Prescriptive Analytics- Meaning and its Importance
Prescriptive analytics is the third and final step in data analytics. Prescriptive analytics is also conferred to the final step of data capabilities, prescriptive analytics uses applications of statistics and mathematical modelling and algorithms and suggest business decisions options to take full advantages of descriptive and predictive analytics. But however, many companies still believe that descriptive analytics is the final steps of their data analytics. Descriptive analytics looks at the data and calculate past performance and understand the reason behind their decision success and failure.
Prescriptive analytics tries to answer all three W’s- What, When and Why. It clearly tries to answer What will happen, When will it happen and Why will it happen? Not only this, prescriptive analytics states decision option which tells how you can take the full advantage` of future opportunity and mitigate the risk and shows the implications of each suggested decisions. Prescriptive analytics has the ability to process new data and convert its decision to re-prescribe and re-predict. Thus, this automatically increases accuracy and prescribing a better opinion.
Prescriptive analytics suggests hybrid model which is the combination of structured (numbers, categories) and unstructured (videos, audios, images, texts) and business rules to predict what lies ahead and how to take the advantage of this predicted future without causing any effect to present situation. Prescriptive analytics comprises various discipline which includes:
- Machine Learning
- Natural Language Processing
- Applied Statistics
- Computer vision
- Signal processing
- Operation research
- Image processing
?Prescriptive Analytics deals with both structured and unstructured data and uses the application of statistics and mathematical modelling to predict, prescribe and adapt. The termed was first coined by IBM and later trademarked by Ayata. However, the concept behind Prescriptive analytics is not new. Its concepts were developed around for hundreds of years ago. The technology behind prescriptive analytics gradually combines hybrid data, computational models and business rules of the mathematical model.
The data inputs to prescriptive analytics emerged from internal and external sources. Internal sources include data from the corporation whereas external sources include environmental data. The data may be structured which includes numbers category and unstructured which includes unstructured category such as images, videos and audios. According to IBM, more than 80% of the world’s data is unstructured.
The importance of Prescriptive analytics:
Prescriptive analytics is typically used to solve highly complex, niche problems such as scheduling, routing and staffing. These activities are highly complex and have historically involved Data scientist rather than business persons. Prescriptive analytics is moving its applications from the hands of Information Technology (IT) to Business Units. This shift has shown the importance of prescriptive analytics to business organisation.
Prescriptive analytics belongs to future leaders
Four factors which have caused the shift from optimisation to solve complex problems to use it to solve more complicated problems, cross-functional problems that business management consider as important for their growth.
- We have more diverse data.
- Prescriptive analytics is becoming less rocket science to study and execute, allowing business users to grow and draw better conclusions without being dependence on Data Scientist and Data Analyst.
- Business Leaders understand the more important problems need to be addressed.
- More and more organisations are using therefore it?s better to use it and draw a better conclusion for the success of the business.
The Value of Prescriptive analytics to Business Organisation
The typical value realised after using prescriptive analytics by the different business organisation is the change in ROI. From the exact ROI depends on the scientific approach of prescriptive analytics, it is crystal clear that prescriptive analytics offers a better result than any other type of analytics using structured and unstructured data. Further, this impact can become more transformational when it is applied to end-to-end business.
Achieve Higher Confidence with lower risk
Optimisation based plan provides solid foundations and effective plans. This can only be achieved by using Prescriptive analytics. Plans can be feasible and non-feasible, depending upon the terms and conditions of the plan. Prescriptive analytics solves this problem using optimisation. The operational and financial flow of the business is very merely represented and there are fewer chances of achieving that goal.
By using optimisation, an organisation develops an ability to deliver a plan and understand the important steps required to implement the plan. A manager can give better plans with high confidence and ability to affect further change in the business model and management.
Prescriptive analytics provides a better sight that can lead to better financial and operational performance, especially when implemented through tools relying on user intuitions (Eg-MS Excel, BI). Different types of impact include:
- Improving the efficiency and effectiveness of business against one or more objectives (Eg- Net Income, Operational Cost).
- Increasing the efficiency of operation (i.e. using the same materials for making more than one product, utilising the waste products).
- Maximising the return from by changing or shifting the machines, subject to minimum returns (Eg- for optimising the allocation of investments in a different category).
Earn a higher and better return on existing asset
Prescriptive analytics helps businesses to forecast how to leverage their previous investments using Electronic Resource Planning (ERP) software that helps to provide with the fresh data. lenders can study and utilise that data for actionable insights and also guide them where they are missing some important data. Prescriptive analytics provides the best effective path, employees can have a true impact on business objectives and quickly access the status and reports of the company. This boost the confidence of the employees and they are highly motivated to use prescriptive analytics solutions.
Address new challenges and problems
Prescriptive analytics is the best form of data analytics that can easily address complex questions and provide the best solution which cannot be performed by any other type of analytics. Further, it can help to discover modern opportunities across businesses that management think is impossible to solve.
Business Organisation usually quantifies risk into two terms. One is Financial and other is Operational. Prescriptive analytics helps to identify and better elaborate the risk associated with the long term and the short term. Not only this, prescriptive analytics uses optimisation strategy to develop advance potential risk mitigation strategies.
Develop higher agility in the organisation
Different plans and difficult decisions take weeks or even months to execute, often taking a lot of time and sometimes uses external consultants. This process, sometimes, didn’t get the same level of security and scrutiny analysis, as there is not much time to go through every individual data. Prescriptive analytics increases the organisational knowledge of how one function is dependent on the other and how it impact one another. It also recommends the best path which helps to increase the ability to evaluate more important problems and deliver fast approach.