The Definitive Guide to Business Intelligence

Business IntelligenceBusiness leaders have access to more data than ever before.

But data by itself doesn’t generate insights.

Business Intelligence Tools have become the go-to resource for helping companies harness the power of big data and analytics and make smarter, data-driven decisions.


  • Overview


  • Big Data


  • Data Warehousing


  • Analysis


  • Use Cases


  • Challenges


  • Future


What is Business Intelligence?

The specific definition of BI can vary depending on who you ask.

Here are a few examples of some of the ways business intelligence is defined:

A variety of software applications used to analyze an organization’s raw data.

A broad category of computer software solutions that enables a company or organization to gain insight into its critical operations through reporting applications and analysis tools.

A set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.

Technologies, applications and practices for the collection, integration, analysis, and presentation of business information.

The use of computing technologies for the identification, discovery and analysis of business data – like sales revenue, products, costs and incomes.

In our view, each of those definitions is incomplete.

Many of them are focused only on the software used for business intelligence. While the term is often heard in relation to software vendors, there’s more to BI than just software tools.

In addition, many of the common definitions of BI neglect to include the primary goal of business intelligence.

Our definition of BI is as follows:

Business Intelligence helps derive meaningful insights from raw data. It’s an umbrella term that includes the software, infrastructure, policies, and procedures that can lead to smarter, data-driven decision making.

History of Business Intelligence

The term “business intelligence” has been around for decades, but it was first used as it is today by Howard Dresner in 1988.

Dresner defined business intelligence as the “concepts and methods to improve business decision making by using fact-based support systems.”

Today, business intelligence is defined by Forrester as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.”

In the first stages of business intelligence, IT teams ran reports and queries for the business side, though today’s systems are focused more on enabling self-service intelligence for business users.

As with any technology, the offerings from vendors have evolved over time and continue to do so. As core features like reporting and analytics are becoming commoditized, vendors are looking at other features to differentiate themselves. Likewise, as the business environment changes, so do the requirements organizations have for their business intelligence applications.

These are a few of the biggest trends and developments in business intelligence right now:

  • The blending of software and consulting services – Vendors are beginning to offer “information as a service” and presenting intelligence to clients, as opposed to selling the software and infrastructure businesses need to access intelligence on their own.
  • Increasing Self-service – Software is increasingly focused on increasing the functions that be performed without having to involve IT staff or data scientists.
  • Cloud-based business intelligence – While cloud computing has taken hold in other areas, it’s beginning to catch on in business intelligence, too. As this progresses, it will allow businesses to use intelligence without dedicating internal resources to manage infrastructure and perform software upgrades.
  • Mobile intelligence – Mobile is becoming a key part of day-to-day business and it’s no different in business intelligence. Mobile tools allow decision makers to access intelligence wherever they need it, not just when they’re at their desks.
  • Big Data – Businesses have access to more data than ever, and a lot of it comes from outside the organization in non-structured form. Business intelligence is increasingly being combined with Big Data analytics, so businesses can make decisions using all the information they have at their disposal, regardless of what form it takes.

Components of Business Intelligence

While ideally the end result of business intelligence is not complex, there is a lot of complex technology involved in turning raw data into actionable information. Here are a few of the core components of a typical business intelligence deployment:
Source Data

Source Data

Business intelligence all starts with the data.

As we mention above, businesses have access to more data than ever. Much of that comes from transactional systems, such as CRM systems, ERP systems, inventory databases, HR and payroll systems, and many others.

Data used in BI also comes from external sources. One common source is social media, which organizations use to capture statements in which users mention the company. Other sources can vary greatly depending on what questions the organization is trying to answer, but may include public data from government reports, weather information and industry news reports.

Extract, Transform, Load

Extract, Transform, Load (ETL)

Simply having access to the data doesn’t mean it’s ready to be used for intelligence.

A key part of BI is the tools and processes used to prepare data for analysis. When data is created by different applications, it’s not likely all in the same format, and data from one application can’t necessarily be looked at in relation to data from another. In addition, if business intelligence is relied on to make critical decisions, businesses must make sure the data they’re using is accurate.

The process of getting data ready for analysis is known as Extract, Transform, Load (ETL). The data is extracted from internal and external sources, transformed into a common format, and loaded into a data warehouse. This process also typically includes data integrity checks to make sure the data being used is accurate and consistent.

Data Warehouse

Data Warehouse

A data warehouse is a repository containing information from all the business’s applications and systems, as well as external sources, so it can be analyzed together.

The ETL process ends with data being loaded into the warehouse, because when the data is contained within the separate sources, it’s not much use for intelligence. That’s for two primary reasons. First, those sources are typically applications that are designed for processing transactions, not for performing analysis. Analyzing the data in that state would take too long and disrupt critical business operations.

Second, the point of business intelligence is to generate more insight about the organization as a whole, so the data from all of those systems must be combined in order to understand a single, holistic view of what’s happening in the company.


