TYPES OF BUSINESS ANALYTICS AND ITS APPLICATIONS
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The document discusses five types of business analytics: descriptive, diagnostic, predictive, prescriptive, and cognitive analytics. Each type serves a different purpose, from explaining historical data and understanding past events to predicting future outcomes and generating recommendations based on data. The document also highlights various applications and examples of each analytics type across different sectors such as healthcare, finance, and marketing.
TYPES OF BUSINESS ANALYTICS AND ITS APPLICATIONS
- 1. Types of Business Analytics Dr.A.Karthikeyan MBA.,Ph.D
- 4. 1. Descriptive Analytics To Know, What Happened? • Descriptive analytics is usually the first type of data analysis that explains what happened recently and in the past. • Both data mining and data aggregation practices are applied for this. • Descriptive analytics is defined “as a branch of data analytics that focuses on summarizing and interpreting historical data to gain insights and understand patterns, trends, and relationships within the data” Dr.A.Karthikeyan MBA.,Ph.D
- 6. Applications of Descriptive Analytics ● Tracking course enrollments, course compliance rates, ● Recording which learning resources are accessed and how often ● Summarizing the number of times a learner posts in a discussion board ● Tracking assignment and assessment grades ● Comparing pre-test and post-test assessments ● Analyzing course completion rates by learner or by course ● Collating course survey results ● Identifying length of time that learners took to complete a course Dr.A.Karthikeyan MBA.,Ph.D
- 7. 2. Diagnostic Analytics • Diagnostic analytics is a sort of business analytics that assists in understanding why things happened in the past. • You may understand the driving causes by using drill-downs, data mining, data discovery, and correlations. • This advanced analytics strategy is typically used as a step before Descriptive Analytics to determine the reasons behind certain outcomes in finance, marketing, cybersecurity, and other fields. Dr.A.Karthikeyan MBA.,Ph.D
- 8. Diagnostic Analytics Examples • Analyzing market demand • Recognizing technological difficulties • Customer behavior explanation • Enhancing the organizational culture Dr.A.Karthikeyan MBA.,Ph.D
- 9. 3. Predictive Analytics To Predict, What May Happen in the Future? Predictive analytics breaks down past information to help businesses make practical guesses about what could happen in the future. That helps analysts to: 1)Plan effectively, 2)Set practical goals, 3)Restrain expectations. DR.A.KARTHIKEYAN MBA.,PH.D
- 10. How does predictive analytics work? • Predictive analytics is based on probabilities. • Using a variety of techniques – such as data mining, statistical modelling (mathematical relationships between variables to predict outcomes) and machine learning algorithms (classification, regression and clustering techniques) • predictive analytics attempts to forecast possible future outcomes and the likelihood of those events. • To make predictions, machine learning algorithms, for example, take existing data and attempt to fill in the missing data with the best possible guesses. Dr.A.Karthikeyan MBA.,Ph.D
- 11. • A newer branch of machine learning is deep learning, which, according to Cornerstone Performance Management, mimics the construction of ‘human neural networks as layers of nodes that learn a specific process area but are networked together into an overall prediction.’ Deep learning examples include credit scoring using social and environmental analysis and sorting digital medical images such as X-rays to automate predictions for doctors to use when diagnosing patients. Dr.A.Karthikeyan MBA.,Ph.D
- 12. Advantages and Disadvantages of Predictive Analysis? • It is based on probabilities, it can never be completely accurate • Predictive analytics can also improve many areas of a business, including: • Efficiency, which could include inventory forecasting • Customer service, which can help a company gain a better understanding of who their customers are and what they want in order to tailor recommendations • Fraud detection and prevention, which can help companies identify patterns and changes • Risk reduction, which, in the finance industry, might mean improved candidate screening Dr.A.Karthikeyan MBA.,Ph.D
- 13. Predictive Analytics Examples • E-commerce – predicting customer preferences and recommending products to customers based on past purchases and search history • Sales – predicting the likelihood that customers will purchase another product or leave the store • Human resources – detecting if employees are thinking of quitting and then persuading them to stay • IT security – identifying possible security breaches that require further investigation • Healthcare – predicting staff and resource needs Dr.A.Karthikeyan MBA.,Ph.D
- 14. 4. Prescriptive Analytics • Prescriptive analytics generates recommendations for dealing with comparable future scenarios based on prior results. For the available internal and external data, it employs a variety of tools, statistics, and ML algorithms. • It tells you what might happen when it might happen, and why. Dr.A.Karthikeyan MBA.,Ph.D
- 15. Advantages and Disadvantages • Provides invaluable insights in order to make the best possible, data-based decisions to optimize business performance. • It requires large amounts of data to produce useful results, which isn’t always available. Dr.A.Karthikeyan MBA.,Ph.D
- 16. Prescriptive Analytics Examples • Monitoring changes in manufacturing pricing • Improving Asset Management • Price forecasting • GPS technology, since it provides recommended routes to get the user to their desired destination based on such things as journey time and road closures. • Testing Identification • Youtube uses prescriptive analytics to offer a customized viewing experience. Dr.A.Karthikeyan MBA.,Ph.D
- 17. Other areas of application • Oil and manufacturing – tracking fluctuating prices • Manufacturing – improving equipment management, maintenance, price modelling, production and storage • Healthcare – improving patient care and healthcare administration by evaluating things such as rates of readmission and the cost-effectiveness of procedures • Insurance – assessing risk in regard to pricing and premium information for clients • Pharmaceutical research – identifying the best testing and patient groups for clinical trials. Dr.A.Karthikeyan MBA.,Ph.D
- 18. 5. Cognitive Analytics • Cognitive Analytics, which combines Artificial Intelligence and Data Analytics, is one of the most recent types of business analytics. • It examines the available facts in the knowledge base to get the best answers to the queries given. • Cognitive analytics encompasses a wide range of analytical tools for analyzing massive data sets and tracking customer behavior patterns and developing trends. Dr.A.Karthikeyan MBA.,Ph.D