Bilal Khan Modern businesses are surrounded by a lot of data, but how much of it is actually being used? According to McKinsey, only 30% of the information collected is used in strategic management. The rest are repositories of useless numbers. It’s a paradox: companies invest in analytics but don’t know how to apply it. Predictive analytics teaches us to look into the future, while prescriptive analytics teaches us to manage it.
However, the rapid growth of information volumes is challenging companies far beyond the capabilities of statistical data. Companies are faced with the need to turn data into useful solutions. That is why more and more organizations are turning to data analytics consulting company to implement advanced information processing techniques and transform their business processes. Analytics has long ceased to be just a reporting tool — now it is a strategic asset that influences the speed and accuracy of decision-making. The question is, how exactly should data be used: for predicting the future or for automated decision-making? Let’s look at how these two approaches are changing business strategies and what role they play in digital transformation.
Predictive analytics: Seeing tomorrow today
Predictive analytics is the analysis of historical data to predict future events. Under the hood, algorithms identify patterns that allow you to build probabilistic models. Companies use data analytics consulting as a predictive approach to anticipate demand, identify risks, and assess market dynamics. This tool is particularly useful for long-term planning and growth strategies, as in industries such as retail or insurance.
In the latter case, predictive work includes insurance risk assessments that help prevent fraud and improve customer service.
Key aspects:
- Relies on statistics, machine learning and Big Data.
- Allows assessment of probabilistic scenarios and trends.
- Also applied to marketing (personalized offers), finance (credit assessment), logistics (supply chain optimization).
Limitations and challenges
Predictions remain probabilities. They do not provide answers about what to do next. If the model indicates an impending downturn in sales, what steps should be taken? What strategy to follow? This is where another variable comes into play — prescriptive analytics, which provides concrete solutions based on the predictions.
Prescriptive analytics: Managing through recommendations
Prescriptive analytics is the next stage in the evolution of data, the most important of which are historical, current and background — transactional, socio-demographic and economic-market data. Such analytics uses the resulting predictions to make specific recommendations and automated decisions. For example, if the algorithm detects a decrease in demand for a product, it can suggest changing pricing strategies or launching personalized marketing campaigns at the right moment. The right moment is key to business success.
Key features:
- Incorporates AI elements and optimization algorithms.
- Does not just analyze, but recommends specific proactive steps.
- Enables real-time automation of management decisions.
Implementation challenge
First, in the experience of consultants at N-iX, a company that specializes in implementing prescriptive analytics, skeptics have concerns about the transparency of the algorithms. Who makes the final decision — humans or AI?
The answer depends on the specific system and business processes. In most systems, and most often, the final decision is made by a human when the system provides them with recommendations. If another approach — automated decision-making under human control. Finally, there are fully automated decisions — where speed and accuracy are more important than human skepticism.
How do you choose between predictive and prescriptive analytics?
Not all companies are ready to fully automate decisions, but virtually every organization wants to get the most out of data. So on the line between greed and the desire to control everything comes the question: how do you determine which approach is best?
Key factors to decide from:
- Data structure and quality. If a company has a huge amount of historical data, predictive analytics can help identify valuable patterns — this approach is for large and experienced companies. If quick reactions to dynamic changes are needed, prescriptive analytics will be more useful — an optimal approach for startups.
- Level of automation. Small and medium-sized businesses more often use predictive analytics in manual mode, while large corporations are moving to automated solutions, such as in such industries as IT technology and the automotive industry.
- Risk management. In some areas, such as the financial sector, it is important to minimize uncertainty — to eliminate the human factor. In such cases, prescriptive analytics reduces the probability of errors by optimizing decision-making processes.
Why is a hybrid approach the best option?
Many advanced companies do not choose between these two methods, but integrate them together. This approach provides a powerful decision-making tool: first trends are analyzed, then algorithms help find the best solution — what could be better?
The three stages of a hybrid solution:
- Predictive analytics identifies a problem.
For example, a drop in demand for a particular product).
- Predictive analytics suggests a solution.
For example, a temporary price reduction or an expansion of a marketing campaign in a particular region).
- The feedback system adjusts the algorithms.
It does this on the basis of the effectiveness of previous decisions.
At each iteration, the algorithms are enriched with new experience. Using a hybrid approach allows a business to both adapt to the future and actively shape it.
Conclusion
Today, it’s not enough for businesses to simply “see the future” — it’s important to be able to adapt to it. According to insights shared with us at N-iX, companies using prescriptive analytics improve process efficiency by 15-30% compared to competitors who limit themselves to forecasts. Adopting such technologies requires investment and gradual adaptation, but the benefits are clear.
Bottom line, companies that implement prescriptive analytics gain a competitive advantage. If your organization wants to use data more effectively, now is the time to start the process. Contact the experts in the field of data analytics consulting to learn how you can customize analytics for your business.
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