Discussed about important points Four Stage Business Analytics Model

1. FOUR STAGE BUSINESS ANALYTICS
MODEL
Presented By
Anitha V

2. 4-stage model

3. What the data tells you?

4. Gartner Analytics Ascendancy Model

5. Gartner model continued..

7. Descriptive analytics
• historical data is collected
• organised and then presented in a way that is easily understood.
• focused only on what has already happened in a business
• not used to draw inferences or predictions from its findings
• uses simple maths and statistical tools, such as arithmetic, averages and per cent
changes
• Visual tools such as line graphs and pie and bar charts
• 2 ways- Data aggregation-collecting and organising
Data mining- finding patterns

8. Continued..
• Advantage: relies only on historical data and simple calculations, this
methodology can easily be applied in day-to-day operations
• doesn’t necessarily require an extensive knowledge of analytics
• businesses can relatively quickly and easily report on performance and gain
insights that can be used to make improvements.
• Disadvantage: doesn’t look beyond the surface of the data
• Example:
• Summarizing past events such as sales and operations data or marketing
campaigns
• Social media usage and engagement data such as Instagram or Facebook likes

10. Diagnostic Analysis
• Why did this happen?
• deep-dive into your data to search for valuable insights
• Diagnostic analytics takes it a step further to uncover the reasoning behind certain
results from descriptive analytics.
• Techniques used are data discovery, drill-down, data mining, and correlations.

11. Continued..
• Advanatge: Ability to draw correlation between two variables.
• Disadvantage: However, this type of analytics has a limited ability to give
actionable insights. It just provides an understanding of causal relationships and
sequences while looking backward.
• Example: In a time series data of sales, diagnostic analytics would help you
understand why the sales have decrease or increase for a specific year or so.

13. Predictive Analytics
• focused on predicting and understanding what could happen in the future.
• Analysing past data patterns and trends
• 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)
• forecasting customer behaviour and purchasing patterns to identifying sales
trends.
• Predictions can also help forecast such things as supply chain, operations and
inventory demands

14. Continued..
• 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
• Since predictive analysis is based on probabilities, it can never be completely
accurate

15. 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

16. Organization Predictive analytics model
Amazon.com Uses predictive analytics to recommend products to
their customers. It is reported that 35% of Amazon’s
sales is achieved through their recommender system
Flight Caster Predicts flight delays 6 hours before the airline’s alerts
Netflix Predicts which movie their customer is likely to watch
next (Greene, 2006). 75% of what customer watch at
Netflix is from product recommendations
Capital One Bank Predicts the most profitable customer
Google Predicted the spread of H1N1 flu using the query
terms
Farecast Developed a model to predict airfare, whether it is
likely to increase or decrease, and the amount of
increase/decrease
Hewlett Packard Developed a flight risk score for its employees to
predict who is likely to leave the company

17. PRESCRIPTIVE ANALYTICS

18. Prescriptive Analytics
• prescriptive analytics tells you what should be done
• calls businesses to action, helping executives, managers and operational
employees make the best possible decisions based on the data available to them.
• most complex stage of the business analytics process, requiring much more
specialised analytics knowledge to perform, and for this reason it is rarely used in
day-to-day business operations.
• Advantage: Prescriptive analytics, when used effectively, provides invaluable
insights in order to make the best possible, data-based decisions to optimise
business performance

19. Continued..
• Disadvantage:This methodology requires large amounts of data to produce useful
results, which isn’t always available.
• Example:
• 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.

20. Analytical Techniques Application
Decision Trees Decision trees or classification trees are usually used
for solving classification problems.
Markov Chains Markov chains are one of the key analytics tools in
marketing, finance, operations, and supply chain
management.
Random Forest Random forest is one of the popular machine
learning algorithms that uses ensemble approach to
solve the problem by generating a large number of
models.
Logistic and Multinomial Regression Logistic and multinomial logistic regression
techniques are used to find the probability of
occurrence of an event.
Regression Regression is the most frequently used predictive
analytics tool. It is a supervised learning algorithm.
In management and social sciences, almost all
hypotheses are validated using regression models
Social Media Analytics Tools Social media analytics is a collection of tools and
techniques used for analysing unstructured data
such as texts, videos, photos, and so on.

21. Reference
• “Business Analytics-The Science of Data-Driven Decision Making” by U Dinesh
Kumar
• https://www.analyticsinsight.net/four-types-of-business-analytics-to-know/
• https://www.gartner.com/imagesrv/summits/docs/na/business-
intelligence/gartners_business_analytics__219420.pdf
• https://www.slideshare.net/sasindia/keynote-thomas-
davenportanalyticsatwork?from_m_app=android
• https://studyonline.unsw.edu.au/blog/descriptive-predictive-prescriptive-analytics-
what-are-
differences?utm_expid=.iRGn1tkHQz6MtwChi0TqLw.1&utm_referrer=https%3A
%2F%2Fwww.google.com%2F

22. Thank You