Is Business Analytics required

Why Business Analytics

Business Analytics is required to cover the road from past actions to future decisions. It is the superlative amalgamation of technology, processes, skills, and thinking and creativity for non-stop performance evaluation of past business executions to gain knowledge and derive strategy and planning for future business executions. Data Collection, Data Cleaning/Sorting, Data Transformation, Data visualization, and underrating the business insights by studying and applying statistical models is called business Analytics by definition, but for common person’s point of view, the knowledge required to understand the business problem and what it takes to solve it and business productivity, revenue and performance. When a business analyst derives business strategy not only just on the domain and business expertise but also derive using data to support his thinking and proportion, Business Analytics comes into play. Business Analytics combines the field of Computer Science, Business Management, and Strategy. It bridges the gap between technology and Management. Three W’s Why it happened, What is going to happen, and What to do are questions answered through Business Analytics. Clear & timely communication along with the ability to solve the problem is a must skill for a business Analyst to transform the insights from data to information needed for executives.

Business Analytics Vs Business Intelligence

Business Intelligence can be considered as a subset in the Business Analytics periphery. BI answers what whereas Business Analytics should answer why and when in future it can reoccur.

The business analyst performs analysis on the historical and current weakness of an organization, dwell into data sea, transform the existing processes, and strategize for the future plan and implement them. Business intelligence focuses on the present and Business Analytics focuses on future plans and paths. BI prioritizes Descriptive Analytics where BA prioritizes Predictive Analytics. You can anticipate what’s going on and make your path accordingly with BA practices.

Business Analytics Vs Data Analytics

It’s more confusing to make difference between Business Analytics and Data Analytics. They both deal with the data in the same way and with the same set of tools and processes.

Business Analytics is more towards the business value/implications of the data. Effectively BA involves analyzing data and use that data to create an important strategy to improve business throughput. Business Analyst uses the data insights to create real-world business solutions.

Data Analytics mainly deals with analyzing data and creating reports and visualizations to explore hidden insights. It focuses on computational tools, Programs to deal with data and create the hypothesis and pass on the details to business owners.

Examples of Business Analytics

Consider a cricket match where team A won the match against team B. Being a coach of team A, you want to review the match. You want to do this so that you can fix the mistakes in the match and keep on winning.

So BI will give you the stats of the match. BI will let you know that team A scored double the number runs in over 25-40 as compared to team B. Also most of the runs have come on the leg side.

BA will give us more details and tell the ‘Why’. Why team A got more runs in over 25-40 and why the runs mainly come from the leg side. 

Reasons are: 

  1. Team B’s main spinner was injured and they were playing with their part-time spinner.
  2. Team A got most of their runs on the leg side as 3 out of 4 main bowlers in team B are a left-handed bowler and they were missing their main boundary fielder as well.

These insights are important as team A can strategize themselves for future games and change the game plan accordingly. So Business Intelligence in the right direction leads to the right Business Analytics.

Business Analytics in Industry: Applications

  1. Uber – A Ride-Hailing Giant implemented COTA (Customer Obsession Ticket Assistant) – a tool to help agents to improve Ticket support and response, built using ML(Machine Learning) and NLP(Natural Language Processing). ( https://online.hbs.edu/blog/post/business-analytics-examples)
  1. Casinos and Betting Companies are using Business Analytics to analyze customer spending patterns and identify the customers who are more profitable to them. They can offer more incentives to big spenders to keep them motivated. ( https://www.mastersindatascience.org/resources/what-is-business-analytics/)
  1. One of the major Indian banks – Axis Bank, implemented RPA – Robotics process automation and other Machine learning and Deep Learning techniques to understand Customer Behaviour and created an NBA engine to reduce churn and segment the customers and offered more customized products to suit customer’s need. ( https://www.microstrategy.com/us/resources/introductory-guides/business-analytics-everything-you-need-to-know)
  1. UA – Under Armour built an app using the IBM Watson Cognitive Computing platform. The “Cognitive Coaching System” is helped to serve as a personal health assistant by real-time data capture for the users and prompting users to take appropriate actions based on data-based analytics using sensors and user inputs for sleep, activities, and food. (https://emerj.com/ai-sector-overviews/5-business-intelligence-analytics-case-studies-across-industry/)                   
  1. Coca-Cola (Soft drink Giant) used Imaged based technology and predictive analysis to generate actionable reports within minutes to sales reps and providing more detailed online assessments to management. Store management improved in many countries for companies. ( https://emerj.com/ai-sector-overviews/5-business-intelligence-analytics-case-studies-across-industry/)

