Modern Business Intelligence

Stephen DeAngelis

June 18, 2020

Intelligence is a word packed with nuance. If you’re talking about people, intelligence is a great quality to have. If you’re talking about computers, intelligence is something some computer scientists aspire to create in their machines. If you are talking about national security, intelligence conjures up images of shadowy figures, covert dealings, and secrets. But what does intelligence mean when you are talking about business? According to Margaret Rouse, “Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information which helps executives, managers and other corporate end users make informed business decisions.”[1] She adds, “Business intelligence is sometimes used interchangeably with business analytics. In other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.” In other words, business intelligence (or business analytics) involves the collection and analysis of data to improve enterprise decision-making.

Data and analysis were being used in businesses long before the term “business intelligence” was coined. Rouse notes, “Sporadic use of the term business intelligence dates back to at least the 1860s, but consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella phrase for applying data analysis techniques to support business decision-making processes.” As Kartik Patel (@p_kartik), Founder and CEO of Elegant MicroWeb, observes, “The primary difference between traditional BI and the new, more modern approach to BI lies in flexibility and accessibility. The more traditional tools were designed for use by analysts or IT staff and while these tools provided sophisticated features, these features were not accessible or easy to understand for anyone outside the analyst or technical community. The traditional tools were not scalable or flexible enough for mobile, nor did they provide guided, auto-recommendations in a natural language environment.”[2] Confining business intelligence to a small group of analysts means many decision-makers might not benefit from advanced analytic capabilities. That’s not a good situation because, as Bain analysts, Michael C. Mankins and Lori Sherer (), explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[3] Since decisions are made throughout an organization, the benefits of BI should be available throughout the organization as well. As Rouse notes, “The role of business intelligence is to improve all parts of a company by improving access to the firm’s data and then using that data to increase profitability.”

Benefits of modern BI

Rouse asserts, “Companies that employ BI practices can translate their collected data into insights of their business processes. The insights can then be used to create strategic business decisions that improve productivity, increase revenue and accelerate growth. Other potential benefits of business intelligence tools include: accelerating and improving decision-making; optimizing internal business processes; increasing operational efficiency; driving new revenues; gaining competitive advantage over business rivals; assisting companies in the identification of market trends; and, spotting business problems that need to be addressed.” Although that sounds great, achieving those benefits takes effort. Piyush Goel, Founder and CEO of Beyond, reports a study published by the Boston Consulting Group found, “Managers now spend 40 percent of their time writing reports. The vast remainder of that time is for meetings where they coordinate with different groups to analyze those reports. Even with all these resources, more than 99 percent of data never gets used or analyzed according to the Harvard Business Review. The process can be infuriating, which is precisely why many companies are turning to business intelligence systems to automate this wasteful process.”[4]

One of the technologies helping to democratize BI throughout an enterprise is natural language processing (NLP). Alberto Pan (@AlbertoApan), Chief Technical Officer at Denodo, explains, “Although BI tools are becoming increasingly user-friendly for business stakeholders, true self-service BI is still difficult to achieve. This is because IT power users are still routinely called in for report creation and the process of modifying existing reports — for the purpose of asking related questions — is still prohibitively cumbersome for most business users. However, NLP has the potential to enable true self-service BI by seamlessly translating spoken commands into SQL or any other technical query language.”[5] Is BI really necessary for a business to succeed? Goel notes, “Forbes featured an Accenture study that found 79% of executives now believe that companies who do not embrace big data will lose their competition and go extinct. However, they don’t just need an influx of big data. They need timely data.”

BI and cognitive technologies

Nowadays companies collect so much data it cannot be analyzed in a timely fashion without the assistance of cognitive technologies, like the Enterra Cognitive Core™, a system that can Sense, Think, Act, and Learn®. Lauren Adley (@LaurenAdley1) notes, “Business intelligence is a complex field representing a process that depends on technology to acquire, store, and analyze business-related data. The goal of BI is to reach optimal courses of action in as short time as possible, so the process includes several different aspects, such as analytics, predictive modeling, performance management, data mining, etc. … When it comes to processing large amounts of data, there simply is no comparison between what a human and a machine can do, so machine learning naturally appears on stage as a potent tool BI can greatly benefit from.”[6] Researcher Keith D. Foote cautions us not to confuse business intelligence with the technology that provides that intelligence. He explains, “Contrary to the theme of several current articles, business intelligence is not a combination of tools, best practices, and software programs, but is the result of those tools and software programs. The same is true when using artificial intelligence to unearth BI. The tool (AI) is not business intelligence, but a source of business intelligence.”[7] Foote goes on to list some of the benefits of using cognitive technologies to create business intelligence. They are:

Natural Language: “The recent advances in natural language processing now allow both experienced users and novices to ask questions about analytic outputs and then shift to related information without struggle.”

Actionable Analytics: “Actionable analytics places data in the place it will be most useful, working in real time.”

Explainable AI: “Explainable artificial intelligence seeks to [make] AI processes more transparent, allowing users to drill down more deeply into the data and understand how the conclusions were reached.” At Enterra Solutions® we use Massive DynamicsRepresentational Learning Machine™ (RLM). The RLM helps determine what type of analysis is best-suited for the data involved in a high-dimensional environment and it accomplishes this in a “glass box” rather than “black box” fashion (i.e., it makes decisions explainable).

Streamlining Operational Processes: “With intelligent IT automation, productivity can be increased significantly. AI-powered ‘bots’ (programs that can automatically execute actions) are becoming common place in factories, with computer vision and machine learning software improving safety and quality control.”

Boosting Sales: “The majority of sales teams spend much of their time performing repetitive, tedious tasks. Artificial Intelligence takes over the tasks of finding leads, sorting them, monitoring orders, and communicating with both customers and potential customers.”

Building Customer Loyalty: “Innovative organizations are dropping the more traditional methods of attracting more customers by way of price wars and incremental product improvements and are instead investing in AI platforms that offer a personal touch, attempting to create tailor-made experiences.”

Concluding thoughts

Adley concludes, “Artificial intelligence and machine learning are no longer vague futuristic, Sci-fi-like concepts, but the fast-developing reality present in numerous processes we encounter on a daily basis. … Machine learning is already improving many BI-related processes, and it’s expected to become even more potent and useful in the years to come.” As Goel noted above, most executives now believe their companies must embrace business intelligence processes or risk losing to their competition — or, even worse, going extinct.

Footnotes
[1] Margaret Rouse, “business intelligence (BI),” TechTarget, September 2019.
[2] Kartik Patel, “What is Modern BI and How is it Different From Traditional BI?Dataversity, 24 January 2020.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Piyush Goel, “How Business Intelligence is Changing the Future of Business,” Dataversity, 15 April 2020.
[5] Alberto Pan, “How natural language processing can transform business intelligence,” Information Management, 18 January 2020 (out of print).
[6] Lauren Adley, “How Machine Learning is Improving Business Intelligence,” insideBIGDATA, 16 February 2019.
[7] Keith D. Foote, “Utilizing Business Intelligence with AI,” Dataversity, 16 April 2020.