What Is The Difference Between Predictive & Prescriptive Analytics – Beginners Guide

What Is The Difference Between Predictive & Prescriptive Analytics

Modern businesses have taken a futuristic approach to business operations, and the majority of them use analytics (well, who doesn’t?). Businesses are using these analytics to predict the future, assisting them to develop robust and fool-proof strategies and plans. It’s needless to say that big data has gained a lot of buzz in the corporate sector and data analytics have the capacity to provide insights about the business.

Still, understanding data interpretation and implementation of data to business strategy is crucial. On the contrary, business analytics is incomplete without predictive and prescriptive analytics. So, if you want to implement data analytics, we are sharing the differences between prescriptive and predictive analytics, so you can understand these concepts and see how they can deliver practical insights into your business systems and processes. Are you ready to dive into the details?

Predictive Analysis Vs. Prescriptive Analytics 

Be it prescriptive analytics or predictive analytics, both of them are for creating well-informed business strategies, respective to collected data. However, the prime difference is that predictive analytics can estimate the future forecast while prescriptive analytics help generates the recommendations. Both these analytics can transform descriptive metrics into actionable decisions and insights. Still, you cannot rely on one analytical method. 

That being said, the integration of prescriptive and predictive analytics helps with effective and strongest business strategies. Truth be told, predictive analytics alone is not ample to keep up with the competitive landscape, since businesses also require the smart recommendations offered by prescriptive analytics for strategic processes and tech applications. It helps drive promising outcomes and even amps up these outcomes. 

So, while both prescriptive analytics and predictive analytics are important to businesses and have specific roles to play, still, predictive analytics tends to lack a little as compared to prescriptive analytics. That’s to say because predictive analytics outlines what will happen but doesn’t provide any decision-based guidance. On the contrary, prescriptive analytics not only estimates the future forecast but also outlines the strategies for overcoming various situations. 

Predictive Analytics – What Is It?

When it comes down to analytics, predictive analytics is the most advanced approach which makes the businesses identify patterns in the potential outcomes. Also, it helps make sense of the repercussions of specific strategies and decisions. With proper leveraging of data, stats, and figures, predictive analytics utilize the updated and raw data for peering the potential business scenarios. To illustrate, predictive analytics forecasts the future and predicts the timeline. 

Predictive analytics help with models that affect different business aspects, but it delivers non-actionable outcomes (yes, they will only identify the need for decision-making). It is responsible for optimizing one function. It’s safe to say that predictive analytics are based on hypotheses with the utilization of prearranged consequences with limited options. 

Prescriptive Analytics – What Is It?

Sure, prescriptive analytics will focus on future scenarios, but it utilizes a more tech-oriented approach. With the utilization of mathematical algorithms, machine learning, and artificial intelligence, the prescriptive analysis will also deliver deeper answers to the whys and whats, as well as, estimating the future outcomes. Even more, prescriptive analytics can be utilized for altering the suggestions and predictions. For the most part, prescriptive analytics can help businesses change forecast future scenarios, and make intelligent business decisions.

It can model across the entire business niche and is completely data-oriented. Prescriptive analytics is suitable for making specific business decisions and will also consider the interdependencies. Topping it all, it is not bound by static rules and can deliver measurable and highly tangible benefits. The most intriguing thing about prescriptive analytics is that it supports the potential and what-if situations, and there are no personal biases. 

Prescriptive analytics utilized the highly validated and calibrated models that influence the business operations. Also, it takes into account different outputs, inputs, and variables. 

Do These Differences Matter Between Two Analytics?

For the most part, people believe that these differences are minor and won’t impact the overall outcomes much. However, these differences can make a huge difference when it comes down to practicality. In addition, these different analytics will point out the fact that there is much more to optimization of prescriptive analytics solution as compared to experimenting with small-scale predictive analytics. That being said, it’s essential to determine the business-related maturity of potential customers. 

Sure, the majority of businesses are using business intelligence, but not all of them have spiked up to prescriptive analytics. According to Gartner, 11% of medium-scale to large-scale businesses are using prescriptive analytics. On the contrary, Gartner has anticipated that prescriptive analytics will experience around 20.6% higher CAGR by 2022. To illustrate, it means that around 37% of businesses will start utilizing prescriptive analytics. 

Since business growth is increasing and a number of businesses have spiked, these stats illustrate that the differences between prescriptive analytics and predictive analytics do matter. Sure, predictive analytics is great for identifying the underlying challenges and problems, but prescriptive analytics helps determine the best way to maximize business opportunities. It’s needless to say that these analytical techniques are catering to unique business needs. 

To begin with, predictive analytics tend to be reactive where management needs to react. On the contrary, prescriptive analytics tend to be proactive because it determines a way forward for management. Still, both these analytics techniques use the real-time data captured by businesses. That being said, the key difference is that prescriptive analytics use rule-based optimization and automation, while predictive analytics only interprets the trends. 

Overcoming Specific Issues With Prescriptive Analytics Vs. Predictive Analytics

It’s a well-established fact these two analytical technologies cannot be used in isolation with each other and aren’t standalone tools. Every business has space for different types of analytics to fix different errors. For the most part, predictive analytics is used for the identification of small-scale to medium-scale trends (it might be used in isolation). For instance, it can outline and identify demand planning, conduct short-term risk analysis, and sales trends for specific products. 