Online Analytical Processing (OLAP)

The data warehouse and ETL process represent the back end of business intelligence, while Online Analytical Processing (OLAP) represents the front end. OLAP tools present data to users and allow them to group, aggregate and sort the data based on various criteria.

This is the function that allows users to pull out the data they want and make the comparisons they need in order to have their questions answered.



As mentioned above, one of the goals of business intelligence is to make data accessible and useful to non-technical business users. As such, data must often be transformed into something beyond spreadsheets and lists of numbers so that it can be properly understood.

Visualization tools present data using charts, graphs and other formats to aid understanding. Traditional formats include bar graphs, pie charts and scorecards, while advanced data visualization can create interactive and dynamic content, automatically choosing the best type of representation and personalizing content for the user.



The dashboard is the primary graphical interface used when working with a business intelligence system. Typically the first thing the user sees when logging on, the dashboard presents the most important reports and data visualizations for the user, customized based on the person’s role.

The dashboard is a simple way to organize information in one place and allow the user to dig deeper for more.

Goals of Business Intelligence

Why do companies use business intelligence? The primary goal is stay ahead of the competition and make the right decision at the right time. Those decisions can be made around pretty much any aspect of running a business, such as:

  • Figuring out how to increase the effectiveness of marketing campaigns
  • Deciding whether and when to enter new markets
  • Improving products and services to better meet customers’ needs

One of the key aspects of business intelligence is that it’s designed to put information in the hands of business users. Organizations are required to make decisions at an increasingly faster pace, so today’s business intelligence tools help decision makers access the information they need without having to first go through the IT department or specifically designated data scientists.

Rather than request a report and then wait for it to be created, the user can log into the business intelligence application and view all the critical information presented in a way that doesn’t take a specialist to understand.

Since the goal is to help business leaders use intelligence to make better decisions, BI tools must be easy for those users to understand

Best Practices for Business Intelligence

As mentioned above, business intelligence is more than just software. For a successful implementation, businesses need to have the right processes and infrastructure in place in addition to the right business intelligence applications.

Unfortunately, a lot of implementations aren’t successful. According to a 2011 report from Gartner, 70%-80% of business intelligence projects fail.

In order to prevent that, here are some of the best practices organizations should follow when they formulate their business intelligence strategy:

1Decide whether you need business intelligence

There’s a lot of hype around business intelligence, and many companies may make the mistake of investing a lot of money into the technology just because they think they need to. Instead, the organization must first be clear on what it wants to accomplish and identify a specific business need business intelligence can help solve.

According to Gartner, one of the top reasons for such a high failure rate is that many organizations assume that business intelligence is a requirement, rather than fully understanding the needs of the business.

Figuring out what those needs are should be the first step in any business intelligence strategy. The key is for IT and the business units to work together to list the needs and determine how and if they can be met using business intelligence, and whether business intelligence or some other solution is needed.

2Standardize systems and processes

Even when a business intelligence project is completed and all the necessary components are installed and deployed, that doesn’t mean the organization is getting the most out of its investment.

One reason businesses run into challenges is because they rely on many different systems and applications used throughout the organization. That makes it hard to get a holistic view of the company and the “single version of the truth” that is critical to business intelligence success.

Only 35% percent of organizations have standardized on one or a few business intelligence products throughout the company, according to InformationWeek’s 2014 Analytics, BI, and Information Management Survey. The rest use different software and systems in different business units. However, a successful business intelligence should come from the top down, with standardized tools and process that work for all departments. It helps to have the entire organization involved from the beginning so that everyone’s input is taken into account.

3Focus on usability

When evaluating software options, it’s especially important to pay attention to how easy the systems are for the people who will use them on a regular basis. Executives, managers and others from the business side are increasingly using business intelligence tools without the help of IT, analysts and others.

Software should have self-service functionality and the ability to display information and reports in a way that the average business person can understand. Again, this is one area in which it helps to have input from everyone during the planning stages.

In addition, the business also needs to give people the right training so they can get the most of the tools that are selected. If the company simply hands access to people who are used to getting all of their information from spreadsheets, they likely won’t get much out of it.

4Get the data ready

Good intelligence starts with good data. When asked what was their biggest barrier to successful business intelligence initiative, 59% of respondents in InformationWeek’s survey answered data quality issues.

Coming up with a plan for a business intelligence deployment takes more than just deciding what software to use. A key piece is figuring out a strategy to ensure data quality. Businesses must look at what data they have or will be able to capture, and decide what they need and how they ensure its integrity.

According to Aberdeen research, data quality must be addressed first, before any other action is taken. Companies with the most success in business intelligence are those that invest in tools and processes to make sure records are complete and accurate. Governance processes must also be used to avoid data duplication and make sure old, outdated, or no-longer-relevant data is deleted.

Download a free PDF of this resource guide

Download Now