What is business intelligence and why it matters?

Business Intelligence is a set of methodologies, platforms, processes, applications, architectures and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical and operational insights and decision making. In other terms Business Intelligence (BI) refers to technologies, applications and practices for collection, integration, analysis and presentation of business information.

Business Intelligence and Business analytics are sometimes used alike, but there are different explanations for each term. Business analytics is a sub set of Business Intelligence which focuses on statistics, predictions and optimization rather than reporting. Business analytics is just a part of Business Intelligence which also execute on querying, data mining, data preparation, visualization and reporting.

Not only Business Intelligence and Business Analytics are mistaken. But also Business Intelligence is used as a synonym for Competitive Intelligence because they both support decision making. BI uses technologies, processes and applications to analyze internal structured data and business process while Competitive Intelligence gathers, analyzes and report information of company competitors. In holistic perspective Business Intelligence can include the subset of Competitive Intelligence.

BI plays a vital role in making use of existing data to find new opportunities and implementing effective strategies to provide competitive market advantage and long term stability.

A few ways that business intelligence can help companies make smarter, data-driven decisions are:

  • Identify ways to increase profit
  • Analyze customer behavior
  • Compare data with competitors
  • Track performance
  • Optimize operations
  • Predict success
  • Spot market trends
  • Discover issues or problems

BI process

Over the past few years, business intelligence has evolved to include more processes and activities to help improve performance. These processes include:

  • Data mining: Using databases, statistics and machine learning to uncover trends in large datasets.

  • Reporting: Sharing data analysis to stakeholders so they can draw conclusions and make decisions.

  • Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards.

  • Descriptive analytics: Using preliminary data analysis to find out what happened.

  • Querying: Asking the data specific questions, BI pulling the answers from the datasets.

  • Statistical analysis: Taking the results from descriptive analytics and further exploring the data using statistics such as how this trend happened and why.

  • Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data.

  • Visual analysis: Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis.

  • Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis.

Importance of Business Intelligence tools

Business intelligence tools helps to retrieve, analyze and transform data into useful business insights. Business intelligence tools include data visualization, data warehousing, dashboards and reporting. When compared to competitive intelligence, business intelligence software pulls from internal data that the business produces rather than outside sources.

BI Trends

1.Self Service Business Intelligence (SSBI) – SSBI empowers the end-users to access, analyze the data and plan and forecast by their own without the direct IT involvement.

2.Embedded Analytics - Businesses have recognized the potential of embedding various BI solutions such as KPI dashboards or reports into their own application and thus improving their decision-making processes and increasing productivity.

3.Augmented Analytics - The central notion of augmented analytics is that uses machine learning automation and AI techniques to “augment human intelligence and contextual awareness.”

4.Artificial Intelligence - Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Solutions such as an AI algorithm based on the most advanced neural networks, provides high accuracy in anomaly detection as it learns from historical trends and patterns.

5.Data Quality Management (DQM) – DQM consists of acquiring the data, implementing advanced data processes to analyze and extract value from the countless sources of data which gather at a high scale.

6.Data Discovery and Visualization – Users can identify specific patterns in data sets and visualize which goes beyond traditional reports.

7.Data Automation - BI has come to the solution to enable users to consolidate all the data that a company manages and provides methods to discover, analyze, measure, monitor and evaluate large scale data.

8.Data Driven Culture – The agility to a data driven culture will provide unmeasurable benefits fir an organization. It helps to detect market changes easily and implement the responses quickly.

9.Mobile BI - Mobile BI enables companies to have access to their data also in real-time, ensuring faster reactions to any business occurrences and giving more freedom to users that are currently not in the office but need to access their data.

10.Collaborative Business Intelligence - These BI tools make the sharing easier in generating automated reports that can be scheduled at specific times and to specific people.


  1. https://olap.com/learn-bi-olap/olap-bi-definitions/business-intelligence/
  2. https://www.tableau.com/learn/articles/business-intelligence
  3. https://technologyadvice.com/business-intelligence/
  4. https://www.datapine.com/blog/business-intelligence-trends/
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