Business Intelligence in the Digital Age

Stephen DeAngelis

March 20, 2019

When most people hear the term “gathering intelligence,” they think of fictional spies like James Bond or George Smiley or about spy agencies like the CIA or MI6. In business circles, gathering intelligence is generally a bit less sinister. Business Intelligence (BI) means gathering necessary information to make informed decisions. Gartner defines the term this way: “Business intelligence is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.”[1] TechTarget’s definition is a bit longer: “Business intelligence is a technology-driven process for analyzing data and presenting actionable information to help executives, managers and other corporate end users make informed business decisions. BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against that data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision-makers, as well as operational workers.”[2] TechTarget adds, “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. What came to be known as BI tools evolved from earlier, often mainframe-based analytical systems, such as decision support systems and executive information systems.”

 

Some analysts have tried to make a distinction between Business Intelligence, data analytics, and data mining. For example, Kate Bondar writes, “Both business intelligence and data mining can be extremely valuable to your business. However, because the two terms are often used interchangeably, it can be confusing to understand exactly what they are, how they’re different and how they can be used.”[3] Obviously, in her mind, there is a difference. She explains, “BI is used to provide insights about both your own company and others such as rivals or business partners. It involves collecting and often processing large volumes of data, whether it be through your own internal metrics or third-party resources. … Ultimately it is used to help make more informed and therefore better business decisions, as well as making cost savings and finding new prospects. It can also be used to identify which processes and systems aren’t performing well enough, so managers can alter them accordingly. … Data mining is the process of analyzing data to identify useful patterns and insights. The software involved allows companies to analyze information from multiple sources to find trends. Given the huge amounts of data now available, companies use big data management solutions that can enact intelligent data mining to provide them with insights to make informed decisions.” The important thing to remember is that BI, data analysis, and data mining are about making better decisions.

 

The importance of better decisions

 

Bain analysts, Michael C. Mankins and Lori Sherer (), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[4] They add, “We know from extensive research that decisions matter — a lot. 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.” Simply put, business intelligence supports better decision-making. With that idea in mind, Chris Lukasiak, Senior Vice President of MyHealthDirect, asks, “Why aren’t more executives paying attention to business intelligence?”[5] He adds, “With more data at our hands, business intelligence is critical to making informed business decisions and can be a key component of forming predictive analyses for the future of a company.” He lists five ways business intelligence help companies. They are:

 

  • Reducing costs
  • Improving efficiency and productivity
  • Supporting decision-making
  • Improving sales
  • Revealing opportunities

 

He concludes, “There’s no question — companies that successfully use business intelligence make better business decisions and are poised to outpace and outlast their competitors.”

 

Leveraging cognitive technologies for BI

 

Many companies are data rich and analytics poor. So much data is being generated companies can struggle to make sense of it all without leveraging some sort of artificial intelligence (AI). Cognitive computing, a form of AI, is predicted to be the go-to platform for most businesses because it can handle both structured and unstructured data, employs machine learning (ML), and utilizes natural language processing so users can easily communicate with and understand the analysis. Lauren Adley (@LaurenAdley1) observes, “There is a lot of information a business can harvest from (potential) customers’ purchasing behavior online. ML brings a significant improvement in understanding a target audience and its needs, providing businesses with valuable information that can be used in marketing in order to skyrocket sales. Data collected from personal profiles (realized purchases, browsing searches, personal details) are irreplaceable, powerful information a company can use to predict, for example, how a new product will be accepted on the market, or which qualities should be included when a new product is made, according to what consumers want and look for. … ML is already improving many BI-related processes, and it’s expected to become even more potent and useful in the years to come.”[6] Much of that data comes from consumers using smartphones. Smartphones are as close as many businesses come to the spy game. Cybersecurity expert John McAfee once stated, “Our mobile phones have become the greatest spy on the planet.” Kevin McGirl (@kevin_mcgirl), President of sales-i, asserts companies need to harness the potential business intelligence in order to create efficiencies company-wide.[7] He suggests five rules that can help achieve this goal. They are:

 

1. Keep your data clean. “You can’t hope to implement BI without making sure your databases are clean and accurate first.”

 

2. Build a clear BI roadmap for the entire organization. “Implementing BI is not a one-off process, it’s an ongoing journey and you’ll need a map to guide you. Don’t expect to see benefits overnight. However, a good roadmap will help you speed up the process and ensure greater success.”

 

3. Educate and empower your team to be data-driven. “Communicate with your team to understand their possible concerns with adopting BI. Change is not easy to adjust to, so help them understand how the new approach will benefit them in their specific roles.”

 

4. Consider different integration options to maximize efficiency. “Consider how BI will integrate with your existing IT infrastructure and how you might evolve your infrastructure to accommodate BI. Your choice of BI software should factor in easy integration to enable better connected processes.”

 

5. Monitor, evaluate and iterate usage. “To make sure you are pushing your BI system to its maximum potential, you need to monitor, evaluate and iterate how it’s being used and what results it’s delivering.”

 

Concluding thoughts

 

Business intelligence is not about espionage; it’s about making better (i.e., more informed) decisions. As Mankins and Sherer note, better decisions always reflect positively on the bottom line. In the digital age, companies need to transform into digital enterprises capable of leveraging the oceans of data available to them. Cognitive technologies will prove to be the foundation upon which digital enterprises are built.

 

Footnotes
[1] Staff, “Business Intelligence (BI),” Gartner.
[2] Staff, “business intelligence (BI),” TechTarget.
[3] Kate Bondar, “BI vs Data Mining: What’s the Difference and How Can They Be Used?” Datafloq, 15 November 2018.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Chris Lukasiak, “What You Need to Know About Business Intelligence,” Forbes, 7 August 2018.
[6] Lauren Adley, “How Machine Learning is Improving Business Intelligence,” insideBIGDATA, 16 February 2019.
[7] Kevin McGirl, “Five Steps to Implementing a Successful Business Intelligence Strategy,” Supply Chain Management Review, 28 February 2018.