# Predictive Data Analytics Task

A Predictive Data Analytics Task is a data analytics task that is a predictive task.

**Context:**- It can (often) produce a Predictive Model Structure.
- It can (often) be preceded by a Historical Data Analytics Task.
- It can range from being a Heuristic Predictive Analytics Task to being an Data-Driven Predictive Analytics Task.
- It can involve Prediction Metrics Design, Predictive Model Creation, Predictive Model Evaluation, ...
- It can be solved by a Predictive Analytics System (such as a predictive analytics service).

**Example(s):**- a Prescriptive Analytics Task, a Statistical Modeling Task, a Machine Learning Task.
- a House Sales Predictive Analytics Task, such as with a Boston real-estate dataset.
- a bank manager wants to identify the most profitable customers or predict the chances that a loan applicant will default, or alert a credit card customer to a potential fraudulent charge.
- What will happen if demand falls by 10 percent or if supplier prices go up five percent?
- What do we expect to pay for fuel over the next several months?
- What is the risk of losing money in a new business venture?

**Counter-Example(s):****See:**Prospective Cohort Study, Historical Data Analytics, Data Mining, Pattern Detection, Decision Making, Actuarial Science, Business Analytics, Business Rules.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/predictive_analytics Retrieved:2015-6-28.
**Predictive analytics**encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.

One of the most well known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

### 2012

- (Evans & Lindner, 2012) ⇒ James R. Evans, and Carl H. Lindner. (2012). “Business Analytics: The Next Frontier for Decision Sciences.” In: Decision Line, 43(2).
- QUOTE: Predictive analytics analyze past performance in an effort to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. For example, a marketer might wish to predict the response of different customer segments to an advertising campaign, a commodities trader might wish to predict short-term movements in commodities prices, or a skiwear manufacturer might want to predict next season's demand for skiwear of a specific color and size. Predictive analytics can predict risk and finds relationships in data not readily apparent with traditional analyses. Using advanced techniques, predictive analytics can help to detect hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and detect trends. For instance, a bank manager might want to identify the most profitable customers or predict the chances that a loan applicant will default, or alert a credit card customer to a potential fraudulent charge. Predictive analytics helps to answer questions such as: What will happen if demand falls by 10 percent or if supplier prices go up five percent? What do we expect to pay for fuel over the next several months? What is the risk of losing money in a new business venture?

### 2011

- (Shmueli & Koppius, 2011) ⇒ Galit Shmueli, and Otto R. Koppius. (2011). “Predictive Analytics in Information Systems Research.” In: MIS Quarterly Journal, 35(3).
- QUOTE: This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research.