Ask any business leader and they’ll tell you they’re ready to leverage generative AI to find efficiencies, gain a productive advantage, and drive innovation. But dig beneath the surface and many are realising their underlying data isn’t ready. In fact, our annual survey of UK chief data officers (CDOs) shed some light on the challenges facing early adopters of generative AI, with domain-specific data quality for training and tuning large language models (LLMs) (40%) and data quality (38%) emerging as issues.
Before companies can even begin to leverage generative AI to transform their business, they need the right data foundations. However, it’s clear that organizations face serious challenges in accessing trusted and reliable data, and our study shows that one-third of CDOs lack 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.
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The technical bridge
With the right approach, Gen AI offers the opportunity to empower non-technical users with the ability to effortlessly access, understand, and use data sets. For too long, business users across a variety of 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 has 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, it enables data-hungry employees to navigate large, complex data sets with simple, plain-language prompts. Organizations that get their data layer right are already reaping the rewards. For example, marketing analysts can ask a Gen AI model to “analyze customer churn data and identify key drivers,” or a supply chain manager can ask for “product demand forecasts based on historical sales and market trends.” Gen AI is bringing intelligence and automation to data, giving companies the power to gain insights from data in an instant.
Principles first
To fully harness the power of AI and put the power in the hands of business users, all the problems in the data supply chain need to be solved first. Therefore, organizations must prioritize data management principles to ensure that all the data they use 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 that businesses can accelerate the delivery of data products and empower users with data at their fingertips, enabling data-driven decision making.
Second, investing in data literacy is equally crucial to successful Gen AI adoption. Employees must understand how to structure cues, 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 data sources that feed their models). Companies need to be confident that they can understand and trace the data sources that feed their models, and transparency will foster trust in the insights generated by Gen AI.
For example, we are already seeing that pharmaceutical and healthcare companies are 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 bright promises of generative AI are abundant – from accelerating drug discovery and development to revolutionizing creative processes. By embracing generative AI and prioritizing data management best practices, organizations can open the door to a future of increased productivity, accelerated innovation, and data-driven transformation across all industries.
However, for organizations that truly want to become AI-first organizations, next-generation AI must also be used as a key to unlocking how they explore, manage, and analyze their own data. A capability that will quickly move from a nice-to-have to a necessity in the AI era.
As next-generation AI and LLMs mature and become integrated into diverse contexts, data management technology is becoming increasingly ubiquitous. From specialized intelligence dashboards that offer consolidated visibility into key metrics to chat applications that provide instant access to data points, next-generation AI is making business insights more accessible than ever before, enabling increased productivity and truly data-driven decision-making.
But business leaders will also need to think carefully about their own data culture. Navigating the AI era requires having the right data foundations in place, but also raising employee awareness of how important data will be to them in the future. Only with these considerations can users have an intelligently guided experience that makes it easier to complete complex data tasks and seize the opportunity to gain a competitive advantage that makes AI ambitions a reality.
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