Ask any business leader and they’ll say they’re ready to use generative AI to find efficiencies, gain a productive edge and drive innovation. But dig beneath the surface and many realise their underlying data isn’t ready yet. 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 large language models (LLMs) (40%) and data quality (38%) emerging as issues.
Before companies can even begin to use generative AI to transform their business, they need the right data foundations. But it’s clear that organizations face serious challenges in obtaining reliable, trusted data. Our research shows that one-third of CDOs don’t have a complete view and holistic understanding of their organization’s information. Without this view, it’s nearly impossible for a company to develop a fully formed generative AI strategy.
Chief Architect EMEA-LATAM at Informatica.
The technical bridge
With the right approach, Gen AI presents an opportunity to empower non-technical users with the ability to effortlessly access, understand, and use datasets. For too long, business users across roles have faced significant obstacles 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 barrier. It has kept valuable data inaccessible to many, from researchers developing new medicines to sales teams trying to better understand customer needs.
But Gen AI encourages a more inclusive approach. For companies with well-governed, high-performance foundations, it empowers data-hungry employees to navigate large, complex data sets with simple, understandable language prompts. Organizations that get the data layer right are already reaping the rewards. For example, a marketing analyst can ask a Gen AI model to “analyze customer churn data and identify key drivers,” or a supply chain manager can request “product demand forecasts based on historical sales and market trends.” Gen AI brings intelligence and automation to data, enabling companies to extract insights from data in seconds.
Principles first
To fully harness the power of Gen AI and put the power in the hands of business users, all the issues in the data supply chain must first be solved. So, organizations must prioritize data management principles to ensure that all the data they use is holistic, accurate, up-to-date, accessible, and protected. Initially, this includes investing in simplified data management platforms to alleviate technical debt and foster innovation. A unified platform will bring together diverse data sets, allowing businesses to accelerate the delivery of data products and putting data at users’ fingertips, enabling data-driven decision-making.
Second, investing in data literacy is equally critical to the successful adoption of Gen AI. Employees must understand how to structure prompts, interpret data, and apply data management best practices. Additionally, companies must prioritize data accuracy, relevance, privacy controls, and “explainability”—the ability to understand and trace the sources of data that power their models. Companies must be confident that they can understand and trace the sources of their data models, and transparency will foster trust in the insights generated by Gen AI.
For example, we are already seeing healthcare and pharmaceutical companies putting a unified data platform – integrated with Gen AI – at the heart of their strategy. By integrating trusted and reliable AI into their systems, they are improving data accessibility for everyone, accelerating the discovery of valuable insights, and boosting R&D.
An intelligently guided AI experience
The glittering promises of Generative AI are numerous – 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 improved productivity, accelerated innovation, and data-driven transformation across industries.
For organizations that truly want to become AI-first organizations, however, Gen AI must also be used as a key to unlock how they explore, manage, and analyze their own data—a capability that will quickly go from a nice-to-have to a necessity in the AI era.
As Gen AI and LLMs mature and become embedded in different contexts, data management technology is becoming increasingly ubiquitous. From specialized intelligence dashboards that provide consolidated visibility into key metrics, to chat apps that provide instant access to data points, Gen AI is making business information more accessible than ever before, enabling greater productivity and truly data-driven decision-making.
But business leaders will also need to think carefully about their own data culture. Navigating a Gen AI era requires having the right data foundations, but also creating awareness among employees about how important data will be to them in the future. Only with these considerations can users have an intelligently guided experience that makes it easy to perform complex data tasks. And seize the opportunity to gain a competitive advantage that makes Gen AI ambitions a reality.
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