Business Analytics and Data Analytics - How are they different?

Whenever we talk about business, the first thing comes to mind is the management of the same. Business management is an essential thing for business development. In today’s technical era, the entire business management is undertaken through technologies.

 

Business analytics and data analytics are the two popular tools that are used in business management. Often the readers get confused and treat both the terms the same. Though they are similar in few components, they still have different meanings and applications.

 

In this blog we will read the concept and components of data analytics and business analytics. We will also discuss the key differences of the two.

 

(Learn about AI analytics)

 

 

What is the meaning of Business Analytics?

 

Business analytics is a common term used in business management. Business analytics is the subset of business intelligence. It is a data management system. It may be defined as a process by which businesses use statistical methods for analyzing site data to form new insight. 

 

Business analytics helps in predictive analytics, data transformation and data mining. This further helps in improving strategic decision-making capability.

 

 

Components of business analytics

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Components of business analytics

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There are different components involved in business analytics and its process. The major components of business analytics are mentioned below:

 

1. Data mining: Business analytics involves data mining. It sorts through large datasets and identifies trends to establish relationships. Data mining uses technologies such as databases, statistics, and machine learning. 

 

2. Data aggregation: Data aggregation is also a concept of business analytics. It is exhibited prior to the analysis. The data is first gathered, organized, and filtered. This includes both volunteered data and transactional records.

 

3. Forecasting: Forecasting is the other component of business analytics. Business analytics tools make informed estimates by analyzing historical data. The derived estimates are predictive in defining future behaviors.

 

4. Sequence identification: Business analytics involve association and sequence identification. It refers to the process of identification of predictable actions. The actions are often linked with other actions.

 

5. Predictive Analytics: Predictive analytics is one of the major features of business analytics. The software uses statistical techniques to frame and conduct predictive models. 

 

This helps in extracting information from several patterns and datasets. At the same time, it provides a predictive score for the sample array of outcomes.

 

6. Text mining: Text mining helps in exploring and organization of large and unstructured datasets. It is used to serve qualitative and quantitative analysis of the data.

 

7. Data visualization: Business analytics tools help in providing visual representation of data. This is done with the help of charts and graphs for easy and quick data analysis.

 

7. Optimization: Business analytics and its optimization helps in identification and predictions of best-case scenarios using simulation techniques.

 

 

What is the meaning of Data Analytics?

 

Data analytics refers to the process of analyzing raw data to find trends and then answering the questions related to it. Data analytics includes several technologies to serve different roles.

 

These techniques have been automated into mechanical processes. This also includes advanced algorithms for raw data processing. It is the science of studying and analyzing raw data to make conclusions. 

 

Data analytics help in driving conclusions from raw information and unprocessed data. It helps in optimizing the business performance.

 

Components of data analytics

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Components of Data Analytics

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There are several components involved in data analytics. The key components of data analytics include the following:

 

1. Roadmap: Roadmap or operating model is the basic requirement of data analytics. Roadmaps utilize mapping tools to make sustainable designs. It helps the organizations to manage their data. 

 

At the same time, it facilitates the invention of remote data structures. It maintains the performance record of the employees and authorizes the quality of key performance indicators. Thus, data analytics help in formulating strategies and plans.

 

2. Data security: Data security is one of the major features of data analytics. It helps in monitoring and detection of suspected and malicious activities. It is essential to resolve global security threats. 

 

Data analytics employ the latest generation of security tools to detect the malicious things in the network. These tools work by reducing cyber-attacks and by enabling quick detection. 

 

The system gathers the data by number of ways such as cloud resources, network traffic and user behavior data. It helps in maintaining the vulnerability of data. 

 

3. Data visualization: Like business analytics tools, data analytics tools also include data visualization. It refers to the concept of presenting data in sheets, graphs and other pictographic material.

 

4. Data standards: Data analytics tool contains the set of standards that are essential to govern the measures of a company or the institution. Such regulations are important to ensure efficient operations and high-quality data. Thus, helping in data management.

 

5. Insight analysis: Data analytics help in building the insights. These insights help in covering up business fluctuations and drawbacks. 

 

6. Data storage: Data storage is the other feature of data analytics. The data formed and framed is stored in proper storage modules to avoid data leaks and other risks. Data is stored in such a way that it is easily accessible in the near future.

 

7. Data acquisition: Data acquisition is the process of identifying physical or electrical change in a computer system. This includes voltage, current or temperature.

 

8. Data optimization: Data optimization refers to the set of techniques used in a data system to reduce response between the action and the system.

 

(Also see, the difference between embedded analytics and augmented analytics)

 

 

Differences between business analytics and data analytics tools

 

Business analytics tools and data analytics tools are quite different from each other in the following ways. 

 

1. Data analytics use the latest technologies to identify business correlations and patterns. While business analytics use technologies to analyze data for the organizational outputs and activities.

 

2. Data analytics include the analysis of datasets to frame insights and trends. While business analytics includes the analysis of information to make effective business decisions.

 

3. The end results of data analytics that is structured information is used by the business analytics to serve its purpose.

 

4. While a business analyst uses the data to make decisive decisions for the business organization, the data analyst gathers data and processes it into well-structured information.

 

5. A business analyst is assigned a practical job of decision making, while a data analyst is assigned the responsibility of gathering and processing of data.

 

6. Business analytics is used to determine weakness of an organization or the company, while data analytics doesn’t serve any such specific purpose.

 

 

What are the common points between business analytics and data analytics?

 

Though business analytics and data analytics are quite different from each other, they share some common concepts. Here is the list of similarities between the two:

 

1. Both business analytics and data analytics are divided into four types, i.e., descriptive, predictive, diagnostic and prescriptive.

 

2. Both business analytics and data analytics emphasis on data visualization.

 

3. Both business analytics and data analytics reduce the response time by executing data optimization.

 

In simple language, the concept of data analytics and business analytics can be concluded in the following way. The data analytics generate structured information. 

 

Later, this generated information is used in the process of business analytics for taking effective decisions. In other words, the end product of the data analytics is the raw material for the business analytics. Both share an important role and have their very own purposes in business development and management.