Business analytics is surely the next major evolutionary step in the continuously changing field of business intelligence (BI). First we tackled data integration – that was the data warehousing era of the early 1990s. In the late '90s and the early part of this century, attention shifted from data to delivery of information – the OLAP, scorecards and dashboards movement. Today we are pretty good at delivering information. Yet for many, true intelligence remains elusive. Surprise! Intelligence is not about how you acquire information; it is about how you use the information that you have.

Redefining Business Intelligence

Let's step back for a moment and think about the definition of business intelligence. Among the popular definitions today are David Loshin's " ... the processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business actions" and Larissa Moss' " ... an architecture and a collection of integrated operational as well as decision-support applications and databases that provide the business community easy access to business data."

I suggest that both of these definitions miss the point. Business intelligence isn't about processes, technologies, tools, applications, data and databases. Nor is it about OLAP, scorecards and dashboards. When a BI program gives more attention to technology than to finance, R&D, marketing, sales, operation, and customer support, then it is time to put the business back into business intelligence!

To that end, I propose a new definition of business intelligence. Let's begin with the essence of intelligence. Wikipedia provides a simple layperson description of intelligence as "the capacities to reason, plan, solve problems, think abstractly, comprehend and learn." Wikipedia also defines business as "the social science of managing people to organize and maintain collective productivity toward accomplishing ... goals." Combining these thoughts, I submit the following as the next-generation definition of business intelligence:

Business intelligence is the ability of an organization or business to reason, plan, predict, solve problems, think abstractly, comprehend, innovate and learn in ways that increase organizational knowledge, inform decision processes, enable effective actions, and help to establish and achieve business goals.

Processes, technologies, tools, applications, data, databases, dashboards, scorecards and OLAP all have roles to enable the abilities that define business intelligence. But they are only the means to BI – not the intelligence itself.

The "Intelligence" in Business Intelligence

The intelligent business, then, is one that has the capabilities itemized in the definition. Consider what it means for the culture, efficiency, effectiveness, sustainability and profitability of a business that is capable of:

• Reasoning – the ability to perform or the act of performing three activities – identifying root causes, understanding cause and effect, and logically developing conclusions based on understanding of root causes.
  
  
• Planning – determining a course of action based upon understanding and reasoning.
  
  
• Prediction – envisioning the future with a sound basis of reasoning and with a high degree of probability that the actual future will be much like the envisioned future.
  
  
• Problem solving – getting beyond symptoms to address fundamental causes of undesirable patterns and behaviors that are found in root cause analysis.
  
  
• Abstraction – removing details and specifics from a situation to see general concepts, patterns, ideas and impacts.
  
  
• Comprehend and understand – the ability to perceive, discern and distinguish – in particular, to perceive situations and conditions, and to distinguish problems from symptoms.
  
  
• Innovate – the ability to create something new through study and experimentation. Innovation often occurs by combining or connecting existing things in different ways.
  
  
• Learn – a cognitive process of acquiring skill and knowledge; learning is the ultimate feedback loop.

Business Analytics and Business Intelligence

So what is it that distinguishes business analytics from business intelligence? Where does the subject of analytics fit in the scope of business intelligence? Analytics is the science of analysis – the processes by which we interpret data, draw conclusions and make decisions. Business analytics goes well beyond simply presenting data, numbers and statistics. The essence of analytics lies in the application of logic and mental processes to find meaning in data. Through these mental processes, we create the capacities that define intelligence – abilities to reason, plan, predict, solve problems, abstract, understand, innovate and learn.

Viewed in this context, business analytics is a powerful thing. Yet it is also a large and complex field that encompasses statistical analysis, predictive analytics, text and speech analytics, web analytics, visualization, causal analysis, decision processes and much more. Most importantly, business analytics involves people – the business analysts who apply the logic and mental processes described above.

Business analytics, then, is an integral part of business intelligence. It takes its place alongside data integration, data access, and reporting to complete the sequence that The Data Warehousing Institute (TDWI) describes as the BI value chain – the sequence that begins with data and ends by delivering business value.

By mapping the value chain to the activities of business intelligence, it becomes easy to see the role of business analytics. Conventional data warehousing and reporting ends at the data-to-information stage. Business analytics extends through the knowledge stage with analysis and understanding, which in turn support decision and action. A complete analytics system measures the results that are produced and provides a feedback loop that facilitates organizational learning.

Business Analytics with a Point

So business analytics isn't really about linear regression, although it is a useful technique in analysis. Nor is it about time-series analysis, though many of your analytic studies are likely to involve time series. But the heart and soul of analytics is about making a difference – providing the insight and understanding to support informed decisions and confident actions, and providing the feedback that is needed to create a learning organization. To satisfy these goals, analytics must meet three criteria:

• Purposeful: Business analytics are purposeful when we know why we create the information and perform the analytic activities. The understanding derived from analysis aligns with business functions (finance, marketing, sales, etc.) and with the issues and objectives of management (performance, compliance, risk, etc.).
  
  
• Insightful: Business analytics are insightful when they help us to discover new facts or information, to become aware of things previously hidden. Cause-and-effect insight is often the most valuable in business decision making. Analytics that simply confirm status quo or reaffirm conventional wisdom offer no insight – they don't have the power to make a difference. Perhaps Oliver Wendell Holmes best expresses the power of insight: "A moment's insight is sometimes worth a life's experience."
  
  
• Actionable: Actionable information is an often used but rarely defined term. The popular definition is "information that makes action possible" – a circular but not very informative definition (a loop without feedback?). I first put forth the following definition in 2004. I believe that it holds true today and applies as much to business analytics as to other kinds of information:

"Information is actionable when it supports the entire process of action-taking including discovery and insight, determination and resolve, decision making, innovation and creativity, and the implementation of decisions. Actionable information is aligned with the knowledge of the person taking action and integrates with the processes where actions are to be implemented."

Recent articles by Dave Wells

• What’s Up with Cloud Analytics?
• Measuring Intangibles: Breaking Down Analytic Barriers
• A Capability Model for Business Analytics, Part 3
• A Capability Model for Business Analytics, Part 2