Data Analytics vs. Business Intelligence: A Comparison
Business Intelligence and Data Analytics are the two terms that oftentimes are used interchangeably - which is a misconception. While they have some characteristics in common, Business Intelligence and Data Analytics have some subtle differences.
Business Intelligence is a mix of strategies and technologies, with the end goal of helping end-users analyze data themselves and make data-informed decisions. BI is essential to data management and performance management.
Data Analytics is about turning raw or unstructured data into formats that are understandable and meaningful to non-IT users. Data after being converted can be processed further in many ways: cleansing, transforming, or modeling. The goal of data analytics is also to inform decision-making and lay the basis for predictive analytics.
In this article, let’s explore some of the key differences between Business Intelligence and Data Analytics.
Table of Contents
Business Intelligence was created way before Data Analytics.
The first use of Business Intelligence dates back to 1865 when Richard Millen Denvers published his book Cyclopædia of Commercial and Business Anecdotes. The book describes how a banker named Sir Henry Furnese utilized data to understand customers, gain a leg up on the competition, and drive profit. The essence of BI, according to Richard and which is still true today, is the ability to collect data and act effectively on it to generate business values.
The term Data Analytics was allegedly coined somewhere in the 19th century. But it’s not until the late 1960s that Data Analytics become more popular since computers become more sophisticated yet lightweight. Businesses at this time use data analytics as a way to understand the inside and outside of their environment. Recently, with Big Data, data warehouse, and cloud, Data Analytics has evolved as a distinct business function to cover most aspects of organizational data.
At its core, business intelligence is the utilization of data to improve the day-to-day operations of a business. People leverage business intelligence tools and experts if they wish to collect and manage data to optimize workflow, generate reports, and support business goals.
Business intelligence has two definitions in its broadest sense. It begins by outlining the methods, developments, and equipment that businesses employ to gather (and communicate) business insights. Second, it defines the insights that are produced as a result of this process.
There are various tactics and tools that can be labeled as BI. They can range from basic spreadsheets to more specialized tools such as:
- Real-time monitoring
- Dashboard development and reporting
- Implementation BI software, like Power BI
- Performance management
- Data and text mining
- Data analytics
In general, business intelligence would allow businesses to navigate their businesses more effectively and stay focused on their goals
The process of collecting, cleaning, inspecting, manipulating, storing, modeling, and querying data is known as data analytics (along with several other related tasks). Data analytics is more of a statistics tool. Businesses use to predict future trends and accordingly develop future strategies.
While business intelligence tells what's happening, data analytics tells what would likely happen. There are experts who look at data analytics as tools for predictive analytics. Data analytics serve numerous use cases that help businesses exploit their data to the fullest. Some of them are text mining, forecasting analysis, regression analysis,...
Business Intelligence is used by all business departments to collect and analyze data, and from that make reports. It ultimately helps users make informed decisions based on those insights. One key characteristic of BI is that it looks at historical data to describe the current situation. Typically, data used by BI is stored in Data Mart or Data Warehouse. BI, unlike Data Analytics, is more about understanding what had happened in the past.
Regardless of the industry, corporation, or objective, BI is primarily concerned with improving processes to increase profit. In general, this denotes the use of metrics that guide the operation of an organization, such as supply chain data, sales revenue, profit margins, staff attendance, etc. The clue is in the name: business is the focus!
On the other hand, people use data analytics when they want to analyze all available data to forecast future patterns. Its purpose is to generate insights that guide decision-making—not only in business but also in the sciences, government, and education. Moreover, analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world.
One of the earliest adopters is the financial sector. In the banking and finance industries, data analytics is used to forecast market trends and analyze risk. Credit ratings are an example of data analytics that has a broad impact. These ratings use a large number of data points to estimate lending risk. Data analytics is also used to detect and prevent fraud, which helps financial organizations increase efficiency and minimize risk.
When to use
Data Analytics is used when businesses are new to analyzing data and need significant change to the way it analyzes data. Data Analytics gives business users the tools they need to analyze historical and current data. From that, they can forecast future trends to support their decision-making and business planning.
Business Intelligence is used when a business is already familiar with data analysis. It doesn’t need any significant change to its current business. Most of the time, Business Intelligence helps its users detect the bottleneck in the way businesses manage data and fix them with more effective decision-making scenarios.
Both Business Intelligence and Data Analytics support reporting and data visualization. But whether to use BI or Data Analytics for these 2 functions depend on business data and business scenarios.
For business scenarios where users want to deep dive into present market trends and generate ad-hoc reports, data analytics would be a better choice. Data Analytics can also be ideal when users seek to forecast future patterns based on available data.
On the other hand, if users want to analyze data stored in the data warehouse and generate reports by pulling data from this warehouse, Business Intelligence would be more ideal. BI is also suitable for tracking targeted sales delivery and organizing data to provide sales intelligence.
There are various tools in the market that can be used to implement BI. As is the case, BI is implemented only on past data that is stored in data warehouses or data marts.
There are also a wide variety of tools for Data Analytics. Most of them have the ability to automatically collect and analyze data - the so-called self-service tools.
Sometimes, BI tools can also be used for Data Analytics, which depends on the approach or strategy defined by your organization.
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