We recently shared straightforward definitions of business intelligence (BI) to help you explain it in plain terms that anyone in your organization can understand. In this article, we’ll do a deeper dive on another question IT leaders will face - the “why” behind BI – specifically, why is it important?
Explaining the “why” of a particular technology matters greatly to any internal evangelism, especially during budget season or in any scenario where there’s cultural resistance to adoption or usage.
What are the benefits of BI?
In BI’s case, this sort of evangelism often involves reminding people of its importance, since the discipline and corresponding technologies the term represents have been around for decades. In large enterprises in particular, BI has probably been around in some form for long enough that it goes relatively unnoticed, even by people who rely on it every day.
BI’s importance fundamentally boils down to this: It’s the practice of making data-driven decision-making – long the rage in business and other contexts – a tangible reality.
“Data, workflows, and collaboration are intersecting, and whether it’s adjusting supply chains or deciding which product lines to focus on, it’s clear that intuition is nothing without data-based insight,” Angshuman Guha, co-founder and CEO of bipp. “For many companies, true business intelligence is a big part of the answer.”
BI also fits with digital transformation efforts across many industries, as E.G. Nadhan, Red Hat Chief Architect and Strategist, North America, recently told us. “Business Intelligence is poised to yield more value going forward with more data sharing between enterprises,” Nadhan says. “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.”
[ Where does AI fit in? Read also: How big data and AI work together. ]
4 reasons Business Intelligence (BI) is important
Let’s dig into four related reasons why BI remains so vital to data-driven enterprises.
1. BI catalyzes rapid decisions
The term “data-driven decision-making” doesn’t fully encapsulate one of its important subtexts: People almost always mean fast decisions.
This distinction matters because it’s one of the capabilities that modern BI tools and practices enable: Decision-making that keeps pace (or close enough to it) with the speed at which data is produced.
“Data is now produced so fast and in such large volumes that it is impossible to analyze and use effectively when using traditional, manual methods such as spreadsheets, which are prone to human error,” says Darren Turner, head of BI at Air IT. “The advantage of BI is that it automatically analyzes data from various sources, all accurately presented in one easy-to-digest dashboard.”
Sure, everyone talks about the importance of speed and agility across technology and business contexts. But that’s kind of the point: If you’re not doing it, your competitors almost certainly are.
“The ability to act on insights sooner allows businesses to spot internal trends and improve inefficiencies before it’s too late,” Turner says. “In a marketplace where the volume of data is ever-increasing, the ability for it to be processed and translated into sound business decisions is essential for better understanding customer behavior and outperforming competitors.”
[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]
2. Hello, big data: BI wrangles copious data sets into usable form
There’s still a place for experience and instinct in business decisions; try telling the rest of the C- suite otherwise. But there’s a difference between a gut call and a wild guess. BI is about making information readily available and accessible to the people who need it to do their jobs at any level of the organization.
“Knowledge workers depend on data for everything from underpinning a strategy to measuring their impact,” says Amy Hodler, director of graph analytics and AI programs at Neo4j.
The challenge is that we often have too much information. Moreover, it’s often incomprehensible in its raw form, or would require incredible amounts of time to sift through for value. This is the proverbial needle in a haystack – except the haystack gets bigger by the day.
“The issue is that with the advent of big data, the size of those haystacks has grown exponentially, making finding those needles a lot more challenging,” Hodler says.
BI done right presents information in a usable form that gets rid of a lot of the “hay,” allowing end users to actually utilize the data they’re presented with. This is why many tools and platforms place a heavy emphasis on dashboards and visualization as the primary UX/UI.
“Visualizing the data set – guided by contextual relevancy and an intuitive user experience – will ensure that we’ll always get significant value from BI,” Hodler says.
This also links back to speed: What once might have been a time-consuming end-of-month or even quarterly reporting process can now happen continuously, again in a manner that people can understand and act on.
“Rather than spending hours collating data at the end of each month, BI platforms make it possible to see data in real time – shaving hours off your working week and allowing you to view insights as they happen,” Turner says.
This spans both the earliest stages of a business strategy as well as its ongoing measurement: BI brings information together and renders it in a consumable format.
“Business intelligence platforms with strong data modeling capabilities can turn disparate databases into a single source of truth that defines a company’s business logic,” Guha says. “This can then be used across the entire company, so everyone is using the same language to represent critical KPIs and data.”
[ Read also: 6 misconceptions about AIOps, explained. ]
3. BI offers industry-specific use cases and benefits
As Red Hat’s Nadhan noted, BI is not a one-size-fits-all proposition. Organizations tailor it to industry and business contexts.
“The benefits of business intelligence can vary greatly by the industry and what kind of application the business is utilizing,” says Bill Szybillo, business intelligence manager at VAI. “For example, a food distributor or processor can use business intelligence tools to gain insights into their supply chain, such as inventory levels, supply and demand trends, order status, and more. This kind of data is not only valuable for streamlining warehouse operations but can also be used by sales and marketing teams to better communicate with current and potential customers.”
As we noted previously, a useful way of thinking about BI’s capabilities is to frame your goals as data-driven questions. BI may be able to provide the answer, or at least the basis for an answer.
Turner notes that a retail store owner who wants to determine whether an investment in new equipment will be profitable can run an ad-hoc report to make an informed decision. A restaurant ownership group can use BI tools to compare efficiency across different locations. BI is widely applicable; there aren’t many industries where it’s irrelevant.
4. BI is evolving for the cognitive and AI age
A different form of technology-fueled intelligence – artificial intelligence – has decidedly more star appeal than traditional BI. But BI and AI aren’t mutually exclusive; they’re increasingly intertwined.
[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
“Traditionally, BI has referred to dashboard and visualization platforms that render data into charts, graphs, and pivot tables. However, AI has greatly accelerated analytics beyond these basic capabilities,” says Mike Finley, chief data scientist at AnswerRocket. “AI can automate data analysis, generating visualizations as well as diagnostic and predictive insights. Simply put, these insights explain key drivers that impact performance and forecast future trends, telling a more complete story of what’s going on in the data – and why.”
The pairing of BI and AI can bring more automation to areas like data analytics and corresponding actions or responses to the analysis, for example, requiring less human intervention on more mundane tasks. This is similar to security automation and the automation of certain kinds of incident responses triggered by data analysis. The goal is similar, too: Increasing speed and improving outcomes in the face of overwhelming amounts of information.
“Business intelligence coupled with artificial intelligence can make a huge difference in solving problems such as customer retention, performance and operational management, visibility, and more,” Szybillo from VAI says.
Another way to think of it: AI should improve BI, not replace it. It’s ultimately concerned with similar business goals and challenges.
“Driving intelligence from customer data and using this data to make more informed decisions on behalf of clients is one of the most important goals of implementing a BI strategy,” Szybillo says. “Other common problems are performance or operational inefficiencies – such as employees traditionally having to manually create customer reports or manually track inventory, wasting valuable time. BI tools can do this for employees, allowing them to spend their time more efficiently.”
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
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