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The Resource for the Global Finance Profession

Analytics and Business Intelligence: One in the Same?

  • By Gary Cokins
  • Published: 2014-04-23

Once a “nice-to-have,” applying analytics is now mission-critical due to increasingly thinner margins for decision error. Yet analytics often is confused with business intelligence, or BI.

Here is a useful way to differentiate BI from analytics: Analytics simplify data to amplify its value. The power of analytics is to turn huge volumes of data into a much smaller amount of information and insight. BI mainly summarizes historical data typically in table reports and graphs as a means for queries and drill downs. But reports do not simplify data nor amplify its value. They simply package up the data so it can be consumed.

In contrast to BI, decisions provide context for what to analyze. A useful rule is to work backwards with the end decision in mind. Identify the decisions that matter most to your organization and model what leads to making those decisions. By understanding the type of decision needed, then the type of analysis and its required source data can be defined.

Many believe that the use of BI software and creating cool graphs are the ultimate destination—that BI is the shiny new toy of information technology. The reality is that much of what business intelligence software tools provide, as just described, has more to do with query and reporting often by reformatting data. A common refrain is: “There is no intelligence in business intelligence.”

It is only when data mining and analytics are applied to BI within an organization that has analytical skills, competencies, and capabilities that deep insights and foresight is created. These are needed to understand the solutions to problems and to select the appropriate actions for improving business operations and opportunities.

Data mining that uses statistical methods is the foundation and pre-cursor for analytics. For example, data mining can identify similar groups and segments (e.g., customers) through cluster or correlation analysis. This allows analysts to frame their analytics to predict how their object of interest (e.g., customers, new products) are likely to behave in the future—with or without interventions. As a result analytics moves from being descriptive to prescriptive.

To clarify, BI consumes stored information. Analytics produces new information. Analytics leverages data within an organizational function that has the mandate, skills, and competencies to drive better, faster, decisions and achieve targeted performance.

Analytics lead to faster and smarter decisions. One can assume that this implies executive decisions, but the relatively higher value for and benefit from applying analytics is arguably better suited for daily operational decisions. That’s because decisions can be segmented in three layers:


  • Strategic decisions are few in number but can have large impacts. For example, should we acquire a company or exit a market?
  • Tactical decisions involve controlling with moderate impacts. For example, should we modify our supply chain?
  • Operational decisions are daily, even hourly, and often affect a single transaction or customer. For example, what deal should I offer to this customer or should I accept making this bank loan?


There are several reasons that operational decisions are arguably most important for embracing analytics. One reason is that executing the executive team’s strategy is not solely accomplished with strategy maps and their resulting key performance indicators (KPIs) in a performance scorecard and dashboards.

The daily decisions are what actually move the dials. Another reason is that in the sales and marketing functions operational decisions maximize customer value much more so than do policies. For example, what should a front-line customer-facing worker do or say to a customer to gain profit lift? Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions.

Predictive business analytics is arguably the next wave for organizations to successfully compete. It should be used not only to predict outcomes but to reach higher to optimize the use of their resources, assets and trading partners among other things. It may be that the ultimate sustainable business strategy is to foster analytical competency and eventually mastery among an organization’s work force.

Gary Cokins is the founder of Analytics-Based Performance Management LLC. He served as CFO of a Fortune 100 company. This article is excerpted from his most recent book, Predictive Business Analytics, co-authored by Lawrence Maisel and published by John Wiley & Sons. Reach Cokins at gcokins@garycokins.com

Copyright © 2015 Association for Financial Professionals, Inc.
All rights reserved.

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