A great way to explain the analytics maturity and value curve is to use the idea of descriptive, predictive and prescriptive analytics. Analytics consultants and thought leaders love to frame the field in these terms, if for no other reason than to give a broad, important topic some structure. Briefly, let me define these concepts:
- Descriptive analytics is backward-facing; focuses on telling users what happened; and answers questions such as how many, when and where. Traditional business intelligence tools such as IBM Cognos BI excel in this area.
- Predictive analytics is about anticipating what comes next. By leveraging historical data and predictive algorithms, users can create models to predict the likelihood of future outcomes. This opens up huge new ways of operating for businesses by giving them answers to questions such as who will buy this product, who will default on a loan, and which message would be most effective for this consumer. The SPSS Modeler tool is most effective here.
- Prescriptive analytics takes predictive one step further by suggesting the best action to take within a business context to give a business the best chance of achieving its goals. This takes the form of decisions: Which marketing campaign should I sent to a customer? How much inventory should I stock to prepare for expected demand?
A lot has been written about these degrees of maturity and the business value they can create. Regular readers of this blog are familiar with descriptive/BI and predictive modeling. But how do you move from just predictive into the realm of prescriptive analytics? What’s the secret to success at that level? That’s where IBM Analytical Decision Management (ADM) comes into play.
IBM Analytical Decision Management is IBM’s answer to this transition. ADM is available through IBM SPSS Modeler Gold and it combines three core capabilities: the ability to manage business rules, predictive models and optimization.
Let’s take a look at each of these and see how they work.
Business rules and predictive models
Business rules and predictive models are very different things. The former is a seemingly subjective, arbitrary condition that must be met. Rules can be driven by corporate fiat, industry tradition or regulatory mandate. Predictive models are the height of objective reasoning, indicating a probability of a future outcome based on historical data.
The reality is that businesses need both—and they should work together but be kept separate. An insurance company can build a predictive model to get the probability of someone making a claim before the policy is issued. But that predictive model won’t (and shouldn’t) consider the fact that the company’s reinsurer has placed new risk thresholds on their portfolio which may preclude some policies from being profitable. By combining these two factors, the insurance company can make a repeatable, justifiable and profitable decision.
This is the general term for the prioritization or selection of actions to meet a mathematical outcome. Optimization is a cornerstone of management science and a vital function of modern industry. It can help a company determine the best way to reroute planes after a storm, decide how to pack merchandise on a truck and much more.
IBM Analytical Decision Management lets users take advantage of an optimization equation that essentially defines the outcome they are trying to achieve. This might be the steps needed to maximize revenue or to boost a marketing campaign’s profitability. And importantly, ADM lets you define these terms using a mixture of predefined rules and predictions. This way, your optimization can be done dynamically.
Climbing the continuum
When combined with predictive analytics, optimization is ultimately what moves a company along the maturity continuum—from predictive into prescriptive. It is this mixture that moves a user from making a prediction toward taking action on a prediction.
ADM automates decisions throughout the organization by selecting an ideal action to be taken. This decision is then consumed at many levels throughout the organization—by retail sales employees, insurance agents, call center staff and more. All these groups can benefit from having their normal work supplemented and improved by an infusion of analytics.
Traditional BI and even predictive analytics produce an output that must be studied and understood for insight to be drawn and for the proper action to be taken. The complexity and domain knowledge required in this step ultimately limits the end “consumer” of insight. ADM democratizes analytics, making it easier for regular employees to generate consumer analytics and benefit from insight drawn from advanced analytics. And that’s the real power of decisions.