The ability to gather data, analyze it, and make informed decisions is what separates successful businesses from the rest. This process of gathering and analyzing data is known as business intelligence (BI). While BI has been around for decades, its use has evolved over time to meet the demands of ever-changing business environments. Here’s a look at the history of BI and how it has evolved over the years.
Business Intelligence in the Pre-Digital Era
BI in the pre-digital era refers to the use of business intelligence tools and techniques that were used in a time before the widespread adoption of digital technologies. The main goal of BI in the pre-digital era was to help organizations collect, analyze, and make sense of large amounts of data that would otherwise be overwhelming or inaccessible for human analysis.
Some key features of BI in the pre-digital era included data visualization techniques, such as pie charts and bar graphs, which could be used to understand trends and patterns across different types of data. Additionally, some BI tools also allowed users to perform basic statistical analyses, such as calculating averages and standard deviations.
OLAP is one of the tools used with decision support systems (DSS) over the years. First introduced in the 1970s, OLAP was initially used as a tool to help businesses gain insights into their data and analyze trends. It allowed users to quickly explore large volumes of disparate data by slicing and dicing the information in different ways, using visualizations like charts and graphs to make it easier to understand.
As technology advanced over time, OLAP became an even more powerful tool for businesses. It could be used not only for analyzing historical data, but also for processing real-time streams of incoming data from sources like social media or sensors on manufacturing equipment. This made it ideal for use in areas like marketing and sales analytics, predictive modeling, risk management, and fraud detection.
EIS, or executive information systems, has been a valuable tool for businesses since its inception. As one of the earliest forms of business intelligence tools, EIS was developed as an early predecessor to today's analytical tools that help companies make effective decisions about their products and services.
Some of the key features of EIS that made it so valuable were its ability to handle large datasets and complex computations in real-time. These features allowed programmers to easily extract data from multiple sources and create useful visualizations using dashboards and other user interfaces. This enabled businesses to quickly identify trends and patterns in their data, which provided critical insights into how they could improve their operations and better serve their customers.
Data warehouses have been a staple of business intelligence in the 2000s. They provide a centralized repository for data that can be used for reporting and analysis. Data warehouses have evolved over the years, and their use has become more widespread. In the early days of data warehousing, businesses used them to store data from disparate sources.
Today, data warehouses are used to store data from SaaS tools, databases, and more. This data is then integrated and made available for reporting and analytics. Data warehouses have helped businesses gain insights into their operations and make better decisions.
Business Intelligence vs. Analytics
To put it simply, business intelligence is all about collecting and storing data, while analytics is about making sense of that data and using it to make decisions.
Business intelligence tools are typically used to track information such as sales figures, customer behavior, and other types of operational data. This information can be used to improve marketing campaigns, inventory management, and other initiatives. Analytics, on the other hand, goes a step further by using statistical techniques to uncover trends and patterns in data. This information can then be used to make predictions about future behavior or trends.
Descriptive analytics is the process of organizing, analyzing, and presenting data in a way that allows humans to understand it. This can be done through various methods, such as statistical analysis, data visualization, and natural language processing.
The goal of descriptive analytics is to provide people with a better understanding of the data, so they can make better decisions. It's often used to find trends, patterns, and outliers in data sets.
Predictive analytics is a type of data analysis that uses advanced machine learning algorithms to identify patterns, behaviors, and trends in vast amounts of historical data. These analyses help companies make more accurate predictions about future consumer behavior or business outcomes, which can be used to improve their bottom line and better serve their customers’ needs.
Prescriptive analytics is the next step in the evolution of business analytics. Where predictive analytics uses historical data to identify patterns and trends, prescriptive analytics takes those predictions one step further by prescribing specific actions that should be taken to optimize outcomes.
The Future: What’s Next For BI?
In general, experts suggest there are three main areas of focus for BI moving forward. The first is data quality. For businesses to make effective decisions based on their data, it must be accurate and well-organized.
The second focus is on analytics. As businesses become more data-driven, the need for sophisticated analytical tools will continue to grow. This includes both traditional reporting and visualization tools, as well as newer predictive and prescriptive analytics platforms.
Finally, the third area of focus is on user experience (UX). As businesses increasingly adopt self-service BI solutions, it’s important that they be easy to use and provide a great user experience. This means having an intuitive interface, clear and concise visuals, and helpful guidance along the way.
Business intelligence has come a long way since its inception in the pre-digital era. The technology has evolved from simple data collection and reporting to sophisticated analytics that can predict future outcomes and prescribe actions. As BI moves into the streaming age, where data is collected and analyzed in real-time, businesses will be able to make better decisions faster than ever before. What’s next for BI? Only time will tell, but we can be sure that this powerful tool will continue to play a critical role in business success.
At Canvas, we’re thrilled to be working on the future of BI - a future that's no-code, collaborative, and integrated with the modern data stack. Canvas is a collaborative data exploration tool for operators to make decisions without SQL. Unlike traditional BI tools, Canvas has:
- A spreadsheet-like interface for your teams to pivot, write formulas, join, and create charts without SQL
- Figma-like collaboration lets your teams communicate where the data lives, while usage metrics inform data teams of who is using specific models
- A native dbt integration so your data models and documentation about the tables and columns are automatically synced and surfaced directly to operators
Head to canvasapp.com/signup to try it out for yourself!