Business Analytics

Beginner's Guide: Business Analytics In India And How You Could Become A BA.

April 7, 2020

You have landed at the right place for a better understanding of Business Analytics. We have curated this guide to business analytics from a beginner’s perspective for you. After reading this post, you’ll know 

  1. What is business analytics? 
  2. How are data science and analytics related to each other? 
  3. Fundamentals of business analytics 
  4. Types of data analytics 
  5. The best business analytics tools
  6. Career scope in the field of Business analytics in India 

So without any further delay, let us get started with this guide.

Let us start with our guide for Business Analytics, knowing What Business Analytics or BA is

The term ‘Business Analytics’ refers to technological processes and practices of gaining business insights on performance, planning, decision making, and strategic execution, through iterative exploration and investigation of historical business data or real-time data or both. In short, BA involves identification of patterns between numerous business factors through data analysis and uses this information to make decisions, and act on them. All the steps and processes of business analytics operations are mentioned in the guide and discussed in detail later part.

Steps and processes involved in Business Analytics

These are the basic steps involved in executing a Business Analytics strategy.

  • Defining business needs: This is the very first step of BA strategy. Here the organization defines ‘the business problem it needs to solve or what needs to be improved using business data analysis’ 
  • Data exploration: This step is where the relevant business data is cleaned, structured and prepared to be loaded in the business analytics tool.
  • Data analysis: Here, statistical analytical methods are used to find correlations between multiple factors and the target variable. This operation is executed using computational algorithms.
  • Making predictions: Once the analytical model starts giving accurate predictions, it can be deployed to make predictions on new data to identify actionable insights.
  • Optimization: This step involves finding the ‘best possible analytical model’ that can be used to solve business problems. The business analytics model is tested under various circumstances, and its performance is measured.
  • Forecasting and taking decisions: At this stage, the model is optimized and ready to perform with real data. The user can now forecast most likely business scenarios and market predictions, and then make decisions based on it.
  • Updating and maintaining the analytics model: In this final step, the deployed business analytics model is updated with new data, trained and maintained by the engineering team.

The above steps will give you the basic idea about the end-to-end steps involved in a business analytics project. Let us now see some applications of business analytics.

Applications of business analytics

Business analytics is majorly used in these domains;

  • Marketing: BA models can be used to identify the target audience, finding the optimum advertising strategy, predicting sales and inventory requirements, etc.
  • Finance sector: Organizations and financial institutions use massive finance datasets for portfolio management, budgeting, financial planning and risk management.
  • Manufacturing domain: Business analytics is used in the manufacturing sector for optimizing manufacturing processes, logistics, supply-chain management, inventory, and demand forecasting, etc.
  • Customer Relationship Management (CRM): BA models are used to manage customer and client relations and analyze their preferences, purchasing patterns, demography, lifestyle, etc. Such information is highly valuable for the businesses to adapt according to key customer trends.

There are many more applications of business analytics. But the above list is just to give you an idea about how enterprises employ data analytics technologies to gain an edge in an uncertain and competitive environment. You may have noted that the business analytics model relies heavily on data. Therefore, it is important to understand the relation between data science and analytics. Let us see what it is!

What is data science and analytics? How are these two terms related?

Data analytics processes are based on the principles of data science. Data science is nothing but scientific methods, systems and processes to extract insights and knowledge from structured or unstructured data. Business data analytics represents techniques (qualitative and quantitative) to process business data and gain meaningful, actionable insights for business gains.

Thus, from the above paragraph, it may have become clear to you that Data Science and data analytics are closely related. The only real difference lies in the specific application of data analytics. Also, ‘Data Science’ is where the principles and processes of Data analytics applications are derived. Now that you have a clear understanding of the terms, let us move on to the fundamentals of business analytics. Knowing them is crucial for you to understand Business Analytics and its applications.

Fundamentals of Business Analytics

You have already come across the steps and processes involved in Business Analytics operations discussed in brief earlier. Let us now dive into them for more details and know the fundamentals of Business Analytics.

#1. Defining the objective of BA for a business

Every BA operation starts with setting an objective for the enterprise. Here, the organization decides on what business problem needs to be solved, or what factor of business performance needs to be improved. For example, a company might need to predict the sales of a product ‘X’ based on the past year performance. Hence, it would require a ‘predictive business analytics’ model for doing so. The objective is decided or influenced by the enterprise stakeholders, business analysts, and users/clients or customers. Once the goal is clear, the BA operation can be taken to the next fundamental step as follows.

#2. Exploring the data

This is a time-consuming but crucial step for Business analytics. The prediction accuracy and success of the BA model for an organization depends on thorough data exploration i.e., its ‘cleanliness’ and ‘reliability.’ The relevant raw data is first taken for preprocessing or munging or cleaning. 

In munging the raw data, missing information is accounted for, wrong information and biases are checked for and finally, the data variables are transformed into a suitable format. Example of transformation can be ‘Sales dates’ which is not a useful feature as such. But one can convert the dates into days, months and hours to reveal meaningful patterns relating to the sales. Then, the ‘Outliers’ or ‘Rare events’ are mapped and traced. Outliers are often removed to minimize calculation time and computational bias in the final predictions. Often, time-series graphs are plotted to detect the outliers and relational patterns. 

At last, the data is structured into a suitable format (.CSV, .XML, .XLSX) and loaded into the business analytics model for training and prediction.

#3. Data Analysis

This is the third fundamental step for a Business Analytics project. Here, the prepared or cleaned data is processed via analytics models. These analytics models run on computational learning algorithms. For example, a binary classification analytics model may use ‘Logistics regression’ algorithm. Or if it is a multi-class classification model, it may use ‘Decision tree’ or the ‘Random forest’ algorithm. 

