DECEMBER 2020CIOAPPLICATIONS.COM8Three Types of AnalyticsThe ability to analyze data, draw business insights, predict future outcomes, and optimize decisionshas become a competitive business advantage. Analytics fieldssuch as Artificial Intelligence (AI), Machine Learning,Statistics, Data Science, Econometrics, and Operations Research are available to meet the challenges of delivering the value of Big Data to drive business successes, as introduced in Lo (2019).Analytics, also known as Data Analytics or Business Analytics, can be classified into the following three types (Figure 1): Descriptive Analytics ­ reporting what happened, often including business reports, summary statistics, and data visualization (e.g., scatterplots, histograms, box plots, line charts). Multi-dimensional tables and statistical graphics are sometimes employed to provide deeper analysis of data. As an example, reporting the recent weather pattern is a form of descriptive analytics. Predictive Analytics ­ predicting what will happen such as future customer behavior or probabilities of some events happening in the future. For example, weather forecasting is a form of predictive analytics that can be based on Statistical Modeling or Machine Learning. Prescriptive Analytics ­ withsome knowledge of the future from predictive analytics, you canevaluate potential alternative actionsto guide your decision makingand determine the best action. For example, if you know there is a high chance of snow impacting your commute tomorrow (from weather forecastingvia predictive analytics), you may evaluate VICTOR S.Y. LO, PHD A.I. AND DATA SCIENCE CENTER OF EXCELLENCE LEADER, WORKPLACE INVESTING, FIDELITY INVESTMENTSIN MY ViewThree Types of Analytics Used in Practice and Their Linksthe risk and benefit (e.g., safety and productivity) of working from home versus going to the office(using experience or historical data) and then determine the best action by balancing risk and benefit. Figure 1 Three Types of AnalyticsThe three types of analytics areclosely related and dependent on each other, as described below. From Descriptive to PredictiveLearning about what happened in the past (descriptive analytics) is often a prerequisite to predicting the future (predictive analytics).While descriptive analytics such as a summary of past prices, past sales data, revenue and profitability provide essential reports to run your business,learning about the past enables us to identify the trend and pattern, understand what happened, and possibly why something happened. Such knowledge allows data scientists to predict future outcomes (or a range of possible outcomes).As an illustration, suppose you are sellinga special kind of juice in a local shop, you may gather historical data on prices and weekly sales and display it in a graph to understand the association between price and sales (descriptive analytics), see Figure 2. To learn about how sales may vary with price (the demand curve), you may fit a regression line as in Figure 2, which is: S =1867-234P, where S Victor S.Y. Lo
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