Business intelligence (BI) doesn’t seem like a tech term that requires much introduction. It doesn’t even sound all that techie: We know what a business is, and we know what intelligence is – put them together and you’ve got what, a smart business?
That’s not that far off, actually. The challenge with defining, understanding, and explaining BI is that the term tends toward the abstract. That’s really what it boils down to, though: Helping people and organizations to make smarter decisions based on all of the relevant information available to them.
“Today, every company on the planet has to be ‘data-driven,'” says Amy Hodler, director of graph analytics and AI programs at Neo4j. “Business intelligence is how companies actually achieve that.”
[ Where does AI fit in here? Read also: How big data and AI work together. ]
BI vs. Big Data
Consider the Big Data (with a capital B and a capital D) frenzy: You could have the biggest data, but that’s not worth much if you can translate said data into usable information. BI is essentially a discipline (with a corresponding category of platforms and tools) that aims to make data-driven strategy and decision-making actually possible, instead of just a good soundbite.
“Business intelligence refers to the strategies and technologies used by companies to analyze valuable business data beyond traditional simple reporting.”
“Business intelligence refers to the strategies and technologies used by companies to analyze valuable business data beyond traditional simple reporting,” says Bill Szybillo, business intelligence manager at VAI. “Data is the most valuable asset of today’s industries, and if utilized correctly and securely, it’s the best way for company leaders to make informed decisions.”
Let’s add a few more clear-cut definitions that focus on the “what” and the “why” of BI. Use these to refresh your own grasp of the concept or to help explain BI to a wider audience of stakeholders around your organization.
Business intelligence (BI), explained: 4 definitions
1. “Business intelligence is the process of bringing together all types of information a company has in a way that’s relevant to the question or opportunity at hand – from customer behavior to manufacturing performance to employee productivity – and using it to make important decisions that will lead to a greater profit and a competitive advantage in that company’s industry.” – Amy Hodler, director of graph analytics and AI programs at Neo4j
2. “BI unlocks the power of data. On its own, data may not reveal much, and won’t drive consensus, reveal problems, or inspire solutions. But business intelligence has transformed enterprises, allowing them to gain insights and make decisions in real-time.” – Jason Beres, SVP, developer tools at Infragistics
3. “Simply stated, business intelligence is collecting data from multiple sources, both internal and external, [and] transforming and normalizing that data in order to provide valuable actionable insights for making data-driven business decisions.” – Dennis Faust, business intelligence & analytics manager at Anexinet
4. “Business Intelligence is poised to yield more value going forward with more data sharing between enterprises. The digital ecosystem is one that can grow in an open, collaborative environment, yielding more meaningful insights for the end consumer. This can be the patient in the healthcare ecosystem, the consumer walking into the retail store, the human with their device of choice, or the passenger in their transport of choice etc. Culture is more often the barrier to the advancement of such collaboration than technology. The prevailing mindset of the data stewards across the extended enterprise will determine the value that Business Intelligence can provide going forward.” – E.G. Nadhan, Red Hat Chief Architect and Strategist, North America
The key takeaway here: BI takes raw data that most people would not be able to make any sense of and turns it into comprehensible information that they can use to do their jobs well.
What is BI software?
There are a wealth of BI technologies, from complete platforms to specific tools to applications embedded inside other applications (such as a CRM or ERP system.)
Common business intelligence applications include data analytics, supply and demand planning, predictive analytics, and warehouse management systems.
“Some of the most common business intelligence applications are data analytics tools, supply and demand planning tools, predictive analytics, and warehouse management systems,” Szybillo says. “These automated applications take time-consuming, tedious tasks off the hands of employees, so they can spend valuable time on knowledge-based tasks.”
Even a spreadsheet could technically qualify as a BI application – a handy, if relatively rudimentary, reference point when explaining BI to non-technical people. Speaking of which, wider audiences probably care most about how they’ll interact with BI, rather than how it works. Two of the most common user interfaces or user experiences (UI/UX) they’ll interact with are dashboards and data visualizations. Sometimes, those are one and the same, though not every dashboard is a graphical interface.
“No longer merely a spreadsheet, BI platforms can be used to provide visualizations of everything from workplace efficiency to cybersecurity threat patterns,” Beres says.
Dashboards are essentially the UI/UX payoff of BI’s initial promise: turning massive amounts of raw data into insights people can understand and use. They don’t need to be data scientists or have any other technical expertise to access, consume, and act on the data expressed in a dashboard.
“Dashboards allow decision-makers and employees to visually see and understand business performance metrics at a quick glance,” Szybillo says. “Business intelligence tools pull and analyze data, and dashboards present that data in an easy-to-understand format to improve business decisions.”
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What can you do with BI?
Given all of the above, the better question is probably: What can’t you do? The possibilities are significant. One way to wrap your head around them and apply them to your particular business function or job is to think in terms of questions and answers. Let’s say a marketer wants to understand something about a particular customer segment’s behavior based on certain variables: Frame it as a question, and BI is the mechanism (or one of them) that can provide credible, valuable answers. Perhaps a logistics manager wants to understand a correlation between weather events in one part of the world and shipping delays in another. Again, pose it as a question. Turn to BI for answers.
This approach can be strategy-oriented but also results-oriented. BI can produce the metrics by which various business units measure themselves, and well as the information those departments use to make new decisions. Faust from Anexinet shares a handful of big-picture questions where BI can help:
- What type of customers are likely to buy a particular product or service?
- Which marketing campaigns are more successful than others, and why?
- What factors are impacting my customer retention?
- What is my customer satisfaction by region, by product, by vertical?
- Where should I be putting my marketing dollars?
You can apply the same principles behind these questions to various areas of the business.
Is BI still relevant?
This one’s easy: Absolutely, positively yes.
The reason the question might come up in conversation is because the term itself is not especially trendy. Other terms and technologies have come along to soak up the limelight. It’s not even the hottest term with the word “intelligence” in it: That would be artificial intelligence, of course. By tech hype standards, BI has more in common with ERP and CRM rather than cloud or AI.
This is also a function of the term’s staying power. Tech and business circles sometimes focus on the new and shiny. The history of the term “business intelligence” is commonly traced back to – wait for it – 1865. A book published that year, Cyclopaedia of Commercial and Business Anecdotes, uses the phrase to describe the strategy of a banker named Sir Henry Furnese, who depended upon being the first to have information that could boost his profits: “Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence.” (The passage goes on to say that he also sowed disinformation to manipulate markets to his advantage, but that’s a whole other story.)
If you want a more recent lineage of the term, computer science researcher Hans Peter Luhn published the seminal paper, “A Business Intelligence System,” in the IBM Journal of Research and Development – back in 1958. Luhn is viewed as a originator of the modern term and its meaning in business and IT contexts.
They say age is just a number; here, that number speaks to the fundamental necessity of BI to organizations in virtually any industry. The past year-plus has underlined that rather than diminish its importance.
“Business intelligence continues to be a huge part of many companies’ digital transformation strategy, especially to survive in this new normal,” Szybillo from VAI says. “Predictive analytics, for example, is crucial for sales teams and C-suite executives to gain insights into purchasing decisions, future trends, and customer sentiment.”
Again, it’s also a measurement discipline: “How did we do?” is as important as “what should we do next?” That’s also part of BI’s endurance.
“Today’s decision-makers also rely on business intelligence tools to pull data reports that prove the ROI of the various investments a company has made,” Szybillo says. “Since many companies are still working remotely, understanding this data is very important to see what’s working within the company.”
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