Business Intelligence vs Business Analytics
Businesses generate a huge amount of data each day. For owners to identify issues, make smart decisions, and get a better bottom line, tools that can turn information into actionable insights are needed.
Business intelligence and business analytics are two solutions that professionals in the business technology world use prominently today — but what is the difference between the two, if any, and how can you know which is right for your needs?
Let’s discuss the definitions and the differences between the two below.
What Is Business Intelligence?
According to IBM, business intelligence is an umbrella term that refers to the technology used for data preparation, data science mining, data visualization, and data management. The role of business intelligence tools and processes is to provide users with the ability to gain useful data that facilitates data-driven decisions in organizations.
Business intelligence has been traditionally employed for managing daily operational management needs in businesses. Most leaders leverage business intelligence expertise and tools to ultimately allow them to achieve their business goals efficiently and effectively.
The tools that are used for business intelligence can involve a wide range of software programs and other systems. Some of these may even include online analytical processing, data spreadsheets, business activity tracking software, data mining systems, and reporting apps.
Meanwhile, some experts believe that business intelligence tools can incorporate the more statistical and predictive components that are used in business analytics.
Overall, the goal in leveraging business intelligence is so that organizational leads can navigate the various challenges they face while ensuring that their companies can stay on target to accomplish what they want.
What Is Business Analytics?
On the other hand, we have business analytics, which is a field that leans more towards statistics. In business analytics, data experts use quantitative software that allows them to make predictions and develop strategies for future growth.
According to a 2018 study by MicroStrategy, the primary uses for business data analytics were to improve business processes, cost-efficiency, and strategies.
Companies employ business analytics tools to achieve numerous functions, such as regression analysis, correlational analysis, forecast analysis, image analytics, and text mining. Most organizations would hire data science specialists since these complex tools require professional training and expertise to use effectively.
The Difference Between Business Intelligence and Business Analytics
Although the two terms have significant overlap, business intelligence prioritizes the events that occur in a business and why. Meanwhile, the scope of business analytics is broader as it includes solutions that allow you to leverage insights to prepare for the future.
Business intelligence makes use of descriptive analytics to come up with conclusions regarding current and historical performance. This provides context regarding changes in the company’s key performance indicators.
Both business intelligence and business analytics employ predictive and prescriptive practices in analytics. These practices are useful for decision-makers since the data can help guide their future actions. In other words, business intelligence and analytics allow stakeholders to make informed decisions.
Key Components of Business Intelligence and Business Analytics
To draw the line clearer between business intelligence and business analytics, let’s take a look at the main elements that you can expect from their respective solutions.
Components of Business Intelligence
- Data warehouses: Once data science has been pre-processed and aggregated, business intelligence specialists move the information to a single repository, such as a data warehouse or data mart. Both are capable of supporting business analytics and other reporting tools.
- ETL: Business intelligence depends on data integration as it combines information from several sources into one. The three steps in the process are called ETL: extract, transform, and load.
- OLAP: OLAP stands for online analytical processing. It’s the technology that extracts data science and reorganizes them for faster and more insightful analysis.
- Natural language processing: Business intelligence solutions have started using natural language processing to allow customers to access business data in new ways.
- AI-assisted data preparation: The ability to identify problems in data automatically — while suggesting solutions — allows business intelligence specialists to adapt their datasets as needed.
- Smart reporting: Smart reports are used in business intelligence solutions, so experts can learn from users and discover insights.
Components of Business Analytics
- Data mining: To delve deep into business analytics, data mining models must be created and used. Classifying demographics and similar parameters are necessary to sort data here.
- Association and sequence identification: This component in business analytics is responsible for helping scientists understand what consumers will buy by analyzing their purchasing patterns and behavior.
- Text mining: Text mining data is vital in helping improve customer service. It is also useful in developing new products based on the collected information.
- Data visualization: Data visualization is important in business analytics as it helps present information effectively while allowing the output to be easily understood and accessible.
- Evaluation and validation: In this stage, predictive models are used to help specialists understand if their business outcomes will be the same whether a situation is different or not. They also use various simulation techniques to help in identifying the most possible results.
- Data governance and standards: This is necessary for businesses to meet the growing requirements while continuing to attain high-quality data.
Deciding on the right solution for your business will depend on your plans and intentions. If you believe your business model is already operating effectively, and you only wish to improve the efficiency of your operations, then using business intelligence is a good choice.
Companies that rely on using real-time data science reporting often lean towards using business intelligence. These entities are often concerned with what areas they can improve immediately.
On the other hand, if you plan to alter the way you do business yet lack the necessary data science insights, you will most likely find answers in business analytics.
Fortunately, both business intelligence and business analytics can be applied together to handle the here and now while preparing for the future. Deciding to use either of the two solutions (or both) can be extremely beneficial for businesses to succeed in the modern world.
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