The cleaned data is partially used to train the business analytics model, and the rest is used to test the prediction accuracy. This split is usually 70/30. Once the model starts giving predictions, the results can be evaluated using several metrics. These metrics are different for different types of algorithmic models. For example, a multi class-classification model is judged by the ‘Confusion Matrix’ and so on. After evaluating the model on its performance, it can be tuned through its ‘Hyperparameters’ for required results.

#4. Making predictions and forecasts

Based on the quality of the data supplied to the BA model and its proper evaluation, it becomes ready to make predictions. The ‘target variable’ is the one that needs to be predicted and the feature variables are the ones that are processed by the BA model to find patterns and correlations. At this stage, an organization might test more than one model to evaluate its performance.

#5. Optimization of the BA model

In the optimization stage, several constraints and limits on the target problem are evaluated. The model that performs the best (Accuracy wise) is selected to be deployed for real-world operations. Such a model returns minimalistic errors, has high forecasting accuracy and is designed to align with the enterprise goals.

#6. Deploying the Business Analytics model for real-world operations

The sixth fundamental step is deploying the optimized BA solution. At this stage, the organization can make use of its BA model to take decisions and forecast the most likely scenarios to occur. A BA model can be deployed over the web. However, most of the models are coded in Python or R, but the online environment favors Java. Hence, necessary changes are made before the solution is deployed over the web.

#7. Updating and maintaining the system

The deployed BA system is now supposed to be updated and maintained on a regular basis. This is achieved by constantly feeding it with new relevant data so that it is trained continuously for better performance. A BA solution will get outdated if it’s not trained as per the newly generated information. Another aspect to take care of is regular maintenance. The database and cloud hosting must be monitored for security and performance integrity.

Let us now see what some other ‘types of analytics’ based on these fundamentals are.

Types of Analytics based on Data Science

Here are the types of analytics based on data science

  • Descriptive analytics: As the term suggests, descriptive analytics tells us about what happened based on the historical data. For example, a graphical representation of fire disasters caused by human faults.
  • Predictive analytics: This type of analytics is used to predict or forecast the most likely outcome based on the historical or real-time data analysis. For example, predicting the sales number of a product after its launch.
  • Prescriptive analytics: Majorly used to make business decisions, prescriptive analytics is a way to find the optimum course of action for possible outcomes. Prescriptive analytics function by combining both the descriptive and predictive analytics. For example, sorting the obese people for most likely possibility of heart-related conditions. 
  • Diagnostic analytics: This type of analytics answers ‘why’ something occurred. Diagnostic analytics is somewhat similar to descriptive analytics. Only it aims at describing the ‘cause’ rather than the event itself. For example, running diagnostic analytics on an e-commerce website to optimize it for the online visitors.

Now that you have understood different types of analytics, let us take a look at some Business Analytics tools.

What are some of the Business Analytics tools?

In this part, we have listed a few common Business Analytics tools for your reference. Business analytics tools are applications, systems, and dashboards that aid in business analytics projects. These include machine languages, databases, data visualization tools, ML models, computational algorithms, etc. The following list will give you some examples of BA tools. It is not a complete list, however. Take a look.

  • Python: A popular coding language to develop analytics models and code ML algorithms, Python is a popular language in BA worldwide. It is open-source and easy to learn.
  • R: A popular and robust analytics tool, R is known to handle massive datasets very well and is highly versatile. It features eight-thousand packages for easy integration with big-data platforms.
  • SAS: It is a robust and versatile tool that is popular for commercial usage. It features modular structure and is easy to use. It features various analytics products for different applications. For example SAS analytics for IoT, etc.
  • Tableau: Explicitly used for generating the interactive visualization of the data, Tableau is an easy to learn tool. It crunches the data to produce graphics and dashboards for easier interpretation and action.
  • Apache Storm: If you are supposed to handle large volumes of continuously streaming data, Apache Storm is the tool. This tool is extensively used for real-time analytics and stream data processing.

At this point, you have sufficient knowledge as to what Business analytics is all about. You are briefed on the definition, applications, varieties, and tools used for BA. Now let us explore what potential Business Analytics holds for professionals in India.

Business Analytics career scope in India

Business analytics is powering enterprises across the globe. From being employed for forecasting to making business decisions, companies are relying more and more on skilled professionals for this domain. In India, it is a rapidly growing sector and holds a lucrative career for those who want to make a career in it. By 2020, the analytics industry will double in size, currently growing at the Compound Annual Growth Rate (CAGR) of 23.8 percent. In fact, 60 percent of Indian analytics revenue comes from Business Analytics services export to the USA. It can be anticipated that India will serve as a major analytics outsourcing hub in the coming decade.  

How to get into Business Analytics?

To be a Business analyst or any other professional in the BA domain, you must have a mathematical and engineering background. Couple it with excellent analytical skills and communication skills and you become a great recipe for being a Business Analytics professional. Enroll in the suitable courses (Big data, data analytics, etc.), master BA tools (Like R, Python, SAS, etc.) and you can embark on this promising career.

What is the role of a Business analyst?

Companies usually want Business or data analysts who can derive practicable insights from the data they are using. You can expect the role as a mix of project management and strategic decision making for the organization too. Business analysts are the professionals who drive business growth and help companies deliver more value in the market. India has seen a boom in the demand for Business analytics professionals in the past two years. Surely, this role holds an excellent scope for those who are willing to be an integral part of the analytics industry. 

There you go! Our beginner’s guide to Business Analytics ends here. Did you find it useful? Write back to us and share your thoughts!