Unleashing the Power of Generative AI for Data and Analytics - Part 1

Now is the right time to modernize your business intelligence with generative AI, but the confusion in the market should make you pause.

In the last year, Generative AI has created the wild, wild West for artificial intelligence. The tech world has gone from tacking “.ai” onto the end of their URL to the point where every vendor is now an AI company. The shift from a mobile-first to an AI-first approach was almost instantaneous, and whether we like it or not, we will all be changed by this latest invasion of AI.

In the words of Buckminster Fuller, the great architect, mathematician, and philosopher…

“If you want to teach people a new way of thinking, don’t bother trying to teach them. Instead, give them a tool, the use of which will lead to new ways of thinking.”

With generative AI we have a tool that will lead to new ways of thinking about how we work, how we learn, how we create, how we interact, and how we succeed. Instead of fearing the new model, great thinkers and great leaders will ride the wave of innovation to new levels of success, new models for doing business, and new ways of living. Every leader is faced with a decision on how to implement general AI and generative AI as part of their digital strategy.

The data and analytics market is no exception to the great AI expansion of 2023. Every data management, business intelligence, and analytics platform already uses generative AI in some form. However, with a barrage of marketing madness, it is unclear who to believe, where to invest, where to use AI, how to use AI, and what percentage of resources should be devoted to AI. Fortunately, the maturity and measurability of data management make it easy to separate the leaders from the followers.

The Universal Proliferation of Generative AI in 2023 and the Start of 2024 Eliminates a “Do Nothing” Strategy for Every Serious Leader

To do nothing is to fall behind at such lengths that it may be impossible to catch back up. This urgency is not just because everyone is using generative AI, the imperative comes from the present proven value and the obvious future benefit of AI.

The real fear of falling behind is already motivating leaders to drive implementation and accurately measure the return on their investment. Even with many unanswered questions, the potential benefits and advantages of both general AI and generative AI far outweigh the risks. There are three strong areas of generative AI value creation: acceleration, innovation, and digital dominance.

Acceleration

Early adopters are already seeing a boost in data management productivity and the acceleration of time to insight based on current AI automations and recommendations. On top of AI acceleration, the creation of copilots using generative AI fuels developer and analyst productivity to even greater levels of efficiency and excellence.

For example, when a data analyst is asked for specific insight to meet the needs of a business user, the old world might have required finding the right data, reformatting the data within the business intelligence platform, configuring a dashboard or report, and providing access to the business user. In most cases, this process would require several iterations to arrive at the insight the business user desired. In a generative AI world, the business user can pose questions directly to the data using plain language, and iterate through a series of recommended next questions or explanations of resulting insight.

Innovation

Innovation is almost always dependent on the speed at which organizations can iterate cycles of experimentation and fine-tune new models. Increasing the speed of data management also accelerates both analytical and business innovation cycles, giving digital leaders what they need to establish data-driven differentiation.

For example, a business user might be looking to better understand a profile of which customers are most likely to buy a certain product. Quick access to a large set of data that includes a broad range of related data sets allows the user to build a finely-grained customer profile. Generative AI enables the user to innovate and iterate quickly using recommendations on potentially related datasets and insight.

Digital Dominance

Finally, AI adoption addresses a major shift in the digital economy. It is no longer enough to work toward digital transformation as the end state of modernization. Today’s digital economy demands more. The combination of acceleration and innovation surpasses transformation and helps leaders establish and maintain digital dominance.

For example, a marketer who has already fully digitized their entire marketing operations, will have access to a digital representation of all marketing programs. If there is also a digital representation of all sales, product, and finance operations, then the combination of analytics across all domains will provide insight that leads to dominance. In this case, the business users of all domains will be able to access insight using plain language, without the need for a data analyst. The platform becomes the data analyst. This depth of access to insight speeds time to insight, which in turn, speeds innovation cycles for the business.

For an analysis of what it takes navigate the complexity of generative AI in the data and analytics space, read the full Ferraro Consulting POV paper, Unleashing the Power of Generative AI for Data and Analytics.

See Part 2.