Prescriptive Analytics: Transforming Decision-Making in the Modern Era

Organisations today are continuously in search of ways to make raw data actionable insights in today’s data-driven world. Although descriptive and predictive analytics help explain past trends and predict future trends, prescriptive analytics takes the additional step of suggesting concrete action items. It is not only about what has happened or what might happen, but prescriptive analytics focuses on what should happen for the best outcome. This blog explores the concept, process, applications, and benefits of prescriptive analytics as a tool for changing decisions across industries.

What is Prescriptive Analytics?

Prescriptive analytics represents an advanced stage of analytics, with methods such as optimisation, simulation, and machine learning being used to identify recommendations that should be taken, among numerous alternatives. That said, it analyses the best way to act based on some output and constraints. Ultimately, it answers the question: “What should we do?”

Therefore, by incorporating prescriptive analytics into its plans, a business can:

• Make data-driven decisions.

• Reduce risks and uncertainties.

• Maximise its operation efficiency and profitability.

Prescriptive analytics goes stepwise along with tools to provide actionable recommendations. Here is a simple breakdown of the process-

• Data Acquisition and Consolidation- Here, data is drawn from different sources, including transaction systems, IoT devices, and customer touchpoints. The above data is then digested on a centralised information system.

• Data analysis- Sophisticated techniques attempt to extract patterns, more so, trends and relations from the archival and/or current data sound.

• Forecasting- Through predictive analysis, it tries to give a prognosis of some possible eventualities future cycles may hold, for instance, in modelling a number of customers or in anticipating the future fluctuations of the market.

• Optimisation Techniques – Prescriptive models use aspects like the linear programming technique and decision trees to scrutinise and compare different patterns, admitting various constraints and mentioning which one would be most effective.

• Actionable Insights- This is where actionable recommendations are generated through dashboards or reports so that stakeholders can make informed decisions.

Applications of Prescriptive Analytics

Prescriptive analytics has been widely adopted in various industries. Here are some of the key applications:

1. Supply Chain Management

Stakeholder management involves the management of the supply chain as key stakeholders in order to cut costs and deliver on time. Prescriptive analytics can be used in the following ways:

• Optimising inventory levels.

• They suggest that better demand forecasts can be obtained through the use of better methods and tools.

• Optimising the scenarios of supply chain and supply chain flows.

For instance, prescriptive analytics can be applied by a retailer to identify the right inventory quantities to expect in various warehouses to meet customer requirements without having to order excessive amounts of stock.

2. Healthcare

In the healthcare sector, prescriptive analytics enhances patient care and operational efficiency by:

• Recommending treatment plans based on patient data.

• Optimising resource allocation in hospitals.

• Reducing readmission rates through proactive interventions.

3. Financial Services

Banks and financial institutions leverage prescriptive analytics to:

• Identify the most profitable investment strategies.

• Optimise loan approval processes by assessing credit risk.

• Detect and prevent fraudulent activities.

4. Manufacturing

• Prescriptive analytics aids manufacturers in:

• Predictive maintenance to prevent equipment failures.

• Optimising production schedules.

• Waste reduction and efficiency.

5. Marketing and Retail

Prescriptive analytics helps marketers in the following ways:

• Personalise customer experience.

• Allocate budget effectively on various channels.

• Predict the success of marketing campaigns and change the strategy in real time.

Benefits of Prescriptive Analytics

Organisations can gain many ways from implementing prescriptive analytics. These include:

• Improved decision-making: This aspect of prescriptive analytics enables quick and accurate decision-making by offering recommendations.

• Increased efficiency: Optimisation methods can also be used to solve operational problems, cut expenses, and increase performance.

• Risk Mitigation: Decision-making support tools and prescriptive analytics, in particular, can be used to anticipate and manage a number of risks based on the analysis of many cases.

• Customer Satisfaction: This, in turn, leads to the general improvement of services that are tailored to meet the needs of customers while resources are efficiently deployed.

• Competitive Advantage: Concerning prescriptive analytics, it is crucial for companies to strive to use those insights to outcompete rivals by making the right strategic decisions based on the data collected.

Challenges in Implementing Prescriptive Analytics

Implementing prescriptive analytics has its share of challenges despite its benefits.

• Data Quality: Poor or missing data will make the recommendations wrong.

• Complexity: The development and handling of complex models requires skills and talents which not everyone may have.

• Integration: It may be very challenging to integrate the prescriptive analytics tool into the current systems.

• Cost: The tools and talents come with a price tag, which might be high for small and medium-sized enterprises.

Trends in Prescriptive Analytics

With advancements in AI and machine learning, prescriptive analytics looks promising. Emerging trends are:

• Real-Time Analytics: The integration of IoT and big data technologies will make real-time prescriptive analytics possible for organisations to make instant decisions.

• Natural Language Processing (NLP): NLP will make the prescriptive analytics system much more intuitive. This will also allow non-technical people to use systems since NLP will make systems friendly enough.

• Cloud-Based Solutions: Cloud computing will make prescriptive analytics much more accessible and scalable for all organisations.

• Ethical Decision-Making: Future models will consider the ethical implications of recommendations in such a way that these recommendations align with societal values and norms.

Conclusion

Prescriptive analytics would be the last word in data analytics, providing recommendations that would support organisations with better decision-making. With applications ranging from optimising a supply chain to personalising healthcare, there is a big scope for application. This is only when the challenges of data quality and complexity are overcome.

With the rapidly growing adoption of technology, prescriptive analytics will remain a crucial tool that will be used by organisations in an effort to handle risks in order to succeed in uncertain and competitive environments. Learn more about Prescriptive Analytics with Accelerator Program in Business Analytics and Data Science with Nasscom by Hero Vired and also get a professional certificate.