Prescriptive Analytics: Going Beyond Prediction to Prescribe Optimized Actions
With businesses accumulating vast amounts of data every day, analytics has become crucial for making informed decisions. While descriptive analytics help understand what happened in the past and diagnostic analytics determine why it happened, prescriptive analytics take analytics a step further by advising the best course of action going forward. Prescriptive analytics uses mathematical modeling, machine learning, and AI techniques to forecast various scenarios and recommend optimal decisions and actions. Let's take a deeper look at this advanced form of analytics.
What is Prescriptive Analytics?
Prescriptive analytics takes concepts from descriptive, diagnostic, and predictive analytics to develop actionable recommendations. Unlike predictive analytics that focuses on what may happen, prescriptive analytics focuses on what should happen. It analyzes past performance, current and potential future conditions, and multiple decision outcomes. The objective is to prescribe the best path to achieve defined business goals by determining the likely result of various choices and recommending optimal actions.
Prescriptive models leverage sophisticated algorithms, machine learning, and AI to help leaders anticipate the consequences of millions of potential decisions or scenarios and prescribe courses of action to mitigate risks, improve outcomes, and optimize performance. It provides answers to "what if" questions by predicting the outcome of alternative strategies and recommending the ideal strategy or plan to achieve targeted objectives in the most effective way.
Benefits of Prescriptive Analytics
Prescriptive analytics can help organizations in many ways:
Optimize operations - Prescriptive models help optimize various operational aspects like supply chain management, workforce management, pricing strategies, marketing campaigns etc. for better efficiency and profitability.
Improve decision making - By presenting analysis of potential scenarios and recommendation for actions, it improves strategic and tactical decision making abilities of leaders and managers.
Mitigate risks - Prescriptive insights help reduce exposure to risks by prescribing preventive actions. It can recommend contingency plans in case of possible threats.
Increase revenues and cut costs - Optimization of operations, decisions and risk mitigation can directly impact top and bottom lines favorably through increased revenues and reduced costs.
Continuous improvement - Regular analysis of actual outcomes helps refine prescriptive models continuously for more accurate recommendations over time.
So in summary, prescriptive analytics enables organizations to determine the very best course of action based on modeling of multiple variables and their interdependencies. It transforms decision making from guesswork to an objective, data-driven process.
Applications of Prescriptive Analytics
Prescriptive analytics finds applications in various domains to optimize decisions:
Supply chain management - Recommend ideal inventory levels, most cost-effective production/procurement strategies, optimal fulfillment plans etc.
Workforce planning - Prescribe optimal staffing levels and skills requirements, training needs, incentive strategies for higher productivity and retention.
Marketing campaigns - Suggest personalized promotion strategies, media mixes, pricing plans etc. for maximizing campaign effectiveness.
Preventive healthcare - Recommend personalized treatments, lifestyle modifications, screening schedules etc. to improve health outcomes.
Cybersecurity - Prescribe preventive measures, threat detection strategies, security policy recommendations to reduce cyber risks proactively.
Fraud detection - Analyze past fraud cases and recommend best practices, resourcing, technologies etc. to minimize future fraud losses.
Oil & gas exploration - Prescribe optimized exploration/drilling plans to discover reserves economically with lower risks.
Smart cities - Suggest infra upgrades, traffic management plans, resource allocation strategies etc. to enhance quality of life and experiences.
Finance - Recommend portfolio allocations, lending terms, risk hedging strategies, budgeting plans etc. for robust financial performance.
The possibilities are immense across sectors like transportation, manufacturing, utilities, public administration and more. Prescriptive analytics is finding increasing usage as organizations embrace data-driven decision making for sustainable competitive advantage.
Challenges in Implementing Prescriptive Analytics
While prescriptive analytics holds immense promise, developing and implementing such advanced analytical models also poses some challenges:
Data quality - Garbage in garbage out. Models require clean, complete and accurate data to prescribe reliably.
Model complexity - Developing precise prescriptive models demands sophisticated algorithms and quantitative skills that are scarce.
Business understanding - Without deep domain and business context, models may offer solutions inconsistent with ground realities.
Scalability - Large scale prescriptive solutions require big data processing capabilities and significant computational resources.
Explainability - Complex models must also explain recommended actions transparently to gain leadership and user confidence.
Regulations - Predictive solutions in domains like healthcare and finance need to comply with strict privacy, security and regulatory norms.
Adoption - Change management is required to drive organization-wide usage and benefits realization from prescriptive decisions.
While data and technical challenges are being addressed with continued technological and analytical progress, change management remains key to successfully embed this advanced analytics in decision making processes. Organizations must focus on establishing a strong data culture and emphasize prescriptive insights in governance, strategy and operations.
Prescriptive analytics has tremendous potential to elevate organizations to new levels of productivity, profitability and competitive advantage through optimized, data-driven decision making. While implementation hurdles exist, businesses that overcome challenges to establish prescriptive capabilities will be best equipped to thrive in the digital future. With continued innovation, prescriptive analytics is set to transform how strategic and operational decisions are made across sectors for growth and value creation.
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A single source of truth is a central and controlled source of data from which the entire organization can draw.
It is the master data.
When you lack such data and employee can pull down apparently the same metrics from disperse systems, unavoidably those systems will produce different numbers.
You get into a he-said-she-said situation, each player drawing and defending their position with their version of the ‘truth’.
Or even worse, some teams may naively leverage stale, low-quality, or otherwise incorrect data/metrics and make bad decisions, when they could have leveraged a better source.
