by Prescriptive Analytics: The Future of Data-Driven Decision Making
Prescriptive analytics refers to the use of data and analytics to not just understand the past or predict the future but also to prescribe actions to be taken. While descriptive and predictive analytics have become quite prevalent in helping organizations make sense of data and anticipate outcomes, prescriptive analytics takes it a step further by recommending optimal decisions and courses of action. As data volumes continue to explode and machine learning capabilities evolve rapidly, prescriptive analytics is emerging as a powerful tool for businesses seeking to gain competitive advantage through superior decision making.
What is Prescriptive Analytics?
Prescriptive analytics leverages advanced statistical techniques, machine learning and optimization algorithms to generate and compare scenarios so as to recommend actions required to achieve desired outcomes. It goes beyond merely making predictions to actually prescribing optimal strategies and policies. Prescriptive models are goal-oriented and consider a variety of constraints to recommend decisions that will deliver maximum value or minimize risk.
For example, a prescriptive model for a retailer may determine the ideal pricing, promotions and inventory levels needed across different product categories and stores to maximize holiday season sales while staying within budget. Or an insurance company’s prescriptive model could recommend customized premium rates, coverage limits and risk mitigation strategies for different customer segments based on their propensity to file claims. In essence, prescriptive analytics marries business domain expertise with data science to provide recommendations aligned with organizational objectives.
Key Capabilities
Some of the key capabilities that define prescriptive analytics include:
-Optimization – Ability to systematically evaluate numerous alternatives or scenarios in order to recommend the optimal decision or course of action. This involves optimization techniques like linear programming, integer programming etc.
-What-If Analysis – Capability to model various hypothetical scenarios and quantify their impact. For example, modeling outcomes under different economic conditions or new regulatory requirements.
-Causal Inference – Ability to infer cause-and-effect relationships from large, complex datasets in order to provide recommendations factoring in such causal effects.
-Constraint Management – Explicitly modeling and handling operational, budgetary and other constraints to ensure recommendations respect real-world limitations.
-Goal Alignment – Ensuring recommended actions directly align with and further organizational objectives as specified by decision makers and stakeholders.
-Iterative Learning – Continually learning from new data to enhance recommendations over time as underlying conditions evolve.
Benefits of Prescriptive Analytics
With its emphasis on recommending definitive, optimized actions rather than just predictions, prescriptive analytics has some clear advantages for forward-looking organizations:
Increased Profits and Productivity
Prescriptive recommendations factoring in constraints can help maximize profits, yields and overall productivity whether through better pricing strategies, prioritizing tasks, predictive maintenance or optimized supply chain management. For example, prescriptive scheduling of maintenance visits can reduce downtime.
Superior Decision Making
By surfacing causal relationships and quantifying impacts of various decisions, prescriptive models empower decision makers with the insight and foresight to consistently make high-impact choices aligned with strategic goals. This drives better strategy development as well as execution.
Continuous Improvement
As new data arrives, prescriptive models can be retrained to continuously enhance recommendations through iterative learning. Over time, this continual refinement bolsters competitive advantages through persistently optimized decision making.
Risk Mitigation
Prescriptive analytics helps identify risks proactively and recommend mitigation strategies whether risks are related to financial portfolios, operational disruptions, customer churn or other uncertainties. This enables more robust risk management practices.
Collaborative Decisions
When integrated into collaborative workflows, prescriptive recommendations present data-backed options to decision makers and stakeholders for consensus-building around the best courses of action.
Prescriptive Analytics in Action
Several innovative organizations today have implemented prescriptive analytics to drive step-change improvements:
– A major retailer uses a prescriptive model to optimize product assortments in 5000+ stores on a rolling 8-week schedule based on demand forecast, inventory levels, store capacity and more. This boosted sales by 4%.
– An agriculture technology company’s prescriptive fertilizer recommendation engine analyzes vast soil composition data to prescribe custom nutrient mixes for different fields and crops. This has increased yields by up to 30% for some farmers.
– An energy provider built a model recommending optimal investments and maintenance schedules to minimize blackouts over 10 years considering equipment aging profiles, weather patterns and other factors. It helped save $200 million in avoided outages.
– A government used spatial prescriptive analysis to allocate limited resources for COVID-19 vaccination centers based on infection risk projections, accessibility and population density to maximize coverage.
The Future of Prescriptive Analytics
Looking ahead, as prescriptive analytics matures, we can expect to see more advanced applications leveraging technologies like prescriptive search, robotic process automation with prescriptive workflows, and prescriptive clinical decision support. Integrating prescriptive recommendations seamlessly into decision making processes will be crucial to fully realizing the benefits. Overall, prescriptive analytics promises to revolutionize how organizations operate by systematically optimizing decisions to achieve objectives amid uncertainty.
Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
