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What is Business Analytics: Definition, Types, Salary and Examples

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Business analytics is one of the most growing fields in the modern era. Due to the deadly combination of statistics and computer science, the scope of business analytics has been growing wider and wider. This evolution of business analytics has resulted in various kinds of career opportunities. That’s why it is very important to understand the meaning and the importance of business analytics.

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What is Business Analytics?

Business analytics refers to the processes, tools, and techniques used to gain valuable insight and improve business decisions using data analysis. It involves examining past and current data to find trends, patterns, and opportunities that can influence strategic planning and performance improvements across all levels of an organization.

Business analytics is about transforming numbers and facts into plans that create progress. It enables turning raw data into actionable information so that organizations can take advantage. Common areas in which it provides valuable perspective include operations, budgeting, management, sales, logistics, and more.

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Components of Business Analytics

Modern-world business strategies are centered around data. Business Analytics, Machine Learning, Artificial Intelligence, Data Science, etc. are used to arrive at solutions for complex and specific business problems. Even though all of these have various components, the core components still remain similar. The following are the core components of Business Analytics:

  • Data Storage– The data is stored by the computers in a way that it can be further used in the future. The processing of this data using storage devices is known as data storage. Object storage, Block Storage, etc. are some of the storage products and services.
  • Data Visualization– It is the process of graphically representing the information or insights drawn through the analysis of data. Data visualization makes the communication of outputs to management easier in simple terms.
  • Insights– Insights are the outputs and inferences drawn from the analysis of data by implementing business analytics techniques and tools.
  • Data Security– One of the most important components of Business Analytics is Data Security. It involves monitoring and identifying malicious activities in the security networks. Real-time data and predictive modeling techniques are used to identify vulnerabilities in the system

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Types of Business Analytics

There are various types of Business Analytics that are performed on a daily basis across many companies. Let’s understand each one of them in this section.

1. Descriptive Analytics

Whenever we are trying to answer questions such as “what were the sales figures last year” or :what has occurred before”, we are basically doing descriptive analysis. In descriptive analysis, we describe or summarize the past data and transform it into easily comprehensible forms, such as charts or graphs.

An example would be finding out the percentage of leads that we couldn’t convert and the potential amount of business that we lost due to this.

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2. Predictive Analytics

Predictive analytics is exactly what it sounds like. It is that side of business analytics where predictions about a future event are made. An example of predictive analytics is calculating the expected sales figures for the upcoming fiscal year. Predictive analytics is majorly used to set up expectations and follow proper processes and measures to meet those expectations.

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3. Prescriptive Analytics

In the case of prescriptive analytics, we make use of simulation, data modeling, and optimization of algorithms to find answers to questions such as “what needs to be done”. This is used to provide solutions and identify the potential results of those solutions. This field of business analytics has recently surfaced and is on heavy rise since it gives multiple solutions, with their possible effectiveness, to the problems faced by businesses. Let’s say Plan A fails or there aren’t enough resources to execute it, then there is still Plan B, Plan C, etc., in hand.

4. Diagnostic Analytics

Diagnostic analytics involves analyzing past business performance to identify the causes of certain outcomes. It helps businesses understand what has happened in the organization and why. Diagnostic analytics uses different techniques like data mining and statistical analysis to dig deeper into historical data and gain useful insights. Some common applications of diagnostic analytics include identifying factors that influence customer churn, determining reasons for project delays or budget overruns, and finding causes that impact product quality and customer satisfaction levels.

The insights from diagnostic analytics enable businesses to take corrective actions, make process improvements, and prevent similar issues from occurring in the future. Overall, it helps organizations learn from past mistakes and successes to enhance operational efficiency and results

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Business Analytics Process

Just like any other thing in business, there is a process involved in business analytics as well. Business analytics needs to be systematic, organized, and include step-by-step actions to have the most optimized result at the end with the least amount of discrepancies.

