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.
What Is Business Intelligence?
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
Some other areas of software that often fall under the BI umbrella are business analytics (BA), data mining, big data analytics, embedded analytics, enterprise reporting and data warehousing.
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 perfect term. Like a physical warehouse, it operates as a data storage. 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 complex queries that transactional databases can’t handle. It also has the ability to negotiate different data storage schemas based on the data type to kickstart the cleaning process.
Once data enters a warehouse, it can’t be altered. Data warehouses only perform historical data analysis and can’t 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 business data users should have ownership of the information 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 across the 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 and 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.
Database vs. Data Warehouse
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 an information repository, typically in a table format. Users can periodically index a database 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. Leverage SQL, structured query language, to acces data stored in databases and warehouses.
A database records data, performs fundamental operations and transactions and captures data through OLTP processes. Conversely, a data warehouse performs OLAP to analyze data in order to present it to user 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 update and reflect real-time data from different sources. The data warehouse now contains information from the database, but it won’t update automatically as new information comes in. Data warehouses can draw information this way from various databases to condense it for user queries.
How Are BI and DW Interrelated?
A robust BI architecture describes various layers and components with different capabilities that produce dashboards and reports. Data warehouses form a vital part of BI architecture.
A robust BI architecture leverages:
Businesses gather data from operational systems such as CRM, ERP, finance, manufacturing, supply chain management and more. Users can also collect it from secondary sources like customer databases and market data. Modern BI tools leverage robust data connectors to combine data from disparate sources. The data may exist in structured, semi-structured or unstructured formats.
To analyze data, consolidate different information sources to provide a unified view. It involves extracting data from multiple systems and loading it into data warehouses. This process is known as ETL (extract, transform and load). During data extraction, raw data is extracted from source locations such as flat files, databases, web pages or SQL servers.
The transformation phase involves data filtering, cleansing, de-duplicating, and performing calculations and summarizations on the raw data. It can also include changing the row and column headers, editing text strings and formatting the data into tables to match the target data warehouse schema. In the last step, the data is loaded into the data warehouse.
Data warehouses store structured data in the form of a relational, columnar or multi-dimensional database for further analysis. It helps achieve cross-functional analysis, summarize data and maintain a single version of truth across the organization.
After the data is processed, cleaned and transformed, the next step is to derive useful insights. Data analysis extracts relevant, actionable information from the dataset that helps businesses make better decisions. These insights or statistics are often represented in graphs, charts, tables, maps and other visualizations.
Modern BI tools empower business users to create intuitive dashboards, reports and visualizations via drag-and-drop capabilities without in-depth technical knowledge.
Share reports and dashboards with team members to facilitate collaboration. Dashboards automatically update in real time on a daily, weekly or monthly basis. Users can also share dashboards in a secure viewer environment.
It doesn’t allow other users to change the content, but they can use assigned filters to manipulate and interact with it. Another option is to use a public URL to share reports and dashboards with stakeholders outside the organization.
Deriving valuable insights involves spotting patterns in table representations or numbers trending upwards in a line chart. It can also mean depicting income distribution or the number of hours spent on different tasks on a particular day in a pie chart.
Looking at historical data can provide insights into how the business reacts to different variables, including market fluctuations, seasonality, trends, economic cycles and more. Analyze points and patterns that may align with current conditions so that businesses can make smarter decisions based on facts.
How Data Warehousing Contributes To Successful Business Intelligence Strategies
An efficient data warehouse accelerates load times for preparing and analyzing data. It can boost compliance, security and data shareability. When data warehousing and business intelligence are combined, they include processes such as:
Data Mining: A process used to extract meaningful information from raw data. It helps discover trends, patterns and correlations in big data.
Performance Metrics: Use metrics to measure the performance of business, campaigns or employees. Performance metrics help prove or disprove a hypothesis based on predetermined business goals.
Data Querying: Query data to obtain reliable information. Queries let users perform simple or complex searches based on specific conditions.
Statistical Analysis: Statistical analysis involves collecting data samples from a population to determine trends and patterns while making strategic decisions about the population as a whole.
Data Visualizations: Present tedious data in compelling charts, graphs, infographics and other visuals to inform better decisions.
Data Storytelling: Create intuitive reports and dashboards to convey a persuasive data story. A robust data story effectively garners audience attention and influences their decisions.
Data Warehouse vs Business Intelligence
Although BI/DW cannot function without each other, there are some important differences to cover. Let’s look at data warehouse vs business intelligence with the help of the following questions.
What is the end goal?
BI primarily focuses on generating business insights. It determines quantitative factors related to business such as product positioning and pricing, profitability, revenue, sales performance, forecasting and more. On the other hand, DW is responsible for storing the organization’s data (obtained from multiple sources) in a centralized location.
In a nutshell, BI systems use DW to process and analyze data, while DW serves as a data foundation for BI tools.
What is the end result?
While BI outputs information in the form of intuitive visualizations, dashboards and reports, data warehouses outline information in dimension and fact tables for use in BI tools.
Metadata management, data distribution, storage management, recovery and backup are some data warehousing capabilities. BI functions include creating intuitive visualizations and dashboards, reporting, predictive analytics and modeling, data mining and more.
Who is the audience?
Data engineers and back-end developers deal with data warehouses. It is their responsibility to develop, design and maintain the systems. Meanwhile, executives and managers use real-time dashboards and reports to derive insights, create sales reports depicting useful metrics and KPIs, and forecast strategic organization development.
What are the tools?
Data warehouse processes are managed with Amazon Redshift. It analyzes information from different sources and runs complex analytical queries to manage the data warehouse. Tools like Power BI, Tableau, Sisense and more run complex queries and create intuitive dashboards and reports to facilitate effective decision making.
Purpose of Intelligence Systems
The most important thing about BIDW is that they are both crucial parts of intelligence systems. They share the same goals of improving your business through data-driven business insights.
It’s vital to consider the data dimensions of a warehouse to drive accurate decisions. For instance, one system may define customers as people who make purchases and another may view customers as an organization that made initial contact with 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. Identify the company’s best customers and most profitable avenues using BI/DW while using this knowledge to influence future enterprise direction.
Once users have discovered which features they need to use, they can move on to comparing products. This BI comparison matrix directly compares different BI vendors based on how well their product performs in different categories — namely, how well they deliver different features from the requirements template.
Use scores to create a shortlist of the top three to seven vendors. This shortlist will be used in the proposal submission step.
Now users have a list of specific vendors to contact for a personalized quote, demo, trial and proposal. This BI RFP template will walk through this step so users can correctly format their 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.
So what if my business needs an intelligence system, but I don’t know what kind to purchase? This section will help users identify the best type of BI system for their business, which features they need from a BI solution and how to begin the process of procuring one.
The first step of this process is to identify precise requirements. An interactive BI requirements template streamlines the process. Accurately understanding which features of an intelligence system the business will use is crucial to choosing the best system, so don’t skimp on this step!
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.