Traditionally, enterprises have focused their data strategies around business intelligence (BI), but the rise of predictive and prescriptive analytics platforms, in part thanks to machine learning and artificial intelligence, is changing the equation. Even business intelligence itself is evolving, tipping in capabilities previously exclusive to business analytics platforms.
Analysts and consultants agree that understanding the distinctions between business intelligence and other analytics platforms, as well as the value each brings to the enterprise, matters significantly in getting your data strategy right.
Here, we break down where business intelligence fits in the spectrum of analytics offerings available today — and how business analytics is evolving, thanks to shifts in tools, tactics, and personnel needs.
Business intelligence vs. business analytics
Analytics in the broadest sense applies to all technology-enabled problem-solving activities. Experts generally divide analytics into four categories along a continuum, with descriptive analytics and diagnostic analytics on the least mature part of the curve and predictive analytics and prescriptive analytics on the higher end.
BI is where most organizations start when undertaking an analytics program and it sits within the descriptive phase. Business intelligence leverages software and services to transform data into actionable intelligence that informs an organization’s strategic and tactical business decisions. It is what enables an organization to collect, analyze and present analysis of data.
“It’s information about the data itself. It’s not trying to do anything beyond telling a story about what the data is saying,” explains Beverly Wright, executive director of the Business Analytics Center at Georgia Tech’s Scheller College of Business.
Although some businesspeople might use BI interchangeably with analytics, Wright says data professions do distinguish between the two; some describe BI as offering insight into what has happened with the broader field of analytics — and particularly advanced analytics — anticipating what will happen under various future scenarios.
BI for business use
BI uses more structured data from traditional enterprise platforms, such as enterprise resource planning (ERP) or financial software systems, and it delivers views into past financial transactions or other past actions in areas such as operations and the supply chain. Today, experts say BI’s value to organizations is derived from its ability to provide visibility into such areas and business tasks, including contractual reconciliation.
Like many other parts of the enterprise technology stack, BI tools have evolved to become much more intuitive and user-friendly, Wright says. In the past, organizations needed data scientists to use these systems and build dashboards, she explains. Today they’re automated. That means organizations can more easily establish data programs that allow non-technical businesspeople to use BI tools to produce reports and get much of the information they need without involving data professionals in day-to-day usage. Analysts agree that this alone makes BI technologies important tools in the enterprise.
Wright says this new class of business users, dubbed “citizen analysts,” are the professionals in marketing, operations, finance or the C-suite who “don’t have intimate knowledge of data or modeling or analytics, but they can rely on a tool or system that gives them the information they need in a very simple way.”
Business intelligence as gateway to business analytics
Although BI tools such as reporting solutions still have a place in the enterprise, analysts say they have limited capabilities.
In its 2017 report Six IT Design Rules for Digital Transformation, the global management consulting firm Bain & Co. says its survey of IT leaders showed that more than 50 percent of organizations use at least three different analytics providers to generate performance reports. It further states: “CIOs urgently want to be able to integrate and synthesize separate data sources into one analytics engine that can reach across the entire infrastructure.”
More important, experts say, is the fact that BI tools don’t provide the most in-depth analysis of data that can drive new business opportunities and growth.
“BI is not driving revenue and innovation,” says John Myers, senior analyst of business intelligence at Enterprise Management Associates.
Although Myers estimates that 20 percent of U.S organizations are still at the BI stage of analytics adoption, he says most organizations don’t want to end their analytics efforts there. What Myers has found is that users typically are encouraged by the information that BI tools produce and want the data to start answering increasingly complex questions.
In fact, the Bain report also notes that IT operations managers ranked advanced analytics “as the capability that they would most like to have, yet only a ﬁfth say they have access to the technology now.”
Myers explains that users might start by looking at sales data and then want that data organized by state or product, for example. Then they’ll want to see their top 10 customers this year, their common attributes and, based on that information, they’ll want to know which will be the top 10 customers in the upcoming year.
“You’ve gone from adding things up and presenting it by different dimensions. That’s what lots of people call reporting or static dashboards or traditional business intelligence,” Myers says. “But when you start projecting forward by either time or using predictive analytics, you get into what many people see as analytics, when you have to do more complicated math.”
Myers elaborates saying that BI uses basic calculations to provide answers, while the other forms of analytics — including predictive and prescriptive — use mathematical models to determine attributes and offer prediction. He further notes that machine learning and artificial intelligence sits at the furthest end of the analytics continuum.
BI blurring the line
Although data professionals still have heavy roles to play in advanced analytics, such as around modeling, Myers says how much involvement they have varies based on the business case. For example, the advanced analytics systems used to detect potential credit card fraud requires speed and thus relies on unsupervised models vs. data scientists querying the systems.
Organizations generally buy off-the-shelf BI products as well as commercial advanced analytics products, Myers adds, but they tend to have their own data professionals build the machine learning and AI capabilities they need “because there’s not a set of packages on the market; the products just aren’t there.”
However, the market for solutions is changing as organizations demand more from their BI platforms and their other analytics tools, says Chris Brahm, who leads Bain’s global advanced analytics practice and formerly led its technology practice.
Brahm says many BI tools are bringing in more, and better, data signals to produce more accurate, insightful reports that blur the distinctions that traditionally separated BI from more advanced analytics. As such, BI vendors will need to advance, or risk losing out in the market, he adds.
“Can they evolve to provide real-time high-quality information for managers in the enterprise, because mangers tend to be the main users? And can they provide better real-time information using new data sets and new techniques? Because if they can’t, then new providers will enter in — and they are entering in — and answer the questions that managers have,” he says.
These new systems, he says, are helping users make better decisions by answering questions about how to maximize and optimize the business — questions like whom should the business target, what promotions are offered, and which ones are offered to whom.
“There are a number of players who are entering the market who are providing analytics for managers and front-line workers that go beyond what traditional BI did,” he says, adding that these tools are using new techniques and data sets to provide better, more holistic answers to the questions that managers have in specific segments such as supply chain, operations and R&D.
Technology companies tend to be further along the adoption curve and the ones most likely to have already adopted advanced analytics capabilities, including machine learning and AI.
More traditional industries are behind them, Brahm says, and they, too, view advanced analytics as critical for future success. He says Bain research shows that 70 percent of organizational leaders view advanced analytics and AI as a high priority for the business.
“Everybody,” he adds, “is heading in this direction.”