Companies collect large quantities of operations data as a by-product of doing business. Huge quantities of data are stored in finance, procurement, sales, marketing systems and multiple other data repositories. Being able to analyze and understand this data is extremely important to running the business. For example, Enterprise Resource Planning (ERP) systems typically contain data concerning the supply chain and inventory levels in addition to financial data. HR systems contain all employee records including demographic data, salary level, and performance reviews. Customer Relationship Management (CRM) systems contain customer, sales pipeline, forecasting and sometimes customer support case data. Many of these line-of-business data stores have their own reporting capabilities, and there are multiple third-party tools that provide sophisticated data analysis and related capabilities specifically for these line-of-business tools. For example, products like InsightSquared and C9 provide dashboards and predictive pipeline analysis, respectively, for the Salesforce platform.
The problem is that all this operational data is typically not accessible in one place for analysis in order to make decisions and provide strategic guidance to the business as a whole. For example, inventory data from an ERP system could be combined with sales forecasting information to understand how to optimize inventories in response to demand. This is the problem that business intelligence systems were designed to solve.
Traditional business intelligence software solves this problem by putting data into a common store called a warehouse. The data is then normalized - removing redundancies and duplication - making it easier to run queries and retrieve data for reporting. Newer data discovery and visualization platforms solve the problem differently, by either connecting directly to the various data sources, or storing data in-memory for analysis and visualization. There are many different types of business intelligence technology, not all of which depend on the business warehouse paradigm. Many new approaches have emerged, and the following section describes the major classes of business intelligence technology.
One way to understand the BI market is to think of it as different layers of capabilities with an ever-narrowing set of metrics as we move up the stack from reporting to predictive analytics (see fig. 1).
Fig 1 – BI Pyramid depicting layers of Business Intelligence capabilities.
- The reporting layer represents a focus on providing both static and interactive reports to users across the enterprise. For example, an HR executive might want a regular report showing employee churn by department. This is the traditional domain of BI as it is widely understood.
- The discovery layer represents the activities of skilled analysts who want to query and explore data, and create visualizations on an ad-hoc basis.
- The dashboard layer is concerned with providing an easy way to visually comprehend key operational tracking data like KPIs and scorecards.
- The predictive layer represents the highly specialized domain of using large data sets to understand what may happen next, so that organizations can build reliable forecasts.
Some business intelligence tools focus on one layer, whereas others encompass several or all. For example, full-stack platforms like MicroStrategy, IBM Cognos, or SAP Business Objects support all layers, while SQL Server Reporting Services and Actuate are examples of tools that support only the reporting layer. Discovery and visualization tools like Tableau and QlikView support only the discovery layer. There are also, of course, specialized dashboard tools like Domo, and predictive analytics tools like SPSS and Revolution Analytics R.
The following table summarizes the different classes of BI software products and their various advantages, disadvantages and best-fit use cases. More details along with ratings by key product in each category are available in the following sections.