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Business Intelligence: The Foundation of Performance

Because business intelligence allows leaders to more effectively make crucial decisions, from the selection of new markets to optimization of supply costs, it serves a crucial role in the skill set of any industry leader.

In today’s business world, survival is synonymous with adaptability. Companies have access to multiple data streams that provide information on almost every aspect of operations. Nested inside this information is the knowledge of how an industry is working and where it is going. To find and use that knowledge, industry leaders must cultivate a skill set known as business intelligence.

Defining Business Intelligence

Business intelligence is the capability to transfer raw data into actionable knowledge. It develops through processes, technologies, and systems that can capture this knowledge and identify patterns. These patterns provide leaders with insights into what is working, what isn’t, and what might happen if something changes. 

Because business intelligence allows leaders to more effectively make crucial decisions, from the selection of new markets to optimization of supply costs, it serves a crucial role in the skill set of any industry leader.

What Business Intelligence Looks Like 

Business intelligence develops from an ability to gather and act on the insights that will help a company to get ahead. Today, that mostly means making use of advanced systems and software.

Data Mining 

Data mining is one of the most useful tools at a company’s disposal. It involves the collecting and processing of information which, after a multifaceted process of computer analysis, identifies connections and patterns that highlight relevant trends.

Some data mining processes are known as “descriptive,” in that they offer interpretations of the data presented. For example, by extracting and integrating information about consumer choices, a data mining program can visually represent purchase trends and help a company to understand its buyers’ behaviors.

Predictive data mining can also a step further and offers forecasts that stem from the analyzed data. Whereas descriptive data mining might identify signs of ready-to-buy behavior from a particular customer base, predictive data mining could indicate whether sales of a particular product category is likely to increase or decrease based on those behaviors. 

Complex Event Processing

Complex event processing, or CEP, also collects and integrates data in order to determine patterns.  As in the case of data mining, these common threads help business leaders to identify key opportunities or risks. In the case of CEP, however, the information streams involved are much more complex.

Because of the intricacy of these data streams, CEP operations require advanced technologies that aggregate crucial data and filter irrelevant information. These advanced technologies can analyze batched data merged with real-time event analytics to present a big-picture view of trends.

Through specially designed user interfaces, the CEP program can present this analysis as relevant insights for the business leader. The program enables the user to define the type of analysis, such as operations or unit performance, and extract the most relevant and end-to-end knowledge.


In order to survive, a business must understand and respond to its position within its industry. Benchmarking offers this crucial information by allowing leaders to compare the details of its performance with that of competitors. Through these specific insights, businesses can hone in on performance gaps and process inefficiencies. The result is a targeted action plan and measurable goal that the business can track.

It is also possible for benchmarking to identify where a company is exceeding expectations and surpassing its competitors. These success stories, when backed by detailed information and concrete comparisons, can become the foundation for future publicity campaigns.

Predictive Analytics

This particular business intelligence tool has been increasing in popularity over the past five years. As the name indicates, it uses available information on a business’ operations to determine trends and forecast future events.

The process of predictive analytics requires available historical and current data as well as technologies with machine learning and artificial intelligence capabilities. These technologies function as follows:

  1. Historical data feeds into a program with a predictive algorithm
  2. The algorithm aggregates the data to develop a working model
  3. Users input new data into the model 
  4. The model determines what is likely to occur based on past trends

This process can help a company to determine the likely course of events under current conditions and anticipate the outcomes of potential changes. For example:

  • By determining the warning signs of customer attrition, a company could develop targeted renewal strategies
  • Marketing departments can send offers to customers who are show ready-to-buy behaviors
  • Sales departments can evaluate seasonal buying behavior and determine potential incentive success

Predictive analytics is particularly useful when a particular course of action could succeed or fail based on the actions of others.

Up Your Business Intelligence Game

Knowledge of these advanced processes is no longer optional for today’s business leaders. The sheer availability of information makes it necessary for a company to analyze available data and respond proactively, lest competitors get there first and capture market share.

This is where education comes in. With a bachelor's degree in business management from Lesley University, learn how today’s innovative businesses operate and acquire the tools for success. Continue your studies at Lesley in our accelerated and fully online Master of Business Administration (MBA).