Business Intelligence (BI) Software

Business intelligence (BI) software has gained considerable traction since first being introduced as “decision support systems” in the 60’s. It’s consistently recognized as a top technology spending priority, especially as tough economic conditions force organizations to make effective and efficient use of existing resources. Today, there are over 100 business intelligence software companies selling some type of BI tool.

What is Business Intelligence Software?

So what is an accurate business intelligence definition? Is it online analytical processing (OLAP)? Extract, transform and load (ETL)? Analytics?

The business intelligence market has evolved from IT staff assembling component tools (e.g. OLAP and ETL) to business intelligence software vendors developing functional applications (e.g. marketing analytics, procurement analytics, etc) to address specific market needs. Today, more and more BI users are business users - not IT staff - that need quick, easy-to-understand displays of information. As a result, vendors are hiding the complexity of BI and increasingly focusing on the user experience.

Business intelligence software helps organizations identify and analyze business data in order to make better business decisions. It allows organizations to collect both internal data from company departments, and external data such as marketing or economic data sets. BI provides more accurate reporting, monitoring and assessment of data, and aims to help companies make the most of their existing resources.

Business Intelligence Functions

When evaluating systems, it is important to determine what functions your organization needs and identify products that support these. Here is a list of common functions found in business intelligence software.

Analytics
Data Mining	This is the process of sorting through large amounts of data to identify new or unknown patterns. It is often the first step that other processes - such as predictive analytics - will rely on.
Online Analytical Processing (OLAP)	Allows users to quickly perform ad hoc queries and analyze multidimensional data from different perspectives. It is typically made up of three analytical operations: consolidation which is the aggregation of data; drill-down which allows users to sort through the details, and; slicing and dicing which allows users to extract (slice) a set of data and view it (dice) from different perspectives.
Predictive Analytics	Essentially analyzes current and historical data to make future predictions in order to identify risks and opportunities. An easy consumer example of this is credit scoring, which relies on an individual’s current financial standing to make predictions about their future credit performance.
Semantic and Text Analytics	Extracts, interprets and structures large volumes of text to identify patterns, relationships and sentiment of the text.
Data Management
Data Quality	Helps organizations maintain clean, standardized and error-free data.
Extract, Transform and Load (ETL)	ETL is the process of taking data (extracting) from outside sources, transporting it to the target system and transforming it in the process, then loading it into the end target (i.e. database or data warehouse).
Reporting
Dashboards	Dashboards allow users to create reports with easy-to-understand visual displays. The displays usually compare key performance indicators (KPIs) and other metrics against a goal or target value. Dashboard reports are often web-based.
Report Writer	Allows users to design and generate their own reports from a range of data sources including databases, spreadsheets, HTML/XML files and other sources.
Scorecards	Scorecards allow users to take KPIs and apply them to a strategy map. From here, related reports and analysis can be performed. It’s basically dashboards taken one step further. It is more often used by organizations that have a strategic performance management methodology like the balanced scorecard (BSC) and Six Sigma.

What Type of Buyer Are You?

Before evaluating software, you must determine what type of buyer you are.

• Business users and departmental buyers. This group of buyers favors small data discovery vendors and BI tools over the big traditional BI systems. Ease-of-use and fast deployment are more important than in-depth functionality and integration. They are usually business users rather than IT staff.
• IT buyers. These traditional business intelligence buyers are more focused on functionality and integration within their information infrastructure stacks or other enterprise resource planning (ERP) applications. Integration across different business entities and departments is usually more important than ease of use.

Market Trends You Should Understand

As you begin your business intelligence software comparison and evaluation, there are a couple trends to consider:

• In-Memory Processing. OLAP systems of the past would pre-calculate every possible combination of data. These calculations would be stored in the “cube” and users could retrieve them when they needed a certain analysis. Creating these cubes was very time consuming - sometimes taking as long as a year - and required expertise. Today, computer processors and memory are faster, cheaper and overall more powerful. This same process can happen in-memory, rather than using a disk-based approach with cubes. BI software built on an in-memory architecture can retrieve data and perform calculations in real-time or on-the-fly.
• Big Data. The Internet is rapidly creating vast amounts of data. According to the International Data Corporation (IDC), data use in 2011 will grow by as much as 44 times amounting to 35.2 zettabytes (a billion terabytes) across the world. This phenomenon is being dubbed “Big Data” among IT and business leaders. BI software companies are beefing up their data warehousing and analytics capabilities to keep up with demand.
• Business users to outnumber IT staff. This is a major trend playing out in the market. More business users - rather than traditional IT staff - are evaluating and purchasing software. So usability is becoming more important than functionality during software evaluations. As a result, small data discovery vendors that develop really good interactive visualization tools are gaining market share. Meanwhile, traditional BI vendors are parroting new market entrants by promoting ease of use.
• Software as a Service (SaaS). A growing number of organizations are considering SaaS or “cloud” BI software instead of traditional, on-premise software that you install on location. Cost is a major driver of this trend. The poor performing economy is motivating companies to look at lower-cost BI software from SaaS and open source vendors. Of course, perceived ease of use, faster implementations and reduced IT needs are also driving this trend. On-premise BI vendors are responding by committing development resources to cloud technology.
• Mobile BI applications. Proliferation of the iPhone, iPad and other mobile devices is pushing vendors (e.g. Microsoft business intelligence software and Oracle business intelligence software) to develop on-the-go tools and applications. Analysts think mobile BI could expand the population of BI users to a larger mainstream audience.