Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all?
Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions.
Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:
“Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications.”
Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take
Prescriptive analytics can be used in two ways:
■ Inform decision logic with analytics. Decision logic needs data as an input to make the decision. The veracity and timeliness of data will insure that the decision logic will operate as expected. It doesn’t matter if the decision logic is that of a person or embedded in an application — in both cases, prescriptive analytics provides the input to the process. Prescriptive analytics can be as simple as aggregate analytics about how much a customer spent on products last month or as sophisticated as a predictive model that predicts the next best offer to a customer. The decision logic may even include an optimization model to determine how much, if any, discount to offer to the customer.
■ Evolve decision logic. Decision logic must evolve to improve or maintain its effectiveness. In some cases, decision logic itself may be flawed or degrade over time. Measuring and analysing the effectiveness or ineffectiveness of enterprises decisions allows developers to refine or redo decision logic to make it even better. It can be as simple as marketing managers reviewing email conversion rates and adjusting the decision logic to target an additional audience. Alternatively, it can be as sophisticated as embedding a machine learning model in the decision logic for an email marketing campaign to automatically adjust what content is sent to target audiences.
8 Prescriptive Analytics Technologies To Create Action
Because “prescriptive analytics” is a focused moniker for data and analytics that are specifically designed and used to improve the effectiveness of decision logic there are many technologies that enterprises can use to improve decisions:
■ Descriptive analytics. Descriptive analytics enables analytics users within an enterprise to query data integrated from multiple applications to create reports, dashboards, or aggregated data that firms access through applications either directly or through APIs. Descriptive analytics can inform decision logic either with aggregate variables specific to customers or with historical data that has been integrated from multiple application systems. For example, a simple aggregate analytic of the current sales for the day can plug into pricing logic in other applications to raise or lower the price.
■ Predictive analytics. Predictive analytics is about creating predictive models — models that can predict an outcome with a significant probability of accuracy. Data scientists can use a combination of techniques including statistical algorithms, machine learning, and forecasting to create predictive models. These models can either provide a variable that feeds into the decision logic or make a probabilistic decision themselves. For example, an application could use extensive decision logic to approve a loan based on many factors, including a credit risk variable — which a predictive model can derive.
■ Streaming analytics. Streaming analytics detects events and patterns in real-time streams of data. Many enterprise decisions have to be made in an instant. Streaming analytics allows developers to define patterns in multiple streams of disparate live data sources. Streaming analytics can provide real-time insights for decision logic or it can detect patterns in data that indicate that a decision has to be made. For example, streaming analytics could detect that a shipment will be late due to a breakdown or that a customer is shopping for motorcycle safety products because she has a pattern of clicking on motorcycle safety products such as helmets.
■ Search and knowledge discovery. Information leads to insights, and insights lead to knowledge. That knowledge enables employees to become smarter about the decisions they make for the benefit of the enterprise. But developers can embed search technology in decision logic to find knowledge used to make decisions in large pools of unstructured big data. For example, search technology can bring back a list of options for an application that finds the nearest gas station or products that are most likely to pique a customer’s interest.
■ Simulation. Simulation imitates a real-world process or system over time using a computer model. Because digital simulation relies on a model of the real world, the usefulness and accuracy of simulation to improve decisions depends a lot on the fidelity of the model. Simulation has long been used in multiple industries to test new ideas or how modifications will affect an existing process or system. Firms use it in engineering to test the safety of infrastructure, as well as in manufacturing to test the safety, quality, and design of products. Simulation uses many analytical techniques. For example, financial services firms use Monte Carlo simulation to explore how unexpected events might affect the value of their investment portfolio so they can make decisions to mitigate risk.
■ Mathematical optimisation. Mathematical optimisation is the process of finding the optimal solution to a problem that has numerically expressed constraints. For example, firms use optimisation to determine the best way to allocate advertising dollars to multiple outlets or to determine the best allocation of supply chain resources. The breadth of mathematical optimisation techniques is exceptional because such techniques originate from various mathematical disciplines, including linear and nonlinear programming and integer programming.
■ Machine learning. Don’t think of machine learning as a singular approach to analysing data. There are dozens of specialized classes of machine learning algorithms that focus on specific problem domains. What makes machine learning algorithms unique is that they identify patterns or make predictions by analysing historical data that is representative of the domain. “Learning” means that the algorithms analyse sets of data to look for patterns and/or correlations that result in insights. Those insights can become deeper and more accurate as the algorithms analyze new data sets. The models created and continuously updated by machine learning can be used as input to decision logic or to improve the decision logic automatically.
■ Pragmatic AI. AI can, at present, reasonably be considered the ultimate prescriptive analytics. Enterprises can use AI to program machines to continuously learn from new information, build knowledge, and then use that knowledge to make decisions and interact with people and/or other machines. We are still a long way from pure AI like the kind you see in sci-fi movies like Ex Machina or television series such as HBO’s Westworld . However, the building blocks of AI comprise many of the other technologies we have mentioned here, including machine learning, and have value for prescriptive analytics. (see Forrester report Artificial Intelligence: What’s Possible For Enterprises Now)
Tie Analytics To Decisions
The bottomline: Enterprises must stop wasting time and money on unactionable analytics . These efforts don’t matter if the resulting analytics don’t lead to better insights and decisions that are specifically linked to measurable business outcomes.
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