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What is Business Intelligence? Examples, Uses & More


The Team at CallMiner

March 16, 2020

Woman holding imaginary brain above data workspace
Woman holding imaginary brain above data workspace

Business intelligence (BI) stands at the intersection where big data and data science best practices meet.

Modern business intelligence solutions surface actionable insights to management teams and company leaders through the use of a variety of data mining techniques as well as integrated report generating features and analytics tools. In this article we cover the essential elements of business intelligence and address their potential for helping organizations grow.

Business Intelligence Data Mining

The beauty of business intelligence’s variety of data mining techniques lies in their close relationship with practical business goals.

All too often, the insights big data yields for businesses are limited by the analysts who capture them. Although the field of data analytics is home to a great many brilliant minds, they may not always be well versed in homing in on important company concerns and parsing data to specifically support these.

BI data mining tools keep your efforts more closely aligned with your ultimate objectives by holding fast to a process comprised of the following points:

1.    Identifying Unusual Information

It is in this step that any anomalous data present in an organization’s data stores is sifted out and assessed.

Odd data presents a special opportunity to organizations where vetting core values and testing operational protocol are concerned. True anomalies reflect on inconsistencies between what an organization assumes might happen given certain conditions and what will probably occur. This type of insight is highly valuable at all stages of a company’s development.

2.    Determining Relationships Between Datasets and Data Capturing Parameters

This part of the data mining process pulls together disparate pieces of a vast dataset’s elements and analyzes them with respect to the rules that allowed them to be captured in the first place.

Such a process helps in fleshing out important facets of an organization’s data-capturing methods, sorting actionable information and methods alike for further assessment later.

3.    Grouping Data Together to Form Loosely Related Clusters

This step begins the larger process of actively combing over big data stores for valuable information. Similar data types are grouped based on predefined variables and passed along for closer examination.

4.    Classifying Newly Discovered Data Types

As data comes under further scrutiny, it is grouped intelligently into entirely new data types.

An example of this would be user registration dates and product launch periods being paired to accurately track consumer demand. Many potential data pairings exist, and each can tell a priceless story upon which  business leaders can base major decisions.

5.    Experimenting with Variable Manipulation to Better Understand Results

Testing findings comes into play through the careful manipulation of independent variables in assessment criteria.

Dates and time periods may be altered, price ranges can be changed, demographics selectors can be switched out and so on. This further describes facets of a company’s operations that were likely unexplored.

Business Intelligence Data Analytics

Freshly mined data must be analyzed before it can be deemed inherently useful or of value to a growing company.

Data analytics in business intelligence tools take information and derive actionable insights from it that can be put to use immediately. Through such a process, a company’s growth and performance can be assessed in relation to its past.

Outside of BI analytics, data analysis often dives deeper into data to unearth insights capable of shaping an organization’s future performance in addition to its present.


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Below are a few key types of business data analytics solutions available to companies:

Operational BI

This form of business data analysis centers on real-time information.

Active operational factors at play within a given company are streamed to a central viewing suite where they can be monitored, assessed and quickly acted upon as needed.

Online Analytical Processing

This type of data analysis is most useful for generating comprehensive reports in a wide variety of areas.

Using such analytics, multidimensional datasets can be interpreted more rapidly through simplified data manipulation.

Enterprise Reporting

This branch of BI data analytics focuses on delivering detailed reports to non-technical users at speed. Administrative safeguards for access control, interactive report capabilities and reusable data resources factor into such enterprise reporting tools.

Business Intelligence Data Visualization

Business Intelligence tool sets provide the means for making competent use of an organization’s data. Generating reports in human-readable formats goes a long way in making data more useful, but most BI solutions go beyond basic reporting functionality.

Key Performance Indicators

Many BI tools provide a dashboard from which both custom and predefined KPIs can be monitored.

These indicators can be categorized into any of the following areas:

  • Finances
  • Marketing
  • Customer Relations
  • Human Resources
  • Project Management
  • Retail

The utility of business intelligence is indisputable – most of the world’s leading companies make active use of such technology to further develop their sprawling ecosystems. To best harness the power of your organization’s accumulated data without overworking a dedicated team of data analysts, consider adopting a full-featured business intelligence suite as well.

How has your company incorporated business intelligence into its daily operations?

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