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.
In this blog, we will be covering the following topics:
Business Analytics Definition
In this introduction to business analytics, we first have to understand the term analytics. Analytics generally refers to the science of manipulating data by applying different models and statistical formulae on it to find insights. These insights are the key factors that help in solving various problems. These problems may be of many types, and when we work with data to find insights to solve business-related problems, we are actually doing business analytics.
One of the biggest essentials of business analytics is categorized as descriptive analytics, which analyzes historical data to determine particular reactions from a bunch of people. Business analytics refers to a subset of several methodologies, such as data mining, statistical analysis, and predictive analytics, to analyze and transform data into useful information. Business analytics is also used to identify and anticipate trends and outcomes. With the help of these results, it becomes easier to make data-driven business decisions.
The use of business analytics is very popular in some industries such as healthcare, hospitality, and any other business that has to track or closely monitor its customers. Many high-end business analytics software solutions and platforms have been developed to ingest and process large data sets.
Business Analytics Examples
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 would have to be taken into consideration before arriving at a decision.
Another popular example of business analytics is streamlining restaurants. With the help of business analytics, restaurants can improve their efficiency during peak hours. When there are a lot of orders, the digital order boards change; they begin to highlight the items that can be prepared quickly. In case of a limited number of orders, the boards will feature items that take longer to prepare with high margins. This will help restaurants to respond to real-time needs and improve efficiency.
Casinos can leverage the power of business analytics to improve their profits and retain customers in the long run. In this case, business analytics can be used to track customers’ spending habits. By learning about this, casinos can help the customers make enough money so they keep coming back. Not only this, the collected data also helps casinos to understand the best amenities.
Components of Business Analytics
Below are the main components of business analytics:
Data aggregation: This is the first step in the process of business analysis. Data aggregation refers to gathering, organizing, and filtering data.
Data mining: Data mining sorts through data sets, using databases, statistics, and machine learning, to identify trends and patterns.
Sequencing: Sequencing identifies predictable actions that are performed in association with other actions.
Text mining: Text mining refers to exploring and organizing large, unstructured text data sets for the purpose of qualitative and quantitative analysis.
Forecasting: Forecasting analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviors.
Predictive analytics: Predictive analytics involves using a lot of statistical techniques to create predictive models, which extract information from data sets, identify patterns, and provide a predictive score for an array of organizational outcomes.
Optimization: Optimization is business making data-driven decisions to improve performance after identifying trends and patterns through data.
Data visualization: Data visualization provides visual representations, such as charts and graphs, for easy and quick data analysis.
Check out this quick guide to a Business Analytics career:
Types of Business Analytics
There are various types of analytics that are performed on a daily basis across many companies. Let’s understand each one of them in this section.
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.
Read our blog on Descriptive 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.
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.
In short, business analytics is a combination of all of these types of analytics.
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.
Want to learn more about the steps involved in Business Analytics? Check out our guide on Business Analytics Process now.
Applications of Business Analytics
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
Looking to get started with Business Analytics? Read our blog at Learn Business Analytics now.
Business Analytics vs Data Analytics
Data analytics refers to the science of analyzing data to transform it into useful insights. These useful insights are driven by trends and metrics that are formed by identifying and analyzing data.
While both business analytics and data analytics aim to improve operational efficiency, business analytics, in particular, leans more toward business-oriented goals and uses data and data analytics to make insightful business decisions. Furthermore, the data analytics umbrella includes intelligence and reporting and online analytical processing. Data analytics for business is more focused on solving specific business questions or anything related to business workflow.
In the data analytics process, data scientists, data analysts, and data engineers work together to collect, integrate, and prepare data for development, testing, and revision of analytical models.
Business Analytics vs Data Science
Data science refers to a multidisciplinary field that uses scientific systems, methods, and algorithms to study structured and unstructured data to know everything about something in particular. Data science is a powerful field that can transform any data into valuable insights and empower businesses with meaningful data. There are many methods and techniques in data science that are used to derive meaningful insights. Some of the technologies used in data science, to study and analyze data, are data transformation, data purging, and data mining.
While data science is more about data in general and deriving meaning from it, business analytics is more oriented toward solving particular business problems. Data scientists are tasked with presenting digital information to depict its practical value in data-driven decision-making. However, they do not typically endeavor into solving specific business questions in the way that business analysts do when seeking out meaningful insights.
Career 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
||Developing visualizations, building APIs, and creating and working with dashboards
||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
||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)
These skills are very easy to master if you are ready to acquire them. You can further enroll in our Business Analyst Course to become a Business Analyst professional
Business Analytics Salaries
- The average salary in the business analytics field is INR780,000 p.a. However, it may vary based on the sector and the experience and skills of the professionals.
- As they go higher in their career, these professionals can easily earn INR2,000,000 with six to seven years of experience.
- Professionals 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$80,000 p.a.