Business Intelligence: What Is It?

Stephen DeAngelis

August 30, 2021

“Business intelligence (BI) doesn’t seem like a tech term that requires much introduction,” writes business journalist Kevin Casey (@kevinrcasey). “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?”[1] According to Christopher Rafter, President and COO at Inzata, BI isn’t as straight forward as Casey might like. He explains, “In the age of Big Data, you’ll hear a lot of terms tossed around. Three of the most commonly used are ‘business intelligence,’ ‘data warehousing,’ and ‘data analytics.’ You may wonder, however, what distinguishes these three concepts from each other.”[2] It didn’t take Rafter too long to “get techie” when discussing business intelligence.

 

In today’s digital environment, the “intelligence” part of BI is inescapably tied to data. And, according to tech writer Michelle Knight (@Michell24332586), that’s when things get more complicated and complex. She explains, “What is a company to do when Business Intelligence, designed to leverage data as an asset, costs too much time and money due to failures in traditional Data Management processes, such as the prepping and cleaning of data? … Businesses need to understand Data Management’s impact on BI.”[3]

 

Defining Business Intelligence

 

Casey admits, “The challenge with defining, understanding, and explaining BI is that the term tends toward the abstract.” According to the staff at Tableau, the reason for BI’s abstractness is that its definition has morphed over the past 60 years. They explain, “BI has had a strangled history as a buzzword. Traditional Business Intelligence, capital letters and all, originally emerged in the 1960s as a system of sharing information across organizations. It further developed in the 1980s alongside computer models for decision-making and turning data into insights before becoming a specific offering from BI teams with IT-reliant service solutions. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.”[4] Let’s look at how different organizations define the term today.

 

Investopedia: “Business intelligence refers to the procedural and technical infrastructure that collects, stores, and analyzes the data produced by a company’s activities. BI is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI parses all the data generated by a business and presents easy-to-digest reports, performance measures, and trends that inform management decisions.”[5]

 

OLAP.com. “The term Business Intelligence refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS). Business Intelligence is sometimes used interchangeably with briefing books, report and query tools and executive information systems.”[6]

 

Oracle. “Business intelligence refers to capabilities that enable organizations to make better decisions, take informed actions, and implement more-efficient business processes. BI capabilities allow you to: Collect up-to-date data from your organization; present the data in easy-to-understand formats (such as tables and graphs); deliver data in a timely fashion to the employees in your organization. BI keeps your organization in the know, and success depends in a large part on knowing the who, what, where, when, why, and how of the market. How popular are your products or services with consumers? What are your competitors doing? Why are consumers choosing one brand over another? How — and when — will the market change? What are the trends for the future?”[7]

 

OMNISCI. “Business intelligence is the process by which enterprises use strategies and technologies for analyzing current and historical data, with the objective of improving strategic decision-making and providing a competitive advantage.”[8]

 

Microsoft. “Business intelligence helps organizations analyze historical and current data, so they can quickly uncover actionable insights for making strategic decisions. Business intelligence tools make this possible by processing large data sets across multiple sources and presenting findings in visual formats that are easy to understand and share.”[9]

 

Every one of those definitions has three things in common: data, data analysis, and making better decisions. Casey’s simple BI definition may say it best, “[BI helps] people and organizations to make smarter decisions based on all of the relevant information available to them.” As Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), report, “Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”[10]

 

Data and Decision-making

 

Ratheesh Raveendran, COO of OpsVeda, adds an additional wrinkle to the BI discussion. He believes operational intelligence (OI) needs to be distinguished from business intelligence. He explains, “Operational intelligence enables continuous evaluation of information leading to timely action. It leverages live stream data. With Business Intelligence platforms, the underlying data is stale, so the platform is predicting future conditions based on past information. The data is there for mining and can provide insights, but it’s always within the context of what’s already occurred. Operational Intelligence closes the gap of things that occurred in the past and those that are happening in real time, which allows people to make impactful context-based decisions.”[11] Raveendran makes it sound like all decisions need to be based on real-time data — that’s not the case. It’s important for businesses to know what data is required to make informed decisions about various aspects of their business. Monitoring and analyzing real-time data for decision-making can be costly.

 

Nevertheless, Raveendran makes some good points. He concludes, “Today’s enterprises need operational intelligence to succeed, and arguably to survive, and manage the ever-increasing unpredictability and complexity of business environments. Firms need to react to changing conditions, handle multi-channel sales, and manage a wide range of vendors and product suppliers.” He believes, “Handling this complexity requires that firms move beyond the limitations of BI to operational intelligence platforms that not only offer analytics but also actionable recommendations.” The kind of platform to which Raveendran refers involves cognitive technologies (i.e., artificial intelligence). These cognitive systems, like the Enterra System of Insight and Actions®, which is powered by the Enterra Cognitive Core™ — a system that can Sense, Think, Act, and Learn® — are embedded with advanced analytics capabilities that can perform diagnostic, descriptive, predictive, and prescriptive analysis.

 

Enterra Solutions® is a leader in Autonomous Decision Science™. Not all decisions require human intervention and Autonomous Decision Science understands when decisions can be made by a smart platform and when human attention is required. The technology isn’t smarter than a human, but it is more efficient and scalable. Our technology leverages advanced glass-box mathematical analysis, powerful non-linear optimization, and a human-like reasoning to help organizations make more decisions faster and more accurately. The Enterra® system is always learning from its actions. It can process more data, weigh conflicting evidence, perform optimization, and prescribe an answer that is ‘best’ among alternatives, — and it does this quickly and accurately. As Mankins and Sherer asserted, “Companies that employ advanced analytics to improve decision making and execution have the results to show for it.”

 

Footnotes
[1] Kevin Casey, “How to explain Business Intelligence (BI) in plain English,” The Enterprisers Project, 21 April 2021.
[2] Christopher Rafter, “What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics,” insideBIGDATA, 16 March 2020.
[3] Michelle Knight, “Data Management vs. Business Intelligence,” Dataversity, 8 January 2020.
[4] Jake Frankenfield, “Business Intelligence (BI),” Investopedia, 23 June 2021.
[5] Staff, “Business Intelligence: What It Is, How It Works, Its Importance, Examples, & Tools,” Tableau.
[6] Staff, “What is Business Intelligence (BI)?” OLAP.com.
[7] Staff, “What is Business Intelligence (BI)?” Oracle.
[8] Staff, “Business Intelligence Definition,” OMNISCI.
[9] Staff, “What is business intelligence?” Microsoft.
[10] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[11] Ratheesh Raveendran, “Business Intelligence vs. Operational Intelligence – What Businesses Should Know,” insideBIGDATA, 25 June 2021.