What is Descriptive Analysis in Business Intelligence

What is business intelligence? Your guide to BI and why it matters

Business Intelligence (BI) is the generic term for a combination of business analytics, data mining, data visualization, data tools and infrastructure as well as best practices with the aim of making more data-driven decisions in the company. In practice, modern business intelligence is characterized by the fact that it gives you a complete view of your company data and that you can use this data to promote change, to eliminate inefficiencies and to react quickly to changes in the market and supply.

However, this is a very modern definition of BI. Business intelligence as a catchphrase has a checkered history behind it. Traditional business intelligence originally emerged in the 1960s as a system of sharing information across the company. It developed further in the 1980s with computer models for decision-making and with the conversion of data into knowledge until it finally became the term for a special service offer from BI teams with IT-supported service solutions. Modern BI solutions focus on flexible self-service analyzes, controlled data on trustworthy platforms, supported business users and quick insights.

This article provides an introduction to BI, but the information is only the tip of the iceberg. Other materials:

Examples of business intelligence

Tableau's Explain Data feature helps you quickly identify possible explanations for outliers and trends in data.

Business intelligence is not a specific “thing”, but rather a collective term that includes the processes and methods for collecting, storing and analyzing business data with the aim of optimizing performance. The combination of all these factors results in an overall picture of a company that should enable people to make better, more actionable decisions.

Over the past few years, more features and activities have been integrated into business intelligence that improve its performance. These features include:

  • Data mining: Using databases, statistics and machine learning to identify trends in large amounts of data.
  • Reporting: Providing data analytics to those responsible so that they can draw the right conclusions and make data-driven decisions.
  • Performance indicators and benchmarks: Compare current performance data with historical data to track performance against goals, usually through customized dashboards.
  • Descriptive Analytics: Use preliminary data analysis to determine the current situation.
  • Database queries: Input of data-specific questions, whereby the BI then determines the answers from the databases.
  • Statistical analysis: Using the results of descriptive analytics and further exploring the data using statistics, e.g. B. to analyze how this trend came about and why.
  • Data visualization: Transferring the data analysis into visual representations such as diagrams, graphics and histograms to simplify the use of data.
  • Visual analysis: Explore data using visual storytelling to communicate insights in a snap without having to interrupt the flow of analysis.
  • Data preparation: Compilation of several data sources, determination of the dimensions and measured values ​​for their preparation for data analysis.

Why is business intelligence important?

A powerful BI offers companies the opportunity to ask questions about their data and get the answers to them.

With Business Intelligence, companies can improve their decision-making by providing a complete insight into the current and historical data in their business field. With BI, analysts can provide performance and competitive benchmarks that can be used to ensure the smooth and efficient functioning of the company. BI also makes it easier for analysts to identify market trends and thus increase sales and revenues. When used effectively, the right data can be an important support in all areas, from compliance to recruitment.
Business intelligence offers companies, among other things, Support for smarter, data-driven decisions in the following areas:

  • Identification of methods of increasing profit
  • Analysis of customer behavior
  • Compare data with competitors
  • Track performance
  • Optimization of processes
  • Business success forecast
  • Recognition of market trends
  • Identification of problems

How does business intelligence work?

Companies have questions and goals. In order to answer these questions and to be able to track the achievement of these goals, they collect relevant data, analyze it and then decide which actions are necessary to achieve their goals.

From a technical point of view, raw data from the company's business activities are recorded, processed and then stored in data warehouses. This stored data can then be accessed by users in order to analyze it and answer business-relevant questions.

How BI, data analytics and business analytics work together

Business intelligence includes data analytics and business analytics. These are used as part of a complete process. BI gives users the ability to draw conclusions from data analysis. Data scientists deal intensively with the details of data. To do this, they use advanced statistics and predictive analytics to determine current patterns as well as to forecast future patterns. Data analytics asks why something happened and what can happen next. Business intelligence translates the results of these models and algorithms into a language that can be implemented in practice.

In Gartner's IT glossary, this term is defined as follows: “Business analytics encompass data mining, predictive analytics, applied analytics and statistics.” In short, organizations use business analytics as part of their higher-level business intelligence strategy. BI is designed to answer specific queries and offers clear analyzes for decisions as well as for planning. Companies can also use analytics for the continuous optimization of follow-up questions and for iteration.

Business analytics should not be a linear process, however, as answering a question will likely raise further questions and thus set new processes in motion. Business analytics processes are more like a cycle consisting of data access, data discovery, data analysis and information transfer. This is known as the "Cycle of Analytics" - a modern expression that describes how companies use analytics to respond to constantly changing questions and expectations.

Difference between traditional and modern BI

Modern BI focuses on flexible self-service analytics and quick insights.

Business intelligence tools used to be based on a traditional business intelligence model; H. on a top-down approach in which business intelligence was primarily an IT function and most, if not all, analytics questions were answered with static reports. If someone had a further question about such a report, this question first had to go through the entire report creation process. The result was frustratingly slow reporting cycles and a lack of up-to-date data as a basis for decision-making at any time. Traditional business intelligence continues to be a common approach to regular reporting and answering static queries.

In contrast, modern business intelligence is interactive and accessible to a broad target group. IT departments are still an important factor in managing access to data. However, users of various types can use it to customize dashboards and generate reports with little effort. With the right software, users are also able to visualize data and generate answers to their questions.

How the major industries use business intelligence

Example of a dashboard with economic indicators showing the long-term drivers of the US economy

Many very different industries were already using BI before it became a major issue. These include health care, information technology and the education and training sector. In principle, all companies can use data to transform their operational processes.

Financial services provider Charles Schwab uses business intelligence to get a comprehensive overview of its branches in the United States, understand key performance indicators, and identify areas of opportunity. With the help of a central business intelligence platform, Charles Schwab was able to summarize all branch data in a single view.

Branch managers now have the opportunity to determine which customers may have new or changed investment needs. Managers can also check whether a particular region is performing above or below average, and with a click of the mouse they can call up details of the best performing branches in that region. This releases additional optimization potential and improves customer service at the same time.

Business intelligence tools and platforms

Many self-service business intelligence tools and platforms optimize the analysis process. They make it easier for employees to make their data visible and understandable. They do not have to have the technical know-how to delve into the data themselves. A variety of BI platforms are available for users of all skill levels for ad hoc reports, data visualization and the creation of custom dashboards.

We have put together our recommendations for evaluating modern BI platforms to help you choose the right platform for your company. A common presentation method for business intelligence is data visualization.

Use of visual analytics and data visualization

Visual Analytics ensures an uninterrupted analysis process.

A common presentation method for business intelligence is data visualization. When it comes to perception, we humans rely primarily on our eyes, which are accordingly highly trained in recognizing patterns and color nuances. Data visualizations make use of this fact by presenting data graphically in a clear, easily comprehensible manner.

If such visualizations are then presented in the form of dashboards, data stories, trends and patterns can be read quickly, which may not be immediately recognized when manually analyzing raw data. This increased level of accessibility enables a deeper understanding of data, which in turn can have tangible business benefits.

Use self-service business intelligence (SSBI) for your company

Today, many companies are converting their processes to a modern business intelligence model on a self-service basis. The IT department then only takes care of the security, accuracy and delivery of the data, while the users can interact directly with their data.

Modern analytics platforms like Tableau support companies in every single stage of the cycle of analytics: data preparation in Tableau Prep, analysis and data discovery in Tableau Desktop, and approval and governance in Tableau Server or in Tableau Online. As a result, the IT team can concentrate fully on managing data access, while business users can independently examine data and share knowledge.

The future role of business intelligence

Business intelligence continues to evolve based on business needs and available technology. Every year we try to provide our users with the latest innovations in line with current trends. Remember: Artificial intelligence and machine learning will continue to grow in importance. It therefore makes sense for companies to incorporate the findings of artificial intelligence into a broader BI strategy. As organizations become data-centric, efforts to share data and promote collaboration will increase. Data visualization is increasingly becoming an indispensable basis for cross-team and cross-departmental collaboration.

This article is a first introduction to the world of business intelligence. BI provides near real-time sales tracking capabilities and enables users to gain insights into customer behavior, profit projections, and more. A wide variety of industries such as retail, insurance and the oil industry are already using BI and more will be added year after year. BI platforms are able to adapt to new technology and the innovation of their users. Stay up to date and catch up on the trends and changes in business intelligence as listed in among the current top 10 trends in BI.