and unruly. Most employees also lack the technical skills to understand where data is stored, how it should be analyzed and the methods for extracting insights. The need to master tools like SQL or Python has proved to be a major blocker, making valuable data simply inaccessible for many people. 

Related:Enterprises Must Modernize Before Starting Generative AI Projects

Data difficulties have affected many roles across a diverse range of organizations, including clinical researchers developing new drugs, risk managers investigating fraud, sales teams trying to understand customer demand and government officials delivering new services for their citizens. Yet the work of all these business users can be dramatically enhanced through access to data management tasks like discovery, cleansing and identifying relevant assets. But without the right skills or tools, valuable insights are left undiscovered and productivity gains remain elusive.

This is set to change soon. More line-of-business staff across all sectors will be empowered and upskilled to make the most of data through generative AI. It will give all users access to a self-service data platform that empowers them to extract valuable insights from datasets and use them to drive strategic goals. It will enable non-technical users to access and use data with unprecedented ease. Carrying out sophisticated data analysis will become a matter of simply writing a prompt in plain language that instructs the AI model to perform difficult tasks, ones which were, until very recently, only performed by specialist data management experts.

Related:First Step Toward Responsible Tech is Understanding the Baseline

Democratizing Data with Generative AI

However, generative AI will also quickly magnify any problems in the data supply chain due to the speed and scale at which it operates. Data management principles will need to be applied to ensure the data used for AI is holistic, accurate, up-to-date, accessible and protected. And companies will need to invest in the right places to make it a reality.

Firstly, they should replace diverse data management tools with a simplified platform to alleviate technical debt and foster innovation. This simplification will be crucial to applying generative AI and large language models (LLMs) to data and successfully delivering data products at an accelerated pace, so people have data at their fingertips to make decisions.

Secondly, it will be important to invest in data literacy. It's not enough to simply provide data. Employees need to understand how to use tools, how to interpret data and how it can help them make decisions. They will need to understand how to structure a question for generative AI in a way that generates a valuable answer. It will also be important to train employees on data management best practices, emphasizing the importance of data accuracy, relevance and appropriateness.

Capturing high-quality, accurate data and making it available to the right people is imperative in the generative AI era. Data management technology will become increasingly ubiquitous as generative AI and LLMs mature and are embedded in various contexts. From specialist business intelligence dashboards that offer consolidated visibility of key metrics, to KPIs and data points in a single interface or chat apps. Generative AI is making business information more accessible than ever before, enabling bold steps into a bright future of increased productivity and truly data-led decision-making.

However, it’s important to make sure that data is ready for generative AI first. Clean, accurate and accessible data that has privacy controls baked in, is at the heart of enabling business users to navigate complex data ecosystems. Explainability is also crucial here. Businesses need confidence that they can fully understand and trace the sources of data their data models are fed. It’s this that will give technology teams and business users trust in why their model made a particular decision.

Only by following these steps can users have an intelligently guided experience which makes it simple to perform complex data tasks. When done right, both data professionals and users with limited technical proficiency can enhance their work with generative AI, improving productivity, strengthening the efficiency of operations and speeding up innovations.  

About the Author(s)

Sidd Rajagopal

Chief architect, Informatica, Informatica

Sidd Rajagopal is chief architect at Informatica

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