What are prescriptive analytics meaning?

What do you mean by Prescriptive Analytics?

As per the requirement of data analysis and depending on the stage of the workflow, there are mainly four kinds of analytics ? descriptive, diagnostic, predictive and prescriptive.

All these analytics are interrelated and each of these analytics offers a different insight. These four kinds of analytics all together answer everything a company needs to know ? from what has happened and what is happening to what actions to be adopted and finally to what might happen in future and what will be the outcome of the selected course of action.

Here we will discuss prescriptive analytics in detail.

In a very simple language, prescriptive analytics helps you understand ? What should I do?

Prescriptive Analytics is the devoted area of business analytics or data analytics. Apart from predicting what will happen, prescriptive analytics also provides the best possible outcomes and then, prescribes the best course of action to achieve that particular outcome in a given situation. Hence, prescriptive analytics uses a strong feedback system that learns and update the cause and effect relationship between the action and the outcome.

Prescriptive analytics is related to both descriptive analytics and predictive analytics. But moreover, prescriptive analytics is the basis of predictive analytics but prescriptive analytics goes beyond the other three analytics to suggest future solutions. As descriptive analytics offers insight into what has happened, diagnostic analytics offers an insight into why did it happen, predictive analytics focuses on forecasting possible future outcomes, and prescriptive analytics focuses on providing the best solution or course of action in a given situation.

Prescriptive analytics uses a combination of optimization, Artificial Intelligence, Machine learning algorithms, mathematics and statistical methods to help you understand that ?what you must do?. It can extract data from both structured and unstructured sources. Essentially it predicts the different possible course of actions and outcomes and prescribes the right solution to the decision-maker. Prescriptive analytics is termed as the final frontier of advance business analytics or data science, in today?s digital term.

Merits of Prescriptive Analytics

  • It can help to prevent frauds.
  • It helps to limit and minimize the risk factor.
  • It helps to increase efficiency in the organization?s work.
  • It helps to achieve a business goal.
  • It helps to strengthen the relationship with customers and create more loyal customers.
  • It helps to face immediate uncertainty and changing conditions.
  • It helps in decision making.

Demerits of Prescriptive Analytics

  • It is not foolproof to completely rely upon.
  • If the input assumptions are not valid, the output results will not be correct or accurate.
  • It is not effective if organizations are not clear about the question to which they seek an answer.
  • Future is uncertain therefore the chosen course of action might not always prove to be correct.
  • Helps in reducing human errors or bias.
  • Helps in less time thinking and more time doing.
  • Helps in real-time and long term business operations.

Things to do with Prescriptive Analytics

We can do certain things by using prescriptive analytics, such as:

  • To manage store inventory in a better way by optimizing which products and how many products are in a particular store.
  • It helps marketers to understand different engagement ways of different customers (for example through email, direct mail, Short Message Service, etc.)
  • To determine how to price and discount products and categorize products for different segments of customers.

Importance of Prescriptive Analytics

Prescriptive analytics go beyond descriptive and predictive analytics by recommending the best course of action according to the given data. It helps in effective decision making by extracting actionable insights from data ? there is no need for users to go through and analyze such huge amounts of data. With the help of Artificial Intelligence and machine learning algorithms, prescriptive analytics effectively use structured and unstructured data and help decision-makers to develop what-if cases and accordingly provide correct solutions to achieve the targeted outcomes.

The cloud and the future of Prescriptive Analytics

For analyzing data comprehensively, you need a robust and versatile location for storing data. Cloud data warehouses make enormous undertakings such as understanding prescriptive analytics not only possible but also user-friendly. Cloud data warehouses give users all solutions to data analytics. With the help of the cloud, prescriptive analytics would not only gain more data, but they would also get more accurate, real-time, and secure data. For example, a manufacturing company can get their hands on more than just company data. It could leverage both historical industry trends and customer industry trends, and economic predictive analytics.

Cloud has such power to push prescriptive analytics towards achieving new, exciting possibilities every day.

Advanced Analytics

Predictive analytics and Prescriptive analytics fall under the category of advanced analytics.

Advanced Analytics = Predictive Analytics + Prescriptive Analytics

Advanced analytics answers to the following questions:

  • ?What happened?
  • How many?
  • How often?
  • Why is this happening?
  • What will happen in the future and what should I do regarding it?
  • How can I maximize my marketing Return On Investment (ROI)?
  • What happens if I price Product A over Product B?
  • What are my predicted outcomes for the future?
  • And so many more questions.

Industrial Use Cases for Prescriptive analytics

1. Transportation and Shipping

  • Reduce training costs and improve driver retention.
  • Avoid unnecessary transportation miles through driving, flight, and sea.
  • Increase the productivity of the driver by improving the travelling routes and eliminating the time spent on loading and unloading.
  • Increase speed and reduce costs by optimizing networks of distribution.
  • Eliminate almost all errors involved in warehouse packing.

2. Oil and Gas

  • Establish the best possible pricing by analyzing and predicting the constant rise and fall of fuel markets.
  • Through optimization eliminate downtime due to breakage and maintenance.
  • Improve operational safety and prevent potential environmental disasters.
  • To determine the best possible locations in a given field to drill
  • Improving drilling completion rate by properly training the machine learning models to help in recognizing the most suitable places to set up field operations properly.

3. Financial Services and Banking

  • Decrease the processing time of the transaction.
  • Reduce transaction costs.
  • Prepare better portfolios for financial investment.
  • Reduce the risk involved in the investment.
  • Optimize decisions regarding investment like when to invest, how much to invest, etc.
  • Increase the total amount of transactions to be processed in a particular time period.

4. Retail

  • Optimize the price of products and services.
  • Optimize the assortment of products and services in a retail store.
  • Find the best possible engagement of marketing methods (online, print, radio, etc.)
  • Measure the effectiveness of trade promotion and profitably optimize the expenditure done on promotions.

Prescriptive analytics includes many other domains other than the ones aforementioned. Some other popular sectors that use prescriptive analytics in their businesses are the healthcare, insurance, renewable energy sector, etc.

Getting started with prescriptive analytics

With the use of prescriptive analytics, businesses will spend less time working on a spreadsheet and more time using informed data that will help the businesses to keep themselves ahead of their competitors. With the help of effective cloud-based prescriptive analytics data tools, businesses can achieve these benefits even more quickly.

Prescriptive analytics is still in its budding stage or growing stage and therefore not many companies have completely made use of prescriptive analytics. But, the analytics world is fast changing. Hence, the prescriptive analysis will come to its fuller form very soon.