Also, predictive analysis can monitor and assess maintenance requirements, profitability, customer churning, and inventory control. As far as prescriptive analysis is concerned, it’s designed to look at a broader picture. Generally, it’s considered to measure the combined trends of all business aspects, along with different divisions and functions. For instance, prescriptive analytics help optimize the operations of coal extraction to ensure higher profitability while meeting the consumers’ demands. 

Some other examples of using prescriptive analytics include the establishment of optimized inventory and manufacturing strategies. It also determines the suitable operation strategies for different utilities while adhering to the regulatory compliances. 

What Value Do These Analytical Techniques Bring To Business?

To begin with, both predictive analytics and prescriptive analytics deliver tangible benefits, but prescriptive analytics’ tends to offset predictive analytics’ results. It is generally considered due to operation scales, and it’s impacted by different types of decisions. In addition, prescriptive analytics can optimize business decisions. For the most part, predictive analytics focuses on short-term parameters, such as short-term risk analysis. 

This analysis sure can bring higher rewards by limiting the risks, but it’s not a similar magnitude as prescriptive analytics. This model has the capacity to identify the most profitable strategies while pointing out the result-oriented markets. In addition, it can be utilized for identifying the strategies that deliver sustained growth of businesses. The business executives are also using prescriptive analytics for exploring the potentiality of situations and trade-offs. 

At this point, it’s essential to outline that prescriptive analytic solutions can be more expensive as compared to predictive analytics since ROI has a greater potential. 

Differentiating Tech Requirements For Both Analytical Technologies 

Data analysis is responsible for preserving the data, but the quicker pace of businesses makes it domineering for business executives and line managers. This is because it delivers higher access to analytical tools. Sure, it doesn’t mean that businesses have to get involved in data cleansing and programming, but it does imply the need for dashboards and business intelligence tools. This is because these tools and dashboards allow businesses to inspect the results. 

Truth be told, it’s the hands-on approach that builds up confidence in providing precise information that eventually helps with effective decision-making. Prescriptive analytics have different tools, ranging from ERP tools to natural programming languages and solution-oriented packages. The initial phase is to clean up everything and collect the data to make it utilizable and applicable. Consequently, different analytical techniques are utilized, inclusive of the following;

  • Neural networks 
  • Machine learning
  • Natural Language Processing
  • Linear regression, logistic regression, and time-based regression 
  • Naïve bayes conditional probability 

As for prescriptive analytics, it takes a step ahead by utilizing two analyses, known as optimization and heuristics. The heuristics can be used for operational consequences that have limited defining. This is a rule-based mathematical technique and is suitable for purchasing raw materials. In addition, heuristics are optimal for automated decisions but are not suitable for optimizing the decisions, as such. However, it needs continuous revision to prevent getting outdated.

On the other hand, optimization utilizes the combination of mathematical algorithms and models that point out the optimal solution. The model will represent the development of function or business. As for the algorithm, it answers specific questions. For the most part, the optimization model is used for minimizing and/or maximizing the parameters related to costs and profits. Prescriptive analytics solutions are available as platforms or packaged solutions. 

It is better to opt for the packaged solutions as they are easier to configure and are solution-oriented. Usually, prescriptive analytics is available through the cloud as PaaS and SaaS solutions. The optimization platforms tend to have two components, such as optimization solver and modeling platform. The first one has flexible costs and is implemented in-house. On the contrary, models have the drag-and-drop visual interface and use mathematic writing. 

Implementing The Analytics 

These analytics programs have become the ultimate technologies for businesses to improve the effectiveness. In this section, we are outlining the implementation of predictive analytics and prescriptive analytics, such as;

Start Small Data analytics can be complicated and overwhelming, and nobody wants to lose the insights and data. Sure, you have to think big with analytical strategies, but it is better to start with small tactics. 

  • Development Of Data Sets

In particular, you have to create rich data sets. It can be done by implementing predictive analytics because it helps create rich information and data sets. These data sets will help yield a better outcome. 

Real-Time Examples Of Predictive Analytics And Prescriptive Analytics 

Computer vision has become famous, but analytics has a huge role to play. It wouldn’t be wrong to say that both predictive analytics and prescriptive analytics are applicable in daily lives, and we are sharing some real-time examples in the section below;

  • Navigation Apps

Everyone with an electric vehicle tends to use GPS-enabled apps for navigation purposes. These navigation apps help transit from one place to another, and even small companies are relying on these technologies for delivery services. Predictive analytics can collect the current travel data and identify a faster route for transit and delivery. Prescriptive analytics is built by informing decision-makers about different decisions and potential outcomes. 

To illustrate, Waze is the traffic navigation app that picks up the origin and destination. The app utilizes a variety of factors to provide guidance on different routes. All the routes are outlined with a specific ETA. 

  • Inventory Planning

As a small-scale retailer, it is important to understand the quantity of stock needed for filling up the shelves. Sure, you can depend on educated guesses, but analytics will help create precise stocking and inventory strategies. With the change in business and retail landscapes, the businesses can start using prescriptive analytics for clarifying the data (it will help improve the sales). For instance, a predictive model assumes the potential sales and customer behaviors with the promotion. 

  • Weather Forecasts

When it comes down to predicting the weather, it can get dicey but seasonal changes can be outlined pretty well. For instance, sporting goods stores can utilize this information of weather and climate for scheduling physical activities. For instance, if the summer season will be in full swing and weather forecasts interpret this change in weather, you can stock up on the running shoe inventory by using data suggested by the analytics.

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