Advanced Analytics

Advanced analytics:

• Predictive modeling: using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. Continuously revise and validate the changes and change the prediction.

The top five predictive analytics models are:

• a. Classification model: The simplest model, it categorizes data for simple and direct query response. An example use case would be to answer the question “Is this a fraudulent transaction?”

• b. Clustering model: This model nests data together by common attributes. It works by grouping things or people with shared characteristics or behaviors and plans strategies for each group at a larger scale. An example is in determining credit risk for a loan applicant based on what other people in the same or a similar situation did in the past.

• c. Forecast model: Very popular model, and it works on anything with a numerical value based on learning from historical data. For example, in answering how much lettuce a restaurant should order next week or how many calls a customer support agent should be able to handle per day or week, the system looks back to historical data.

• d. Outliers model: This model works by analyzing abnormal or outlying data points. For example, a bank might use an outlier model to identify fraud by asking whether a transaction is outside of the customer’s normal buying habits or whether an expense in a given category is normal or not. For example, a $1,000 credit card charge for a washer and dryer in the cardholder’s preferred big box store would not be alarming, but $1,000 spent on designer clothing in a location where the customer has never charged other items might be indicative of a breached account.

• e. Time series model: This model evaluates a sequence of data points based on time. For example, the number of stroke patients admitted to the hospital in the last four months is used to predict how many patients the hospital might expect to admit next week, next month or the rest of the year. A single metric measured and compared over time is thus more meaningful than a simple average.

• Augmented analytics: Is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.

• 3. Prescriptive analytics: Combine business intelligence and data analytics experience to evolve data into a resource for prescriptive insights that help make effective and timely decisions and helps draw specific recommendations.

• POC for data analytics in ETL done in Azure

• POC for data analytics using ML require more research.