AI & BI: “It’s a Whole New World”

Technology has changed dramatically recently, Leone said by way of introduction. “We've seen augmented analytics and the rise of augmented analytics and infusing machine learning into these platforms. And then, of course, here comes Gen AI, and it's a whole new world and a ton of opportunities.”

Businesses are placing increasing value on analytics and AI, the research shows. Over the past year, 97% increased investments and 89% were allocating more budget to tools enabling better integration, access, and analysis of data.

That mirrors what we’re seeing at MicroStrategy, said Saurabh Abhyankar, Executive Vice President & Chief Product Officer. Instead of asking for commoditized dashboards and reports, people now want to rethink their entire strategy around business intelligence and analytics.

For the past decade, data visualization has been paramount. Yet now there’s a renewed interest in information democracy, Abhyankar said. With entry-level AI available, even those who aren’t yet ready for augmented analytics are looking to benefit. In fact, just one year in, more than one in three organizations in the ESG research were already incorporating generative AI capabilities.

However, PeggySue Werthessen, MicroStrategy Vice President, Product Marketing, noted that over the past six months, while more people were incorporating MicroStrategy’s AI, she found “they’re expecting magic, and it’s still not magic.”

The evolution of AI in BI is continuing, Abhyankar agreed. Many organizations use AI automation to improve productivity and make it easier to do things you already do.

Some are using AI tools to do things that they didn’t previously have the skills to do (e.g. self-service asking questions and getting answers). But, while customers might expect that they can predict the future with AI, they’re not there yet.

Many don’t recognize generative AI is not predictive analytics, Leone said. Something is needed in order to bridge the gap between the two. “It’s both of them together that are going to really generate the most value.”

Challenges Integrating AI into BI and Analytics

Getting the most out of AI requires understanding your specific use case and working backwards to determine what insights you need from which data, Abhyankar said. All this also requires an understanding of the realities of AI and BI integrations and the challenges businesses face.

Data security and privacy have long topped the list of challenges, and they need to remain a top priority, Leone said. Yet data quality and consistency are also top of mind concerns. Organizations need to consider:

• What data is going in there?

• How am I leveraging it?

• Can I trust it?

• How are we ensuring data quality?

• How are we ensuring consistency across the different use cases?

Each individual might analyze data differently, so it’s a tough but important task to ensure data quality and consistency across the board, Leone said.

That’s a challenge that’s not going anywhere, Abhyankar said. “We all know that at a restaurant you can have a robot cook the food, but if the ingredients are rotten or you don’t know where they are, the robot isn’t going come up with anything great.” That’s why MicroStrategy is proud of its focus on the fundamentals (e.g., our semantic graph and data governance).

Ensuring you have the right ingredients is more challenging now with the influx of new data, Abhyankar said. Quality assurance for data from many sources, including Gen AI, adds complexity just as organizations emphasize—especially as of late—simplifying.  

Realities of the AI revolution

ESG’s research found that 75% of organizations have not seen the promise of self-service AI-powered analytics come to fruition. This frustration likely reflects the challenges of ensuring quality while meeting the requests of their users.

Additionally, 78% of those surveyed by ESG said it still takes too long to act on insights. This is attributed to several factors:

1. Expecting people to become analysts just because the organization has paid for a business intelligence platform seat.

2. Providing access to an overwhelming amount of intelligence rather than the relevant data when it’s most needed.

3. Individuals holding fast to the way they’ve always done things and resisting the power of AI.

4. Having too many tools in place for analytics and business intelligence.

In an engagement with a top five bank, Leone saw two cohorts. One continued with manual processes while the other embraced the new technological tools. The second, tech-friendly cohort dramatically outpaced the other in productivity and efficiency, even taking work from the manual cohort. But, he said, some people just don’t want a new tool and more training.

That’s exactly what prompted the creation of MicroStrategy Hyperintelligence and embeddable AI bots, Abhyankar said. The idea was to get the data to the user, wherever they are. Instead of giving them an umpteenth tool to navigate, the focus is on determining injecting the data end-users need into Salesforce, Workday, their mobile app, or whatever else they were already using.

Steps to Take to Truly Take Advantage of AI in Analytics

The speakers concluded sharing thoughts on how organizations can address these realities. Abhyankar emphasized the importance of empathizing with employees and recognizing that they’re often overworked and overwhelmed.

The way to encourage action on AI in analytics is ensuring you are enabling users to do their jobs better. That requires providing them with the precise data they need, where they are, when they need it. When that happens, people will want to incorporate AI analytics and business intelligence into the way they work.

For example, in the past a user looking at an analytics dashboard might have to go back to IT or the analyst to answer a new question that comes up. Now, with Gen AI infused into BI, a user can ask their questions in that dashboard.

Abhyankar wouldn’t claim the Auto tool could answer 100% of their questions, but maybe 50-60% at this point. “That's a pretty big leap forward in terms of their ability to get the information that they need when they need it without having to call anybody.”

Embedded analytics provide a similar use case. MicroStrategy is working with more organizations to build embedded AI chat or auto dashboards that empower people within the organization to get to value quickly. Of course, these require curated, clean data and business definitions to support data modeling and interactive dashboards, but the result is increased user freedom to work easier and faster.

The Future of AI for BI

“The best BI tool is the one you don't know you're using,” Abhyankar said. Take Apple Watch users. They are using analytics every single day (e.g. heart rate, calorie burn, exercise time), but they don’t think of it that way.

Each speaker agreed that that’s the future for business intelligence. The industry needs to be more thoughtful about end-user experience. The data integration and AI infusion into BI needs to be purpose and data driven for a specific function at a specific time.

Bringing actionable insights to the user simplifies their work and increases productivity. AI and machine learning algorithms make data sources more accessible not only business users but also frontline workers. It is through AI that more turn to BI more frequently, thus making the AI investment more valuable.

We’ve only captured the highlights of this informative MicroStrategy community webinar. Learn more about incorporating AI analytics into your business intelligence by watching the on-demand recording of the webinar. 

Want to read the research for yourself? Download your copy of the eBook, "Unleashing the Power of AI in Analytics and Business Intelligence." Find out how you can transform raw data into valuable insights with AI algorithms, to help your entire organization make informed decisions.