Business analytics involves something from simple reports to the most advanced optimization techniques, such as methods for finding the best course of action. This is generally comprised in three broad categories : descriptive analytics, predictive analytics, and prescriptive analytics.
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Descriptive analytics encompasses the set of techniques that describes what has happened in the past. Examples are data queries, reports, descriptive statistics, data visualization including data dashboards, some data-mining techniques, and basic what-if spreadsheet models.
A data query is a request for information with certain characteristics from a database. For example, a query to a Airline’s database might be for all records of flights to a particular designation during the month of March. This query provides descriptive information about these flights: the number of Passengers, the date each trip, and so on. A report summarizing relevant historical information for management might be conveyed by the use of descriptive statistics such as means, measures of variation, etc. and data visualization tools such as tables, charts, and maps. These simple descriptive statistics and data visualization techniques can be used to find patterns or relationships in a large database.
Data dashboards are collections of tables, charts, maps, and summary statistics that are updated as new data become available. Dashboards are used to help management monitor specific aspects of the company’s performance related to their decision-making responsibilities. For corporate-level managers, daily data dashboards might summarize sales by region, current inventory levels, and other company-wide metrics; front-line managers may view dashboards that contain metrics related to staffing levels, local inventory levels, and short-term sales forecasts.
Predictive analytics consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another. For example, past data on product sales may be used to construct a mathematical model to predict future sales, which can factor in the product’s growth trajectory and seasonality based on past patterns. A packaged food manufacturer may use point-of-sale scanner data from retail outlets to help in estimating the lift in unit sales due to coupons or sales events. Survey data and past purchase behavior may be used to help predict the market share of a new product. All of these are applications of predictive analytics.
Linear regression, time series analysis, some data-mining techniques, and simulation, often referred to as risk analysis, all fall under the banner of predictive analytics. We discuss all of these techniques in greater detail later in this text. Data mining, techniques used to find patterns or relationships among elements of the data in a large database, is often used in predictive analytics. For example, a large grocery store chain might be interested in developing a new targeted marketing campaign that offers a discount coupon on potato chips. By studying historical point-of-sale data, the store may be able to use data mining to predict which customers are the most likely to respond to an offer on discounted chips by purchasing higher-margin items such as beer or soft drinks in addition to the chips, thus increasing the store’s overall revenue. Simulation involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision. For example, banks often use simulation to model investment and default risk in order to stress test financial models. Simulation is also often used in the pharmaceutical industry to assess the risk of introducing a new drug.
Prescriptive analytics differ from descriptive or predictive analytics in that prescriptive analytics indicate a best course of action to take; that is, the output of a prescriptive model is a best decision. The airline industry’s use of revenue management is an example of a prescriptive analytics. Airlines use past purchasing data as inputs into a model that recommends the best pricing strategy across all flights for maximizing revenue. Other examples of prescriptive analytics are portfolio models in finance, supply network design models in operations, and price markdown models in retailing.
Another type of modeling in the prescriptive analytics category is simulation optimization, which combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings. Finally, the techniques of decision analysis can be used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Decision analysis also employs utility theory, which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors.