There tends to be some confusion in the industry concerning the differences between business intelligence tools (BI) and data warehousing (DW). Some people conflate them into a single term – BIDW (Business Intelligence/Data Warehouse) – and consider them to fundamentally be the same thing. Others consider them separate software categories.
As with many conflicts, the truth depends upon your point of view. This article will break down the similarities and differences between data warehouse vs business intelligence, examine the capabilities of BIDW software and explain how to select a BIDW solution.
Business intelligence is defined by Gartner as “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.”
BI goes back as far as the 1800s when financial advisors used knowledge of the market that their competitors lacked to get ahead. It was coined in 1989 by Howard Dresner, a former Gartner analyst and has been evolving and changing ever since.
BI is a category of intelligence systems that gather proprietary data then organize, analyze and visualize it to help users draw business insights. It can blend data from a variety of sources, discover data trends or patterns, and suggest best practices for visualizations and next actions.
Insights can include historical metrics, future forecasting, competitor performance comparisons and much more.
Some of the benefits of business intelligence include:
- Access to/control of proprietary data
- Improved data literacy
- Intuitive visualizations
- Data mining
- Performance management
- Sales intelligence
- Streamlined operations
- Eliminated guesswork
- A competitive edge
What Is Data Warehousing?
That brings us to the next question: what is data warehousing? Gartner defines a data warehouse as “a storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse then combines that data in an aggregate, summary form suitable for enterprise-wide data analysis and reporting for predefined business needs.”
The concept of a data warehouse goes back to 1988 when Barry Devlin and Paul Murphy of IBM coined the term.
So a data warehouse is perfectly named. Like a physical warehouse, it operates as storage for data that has been extracted from another source. Many organizations have proprietary data warehouses that store information on performance metrics, sales quotas, lead generation stats and a variety of other information.
Data warehouses can perform some analytics capabilities: using the extract, transform, load (ETL) process, data warehouses can perform the complex queries that transactional databases cannot handle. It also has the ability to negotiate different data storage schemas based on the data type to kickstart the cleaning process.
Once data has entered a warehouse, it cannot be altered. Data warehouses only perform analysis of historical data and cannot provide real-time data or make future predictions.
The basic features of a data warehouse are:
- Uses large historical data sets
- Allows both planned and ad hoc queries
- Controls data load
- Retrieves large volumes of data
- Lets users manage schema like tables, indexes, etc.
- Lets users generate reports
- Backs up data
What Is BIDW?
Some people believe that a data warehouse merely stores information to form the back end of business intelligence and that they are completely separate entities. Let’s investigate these ideas to untangle what BIDW is and whether it’s a valid categorization of software.
According to the Kimball Group, “data warehousing was relabeled as ‘business intelligence.’ This relabeling was far more than a marketing tactic because it correctly signaled the transfer of the initiative and ownership of the data assets to the business.” While the concept that the users of business data should have ownership of the information, it implies that the storage and access of data (i.e., data warehousing) is the same as analyzing and interpreting it (i.e., business intelligence).
To understand how BI and DW work together, we need to first separate the concept of business intelligence from the tools which support it. Business intelligence is based on collecting information from across a company’s entire enterprise and analyzing the data to form global views and reports.
BI Tools are software applications that facilitate BI analysis by creating visualizations and reports as well as enabling OLAP (online analytical processing). Data warehouses are another facet of a BI toolset and are concerned specifically with aggregating data.
A data warehouse is designed to “consolidate data from disparate databases and to better support strategic and tactical decision making needs.” Simply put, a data warehouse is intended to help companies achieve a single version of the truth by consolidating information from multiple systems, usually including databases.
Data warehouses are one of many steps in the business intelligence process, so the term BIDW is something of a generalization. BI and DW is a bit more accurate, and just using the general umbrella of BI to include business analytics, data warehousing, databases, reporting and more is also appropriate. All of these types of solutions make up a vast ecosystem of intelligence systems with common purposes.
Another pair of terms that are often confused are databases and data warehouses. While the two may seem similar, there are plenty of differences that make them easy to tell apart to the trained eye.
A database is a repository of data where the information is organized, typically in a column, row and table format. A database is periodically indexed to make sure the information is structured and accessible. Databases can perform online transaction processing (OLTP) functions and respond to queries such as a search.
Both databases and data warehouses are relational data systems, which means that they store, organize and transport data points that are related to each other in some way. They are built using SQL, or structured query language, and can be accessed by users performing searches.
This chart identifies the main differences between the two:
A database is designed to record data, perform fundamental operations and transactions and capture data through OLTP processes. Conversely, a data warehouse performs OLAP to analyze data in order to present it to your queries.
Databases are application-oriented, typically limited to a single application (like an HR software solution), and stores detailed real-time data. Data warehouses are subject-oriented collections of historical data that can perform complex queries to retrieve summarized data.
So to break this down into a practical example, data warehouses draw and store data from databases. Those databases are often being updated constantly and reflect real-time data from whatever source it is drawing from. The data warehouse now contains the information it retrieved from the database, but it won’t update automatically as new information comes in. Data warehouses can draw information this way from a variety of databases to condense it for user query.
Purpose of Intelligence Systems
The most important thing about BI and DW is that they are both crucial parts of intelligence systems. They share the same goals of improving your business through data-driven business insights.
The true power of a business intelligence system which relies upon a data warehouse comes from using conformed data dimensions to help analyze and drive business decisions. For example, one system may refer to a customer as someone who has purchased goods within the past twelve months. Another system may define a customer as any company who has ever been in contact about services.
By analyzing data warehoused information based upon dimension, rather than discrete data points, a business intelligence solution can enhance a company’s plans and bottom line. By using BI/DW, a company’s best customers and most profitable avenues can easily be identified based upon multiple criteria. This knowledge can then be used to influence future enterprise direction.
How to Select an Intelligence System
So what if my business needs an intelligence system, but I don’t know what kind to purchase? This section will help you identify the best type of BI system for your business, which features you need from a BI solution and how to begin the process of procuring one.
The first step of this process is to identify your required features. Don’t worry if you’re not sure about this step — our interactive BI requirements template will walk you through the process. Accurately understanding which features of an intelligence system your unique business will most heavily utilize is crucial to choosing the best system for your needs, so don’t skimp on this step!
Once you’ve discovered which features you need to use, you can move on to comparing products. This BI comparison matrix allows you to directly compare different BI vendors based on how well their product performs in different categories — namely, how well they deliver different features from the requirements template.
You can use these scores to create a shortlist of the top three to seven vendors who meet your needs the best. This shortlist will be used in the proposal submission step.
Now you have specific vendors to contact. Now is the time to get a personalized quote, demo, trial and proposal from each vendor you were interested in. This BI RFP template will walk you through this step so you correctly format your request.
An RFQ is the only way to get completely accurate pricing information, but feel free to check out this BI pricing guide to get an idea of the market and see where pricing from some of the industry leaders begins.
Now you should understand the function of data warehouses, databases and the general category of business intelligence. BIDW is an abbreviation of business intelligence and data warehousing, which are two separate entities within the BI umbrella. You also learned how to select an intelligence system and can proceed confidently with your software selection following our requirements template, comparison report and RFP process. Any additional questions? Please let us know in the comments!