Predictive and prescriptive analytics for AI – Data driven decision making

By Mijitha Muralidharan, Associate Director, Pre Sales, Ascendion

Business context is ever evolving and the speed of decision making with precision is crucial to leverage the right opportunities. Predictive and prescriptive analytics are driven by AI analytics that enables organisations to make quick decisions from vast amounts of data. Predictive analytics offers businesses the ability to forecast their capabilities, identifies patterns that are likely to emerge, whereas prescriptive analytics provides insights through strategies that channel desired outcomes. These combined enables organisations to progress from reactive decision-making to proactive decision-making, leading to enhanced innovation.

Predictive analytics answers the question, “What might happen next?” with a wide variety of approaches including looking at regression estimates, machine learning algorithms, and statistical models. It helps companies assess risks, capitalise on opportunities and estimate forthcoming conditions with an overwhelming percentage of reliability. For example, in the retail space, predictive analytics can forecast purchase patterns based on the history. This will help businesses optimise their inventory, have focused marketing strategies and enhance customer satisfaction. In the healthcare space, predictive analytics can help plan for patient admissions and potential health risks, enabling care centres to allocate optimum and efficient resources for patient care.

Prescriptive analytics addresses the angle from “What should we do next?”. It makes suggestions using optimisation algorithms, machine learning models, and simulations of several scenarios. It comes under play when the decision under consideration factors multiple variables and several uncertainties. In supply chain management for instance, prescriptive analytics help determine the most efficient transport routes minimising delivery costs and time. For the financial services industry, it plays a role in portfolio management where it suggests whether adjustments are necessary to portfolios based on expectations of changes in the market conditions. Prescriptive analytics brings about the knowledge from the insights and assists in strategy implementation that takes care of maximising the value and risk exposure.

The use of AI based analytics has a certain edge owing to their ability to learn continuously and improve. With the introduction of new data inputs, machine learning algorithms their capability on predictions and recommendations. With this kind of constant change, the quality of decisions gets better with time. For example, in fraud detection, predictive analytics models will understand the new types of frauds through chat or interactions and hence will reduce false positives and improve the overall detection. With AI-based analytics, virtual assistants in customer service will learn from interactions providing increasingly accurate and personalised support to users.

While these analytics technologies are effective, human judgement will always be necessary. By including ethical frameworks and oversight into our analytics solutions, we decrease the risk of over-reliance on AI, especially in high-stakes circumstances, protecting against unexpected consequences and ensuring decisions are consistent with our clients’ core beliefs and goals.
In today’s data-driven world, incorporating predictive and prescriptive analytics into company strategy is no longer an option—it’s required to be competitive. Organisations that understand how to use the technologies at their disposal will be able to make better decisions, automate procedures, and create something new at the same time.

To fully realise the potential of AI-driven decision-making, businesses must prioritise three essential factors: trust in AI systems, access to comprehensive data, and effective integration of analytical capabilities. Trust is developed by giving AI procedures and outputs that are explainable and auditable to stakeholders who apply the insights.

Availability of good quality and the right type of data is the bedrock for the success of any analytics. Access to high-quality, relevant data is crucial for the accuracy and effectiveness of analytics, while seamless integration ensures that these insights can be acted upon quickly and efficiently.

As we look to the future, the mix of predictive and prescriptive analytics will continue to change decision-making processes in several industries. These analytics tools enable organisations to foresee future occurrences and prescribe best responses, allowing firms to overcome uncertainty and generate long-term success.