Ask any business leader and they’ll say they’re ready to leverage generative AI to find efficiencies, gain a productive edge and drive innovation. But dig under the surface and many are realizing their underlying data is not ready. In fact, our annual survey of UK chief data officers (CDOs) shed some light on the challenges facing early generative AI adopters with the quality of domain-specific data for training and fine-tuning of large language models (LLMs) (40%) and quality of data (38%) emerging as issues.
Before businesses can even begin to harness generative AI to transform their business, they need the right data foundations underneath. However, it’s clear organizations face serious challenges accessing reliable, trusted data, with our study showing one-third of CDOs lacking a complete view and holistic understanding of their organization’s information. Without this view, it’s near impossible for a business to develop a fully formed generative AI strategy.
Chief Architect EMEA-LATAM at Informatica.
The technical bridge
With the right approach, Gen AI offers an opportunity to equip non-technical users with the ability to effortlessly access, understand and use data sets. For too long, business users across diverse roles have faced significant hurdles in accessing and interpreting data due to technical barriers, fragmented data sources, and a lack of data literacy. The need to master tools like SQL and Python has long been a hurdle. It’s kept valuable data inaccessible to many. From researchers developing new drugs, to sales teams trying to better understand customer needs.
But Gen AI is fostering a more inclusive approach. For companies that have well-governed, high-quality foundations in place, it allows data hungry employees to navigate large complex data sets with simple, plain language prompts. Organisations who get the data layer right are already reaping the rewards. For example, marketing analysts can ask a Gen AI model to “analyse customer churn data and identify key drivers,” or a supply chain manager is able to request “forecasts for product demand based on historical sales and market trends.” Gen AI is bringing intelligence and automation to data, giving businesses the power to derive insights from data in moments.
Principles first
To fully harness the power of Gen AI and put power in the hands of business users, all problems in the data supply chain need to be first ironed out. So, organizations need to prioritize data management principles to ensure any data they are using is holistic, accurate, up-to-date, accessible and protected. In the first instance, this includes investing in simplified data management platforms to alleviate technical debt and foster innovation. A unified platform will bring together diverse data sets so companies can accelerate the delivery of data products and empower users with data at their fingertips, enabling data-led decision-making.
Secondly, investing in data literacy is equally crucial for the successful adoption of Gen AI. Employees need to understand how to structure prompts, interpret data, and apply data management best practices. Additionally, businesses must prioritize data accuracy, relevance, privacy controls, and ‘explainability’ – the ability to understand and trace the sources of data feeding their models. Businesses need confidence that they can understand and trace the sources their data models are fed, and transparency will foster trust in the insights generated by Gen AI.
For example, we’re already seeing healthcare and pharmaceutical companies put a unified data platform – integrated with Gen AI – at the heart of their strategy. By integrating trusted and reliable AI into their systems, they’re improving accessibility of data to everyone, accelerating the discovery of valuable insights and turbo charging R&D.
An intelligently guided AI experience
The shiny promises that generative AI offers are plentiful – from accelerating drug discovery and development to revolutionizing creative processes. By embracing Gen AI and prioritizing data management best practices, organizations can unlock a future of enhanced productivity, accelerated innovation, and data-driven transformation across industries.
However, for organizations that truly want to become AI-first organizations, Gen AI also needs to be used as a key to unlock how they explore, manage, and analyze their own data. An ability that will quickly go from being a nice-to-have to a necessity in the AI era.
As Gen AI and LLMs mature and become embedded in various contexts, data management technology is becoming increasingly ubiquitous. From specialized intelligence dashboards that offer consolidated visibility of key metrics, to chat apps that provide instant access to data points, Gen AI is making business information more accessible than ever before, enabling increased productivity and truly data-driven decision-making.
But business leaders will also need to give careful thought to their own data culture. Navigating a Gen AI era requires having the right data foundations in place but also building awareness among employees about how important data will be to them moving forward. Only with these considerations can users have an intelligently guided experience which makes it simple to perform complex data tasks. And seize the opportunity to gain a competitive edge that makes Gen AI ambitions a reality.
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