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Gaining a Competitive Edge through Advanced Analytics

March 13, 2018


The World Economic Forum has declared data a resource as valuable as gold. The good news is almost every organization has access to data and, increasingly, has the means to mine that data. Gary Cokins (@GaryCokins), founder of Analytics-Based Performance Management LLC, asserts, “Analytics is becoming a competitive edge for organizations. Once being a ‘nice-to-have,’ applying analytics is now becoming mission-critical.”[1] He continues, “The use of analytics that include statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There’s a requirement to gain insights and inferences from the treasure chest of raw transactional data that so many organizations have now stored (and are continuing to store) in a digital format. Organizations are drowning in data but starving for information. The application of analytics is becoming commonly accepted, but will senior executives realize it?”


Gaining a competitive edge


Thomas H. Davenport (@tdav), a Professor in Management and Information Technology at Babson College, reports research has shown businesses “have gained an advantage by competing not only on products or services but also on advanced analytical capabilities.”[2] He explains, “Many companies use analytics to drive decision-making and better understand their businesses, markets, and customers; analytical competitors go a step beyond by leveraging analytics extensively and systematically to help outthink and outmaneuver the competition.” The staff at CIO Review agrees big data analytics has become a differentiator for businesses. “The deluge of data in the market — structured and unstructured — such as market trends or customer behavior and preferences has made big data the latest buzzword in business echelons. The onset of high performance analytics has ushered in a new era wherein the insights hidden within the data, are uncovered to improve decision making. Streamlining and efficient execution of processes is a natural outcome of the same that has improved customer satisfaction manifold.”[3]


Both Davenport and CIO Review staffers touch on the importance of decision making. Bain analysts, Michael C. Mankins and Lori Sherer (), note that decision making is one of the most important aspects of any business. “The best way to understand any company’s operations,” they write, “is to view them as a series of decisions.”[4] They explain:

“People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. 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.”

The amount of data needing to be mined for insights can no longer be done manually. Fortunately, artificial intelligence systems (i.e., cognitive computing systems) are now available to conduct advanced analytics on big data. If all of the decisions noted by Mankins and Sherer are made, aided, and/or informed by cognitive computing platforms, the advantage should be obvious.


Diving for pearls: Types of analytics


Alexander Bekker, Head of Database and BI Department at ScienceSoft, writes, “Back in the 17th century, John Dryden wrote, ‘He who would search for pearls must dive below.’ Despite the author did not have advanced data analysis in mind, the quote perfectly describes its essence.”[5] He adds, “There are 4 types of analytics. … As it happens, the more complex an analysis is, the more value it brings.” The four types of analytics referred to by Bekker are: descriptive, diagnostic, predictive, and prescriptive.


Descriptive Analytics

Bekker notes descriptive analytics is the simplest analytics to conduct and answers the question of “what happened.” He adds, “Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics.”


Diagnostic Analytics

Diagnostic analytics, Bekker notes, allows a company to use historical data, measured against other data, to answer the question of “why something happened.” He explains, “Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives a deep insight into a particular problem.”


Predictive Analytics

“Predictive analytics,” Bekker writes, “tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting.” David Weldon (@DWeldon646), editor in chief of Information Management, notes, “As businesses look to cash in on the hype of predictive analytics, artificial intelligence is becoming one of the main drivers behind those efforts.”[6] John Crupi, vice president of IoT analytics at Greenwave Systems, told Weldon predictive analytics can pay big benefits in the area of preventive maintenance. He explained, “AI models can continuously learn as more data arrives and the models refined to achieve increasing accurate results. Predicting abnormalities in minutes or hours in advance can save companies millions of dollars by avoiding catastrophic failures and downtime.”


Prescriptive Analytics

“The purpose of prescriptive analytics,” writes Bekker, “is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend.” Mark van Rijmenam (@VanRijmenam), founder of Datafloq, writes, “Prescriptive analytics comes with some benefits you can leverage with Big Data, such as enhanced awareness of the impact of new technologies or techniques, improved utilization of resources and increased insight into patterns and habits of consumers. For example, you can use prescriptive analytics to determine the best social media engagement opportunities to take.”[7] He adds, “The future of prescriptive analytics will facilitate further analytical development for automated analytics where it replaces the need of human decision-making with automated decision-making for businesses. … With the expanding use and value of prescriptive analytics, it is driving the future of Big Data. Enterprise leaders can avoid risky business moves and reduce financial losses with the power of prescriptive analytics to evolve the logic of their business decisions. When you incorporate prescriptive analytics in your Big Data strategy, it can help you make business decisions faster to enhance efficiency and productivity of your enterprise.”




Davenport labels companies taking advantage of advanced analytics “analytical competitors.” He concludes, “Data analytics has helped rewrite the rules of business competition, creating a growing number of analytical competitors. … To outperform their peers, analytical competitors will likely continue to rely on data and analytics to find and keep loyal customers; develop efficient and effective marketing campaigns and promotions; excel at insights-driven customer service; and create ultra-efficient supply chains. Their increased ability to understand both internal and external business environments will likely allow them to continue to predict competitive challenges and identify solutions ahead of others in their industries. Analytical competitors will likely continue to lead their industries into the future.”


[1] Gary Cokins, “Why analytics will be the next competitive edge,” Information Management, 17 November 2017.
[2] Thomas H. Davenport, “How to Outflank the Competition With Analytics,” The Wall Street Journal, 14 December 2017.
[3] Staff, “The Era of Big Data Analytics,” CIO Review, 15 December 2017.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Alexander Bekker, “4 types of data analytics to improve decision-making,” ScienceSoft, 11 July 2017.
[6] David Weldon, “AI may hold the key to success with predictive analytics,” Information Management, 14 February 2018.
[7] Mark van Rijmenam, “Why Prescriptive Analytics Is the Future of Big Data,” Datafloq, 5 October 2017.

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