Enterprises, in the current day and age, are on the constant lookout to assimilate explainable and actionable insights using the data generated. It is at this moment that the need to incorporate prediction-based decision-making across organizational strategy finds considerable importance. Volatile market conditions and changing economic scenarios have resulted in conventional AI and analytics solutions struggling to deliver value while handling complex business cases. In order to keep up with an evolving market environment, businesses are implementing advanced data modeling techniques leveraging predictive and prescriptive analytics to determine future outcomes and disruptions. Forward-looking enterprises are going one step further in terms of contextualizing future recommendations to set up optimal steps in countering the outcomes and/or integrating these outcomes as part of the organization’s strategic KPIs. As organizations need to stay abreast with a competitive and dynamic environment, the incessant need to predict the future and go beyond in terms of optimizing the right sequence of actions to achieve it will form the future of enterprise decision-making.

The current state of predictive analytics – Anticipating outcomes

Organizations are increasingly deploying predictive analytics to ascertain how to enhance processes, production, customer services, and workforce management. To improve sales and targeted marketing of products, predicting customer preferences to generate personalized experiences through omnichannel marketing strategies will enable organizations to generate a higher customer loyalty and retention rate. With a major influx of internal and external data generated in the insurance segment, assessing customer churn and risks associated with loans, credits, and claims management are use cases witnessing a rising adoption of predictive analytics.

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Predictive analytics finds increasing importance in the healthcare vertical, with variables such as health conditions, past compliance with medication, and health history forming critical tools in delivering personalized care plans, recommendations, or other remedies. Predictive analytics algorithms have played an increasing role in controlling, assessing, and managing the spread of infections, particularly during the covid pandemic, while analyzing key variables such as the medicines patients take and their socioeconomic status and vaccination history. As a result, drug development and personalized treatment plans can be well structured to meet the target group, thereby improving the efficacy of vaccines and control measures deployed by government and health bodies around the globe.

On the manufacturing front, assessing and predicting equipment failure and planning machine downtime are key productivity indicators. Predictive maintenance can help improve machine uptime, control breakdown costs, and manual intervention required to ensure an optimum level of productivity is achieved across the shop floor. Tracking processes and lead times can generate a foray into production demand which can, in turn, enable manufacturers to account for the volatile material costs that fluctuate with geopolitics, disasters, calamities, etc. Besides production, asset management, and demand estimation, manpower planning and workforce scheduling are other areas where predictive demand analytics will play a key role.

Shift to prescriptive analytics – Controlling outcomes

While identifying potential outcomes is a key pain point for businesses, the next step lies in assessing those outcomes and devising control measures. It is here that prescriptive analytics comes into play. Against the backdrop of the benefits achieved through predictive analytics, prescriptive analytics goes one level deeper into establishing an understanding of the variables and manipulating/controlling them to achieve the desired outcome. From the question of identifying ‘what,’ organizations can delve into answering the ‘why’ aspect of variables associated with the process, people and productivity. Prescriptive analytics necessitates data scientists to develop a deeper understanding of cause and effect to control the variables and fine-tune the desired results.

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While research in the field of prescriptive analytics continues to drive the efficacy of algorithms and models being generated, a focus area where these tools are finding increased traction involves the manufacturing vertical. For instance, maintaining the temperature of a manufacturing plant or identifying machine settings can help address the process yield and efficiency, thereby enhancing overall productivity. Other major use cases witnessing the growing adoption of prescriptive analytics include route planning and optimization (transportation and logistics), trade risk assessment (BFSI), best-fit product development (retail), and streamlining new drug development (healthcare and life sciences). Adoption of prescriptive analytics will empower businesses to lower risks and improve accuracy with a data-driven output, increase revenue due to optimized processes, thereby minimizing costs and boosting profit margins.

Future of analytical outcomes in business strategy making

While predictive analytics continues to assist organizations in understanding the likelihood of a particular outcome and identifying the root causes, modern businesses are more inclined towards expanding their customer base, controlling costs, generating new revenue streams, and delivering additional value to clients. In a fast-paced and evolving market ecosystem, prescriptive analytics can assist enterprises in adjusting and shaping up better for future disruptions by controlling the key outcomes and developing a strategy that enables leaders to plan and develop preparedness in facing the unknowns.

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