Business Intelligence vs. Data Analytics (Main Differences)
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Many organizations use the data they collect to make important business decisions. Business intelligence and data analytics are two processes that organizations use to gather, translate, and analyze data so they can benefit from it. If you're a professional who collects, translates, or analyzes data, you can benefit from learning more about business intelligence and data analytics. In this article, we provide the definitions of business intelligence vs. data analytics, explain how they differ, discuss why organizations use these approaches, and identify which professionals use these strategies.
Business intelligence vs. data analytics definitions
It's vital to learn the definitions of business intelligence vs. data analytics to understand the differences between these two business terms.
What is business intelligence?
Business intelligence (BI) is the process of how an organization gathers data, transforms it into useful metrics, and applies those metrics to make informed decisions about various processes. Professionals use the statistics and figures from business intelligence to determine which areas of the organization are excelling and which can use improvements. An organization can then view its historical data in various visual representations, which can help its leaders better understand how it's performing. Stakeholders may also request information that may be easier to share and understand in certain forms.
While business intelligence usually references an organization's actions to make the most of its data, it also refers to the tools an organization can use to collect data. As many organizations rely on the collection and transformation of raw data, there are various software tools you can use to help make this process easier. You can use these software tools to monitor productivity and observe trends within data. Typically, these tools are user-friendly and well-organized.
What is data analytics?
Data analytics refers to various practices that involve translating data into actionable information, allowing organizations to better plan for their futures and remain competitive. Most of the processes of data analytics are now in the form of algorithms, which data professionals adjust and automate so other professionals can better understand the data. While many professionals refer to data analytics as business analytics, business analytics is a category of data analytics. Business analytics concerns how organizations use data to make informed predictions.
Organizations use data analytics to predict their future needs. Often, organizations identify trends in previous data to plan the number of materials they require to help ensure they can meet demands in the future. For example, an online puzzle shop might examine its data from previous years and notice its sales double every year in November and December. With this knowledge, the organization can plan to double its available products to help ensure it doesn't sell out of specific puzzles.
Read more: 18 Data Analyst Skills for Success
What is the difference between business intelligence and data analytics?
Business intelligence and data analytics relate to how an organization uses data to make informed decisions. The fundamental difference between business intelligence and data analytics is that business intelligence reveals what already happened, while data analytics reveal why. To better understand the differences between the two terms, here are three standard methods of examining data with information regarding both approaches:
Descriptive analytics is how organizations examine the historical data that they've archived. A business intelligence approach to descriptive analysis involves interpreting performance metrics and trends to reveal what happened. For example, a professional using a business intelligence approach may examine previous sales reports to determine which products sold well and which ones didn't. Conversely, a data analytics approach to descriptive analytics involves taking data and understanding the reason behind the historical data. For example, a professional using a data analytics approach to descriptive analytics might focus on learning why specific products sold well and others didn't.
Predictive analytics is how organizations interpret data to provide a more accurate forecast of their futures. Typically, organizations perform predictive analytics after finishing descriptive analytics. A business intelligence approach to this process involves examining data sets to determine potential continuations of current trends. A professional taking this approach to predictive analytics may use historical data and more recent data to create a graph of seasonal trends from previous years. This graph can allow professionals to make more accurate predictions about the organization's needs for the future.
While a professional taking a data analytics approach to predictive analytics aims to answer the same questions, how they answer the questions differs. While business intelligence usually uses a limited amount of past data to predict the future, data analytics examines various data sets using an artificial intelligence software. The increased amount of data that professionals use with a data analytics approach to predictive analytics allows them to create more detailed mathematical models from algorithms and simulations.
Read more: What Is Quantitative Analysis?
Organizations usually follow predictive analytics with prescriptive analytics, which is the last step of the analytics process. The prescriptive analysis involves creating a plan for an organization based on the findings from the predictive analytics and historical data. Considering business intelligence concerns the identification of past and current trends, it rarely involves prescriptive analytics. Yet, business intelligence provides a vital data framework that professionals use to perform data analytics.
Conversely, data analytics' primary function is performing prescriptive analytics. Professionals using this approach use the information they collect from descriptive analytics and predictive analytics to write proposals for an organization's future. Applying data analytics to the information professionals collect from business intelligence allows them to better understand past data trends, anticipate future data trends, and write effective business plans.
Why do organizations use business intelligence and data analytics?
Organizations use business intelligence and data analytics to:
Help improve daily operations
Organizations can review the efficiency of their operations to determine ways in which they can increase productivity and improve daily processes. For example, a professional might inspect data and discover that shortages of a specific material occur at a similar time each year. To solve this problem, the organization may order additional materials before the deficiency occurs.
Lower operating costs
When organizations review operations data, they might determine which procedures can become partially or fully automated, reducing costs for hiring professionals. Organizations can also check their most costly areas and help ensure they're contributing to operations, making the investment worthwhile. For example, a professional might inspect operations data and determine that a team isn't using an expensive machine the organization rents. They might decide to stop renting the device or find a less expensive alternative to reduce unnecessary costs.
Make data easier to review
While data is often difficult for most professionals to understand, business intelligence and data analytics can make it less complicated. Professionals use data mining software in business intelligence and data analytics to sort and organize data. They then use this data to create a visual representation that most people can understand. For example, an organization wanting to determine if its mobile phone application is easy to use can gather survey data to create a visual description of the users' experience.
Track customer satisfaction
Organizations can track customer service using business intelligence and data analytics. When an organization stores and organizes customer feedback received from testimonials or surveys, it can discover how customers feel about specific services or products. With this knowledge, organizations can make informed decisions about their future, like whether to reorder a product or discontinue it.
Track professional performance and motivators
Organizations can use business intelligence and data analytics to monitor their professionals' productivity. These organizations can also discover which initiatives or incentives motivate their professionals to contribute their best efforts to fulfil the organization's goals. For example, an organization might determine that there's less employee turnover and better productivity when it increases the bonus amount yearly.
Display patterns in sales trends and customer behaviour
Business intelligence and data analysis allow organizations to time product launches and marketing campaigns when sales peak within their industry. For example, an organization might debut a marketing campaign in November to help ensure that it garners maximum consumer engagement in time for the holidays. Another example is how many organizations cater their opening hours to times when consumers typically shop. If an organization notices a trend that consumers shop more frequently in the last hour on a particular day, it might decide to extend the opening hours in the future.
Promote progressive business initiatives
When organizations use a business analytics approach, it allows them to use their data to make business predictions. For example, an organization might use its growth rate data from previous years to determine whether it can grow its operations by 100% in the next five years. This information helps organizations establish realistic goals and plans for achieving them.
Which professionals use business intelligence and data analytics?
Professionals in various roles use business intelligence and data analytics to help organizations better understand their procedures. Here's a list of 10 roles that might use these strategies:
Business intelligence analyst
Business intelligence director
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