My definition of Business Intelligence is:
"Business Intelligence is the art of collecting, sifting, and reporting about business facts in ways that provides companies a greater vision and clarity about their company's historical activities such as sales, inventory and other business processes so that the companies can make better and more informed decisions".
The Technology Definition
Let's review what the technology definitions are. In general they define business intelligence as using technology tools to analyze business information.
From Wikipedia:
"Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics."
From an article on CIO magazine [ http://www.cio.com/article/40296/Business_Intelligence_Definition_and_Solutions ]: "Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization's raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting."
From Dictionary.com: "the process of gathering information about a business or industry matter; a broad range of applications and technologies for gathering, storing, analyzing, and providing access to data to help make business decisions".
These seem to indicate that BI is a technology "thing" that does something for the company. It is a software application, It is data mining or analysis or reporting, all technology in use. But the key is that these definitions use technology as BI. I disagree.
The need for clarity and vision
Businesses, rather the people who guide them make decisions that affect everything about the business many times each day. Some of these decisions are trivial and do not substantially affect the outcome of the business (usually to make a profit). However, there are decisions that are made that really are important and can grossly affect the business in some way. Perhaps in a good positive way. Sometimes with disastrously outcomes. Let's think about this statement about business today:
Because of lack of information, processes, and tools, more than 35 percent of the top 5,000 companies will regularly fail to make insightful decisions about their business and markets.
It might be interesting to think about how business decision makers are arriving at their decisions. If you look at the above statement, the word "insightful" sums up the issue of BI. People are making decisions in business that have no rational basis, not supporting information or datum to support those decisions nor a vision nor expectation that is other than "it's the right decision" which will lead them to the "right" outcome.
What they really are doing in many if not most cases is guessing. Guessing at what history has / has not told them. Guessing at what the chances are of their outcome becoming the reality. Guesses, not informed guesses, but "just hope we are right" guesses. Worst yet is that many people making the decisions do not even think about how they arrived at their decisions. Not using all the information that is available is key. How much data does the average small business have? They probably have too much data to easily review. Expand that millions of times of the amount of data large companies have. Too many people are making decision solely using an Excel spreadsheet that has some limited information to make strategic business decisions. This is just not cutting it anymore.
Let's look at questions that nearly every company needs to answer. To make correct decisions that advances the business in a positive manner. To increase sales and profit. To reduce costs.
What products are typically sold together? Why? Can we leverage these pairings?
What is the average revenue per dollar spent on a salesman? Which salesman is the highest? What salesmen are below the average? How can we maximize our sales efforts?
What customer is you're most profitable? Why?
What are your customer segments and what are the traits of each?
Can you predict revenue and profits by product? By market segment? By customer?
Each of these questions can be a source of frustration because companies routinely do not have this information available when decisions are being made. All of these represent information challenges that businesses need to solve. I routinely use these data challenges when talking about business intelligence:
How can I utilize historical information? What can I learn from it? How and where do I get that information?
How do I overcome the issues of "too much data"?
How do I get the information I know is out there, but cannot get it into some form that I can make sense of it?
How do I model or predict the future of our company's activity?
How do I discover knowledge that I never thought existing or that I needed?
How do I ignore the "so what" information that I get. I need actionable information not just information.
BI, the technology will help you achieve this need for information.
Why businesses lack knowledge
Information is NOT insightful knowledge. Businesses today certainly lack knowledge yet they are surrounded with data and information. It is the ability of a discipline of business intelligence that can help provide knowledge through use of technology tools and smart business desires. Part of this lack of knowledge stems from all the operational systems that are in companies today. Operational systems are solutions that run your business: order processing, accounting, inventory systems and the like. These generate enormous amounts of information, but rarely insightful information. So what happens to a business that has all these operational systems is:
Reliant on operational systems for information that they think is insightful and analytical.
Lack of clear knowledge needs in the organization to operate effectively
No cohesive plans to get the right knowledge to those who need it
No time to read the information that I have already because they are overwhelmed with information and data but not knowledge
The operational systems leave little or no funding for knowledge based solutions.
The managers of the business do not think they are lacking in insightful knowledge
How Business Intelligence Helps
BI can help the company and its managers by supplying the information and knowledge that is "below" the operational level of information. Below because this information typically needs to be developed with tools and data manipulation in ways that provide the "hidden" knowledge. BI tools assist in these areas:
In depth processing & reporting
Analytical reporting
Dashboards
Data mining
Predictive analysis and modeling
Performance management (KPI) Key Performance Indicators
What these tools work with is the data of the enterprise. Many times this data is collected into central databases [data warehouses] for processing by the BI tools. The process of moving, conforming, reviewing and converting this data from the operational systems into the BI data warehouse is called ETL [Extract, Transform, Load].
BI Database: Many times it is better for companies to create a special database where the data is stored. This prevents reporting and analysis processes from having detrimental effects on the operational data systems. For most BI applications, the data is stored in formats that are more appropriate for BI based tools so that they can perform faster and contain the proper information that is needed.
Data Manipulation: Processes that gather all the data for the BI system. They collect data from other systems. Along the way, the data is checked for accuracy, is standardized and updated into the BI database. Data is conformed to meet reporting and analytical needs. Additionally data may be enhanced with external data that is "married" to the operational data. An example of external data might be to process addresses to adjust and add address information so that each address is properly formatted. Sometimes historical data can be kept for long periods to assist in data analytics.
Dashboards: Used to provide quick summary data and information to users. Typical use is to show data graphically or in pivot tables to allow a user to "drill into" more details as they need.
Reporting & Analytics: Typically pre-determined, pre-written reports that are designed to convey specific information. Statistical modeling is sometimes used. Most BI implementations also have "ad hoc" capabilities that allow the user to form their own report views quickly and efficiently.
