A new trend is coming soon to a bar near you. Self-service taps allow the customers of a handful of cutting-edge venues to pour their own beer from a large selection of taps, according to business intelligence expert Mona Lebied’s “5 Big Data Examples in Your Real Life at Bars, Restaurants, and Casinos” on the Datapine Blog.

Customers at these advanced pubs receive a temporary card, which they swipe before filling their glasses. If they only want to try a half glass of a rare import, they only pay for the amount they pour. If they want four glasses of Guinness, they pay for all four glasses.

This new system is not only convenient for the customer, but it also helps the bar cut down on “generous bartender” issues, where more beer is poured than is accounted for at the end of the day. The system also collects data to provide insights on which types of beers are selling on certain days or times. New kegs can be automatically ordered on time as “flow meters” track exactly how much beer is going out with each pour.

Self-serve beer is just a small example of how business intelligence and analytics can streamline business processes and increase efficiency. New uses for these techniques continue to be created in a variety of industries. For example, Lebied’s article also notes that major fast food chains are now monitoring drive-through lines during the day. If the line is long, the specials on the LCD screen menu will change to meals that can be prepared more quickly. The result is faster service and more profit.

These advances in business intelligence and analytics are important factors for those seeking leadership roles. Professionals pursuing C-suite appointments and upper-management positions should be aware of the latest trends in business executive responsibilities, especially where technological advancements in big data are concerned. A working knowledge of the newest tech provides executives with the tools necessary to guide their companies to better service, improved productivity, and increased profits.

Modern Business Intelligence Defined

Contemporary business intelligence (BI), often called “descriptive analytics,” takes collected data and uses it to derive insights that decision-makers can use to formulate strategies and resolve inefficiencies.

Modern BI is far more robust and useful than the business intelligence of the pre-big data era. Before data analytics, BI mostly referred to the reports produced by accountants for purposes of reviewing past and current performances. Today, BI includes predictions about future performance.

Business analytics (BA) is “a technology-aided process by which software analyzes data to predict what will happen (predictive analytics) or what could happen by taking a certain approach (prescriptive analytics). BA is also sometimes called advanced analytics,” business technology journalist Mary K. Pratt explains in her CIO.com article, “What is BI? Business Intelligence Strategies and Solutions.”

Advanced analytics is further defined as “the analysis of all kinds of data used for sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation, and optimization of prescriptive solutions) to produce insights that traditional business intelligence — such as query and reporting — are unlikely to discover,” according to SAS Institute’s “2016 IIA Business Intelligence and Analytics Capabilities Report.”

“Organizations commonly apply advanced analytics to data to find opportunities, mitigate risks, [inspire] product or service innovation, acquire customers, and improve operational effectiveness,” according to the SAS Institute.

In light of all the predictive and prescriptive capabilities afforded by modern BI, the appeal of advanced analytics to today’s business executive becomes clear.

Effective Advanced Analytics — A Balance Between Technology and Instinct

Today’s business intelligence includes predictions about future performance.

The rise of modern BI hasn’t replaced traditional BI in the world of business. IT departments continue to generate reports by collating and organizing in-house transactional data. But in today’s world, traditional BI only comprises a small percentage of the larger business analytics industry.

Modern BI makes analytics more accessible by allowing non-IT personnel in businesses to interact with user-friendly, agile software systems that utilize machine-learning artificial intelligence. These agile software systems, which combine predictive and prescriptive analytics capabilities into one powerful software suite, are most useful for quick insight into rapidly changing, fluid business dynamics where 100 percent accuracy is not necessary, according to Pratt.

Business leaders who use modern BI are simply looking for help in making better decisions in a fast-paced environment. Executives want to take a look at last year’s sales, compare them with this year’s, and catch a glimpse of next year’s, all while playing out situations of what might happen if particular products, processes, or services are instituted.

A problem arises, however, when companies lean too much on potentially flawed analytics models at the expense of traditional business acumen and intuition.

“An effective business analytics organization balances functional knowledge, business instinct, and data analysis with an operating philosophy to add complexity only when the additional insights justify it,” write L.E.K. Consulting’s directors Todd Clark and Dan Wiesenfeld in their Harvard Business Review article, “3 Things Are Holding Back Your Analytics, and Technology Isn’t One of Them.”

The organizations Clark and Wiesenfeld refer to have three components:

• An analytics nerve center: Ideally, an effective organization will have a small team of dedicated data scientists with advanced degrees. Analytics generalists will then be embedded into each of the company’s departments, reporting actionable analyses back to the data team.
• Representation at the top: The dedicated data team needs to be represented at the company’s C-suite level. A chief analytics officer (CAO) would have good business instincts as well as the analytics information necessary to sway decision-makers when change needs to happen.
• A champion-challenger approach: The data team will take insight and use it to create a minimum viable product (MVP), which essentially offers a minimum acceptable solution to a problem. The MVP acts as the “champion” against which extra levels of complexity, additional solutions, and/or added benefits are pitted as “challengers.” The CAO and the rest of the C-suite can then decide if the simple, minimum solution is the best or if they would prefer additional options.

An effective business analytics organization of the kind Clark and Wiesenfeld describe should be the goal of any business organization using or planning on adopting modern business analytics capabilities. Students in an online executive MBA program may be the next CAO of their organization, so a working knowledge of modern BI could prove instrumental to their future careers.

Washington State University’s EMBA Degree Program

Online Executive MBA students at Washington State University’s Carson College of Business can expect to learn the basics of modern business intelligence and how it concerns business decision-makers. Students will be prepared for top-level management and C-suite positions where they can harness the full potential of their company’s business analytics.

Washington State University offers an online Executive MBA program that provides students the knowledge, skills, and training to rise to the top of innovative industries as strong, influential business leaders and effective decision-makers.

To support future innovation leaders, Washington State University’s Executive MBA curriculum includes managerial leadership and productivity, organizational design, and management of innovation. Contact Washington State University for more information.

Sources:

5 Big Data Examples in Your Real Life at Bars, Restaurants, and Casinos

What is BI? Business Intelligence Strategies and Solutions

2016 IIA Business Intelligence and Analytics Capabilities Report

3 Things Are Holding Back Your Analytics, and Technology Isn’t One of Them