You are currently viewing Prescriptive Analytics in Data Science Institutes in Bangalore 

Prescriptive Analytics in Data Science Institutes in Bangalore 

Prescriptive analytics is used to tell us things we should do. The methodology is the third and final stage in the business analysis process. Prescriptive analysis sets businesses into motion. It assists the operational employees, managers, and executives make the best decisions according to the available data. 

How it works

Prescriptive analytics takes the things learned in descriptive and predictive analytics to move one step forward by recommending the best course of action. However, this is a complex part of business analytics, requiring more specialized analytical knowledge for it to work. This is why it is not always used in the daily operations of a business. 

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If you are to make recommendations and predictions the right way, there are tools and techniques that you would have to use. This includes machine learning algorithms, statistics, and rules. These can be applied to the data available, which includes external data derived from sources like social media and internal data derived from business data. Machine learning capabilities extend very far beyond what we can achieve. 

Some people confuse machine learning and predictive analytics, assuming they are one. Predictive analytics involves historical data, where statistical techniques are used for predictions regarding the future. This is very different from prescriptive analytics. 

The things we can get from prescriptive analytics

Prescriptive analytics is important for businesses. It helps anticipate the what, when, and why something could happen in business. When this is done, it goes a step further to consider the implications of every decision that is available on the table. This is the only way recommendations can be made regarding the best decision that can help a business make use of any future opportunities and mitigate possible risks. Prescriptive analytics predicts all sorts of futures. This makes it easier to consider all possible outcomes before making a decision. Taking such considerations makes it easier to come up with the best decision every time. 

If you want your findings to impact data science course in bangalore business decision-making and strategy, you must ensure that prescriptive analytics is done as effectively as possible. The findings and decisions made can have a significant impact on business growth and customer experience. 

Pros and cons associated with prescriptive analytics

We get the most valuable insights when prescriptive analytics is used as it should. These make it easier for businesses to make the best data-based decisions that help optimize performance. Like predictive analytics, the methodology demands a lot of data if valuable results are to be produced. The main challenge is that such large data sets are not always available. Prescriptive analytics also relies on machine algorithms. These are not always capable of accounting for external variables. Using machine learning, however, reduces human error. 

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Applicable industries

GPS technology is one of the tools used in prescriptive analytics. This is because all possible or recommended routes are offered to the user to reach their destination. This can be based on things like road closures or journey times. In such a case, prescriptive analysis helps optimize objectives that measure distances from your desired starting point to the endpoint or destination. Then, it prescribes the best route, which is often the shortest distance. 

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Some of the other areas where prescriptive analytics has been conveniently applied include:

  • Oil and manufacturing, where fluctuating prices can be tracked
  • Manufacturing: in this case, it is about improving equipment management, price modeling, maintenance, storage, and production. 
  • In healthcare, prescriptive analytics can help improve healthcare administration and patient care. This can be done by evaluating things like readmission rates and cost-effectiveness-related procedures. 
  • Insurance: in this area, it helps in risk assessment related to the pricing and client information. 
  • Pharmaceutical research: prescriptive analytics can help identify the best patient groups and test for clinical trials in this case. 

Prescriptive analytics examples in action

We call prescriptive analytics the data analytics future, and this is because of many good reasons. First, the analysis goes above and beyond predictions and explanations to help recommend the best way forward. It is a great help when you want to drive data-informed decisions.

Some of the best examples of how prescriptive analytics can be used include:

Venture capital for investment decisions

There are instances where we make investments based on gut feelings. To strengthen these, you can use algorithms to help you weigh all risks and make investment recommendations. For example, in venture capital, an experiment was used to test how effective the decision of algorithms was in making decisions regarding startups that you can invest in compared to other decisions. The algorithm gave better results than the angel investors, who were less experienced in investing and without enough skills to control cognitive biases. On the other hand, Angel investors performed better only when they had investment experience and could control cognitive biases. 

With such an experiment, it is easier to shed some light on the role of prescriptive analytics in making decisions. It also shows its potential in decision-making, especially when no experience and cognitive biases are present. