Business Intelligence (BI) – What it is, how it works

What is Business Intelligence (BI)?

Business intelligence, often abbreviated as BI, is the practice of turning data into actionable insights that enable business leaders to improve the performance of the enterprise.

Business intelligence processes include collecting data, creating models, analyzing the data with queries, creating data visualizations such as graphs, and producing reports to be used by business decision makers. BI processes can be applied to both operational and strategic decisions.

The purpose of business intelligence is to produce information that facilitates optimal business decisions that can improve operational efficiency and profitability for a business and provide it with a competitive advantage in the marketplace. Business intelligence processes can provide historical, current and future forecast information related to business operations.

BI uses and cases

Business intelligence processes can provide a wide range of information and analysis that can be used for various purposes. Some examples of how business intelligence can be used are the following:

Using data and statistics to reveal business or market trends – for example, comparing first, second, third and fourth quarter product sales figures to see if sales of an individual product are increasing or decreasing. Analysis of key performance indicators (KPIs) to identify areas where operational efficiency is maximized or can be improved – e.g. examine same-store sales or plant production figures Compare current results (such as sales) to either historical results or company goals – see e.g. look at year-on-year sales figures in different geographical areas where a business operates Create visual representations such as maps and graphs to make data analysis easier to understand and useful for decision makers – e.g. create a chart or histogram that clearly reveals stock sector trends. – e.g. to predict how making one change versus another might affect future growth in sales revenue

The BI process

Although business intelligence is employed in different ways and for different purposes by individual companies, the process is fairly uniform throughout all industries and typically unfolds as follows:

Data from various sources – including internal company data and external market data – is collected, integrated and then stored; because “big data” is commonly used, data is usually stored in what is called a data warehouse, created by a data engineer Data sets are created and prepared for data analysis, often by creating data analysis models. Data analysts run queries against the data sets or models The results of queries are used to produce visualizations in the form of charts, graphs, histograms or other visual representations, along with BI dashboards and reports Decision makers use the data visualizations and reports to help make decisions; they can also use their BI dashboard to further explore the data for more information.

BI Jobs and Roles

Data engineers

Business intelligence requires a group effort that includes multiple professionals, each serving to provide a specific function within the overall BI process.

Data engineers acquire the data to be analyzed, organize it, and create channels and automated data feed processes to transport the data between various database systems. They are also typically responsible for arranging data storage by creating data warehouses and for data security and integrity as it moves between systems.

Data engineers must possess a wide range of skills, such as being familiar with working in programming languages ​​such as SQL and Python, knowing how to create databases, using ETL (Extract, Transfer, Load) tools and algorithms for create data analysis.

Data engineers must be proficient in using programs such as Spark or Hadoop, which help in the organization and utilization of big data. They also need to be able to use programs like Kafka and Pubsub, which are used for integrated streaming, real-time data in existing databases.

Data analysts

Data analysts are primarily responsible for creating models and metrics to perform data analysis. Analysts import data from various sources, such as Excel files and databases, and then organize the data into usable formats to perform data analysis. Their next task is to create data analysis models, which often involves combining related data that may exist in the form of separate tables.

Data analysts also create automated refreshing tools that can update data in real time. In addition, they create formulas for measuring performance, such as KPIs. Finally, they document their work so that other analysts or end users of the data can easily interpret the data analysis models.

Like data engineers, data analysts must possess a wide range of technical skills. They are usually fluent in using not only basic data analysis programs such as Excel, but also software such as Power Query and SQL. The Excel add-in Power Query makes data transformation faster and easier for data analysts. Programs such as Tableau, Power Pivot, and Power BI help analysts combine data from multiple sources and create data analysis models, statistics, dashboards, and visual representations.

Data visualization specialists

A data visualization specialist creates visuals and dashboards that help business leaders identify key issues and take appropriate action to resolve them. To accomplish this, data visualization specialists often create visual representations to highlight key metrics, numbers, or trends identified through data analysis.

They can also create dashboards that combine different visual representations and enable the end users of the data to easily query the data presentations for more detailed or additional information. Both data analysts and data visualization specialists may be responsible for creating and presenting reports to business decision makers or other appropriate personnel.

Data visualization specialists must possess many of the same technical skills as data analysts. They usually have advanced expertise in the use of Excel, Tableau, Power Pivot and Power BI.

Importance of business intelligence

Using business intelligence enables business decision makers to make more informed, and therefore hopefully better, decisions about how to operate and manage their business. It can be used to assess products or services, pricing and product positioning, advertising and marketing programs, different potential markets, inventory and supply chains, and production processes, to name a few things.

Business intelligence can help a company to operate more efficiently and cost effectively and also to be more competitive in the market. A strong business intelligence organization that incorporates real-time data can help business managers recognize changing market trends and identify operational problems early so they can be quickly corrected.

Ultimately, good business intelligence serves the same purpose as any other aspect of running a business – that is, to increase revenue and profitability.

History of Business Intelligence

What we know today as business intelligence began to develop primarily in the 1980s when the advent of widespread computing made data collection and analysis possible for companies to use. Over the years, BI processes have expanded and improved to include extensive data mining, data visualization tools, and various methods of data analysis to provide business decision makers with important insights. Such insights can be used to increase operational efficiency and to make key business decisions related to things like product pricing and marketing campaigns.

Key advances in business intelligence include the ability to collect and manage extremely large data sets, the ability to combine external and internal data, increased data sharing, and the creation of business intelligence dashboards.

BI dashboards enable individual business intelligence users to customize reports to serve specific purposes and run queries on the data to provide more information. A key feature of modern business intelligence dashboards is that they provide easy-to-use data interfaces that do not require technical IT expertise.

Today’s business intelligence processes can incorporate real-time data with existing historical data. It enables business managers to perform data analysis that includes the most up-to-date information available.

Additional resources

Big data in finance

Financial Engineering

Data science

See all business intelligence resources