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 business.
Business intelligence processes include collecting data, creating models, analyzing the data with queries, creating data visualizations such as charts, 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 edge in the marketplace. Business intelligence processes can provide historical, current, and future forecast information related to business operations.
Business intelligence uses technology, such as software programs like Excel, to analyze data and provide actionable information to help business executives make informed choices and decisions.
BI can be applied to making both operational and strategic business decisions.
Business intelligence is created by a team of professionals that includes data engineers, data analysts, and data visualization specialists.
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 marketplace trends — e.g., compare first, second, third, and fourth quarter product sales figures to see if sales of an individual product are increasing or decreasing
Analyzing key performance indicators (KPIs) to identify areas where operational efficiency is maximized or can be improved — e.g., examining same-store sales or plant production figures
Comparing current results (such as sales) to either historical results or company goals — e.g., look at year-over-year sales figures in different geographical areas where a business operates
Creating visual representations such as charts and graphs to make data analysis more easily understandable and useful for decision-makers — e.g., creating a chart or histogram that clearly reveals stock sector trends
Providing “what if” analysis of different possible business choices — e.g., predicting how making one change versus another may impact future sales revenue growth
The BI Process
Although business intelligence is utilized 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 commonly stored in what’s 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 utilize the data visualizations and reports to help them in making decisions; they may also use their BI dashboard to probe further into the data for more information.
BI Jobs and Roles
Business intelligence requires a group effort that includes several professionals, each serving to provide a particular function within the overall BI process.
Data engineers source the data to be analyzed, organize it, and create channels and automatic 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 broad 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 creating algorithms for data analysis.
Data engineers must be skilled in using programs such as Spark or Hadoop, which assist in the organization and utilization of big data. They must also be able to use programs such as Kafka and Pubsub, which are used for integrated streaming, real-time data into existing databases.
Data analysts are primarily responsible for creating models and metrics for doing data analysis. Analysts import data from various sources, such as Excel files and databases, and then organize the data into usable formats for running 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 automatic 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 broad range of technical skills. They are commonly fluent in using not only basic data analysis programs such as Excel but also software such as Power Query and SQL. The Excel add-on Power Query makes data transformation both quicker and easier for data analysts. Programs such as Tableau, Power Pivot, and Power BI aid analysts in combining data from various sources and creating data analysis models, metrics, 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. In order to accomplish this, data visualization specialists often create visual representations to highlight key metrics, figures, or trends identified by data analysis.
They may 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 need to possess many of the same technical skills as data analysts. They usually possess advanced expertise in using 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 marketplace. A strong business intelligence organization that includes the incorporation of real-time data can assist business executives in recognizing changing market trends and in identifying operational problems early on so that they can be quickly remedied.
Ultimately, good business intelligence serves the same purpose as any other aspect of running a business — that is, increasing revenue and bottom-line profitability.
History of Business Intelligence
What we know today as business intelligence primarily began being developed in the 1980s when the advent of widespread computer usage made data collection and analysis possible for companies to utilize. Over the years, BI processes widened 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 help in making key business decisions related to things such as 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 users of business intelligence to customize reports to serve specific purposes and run queries on the data to provide more information. An important characteristic of modern business intelligence dashboards is that they offer easy-to-use data interfaces that don’t require technical IT expertise.
Modern-day business intelligence processes can incorporate real-time data with existing historical data. It enables business executives to perform data analysis that includes the most up-to-date information available.
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