Generative artificial intelligence (GenAI) can improve task-based productivity by quickly summarizing long documents, creating email copy, or translating code, for example. But organizations can derive much greater value from GenAI by going beyond the short-term, task-based uses.
With its ability to rapidly ingest and process massive amounts of data, GenAI can provide deeper insight into what’s going on across your business and recommend actions based on those insights. Using GenAI in such a foundational way to improve your business, however, requires some equally foundational changes in the way you manage your organization’s data.
Leaders need to approach GenAI initiatives as more of a marathon than a sprint. And just as a long-distance runner needs disciplined training and a strict diet for optimal performance on race day, GenAI requires its own healthy input in the form of your organization’s data. The fitness of that data will determine whether your GenAI project makes it to the finish line or struggles and burns out before it hits its stride.
Fueling GenAI with Healthy Data
Consider how GenAI will change the way you currently work with your data:
• Democratization: Traditionally, only certain team members had access to your organization’s data, such as when generating reports and building dashboards. But with the right security and privacy controls in place, using GenAI can allow more employees to make data-driven decisions and solve their business problems.
• Speed: The dramatic reduction in time to insight GenAI provides can lead to faster decisions and more immediate actions.
• Context: Beyond reporting numbers, GenAI can provide a deeper understanding of the conditions that resulted in the numbers. It can offer context and recommendations for each metric to create a comprehensive narrative that drives a better overall understanding of your business.
This enhanced access, speed, and depth of insight demands increased diligence around your data. If the data feeding your GenAI tools were incomplete or inaccurate, the insights and recommendations GenAI produces would be flawed—as would the decisions and actions you take based on those flawed insights. This could cause mistrust of the tools, which could hamper any future rollout of GenAI initiatives.
Don’t Let Data Cramp Your Potential
When there’s as much enthusiasm for a technology as there is for GenAI, it’s tempting to ignore signs of strain and keep running forward at maximum speed. Runners who do this risk career-ending injuries. Running GenAI this way could be just as damaging.
Make sure your data is in the best possible condition by asking these critical questions:
• How are you governing and collecting your data? Your data needs to be of the highest quality and reliability. It’s best to monitor data as it enters your organization rather than when it’s already there and your organization is applying it to your internal systems and processes. A governance program that assigns ownership of and accountability for all incoming data will provide the necessary controls.
Your organization will also need to adapt its methods for collecting data. Traditional extract, transform, and load (ETL) techniques that work well with structured data can be slow when onboarding other data types, such as videos, PDFs, and transcripts. Using metadata-driven pipelines and data lakes or lakehouse architectures can greatly streamline the collection of unstructured data for your GenAI needs.
• How are you storing and managing your data? Does your data reside in departmental, functional, or regional silos? Once you’ve ingested the data, you need to ensure all of it is available to your GenAI tools to make your GenAI output as complete, accurate, and current as possible.
But you don’t have to undergo a large-scale data unification initiative all at once. Piloting smaller projects is a good way to start—keeping in mind that both your data platform and strategy will need to scale across your organization for you to realize the long-term benefits of GenAI.
• Is the data secure, with privacy restrictions in place? Leaders should be—and are—particularly concerned about using GenAI securely. Getting leadership buy-in for your project may hinge on thoroughly addressing legal and compliance risks that come from the increased access to, speed of, and context around your data.
The GenAI Marathon Is a Team Sport
A healthy data program comes down to more than just technology. Your data strategy must also encompass data literacy. Your teams need training on best practices for using data responsibly and an understanding of the best ways to extract useful information from the data. (Prompt engineering is fast becoming one of the most powerful “programming” languages.)
By providing this support, you’ll be empowering your workforce to uncover insights, ask questions, think critically, and challenge conclusions using data and experimentation.
GenAI will play a vital role in your organization’s future, but only when it’s built on a healthy data foundation. Don’t let your GenAI program hit the wall. Keep it fueled with reliable data.
Need a GenAI coach? Slalom and Google Cloud can help.