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10 Predictions For Analytic Decision Support by 2015

By 2015, companies will increasingly blend math, technology and decision sciences, employing user-generated data to infuse analyses with behavioral and social-network insights.

With many businesses still struggling amid anemic economic conditions, increasing numbers of companies are focusing on analytics-driven decision support across all facets of their businesses. Leaders have defined their competitive edge on the foundations of data-driven decision making while others are struggling to play catch up.

This article presents ten predictions about changes in decision science that will have the greatest impact on business over the next five years. Developed by Mu Sigma, the analytics services firm I founded and head, these predictions touch on trends that will challenge conventional wisdom, drive new business models, and enable organizations to differentiate themselves to compete and drive growth.

Conventional wisdom suggests that organizations need to evolve from Descriptive Analytics to Predictive Analytics, but I encourage the concurrent use of math, business and technology decision sciences across what Mu Sigma calls the DIPP Index:

D for Descriptive Analytics -- What happened in the business (using reports and dashboards)?
I for Inquisitive Analytics -- Why something happened in the business (using analyses)?
P for Predictive Analytics -- What will happen in the business (using predictive modeling)?
P for Prescriptive Analytics -- the So What, Now What?

As companies adopt analytics as the new science of winning, the future of analytics will not just be based on applied math, business and technology, as it is today. In the future, decision sciences will make use of Math + Business + Technology + Behavioral Economics, as outlined below.


  • Business + Technology allows us to simply automate
  • Math + Business allows us to present more cogent arguments in the boardroom
  • Math + Technology allows us to anticipate and operate proactively
  • Math + Business + Technology allows us to execute better
  • Math + Business + Technology + Behavioral Economics let us develop nudges (cognitive repairs) against human biases.

I'll explain in more detail by describing our top-ten predictions for decision sciences. Some of these predictions are obvious and some are relatively obscure. The relative importance of each prediction depends on the unique needs and business drivers in your industry and the analytical maturity of your organization.

Prediction 1: Hyper Competition Will Proliferate

The evolution of decision sciences will lead to hyper competition. Organizations will engage in information arbitrage games to exploit information asymmetry against the competition. In analytically mature industries, information advantage will be a swiftly moving target due to the intense competition. Airlines and financial services have historically pioneered the use of analytics as an integral component of their business models. Today, many airlines are unprofitable because they can't maintain a competitive edge due to the swiftly moving information arbitrage in an intensely competitive marketplace. On the other hand, some financial services firms have gained a formidable edge over competitors in high speed algorithmic trading by exploiting cutting-edge models.

To unleash the true potential of analytics, companies will have to move beyond the traditional frontiers of pricing, marketing and risk, using analytics to compete on innovation and relatively obscure areas of the business. Companies in all industries, be it banking, insurance, retail or airlines, will have to innovate in supply chain management and new-value creation to stay competitive on price and quality, and to generate enough capital to outlast competitors.

Prediction 2: Companies Will Compete on Consumption of Analytics, Not Creation of Analytics

Analytics will become more commoditized, with organizations competing on consumption of analytics rather than creating analytics from scratch. Context and relevance will be the key enablers for effective consumption of analytics. Organizations will have to focus on various competencies to get consumption of analytics right. The consumption cycle (shown below) will necessitate a more balanced combination of right-brained and left-brained thinking.

Analytic Consumption Cycle

The increasing use of intuitive technologies such as visual analytics, interactive dashboards and decision-support simulation tools is indicative of the trend toward consumption of analytical insights. Flex-, Silverlight- and Ajax-based Rich Internet Applications are enriching these user experiences. Companies such as Tableau Software, Tibco Spotfire and Qlik Technologies have pioneered visual analytics as a means to give business users quick insight into data. It is noteworthy that Qlik Technologies recently released an IPO and has a P/E ratio of 80 and a market cap valuation of around $1.5 billion on revenues of only about $150 million.

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