Prescriptive analytics is an advanced form of analytics that goes beyond descriptive and predictive analytics to provide recommended actions based on prediction of future events.
With the increasing availability of vast amounts of real-time data and the progression of analytics tools and technologies, prescriptive analytics is becoming an essential aspect of business strategy for organisations across the globe.
Prescriptive analytics uses cutting-edge technology and tools including mathematical models, machine learning algorithms, business rules, and computational modelling methods.
These tools are capable of processing a wide array of data, be it structured, semi-structured, and unstructured data – to predict future occurrences and thus prescribe the optimal course of action.
The ultimate aim of prescriptive analytics is not just to predict the future, but to take advantage of these predictions to achieve the best possible business outcomes.
In business, prescriptive analytics finds use in numerous applications. It can, for example, help org’s to optimise production schedules, inventory management, and supply chains, leading to cost savings and improved operational efficiency.
This relatively new analytical approach also proves beneficial in finding the most effective marketing strategies, predicting customer behaviour and adjusting various factors in real-time to boost sales and revenues.
Prescriptive analytics is being used successfully in various sectors. In healthcare, for instance, it is used to predict health issues in patients and prescribe preventive measures, thereby improving patient outcomes while reducing healthcare costs.
In the retail sector, prescriptive analytics enable businesses to predict customer demands during peak seasons and retain customers by offering personalised promotions and discounts.
Despite its significant potential, prescriptive analytics does have its challenges. Implementing prescriptive analytics requires a substantial investment in technology, data infrastructure, and skilled professionals.
Dealing with real-time data from various sources can give rise to issues related to data quality, privacy, and security.
Nonetheless, the potential benefits of prescriptive analytics outweigh the challenges.
As businesses across the globe continue to produce and collect vast amounts of data, the need for advanced analytics that can make the best use of these data to make informed decisions and boost business outcomes is apparent.
BEST PRACTICES
Businesses can deploy this form of advanced analytics to uncover hidden patterns, relationships, and dependencies and identify potentially profitable opportunities.
The first step to using prescriptive analytics is defining the business problem that needs solutions or opportunities.
Once the problem or objective is defined, the next step is gathering relevant data. This data can be structured or unstructured, and can be obtained from various sources such as databases, transaction records, social media posts, et cetera.
The data collected is then cleaned and analysed to build an ML model. The model learns from the data, recognising patterns and trends. The more relevant data the model processes, the more accurate the predictions.
Next, the predictive model is tested and validated, refining its predictive power.
Once a reliable model is established, it’s used to simulate different scenarios based on various decision options. This is done with the help of optimisation algorithms that analyse outcomes of different decisions to recommend the best course of action.
The simulation process can explore thousands of alternatives, determine their impacts, and thereby propose optimal decision recommendations. It’s crucial, however, that businesses don’t fully rely on predictive models without considering real-world context or intuition.
The predictive model can provide a base-line expectation, but the final decision should combine this with human judgement and industry expertise.
For successful utilisation of prescriptive analytics, businesses should invest in the right talent, technology, and ensure strict data governance.
A skilled team of data scientists, analysts, and IT professionals is essential to manage and interpret the data, and implement prescriptive solutions effectively.