What is Business Intelligence?
Business intelligence (BI) software is a term used to describe tools employed by organizations to report against their data. Organizations use BI to identify trends in data that help plan business strategy, reveal new revenue streams and identify inefficient processes. BI can consist of a variety of tools, workflows, databases, data warehouses and even spreadsheets.
The rapid growth of data has made BI software a necessity in most organizations today. However, getting data to the end users who need it, in a timely and coherent format, continues to challenge organizations. As a result, a new generation of business intelligence has emerged: one which provides self-service business intelligence, in the form of reports and dashboards, directly to the end users who need it. Common traits of self-service BI software include:
- drag and drop and search functionality already familiar to users
- advanced visualizations with drill downs
- integration inside applications and workflows
- fully responsive design for use across devices
- scheduling and alerts functionality
- application scalability to the cloud
Traditional BI often employs legacy reporting software such as Crystal Reports, SSRS or Cognos. These are tools designed for enterprises, not SMBs. Besides being expensive, they require ongoing IT support to code SQL queries and format reports. Many legacy applications use data warehouses to aggregate data for reporting, which prevents them from delivering real-time reporting.
There are several additional drawbacks to traditional BI products. They tend to be standalone solutions that are separate from, not embedded in, the applications end users interact with daily. This forces end users to navigate a separate application, often with a very different UI, in order to view analytics. Another shortcoming of traditional BI solutions is a lack of self-service functionality. Even minor changes to a report can be caught in a bottleneck of program change requests to IT. For these reasons, traditional BI offers little opportunity for iterative exploration by end users which can uncover new insights from data.
The surge in smartphone usage, the growth of BYOD and the consumerization of data means that today’s end users need access to business data on any device. As a result, mobile BI is one of the fastest growing areas in business intelligence and analytics. Organizations looking to deliver mobile BI can choose from two options, either via a traditional browser with a responsive web application, or through a custom app.
There are several unique challenges posed by mobile BI:
- the limited size of user interfaces on devices
- the wide variety of devices served
- security issues such as device loss and exposure of company data
- in the case of custom apps, the need to write and maintain a separate application
When assessing the need for mobile BI, an organization should first identify the functionality that users need on their devices. The smaller screen size, data limitations and touchscreen-based functionality means that a mobile BI solution will not work for very data-heavy analytics. Generally, operational end users, who are often offsite and make decisions based on smaller quantities of data, are well served by mobile BI.
Cloud BI refers to business intelligence applications that are hosted in the cloud. Hosted applications include dashboards, KPIs, charts and other business analytic objects. Some of the benefits of cloud-based BI are accessibility, since users need only a connection to the internet and a browser, so BI is available to them on any device; scalability both of data sources and number of users; and speed of deployments and updates.
Social Business Intelligence (Social BI)
Social BI refers to the use of cloud technology to deliver business analytics. Reports, end user tools or dashboards that are published to the cloud can be considered Social BI. Group sharing of business analytics for decision making is a key marker of this practice.
This term is also used to refer to the use of social media data as source data for analytics, for example, using Facebook or LinkedIn trending to see what consumers are buying.