Now, let us dive into the steps involved in business analytics:

  • Business Problem Framing: In this step, we basically find out what business problem we are trying to solve, e.g., when we are looking to find out why the supply chain isn’t as effective as it should be or why we are losing sales. This discussion generally happens with stakeholders when they realize inefficiency in any part of the business.
  • Analytics Problem Framing: Once we have the problem statement, what we need to think of next is how analytics can be done for that business analytics problem. Here, we look for metrics and specific points that we need to analyze.
  • Data: The moment we identify the problem in terms of what needs to be analyzed, the next thing that we need is data, which needs to be analyzed. In this step, not only do we obtain data from various data sources but we also clean the data; if the raw data is corrupted or has false values, we remove those problems and convert the data into usable form.
  • Methodology selection and model building: Once the data gets ready, the tricky part begins. At this stage, we need to determine what methods have to be used and what metrics are the crucial ones. If required, the team has to build custom models to find out the specific methods that are suited to respective operations. Many times, the kind of data we possess also dictates the methodology that can be used to do business analytics. Most organizations make multiple models and compare them based on the decided-upon crucial metrics.
  • Deployment: Post the selection of the model and the statistical ways of analyzing data for the solution, the next thing we need to do is to test the solution in a real-time scenario. For that, we deploy the models on the data and look for different kinds of insights. Based on the metrics and data highlights, we need to decide the optimum strategy to solve our problem and implement a solution effectively. Even in this phase of business analytics, we will compare the expected output with the real-time output. Later, based on this, we will decide if there is a need to reiterate and modify the solution or if we can go on with the implementation of the same.
business analytics process

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Business Analytics Tools

Business analytics tools empower companies to visualize, organize, and analyze, critical organizational data. These technologies provide a platform that makes business intelligence clear and actionable.

Several types of business analytics tools exist, with capabilities that enable various insights based on data as part of a complete solution. Among the most popular are:

SAS Business Analytics (SAS BA)

SAS Business Analytics is a collection of data visualization, analysis, and reporting tools offered by SAS Institute. It provides self-service and governed data discovery capabilities that enable all types of users to easily explore data on their own while ensuring security and consistency. 


QlikView is a popular business intelligence and data analytics platform, which provides interactive data visualizations and guided analytics features. It utilizes a rapid data indexing engine along with an associative experience that allows users to freely explore data relationships.


Splunk is a data analytics solution focused on collecting, indexing, and analyzing real-time data generated by IT infrastructure and applications to provide operational intelligence. It utilizes machine learning to provide security monitoring, application management, and IT analytics to users through correlating data, regardless of its format or source.


KNIME Analytics Platform is an open-source business intelligence tool built specifically for advanced data science integration, modeling, visualization, and reporting. It enables the assembly of reusable data transformation workflows through an intuitive, drag-and-drop interface. KNIME integrates machine learning and deep learning models, Python & R scripts, SQL database connectivity, and advanced business intelligence operators.

Dundas BI

Dundas BI is a flexible, full-featured business analytics solution offering interactive data visualization capabilities for faster business insights. It provides businesses with the ability to track KPIs, build stunning reports, and access data across departments on one intuitive platform. Dundas BI speeds up informed decision-making through effortless information discovery, exploration, and collaboration.

Applications of Business Analytics and Uses

Business analytics is a very useful process that is used in different sectors. Whether it be the IT sector, the healthcare domain, or any other type of business, business analytics can help improve them immensely. Hence, there are a vast number of applications for business analytics. Some of the notable examples of business analytics are:

  • Optimization of supply chains
  • Forecasting revenue
  • Pinpointing reasons for employee attrition
  • Fraud detection
  • Recommendation systems
  • Finding out the number of cabs required in a region
  • Price point comparison

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Business Analytics Examples

Some of the examples of Business Analytics are:

  • A simple example of Business Analytics would be working with data to find out what would be the optimal price point for a product that a company is about to launch. While doing this research, there are a lot of factors that it would have to take into consideration before arriving at a solution.
  • Another example would be applying Business Analytics techniques to identify and figure out how many and which customers are likely to cancel the subscription
  • One of the highly appreciated examples of business analytics is working with available data to figure out and assess how and why the tastes and preferences change of customers who visit a particular restaurant regularly.

