What Is Business Intelligence?
The purpose of business intelligence is to convert raw data into business insights that business leaders and managers can use to make data-informed decisions. Business analysts use business intelligence tools to create decision-support products for business management decision making. If you want to build decision-support dashboards, visualizations, or reports from complete medium-sized sets of structured business data, then you can use business intelligence tools and methods to help you.
Business intelligence (BI) is comprised of
Mostly internal datasets: Internal means business data and information that’s supplied by your organization’s own managers and stakeholders.
Tools, technologies, and skillsets: Examples here would include online analytical processing, ETL (extracting, transforming, and loading data from one database into another), data warehousing, and information technology for business applications.
Data used in business intelligence
Insights that are generated in business intelligence (BI) are derived from standard-sized sets of structured business data. BI solutions are mostly built off of transactional data — data that’s generated during the course of a transaction event, like data generated during a sale, or during a money transfer between bank accounts, for example. Transactional data is a natural byproduct of business activities that occur across an organization, and all sorts of inferences can be derived from it.
You can use BI to derive the following types of insights:
Customer service data: Possibly answering the question, “what areas of business are causing the largest customer wait times?”
Sales and marketing data: Possibly answering the question, “which marketing tactics are most effective and why?”
Operational data: Possibly answering the questions, “how efficiently is the help desk operating? Are there any immediate actions that must be taken to remedy a problem there?”
Employee performance data: Possibly answering the questions, “which employees are the most productive? Which are the least?”
Technologies and skillsets useful in business intelligence
To streamline BI functions, make sure that your data is organized for optimal ease of access and presentation. You can use multidimensional databases to help you. Unlike relational, or flat databases, multidimensional databases organize data into cubes that are stored as multi-dimensional arrays.
If you want your BI staff to be able to work with source data as quickly and easily as possible, you can use multidimensional databases to store data in a cube, instead of storing the data across several relational databases that may or may not be compatible with one another.
This cubic data structure enables Online Analytical Processing (OLAP) — a technology through which you can quickly and easily access and use your data for all sorts of different operations and analyses. To illustrate the concept of OLAP, imagine that you have a cube of sales data that has three dimensions — time, region, and business unit.
You can slice the data to view only one rectangle — to view one sales region, for instance. You can dice the data to view a smaller cube made up of some subset of time, region(s), and business unit(s). You can drill down or up to view either highly detailed or highly summarized data, respectively. And you can roll up, or total, the numbers along one dimension — to total business unit numbers, for example, or to view sales across time and region only.
OLAP is just one type of data warehousing system — a centralized data repository that you can use to store and access your data. A more traditional data warehouse system commonly employed in business intelligence solutions is a data mart — a data storage system that you can use to store one particular focus area of data, belonging to only one line of business in the enterprise.
Extract, transform, and load (ETL) is the process that you’d use to extract data, transform it, and load it into your database or data warehouse. Business analysts generally have strong backgrounds and training in business and information technology. As a discipline, BI relies on traditional IT technologies and skills.