A prevailing misconception suggests that generative AI platforms, such as ChatGPT, possess an almost mystical ability to provide answers to intricate business queries. “How can I sell more widgets in Wyoming?” seems like a simple question, but without the right data, even the most advanced AI systems won’t help you. At the heart of this misconception is an overestimation of AI’s capabilities. While generative AI is undeniably powerful, it cannot conjure insights from thin air. The adage “Garbage in, garbage out” remains as relevant as ever. In the context of AI, this translates to “You can’t analyze data you don’t have.”
The Imperative of Data Governance
For businesses to truly harness the potential of generative AI, a foundation of robust data governance is paramount. An organization equipped with clean, organized, and accessible data has the potential to stand at the forefront of AI-driven innovation. Such a foundation enables the development of applications that integrate Large Language Models (LLMs) with existing datasets, facilitating near-real-time access through plain language queries.
The Archaic Odyssey of Business Intelligence Reporting
Imagine a non-technical, seasoned sales executive at the helm of a leading brand’s sales organization. They’re preparing for an upcoming board meeting and require a comprehensive business intelligence report to showcase the sales performance over the past quarter. For most companies, the journey to procure such a report is a convoluted dance of multiple steps, each more time-consuming than the last.
- The Initial Request: Our sales exec approaches the data office with their requirements. Our sales exec is clear about what they need, but they lack the technical know-how to extract it themselves. This initial communication could take a day, given the back-and-forth clarifications.
- The Data Office’s Relay: The data office, acting as the intermediary, communicates this request to a data scientist. The scientist must understand the nuances of the sales data required, ensuring nothing is lost in translation. This stage might consume another day or two, especially if there are any ambiguities in the request.
- The Database Expert’s Involvement: Once the data scientist has a clear picture, they turn to a database expert. This expert is tasked with crafting the precise queries to extract the relevant data from vast databases. Given the complexity and the need for accuracy, this phase can stretch over several days.
- The Art of Translation: The database expert passes the raw data to a report writing and data visualization specialist. Their job? To transform these raw, often unintelligible data into coherent plain language, charts, and graphs. This process is both an art and a science, ensuring the data are presented in a manner that’s both accurate and understandable to the nontechnical audience. Depending on the volume and complexity, this stage can take up to a week.
- The Final Delivery: Upon receiving the requested report, there’s a very good chance that modifications are required – and the process will be repeated until the output matches the original request.
This “very typical” data journey could span a week or two – and that’s if everything goes right.
The True Magic of Generative AI
Instead of this multi-week data odyssey, an organization with better-than-average data governance could use an LLM-based chatbot to allow anyone with permission to “talk” to the data – posing their questions in plain language and receiving near-instantaneous insights, visualizations, and reports. What once took weeks could be condensed into minutes or hours.
The Future Depends on Your Data
The implications of this streamlined approach are profound. However, the crux remains: none of this innovation is feasible without the data hygiene and data governance to capitalize on it. AI is not a magic wand; it’s a remarkably powerful tool with the potential to dramatically improve productivity and create value. The age of AI-powered business intelligence is here – if you’ve got the data to take advantage of it.
If you want to learn more about how to use generative AI to talk to your data, sign up for our free online course, Generative AI for Execs. It offers a deeper dive into the practical applications, ensuring you’re not just informed, but equipped to innovate.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it.
ABOUT SHELLY PALMER
Shelly Palmer is the Professor of Advanced Media in Residence at Syracuse University’s S.I. Newhouse School of Public Communications and CEO of The Palmer Group, a consulting practice that helps Fortune 500 companies with technology, media and marketing. Named LinkedIn’s “Top Voice in Technology,” he covers tech and business for Good Day New York, is a regular commentator on CNN and writes a popular daily business blog. He's a bestselling author, and the creator of the popular, free online course, Generative AI for Execs. Follow @shellypalmer or visit shellypalmer.com.
