Predictive Analytics Strategy
Predictive analytics is now sexy in the business and policy-making worlds. While predictive analytics has many benefits and can help organizations gain competitive advantage, the hype may be causing false expectations. There is a mistaken belief that all you need is new data crunching technology, big data and some business analysts to find meaning in the data - and Voilà! - you can make predictions. This is a recipe for disaster.
An organization needs an information management strategy (including both internal and external data as well as both structured and unstructured data), a technology strategy and a data science strategy. The organization must invest in a team of data scientists to use sophisticated analytical techniques, machine learning and statistical algorithms for finding, accessing and crunching relevant data. The data science and business analytics team works with business leaders to design a strategy for using predictive information.
Organizations can hire data scientists in-house (difficult considering a lack of skilled business data science practitioners) or professional data scientists can be engaged on a time or fixed fee basis and be responsible for deploying, managing and scaling the data science and predictive analytics projects. A mixture of both internal and external data scientists may be optimal for ensuring objectivity and creativity. Hiring external data scientists offers the ability to quickly form a data science team and scale-up big data projects without the upfront CapEx of hiring data scientists in-house. Organizations can also scale down equally quickly and pay only for the data science services they use.
There are three types of data analysis:
- Predictive (forecasting)
- Descriptive (business intelligence and data mining)
- Prescriptive (optimization and simulation)
While descriptive analytics (modern data warehouse / business intelligence systems) looks at data and analyzes past events for insight as to how to approach the future, predictive analytics (new data analytical platforms) uses data to determine the probable future outcome of an event or a likelihood of a situation occurring. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future 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.
Three basic cornerstones of predictive analytics are:
- Predictive modeling
- Decision Analysis and Optimization
- Transaction Profiling
Predictive analytics can help:
- Predict market trends
- Predict customer needs
- Create customized offers for each segment and channel
- Predict changes in demand and supply across the entire supply chain
- Hire the right people
- Manage the workforce
- Predict who is likely to quit their job
- Predict how market-price volatility will impact your production plans
- Manage risk
An example of using predictive analytics is optimizing customer relationship management systems. They can help enable an organization to analyze all customer data - exposing patterns that predict customer behavior. Another example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. Credit scoring uses predictive analytics to process a customer's credit history, loan application and customer data to rank-order individuals by their likelihood of making future credit payments on time. An example is the FICO score.
Other applications of predictive analytics includes: decision support systems; collection analytics; cross-selling; customer retention; direct marketing; fraud detection; portfolio design and management; product design; economic forecasts; risk management; underwriting and others.
Analytical techniques include: regression techniques; linear regression models; discrete choice models; logistic regressions; multinomial logistic regressions; probit regressions; time series models; survival or duration analysis; classification and regression trees; and multivariate adaptive regression splines.
Machine learning techniques include: neural networks; radial basis functions; support vector machines; naïve bayes models; k-nearest neighbour algorithms; and geospatial predictive modeling.
Most garden variety business analysts do not have the training or experience to apply these analytical and machine learning techniques or design and execute customized algorithms to find the valuable, actionable insights from the raw data. But most data scientists do have the training and experience to apply all or some of these sophisticated techniques. As a result, it is prudent to distinguish between data scientists and business analysts and create a team assigning proper roles to each to optimize the predictive analytics strategy.
In addition, for an organization to become data-driven and optimize predictive analytics for better decision making, a cultural "mind-set" shift needs to occur between using descriptive analytics with the current business intelligence systems and learning to think prescriptively using data science and prescriptive analytics. Prescriptive analytics automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions.
Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options.
Prescriptive analytics synergistically combines data, business rules, and mathematical models. The data inputs to prescriptive analytics may come from multiple sources, internal (inside the organization) and external (social media and other data sets). The data may be structured (transactional, numerical and categorical) as well as unstructured (text, images, audio and video data). Business rules define the business process and include constraints, preferences, policies, best practices, and boundaries. Mathematical models are techniques derived from mathematical sciences and related disciplines including applied statistics, machine learning, operations research, and natural language processing.