Core Components of Business Analytics

  1. Data Acquisition: Data is collected and stored at a single place and cleaned for duplication, inaccuracy, and incompleteness. Irrespective of the fact whether data is captured manually or digitally, or data is real-time or batched one, transactional or static data, it needs to be a single source of truth.
  1. Data Mining –  Next step is to deep dive into the data and extracts the unknown patterns and trends. Various Statistical models and other mining techniques are used to mine the data.
  1. Classification – Used when variables to be classified are categorical (two or more classes). This is used to sort or group the data.
  2. Regression – Used when the variables to be predicted is the numerical and historical trend of the variables is used.
  3. Clustering – Used when the classes of the variables are known. The data is not labeled.
  1. Forecasting – Repetitive patterns in the human trait. A person tends to follow the trend. This repetitive behavior can be analyzed and transformed into a forecasting trend. E.g. retail sales for holiday season or occasion, Internet search spikes around any event, etc.
  1. Efficiency and Optimization – Business analytics enable effective and efficient processes and implementation. It helps in optimizing the operations and overall workflow. When to increase the production, when to change the product type etc. can be identified and help to efficiently use the resources and power.
  1. Predictive Analysis – To predict a future event that can impact the business throughput is very much possible using BA. Predicting the failure in the machinery, predicting the customer churn has been successfully predicted by companies and corrective actions were taken. Predictive Scoring Models can be created to help businesses plan their future plans and strategies.
  1. Plots and Visualisations – Pictures and Graphics infer more and quick information to the audience. Data Visualisation elements help organizations to use their data to inform and derive new goals more effectively. Exploratory Data analysis builds the base of any further analysis and in turn to the business goal.
  1. Big data and Unstructured Data Analytics – Nowadays when data from every single event is captured including speech, Images, logs. It’s very important to integrate these unstructured data sources with traditional & structured data and bring up more robust and comprehensive insights. Processing and analyzing huge amounts of data in an unstructured format require non-traditional and complex techniques. New technological improvements in Data storage and processing power have enabled organizations to leverage the power of business analytics using Bigdata techniques.
  1. Correlation, Association, and Reference Identification – Customer’s pattern of doing events help to relate events in a certain sequence. Like buying bread with eggs or conditioner with shampoo etc. Sequencing like buying a flight ticket followed by hotel booking, buying a car followed by car insurance, etc. These associated events can be analyzed in more detail which can help related businesses to grow together.

How Organisations can leverage Business Analytics Capabilities:

  • Organizations have huge capacity and requirement to use analytics to improve their business. Companies using Business Analytics have more chances to take better and fast decisions. (published on BetterBuys).
  • In the next 4-5 years, the global Analytics market would be around 150 Billion.
  • Organizations will be creators and managers of the world’s 60% data in the next 5 years.
  • Most of the businesses believe that big data, Analytics, and Real-Time data capture and analysis will change the way business will perform.

It is clear and proven that Business Analytics and tools are very important for any business to grow in the future. Let’s see how businesses can leverage the power of Analytics in their processes and workflows:

  1. Increasing Sales
  2. Improving the Business Processes
  3. Increasing efficiency in the departments
  4. Reducing redundancy
  5. Reducing the Cost      
  6. Improving Resource Management
  7. Integrating the Organisation systems
  8. Improving Customer Experience and hence customer loyalty
  9. Better and Faster Decision Making
  10. Dealing with environmental factors in a better way.

How Challenging is for Organisations to adapt Business Analytics:

Commitment from Management: Even though Analytics packages are considered automated products and supposed to be implemented easily, the ROI of these products is not quick and can be discouraging. Analytical models take time to train and predict the outcomes as expected, so, patience and dedication are required while initiating any analytical process. It’s often become challenging for a business to keep engaged with the process and wait for outcomes to flourish. Business Analysts need to maintain an analytical environment alive and happening through the investment period to keep the business owners engaged throughout.

  1. Collaboration between Departments:

The intersection area between different functional areas of the organization has to be broad enough for deriving analytical initiatives. Business Analyst has to play a very crucial role in this. Business and IT has to be in sync. Lack of collaboration can lead to misinformation, in-synchronized output, and possible break to the overall initiative.

  1. Data Quality:

The type of data being identified for analysis would derive the overall output of the Business Analytics initiative. It’s very important to identify the correct and effective data points. Organisation’s Internal structure and data sources need to be robust enough to support the quality of data.

  1. Technology Overflow:

With continuously changing technology so rapidly, it’s very difficult to keep updated and supportive of the changes. The organization keeps changing its policies needs to support the technology changes and adapt them as quickly as possible without disrupting the Analytical initiatives.

Business Analytics offers multi-suites of roles:

  1. Program/Domain Manager: The program manager is responsible for developing the roadmap of the Business Analytics program. He is responsible for the overall business value of the program and supports IT for that matter.
  1. BI Analyst: BI Analyst is responsible for capturing, integrating, and analyzing the data to bring out insights about the domain of the program. This is very important in terms of keeping management up to date with business performance and help them to strategize.
  1. Bigdata Specialist: Big data specialists help to gaze the benefits of the huge volume of data (unstructured and structured) using new technologies and helps to solve the challenges presented by the new digital landscape.
  1. Data Scientist: Data scientists are expected to collect, organize, analyze data, and build mathematical/ statistical models and derive predictions, recommendations. Building Data Story to support Business Hypothesis using tools, reports, and visualizations is an important aspect of their role.
  1. Management Consultant: Management consultants play an important role to help various business departments to identify the business issues and suggest solutions to overcome them. They help to improve efficiency and reduce overheads and bring collaboration among the departments.

Top 7 Business Analytics Tools in Market:

  1. Excel: Sounds like very trivial but Microsoft excel is one of the best and first to go tool for any analytics requirement. It is capable of performing simple to complex data transformations, manipulations, and visualizations. Excel comes very handy when it comes to starting any analysis.
  2. R: R as a programming language is very powerful to perform many statistical computations. It is free and supported by the R Foundation. It is one of the very easy open-source programming languages which can be used to run Regression, classification, statistical analysis, and other machine learning algorithms with very good performance.
  3. Python: One of the most famous and effective language of current time having a lot of libraries and packages to handle many complex algorithms and computations, visualizations, data manipulation, Web data scrapping, and much more. Huge community support makes this language as one of the highly recommended tools for business analytics.
  4. Tableau: Having the capability of connecting to any data source, Tableau makes data visualization playground very easy and effective for anyone to use and understand the data very quickly. Its non-programming GUI interface makes it one of the first choices for BAs to start the analysis.
  5. Qlikview: Qlik is a BI product for data discovery and analytics applications. It helps to uncover insights from the data source and bring the output in the form of interactive dashboards which is very helpful for MI and reporting.
  6. KNIME: It is a free open-source drag-and-drop platform for data analytics, data integration, and data flow, Data modeling. KNIME has capability to integrate components of ML and DL along with data mining through data workflows.
  7. RapidMiner: It is one of the highly used data science platforms to perform a full data science cycle. It helps to perform data capture, ML, DL, text mining, and Predictive analytics very rapidly and effectively.

Business Analytics Courses and Certifications:

  1. Coursera: Business Analytics Specialization: This course introduces Big data Analytics for business professionals including beginners. The program describes how BAs interpret, analyze, predict, and communicate business decisions in the specific areas of marketing, human resources, finance, and operations. In labs and implementation part, learner will use his skills to analyze a real-world problem data set and come up with solution and business strategy recommendations.
  • Udemy: The Statistics for Business Analytics A–Z course is designed to get a good grasp of subject areas of data science and business Analysis with emphasis on real-world problems and their solutions. It covers concepts of distributions, central limit theorem, and statistics. If someone wants to master Statistics for Business Analytics, this course is perfect fit. 
  • edX: Master program in Business Analytics: Columbia’s Micro Master program in Business Analytics helps learners to gain skills, insights, and understanding to improve business performance using data and with techniques like and qualitative -quantitative analysis, statistics, exploratory and predictive data analysis to bring actionable decisions. With multiple subsections in the course, this whole education program emphasize on the usage of statistical analysis, computing tools, and mathematical models to predict the outcomes of various business decisions and identify the best implementation.
  • Udacity: Business Analytics Nanodegree specialization course learning path covers the data skills that apply across several functions and industries. Main focus of this course is application of data analysis techniques and methods to capture and integrate data and then transform and analyse that data, interpret and model business scenarios, and communicate findings with SQL, Excel, and Tableau.

What’s Next in Business Analytics: 

Although Business Analytics is an Umbrella term used across the industry domains for their analytics and Data Science path, this subject areas in itself cover the major topics with vast explorations. In my next few writings, I will cover the details of topics like Big data, Artificial Intelligence, Deep Learning, Neural networks, IoT with emphasis on their practical implementation, and Usage. Data-driven Decision making is the core concept behind all these terms. How we understand our data and brings value out of it, what we aspire when we use the term ‘BUSINESS ANALYTICS’

Leave a comment