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Business intelligence (BI) is a simple concept. It involves 1) collecting data pertaining to your company from internal and external sources and 2) finding a way to distill it into something actionable. Essentially it involves harvesting the data you need to make good business decisions.

Today, the term “business intelligence” usually refers to the software or tools organizations use to turn data into usable information. It’s come a long way in the last 10 years, and with the recent growth of artificial intelligence, BI tools have powerful potential.

How Business Intelligence Has Changed in the Last 10 Years

In the last decade, companies have consolidated their business systems around a few key players (Microsoft, Oracle, SAP). Microsoft stands out because they’ve built a product into an ecosystem that thousands of organizations use every day. Microsoft 365 developed Power BI as a stand-alone product within the last 10 years.

More than 10 years ago, tools were difficult to use. They required people with specialized skill sets to write code and gather data to turn it into usable information. Microsoft, however, as a leading data architect and office productivity company, has key contributions in the simplification of business intelligence. With Microsoft’s Power BI, companies no longer need an army of database administrators and developers to handle data. It has become more of an intuitive, self-service business intelligence platform people can use themselves.

The Future of Business Intelligence Is Artificial Intelligence

Since Microsoft is a key player in this industry, they’ve already included some basic AI tools within Power BI. One of those is a Q&A box that can be included in a report to make it easier to find information. A user can ask, “What was the revenue in Q1 of 2022 vs. 2023?” Power BI will do its best to pull that information.

As Microsoft continues to develop AI capabilities, we expect users will be able to ask even more complex questions and follow-up questions, just like with ChatGPT.

We also expect AI to be able to examine a set of data and make inferences based on that data. For example, suppose a company needs to review customer feedback on a product line but has 10,000 customer reviews. That’s a lot for one person to parse through. Instead, there’s potential for AI to step in and find common threads with language without having to spend hundreds of human hours looking through reviews. Instead of merely taking a 5-star review at face value, AI could analyze what was said about the product—because a 5-star review isn’t always meaningful if the text says otherwise.

It’s important to note that, no matter how advanced AI is, organizations shouldn’t fully rely on AI to extract data and make inferences. Human intelligence will still be required to manage AI tools and make sure they’re pulling the right data, making accurate inferences, and interpreting language correctly.

AI is all about saving time and allowing employees to add more value to the organization. As AI advances, it will alleviate a lot of time-consuming activity and allow employees to focus on the strategies and conversations that will drive business decisions.

The Right Mindset for AI

Remember, AI is a tool. It’s not a decision maker or a business strategist. While it can replace a lot of human tasks, it doesn’t replace a human. The people using AI tools are the key to making AI tools successful. The right tool in the wrong hands won’t solve anything. But if used correctly, AI has the potential to add significant value to organizations.

A Word on Best Practices

When it comes to managing data the following practices are crucial, with or without AI.

1. Establish Good Governance Around Data

Setting standards around creating data is key. We’ve seen companies that have multiple people entering the same data in their system under different names (e.g. GE, GE Power, General Electric). When someone asks to see information for GE, the data isn’t accurate. Organizations must have good governance and data ownership on the front end so information is centralized.

2. Define & Agree on Metrics

It’s important to agree on and define which metrics are to be tracked. Know what’s included in each metric and what’s not. If people realize data metrics aren’t consistent or correct, they won’t believe the data. They’ll be more likely to create their own databases that are more consistent with the data they need.

Terminology is critical. In many organizations, different departments or divisions within the company have different definitions for the same word. But there shouldn’t be any ambiguity on a well-built report. It’s important to note that BI software or tools can’t solve this problem. It’s about the processes around business intelligence and the people involved. BI software is maintained by people, and the processes for maintaining it must be clear and thorough.

At Trenegy, we help organizations implement fit-for-purpose business intelligence solutions to drive value. For more information, reach out to us at

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