This page aims to clarify some of the key topics related to Business Intelligence.

Business Intelligence

There are so many different definitions for Business Intelligence (BI). BI Vendors expand or contract the scope of Business Intelligence according to their convenience, whichever they find is the best fit for their product/software to be placed in the top list of tools. So which definition of Business Intelligence should we consider?

Based on 10+ years of own experience in BI area and considering the definitions provided by Gartner and Wikipedia we can simplify the definition of Business Intelligence as given below

Business Intelligence is the process/umbrella term of/for deriving Information and Insight from raw data efficiently to enable better decision making in order to improve Business and thereby increase profits.

Raw data could be any data ( big or small, file or DB, Website or log data, structured or unstructured).
Data collection, processing and storage, Data Modeling, Architecting, reporting, analysis, Analytics, and visualizations all of these fall under Business Intelligence. The process of deriving insight is an iterative process. See the image below

Business Intelligence, what is BI
Click to enlarge : Business Intelligence
As mentioned above there is no one single agreed definition for Business Intelligence. Below are the ones that I believe are the widely used definition.

Gartner - BI is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and  performance.

Wikipedia - BI can be described as "a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes".

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

My simplified version - BI is the process* of deriving information from data* efficiently to enable informed decision-making in order to improve Business.

Process includes technologies, methodologies and most importantly the People.

Data refers to all types and from all the sources whether internal to Business or not. It doesn't matter how small or big the data is, where it is, how it is captured and which type it is.

BI can be considered as the black box to which data is passed as input and the output is useful information based on which decisions can be made to improve Business.

DATA + BI = Improved Business.

Businesses are not necessarily commercial Businesses, it could also be non commercial activity like Police Department, Not for Profit organizations, Free Hospitals etc.

See how law enforcements are using BI in couple of examples given below

Why Businesses need Business Intelligence?

In Short, to measure and improve Business. As someone rightly said, If you can't measure it, you can't improve it.
  • To provide Right Information to the Right Person at the Right Time. 
  • To Understand Customers, Employees, Partners, Suppliers, Distributors and Competitors. Basically to understand all parties involved in the Business. 
  • To ensure all parties involved in the Business get the same information. 
  • To make fact based decisions. 
  • To be compliant with legal and regulatory requirements. 
  • To minimize expenses. 
  • To Stop Revenue Leakages. 
  • To find Growth Markets/Products/Services 
  • To Increase Revenue
BI to Business is equal or more important than a Dashboard for a Car. The more intelligent the Car Dashboard is, the better results you get. Can a car be driven without Dashboard? Yes. Can you drive better when you have a Dashboard? Yes. This is exactly what BI does to Business. You can obviously run a Business without BI. However with BI you can run it better. And in this competitive world where you need to run better than your competitors you definitely need BI. Like the Dashboard of the card indicates what speed the car is going, What is the optimum speed, In how much time you will reach your destination, which route to take, Which road to avoid, How many kilometres you have driven in this trip, What is the total number of kilometres driven till date etc BI indicates how the Business is performing, How will it perform in the future and what changes should be done to achieve the objectives of the Business.

Uses of BI - Examples

Excluding the BI Team , BA’s, Interns/Students the real end users are usually from below functions, I have given examples for each function so that you get a better idea

Sales - product/service/program revenues, compare across verticals/horizontals, commissions for sales manager…

Marketing (Marketing Managers) - Campaigns Management, Subscription Management…

HR - Employee cost, Utilization ratio, legal and regulatory compliance, Job application to selection/rejection ratio, forecast growth in employee count…

Finance and Billing - Budgeting, Accruals, cost center reporting and planning, profit and loss reports, Billing Reports…

Product/Service Management Teams - Monitor product/Service performance, compare across products/Services/categories…

Account Management Teams - Monitor account performance, Revenue tracking, Regulatory reporting…

Operations - Ticket backlog, SLA’s , Call centre stats, Technical performance indicators…

In each of these functions the users are usually Managers and Heads

Higher Management - Overview reports/Dashboards

Agile BI

In Agile BI frameworks like Scrum or Kanban are used. Based on own experience I developed KABI, a new agile methodology for implementing BI solutions. Check KABI - The new Agile Methodology for BI Projects - Implement BI projects quicker happily

Should a BI Solution always have a Data Warehouse?
Short answer is No, A BI Solution does not always have a Data Warehouse.

A strategic and tactical BI Solution most often has a data warehouse in the back-end of the BI solution. This is because data from the transactional systems needs to be historized and versioned. Operational BI Solution most often do not have a Data Warehouse. Examples of Operational BI Solutions without Data Warehouse are BMC Remedy Analytics for BSM and JIRA Dashboards. In these examples the BI reports are directly based on the transactional databases or mirror databases and data is not historized like in the case of a Data Warehouse. A BI Solution with a Data Warehouse can answer 100% of the questions whereas a BI Solution without a Data Warehouse will fall short in answering historic and versioned data based questions and hence will also fall short in providing predictive and prescriptive BI.

What exactly is the difference between Data Warehouse and Data mart?
Unfortunately there is no simple answer. Because the answer depends on two main points;
 1) Building approach 2) Data stored

Building approach:

If Bottom up approach (Kimball Methodology) is used for building the Data Warehouse then Data Marts are the building blocks of the Data Warehouse. If Top Down approach (Inmon) is used for building the Data Warehouse then Data Marts are the subsets of the Data Warehouse.

Data Stored:

For explanation purpose lets consider that both Data Warehouse and Datamarts are types of Data Stores that are at the backend of the BI Solution. Data Warehouse stores or plans to store data from multiple subject areas whereas Datamart stores data of only one subject area. Example - A Data Warehouse may contain data from various subject areas like Customers, Employees, Distributors, Sales, Finance, Purchase, Marketing, HR etc in the data store whereas a Datamart contains data only from one subject area for example a Data store based on Job Application System that is used by HR Team.

Whether a Data Warehouse gets built or Datamart gets built largely depends on who owns the BI Solution? Is it a Enterprise level initiative or a department level initiative? In general if it is a enterprise level initiative then a Data Warehouse gets built and on the other hand if it is a department level initiative then a Datamart gets built. Exceptions are definitely there, A department level initiative can also result in a Data Warehouse if the Data store stores multiple subject areas.

Further reading

For all posts related to BI please check -
BI Glossary (TBU)


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