November 05, 2019
November 05, 2019
Every hour, every minute, every second, organizations capture and consume massive amounts of data used to make strategic and tactical decisions. Yet, as the volume of data grows, few finance teams are using that data to provide business partners with actionable insights. Those that do create significant value for the business by using data science skills to predict a series of outcomes over time and use those predictions to identify the best course of action.
“Data science and machine learning (ML) provide the means for organizations to predict a future that looks uncertain,” says Carlie J. Idoine, Senior Director Analyst, Gartner. “They provide prediction insights to drive action, but the appropriate actions are neither obvious nor easy to sort out.”
Business rules can be complicated given competing resources, numerous constraints and continually changing variables. This makes it hard to evaluate all the alternatives and quickly decide on the optimal actions for achieving business objectives using an easily repeatable approach. Finance leaders should bring together two distinct but complementary data science and ML techniques to drive high-impact decisions.
Read more:Reduce the Staggering Costs of Poor Operational Decisions
Combining predictive and prescriptive capabilities is a key first step in solving business problems.
Predictive analytics addresses the question of “What is likely to happen?” It relies on techniques such as predictive modeling, regression analysis, forecasting, multivariate statistics and pattern matching.
Prescriptive analytics addresses the questions of “What should be done?” and “What can we do to make “X” happen?” This technique includes graph analysis, simulation, complex-event processing, recommendation engines, heuristics and, increasingly, neural networks and machine learning.
Bringing together forecasts (predictive) with action (prescriptive) enables an organization to explore how changes to different variables are likely to affect the outcomes or alter the relative trade-off. “This combined approach gets to the heart of the task of adding business value — of proactively making decisions that drive action and influence the future course of an organization,” says Idoine.
For some industries — like manufacturing, transportation and financial services — predictive and prescriptive modeling have long been part of their processes. However, even with the known power of these capabilities, they are often isolated and trapped in specific, dedicated departments instead of being used in tandem with others and leveraged across the entire organization.
In the age of digital business, flexibility, agility, collaboration and responsiveness are key. In effect, the ability to predict outcomes, quickly assess innumerable alternatives and take action is something that can no longer be reserved for the few.
Learn more: The New Finance Mandate
One approach is to use a predefined framework or rules for choosing between alternatives. The other approach is to use an outcome-driven, constraint-based evaluation of an interdependent set of options or optimizations. The resulting actions can be recommended to human decision makers, used for decision support or fully automated as part of a decision management system
The nature of the problem guides CFOs’ choice of whether to use prediction, forecasting or simulation for the predictive analysis component. The complexity of the solution guides their choice of whether to use rules or optimization for the prescriptive analysis component.
To jump into predictive and prescriptive analytics, CFOs need to develop their team members’ skills to improve decision making. Gartner has identified three ways finance leaders can develop and expand their teams’ in-house skills for predictive and prescriptive analytics.
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Recommended resources for Gartner clients*:
Combine Predictive and Prescriptive Techniques to Solve Business Problems
*Note that some documents may not be available to all Gartner clients.