Business Intelligence Evolution

The term Business Intelligence first appeared in Richard Miller Devens’ book The Cyclopaedia of Commercial and Business Anecdotes, from 1865. The character presented in it, the banker Sir Henry Furnese, collected a wealth of information about the market and customers, which allowed him to make more informed business decisions. The bank conducted by Furnese has gained an advantage over the competition by using business intelligence, which allowed it to achieve better results compared to other entities operating in the market. Currently, business intelligence methods work much more efficiently than in the nineteenth century thanks to the use of technology: using the computing power of computers and collecting huge data sets (Big Data).

Currently, every company uses BI solutions to a greater or lesser extent. However, to obtain the best results, it is necessary to be aware of the data in possession, to use it efficiently and to select the right tools. In the article, we will present the various stages of the development of the Business Intelligence concept, which were shaped along with the development of technology and methods of acquiring business knowledge on the basis of data.

1. First BI analyzes

The first contact with BI enables companies to create analyzes in which we deal with single data sources. Typically, they require manual data source selection and processing, and then analysis. Often, data processing takes longer than analyzing it.

Features of basic analyzes:

  • Single data sources
  • Manual data selection
  • Obtaining data often takes more time than analyzing it

2. Traditional Business Intelligence

The next step in the evolution of Business Intelligence applications in the company is traditional BI as we understand it today. There we already deal with many data sources that are integrated with each other, interconnected by relationships. Data and reports are managed entirely within the IT structure, while users are significantly limited in operation, e.g. they can only perform reports with a few selected parameters and have little influence on which data is presented and analyzed. Typically, such multi-page, large-scale numerical reports show data relevant to lower-level employees that help them evaluate their KPIs, but tell little to the management of the organization.

Traditional Business Intelligence features:

  • Multiple data sources
  • IT managed data and reports
  • Lack of independence of business users
  • Limited service of tools by the users themselves (self-service)
  • Focusing on reporting and KPI results

3. Considering data visualization

In response to the shortcomings that characterized traditional solutions, tools appeared that deal very well with data visualization. As a result, multi-page reports have been supplemented with visualizations and dashboards, allowing the results to be gathered together, visualizing them and building interactions between them.

Features of Business Intelligence tools with data visualization:

  • Large multi-page reports supplemented with visualizations, dashboards and interactions between them
  • Direct access to interactive reports for business users

4. Self-service

The next stage is self-service, in which users make their own decisions about which data is visible. They can also independently define the relationship between them. As a result, users gain much greater autonomy in data analysis, and have a partial ability to create their own models.

Features of BI tools equipped with self-service:

  • User-driven analysis
  • Big autonomy in data analysis by business
  • Analysts can partially create their own data models

5. Augmented Analytics

The last of the currently defined BI development phases is the augmented analytics. In this type of solution, business does not need IT support when creating models. In these tools, it is also possible to introduce machine learning, i.e. solutions in the field of artificial intelligence helpful in supporting decision-making and predicting what may happen in a moment. It is currently the most advanced stage in the development of BI tools, but it should not be treated as the final stage, which will not have its successors in the future.

Features of BI tools with augmented analytics

  • Business does not need IT support when creating analytical models
  • Data preparation and analysis are supported by machine learning (ML) and artificial intelligence (AI)

The stage at which the company is developing and using BI tools depends on many factors, such as the amount of information processed in it. It is also important to be aware of the possibilities of Business Intelligence solutions and to have an appropriate budget. The most mature companies are now somewhere between the self-service stage and the augmented analytics stage.


Marek Czachorowski

Head of Business Intelligence Practice at Inetum in Poland. For 10 years Marek has been involved in BI and broadly defined data analysis and processing. Since the beginning he has been mainly associated with Microsoft solutions and tools. Since 2017, a certified specialist in the area of data warehouse design and SQL Server platform management. He is currently developing primarily in the area of cloud analytics. As a consultant, he helps clients define company processes, establish rules for processing and access to data.

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