Differences Between Business Analytics and Data Analytics

Business Analytics means performing data analysis to draw business insights and offer solutions to complex business problems. It specifically involves dealing with business insights, unlike Data Analytics.

Data Analytics refers to the analysis of already existing data to draw conclusions about the information contained in the data. It is a broader concept and involves business analytics too.

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Business Analytics Vs. Data Science

Data Science refers to the performance of data analysis using advanced statistical methods and arriving at insights to drive data-driven decision-making. It is the advanced stage of Business Analytics. However, both the roles differ based on the activities and functions involved in the business decision-making. Both play a very vital role in understanding the fundamental difference that lies in Business Analytics and Data Science.

Data Science explores possible solutions and aims at generally long-term problems and business growth. On the contrary, Business analytics aims at short-term and specific business problems.

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Challenges of Business Analytics

Business analytics can be tricky. Collecting reliable data and making sense of complex information is hard. Still, solutions exist to conquer these common data hurdles.

  • Data Complexity: With data from various systems and sources, understanding connectivity and ensuring quality is significantly difficult. Much effort is needed to ensure accuracy and context.
  • Data Interpretation: Even with quality data, finding meaningful insights requires considerable data science and analytical expertise to explore relationships and patterns effectively.
  • Tool Disconnectivity: Many analytics tools and platforms generate output that is often disconnected rather than integrated, hindering enterprise-wide awareness. 
  • Acceptance Hesitation:  lack of skills and a data-driven culture can make organizational alignment and implementation of analytics methodologies slow, despite extensive proof of concept. Patience and persistence thus become mandatory.

Future Scope of Business Analytics

As we mentioned above, there are a lot of different sectors recruiting business analytics professionals. Hence, the career scope of business analytics is very wide. Business analytics professionals are hired for different kinds of job roles. Their responsibilities may differ a little based on their designation and the sector in which their organization operates, but the end goal is the same—solving business problems.

Some Important Roles in Business Analytics

Designation Description
Business Analyst Developing visualizations, building APIs, and creating and working with dashboards
Data Analyst Analyzing data trends and finding valuable insights and metrics
Decision Analytics Professional Working with data and client requirements to find out the optimum path for a solution and its implementation
Business Consultant Working with partner clients from planning to implementation phases

Learn more about careers in Business Analytics on our blog at Business Analytics Careers.

Skills Required for Business Analytics

The skill set of a business analytics professional includes:

  • SQL (mandatory)
  • MS Excel
  • Statistical expertise
  • Strong analytical skills
  • Business acumen
  • Python coding (preferred by a lot of companies)
  • Proficiency in R (preferred by a lot of companies)
  • Data visualization skills (preferably in Tableau and Power BI)

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Business Analytics Salary

  • The average salary in the Business Analytics field is ₹8.2 LPA. However, it may vary based on the sector and the experience and skills of the candidates.
  • As they go higher in their career, these professionals can easily touch a point of ₹20 LPA with 6–7 years of experience.
  • Candidates with Python and R skills earn higher average salaries than those who do not have these skills.
  • In the United States, the average salary of a Business Analytics professional is around US$93,000 per year.

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Business analytics is the process of collecting, organizing, and analyzing business data to help companies make more informed decisions. By gaining insights from data, businesses can improve operations, and increase sales and profits. Whether you are a small startup or large corporation, analytics can provide valuable information to help any business grow and succeed in today’s competitive world. With the right tools and strategies, analytics makes it possible to transform raw numbers into actionable intelligence that determines real results.


What are the uses of business analytics?

Businesses use analytics to learn from numbers. It shows patterns and guesses what might happen next. This helps businesses make better plans for the future.

What are the benefits of business analytics?

Business Analytics can help companies make smarter choices that lead to more money and lower costs. It also helps give customers a better experience. Analytics finds new ideas to help businesses grow bigger over time

Which isare the best business analytics software?

Some popular choices are Microsoft Power BI, Tableau, and SAS. Other good options include Qlik, IBM Cognos, Oracle Cloud, SAP Cloud, and Google Sheets. Excel can work for small projects too. These tools make it easy to learn from data.


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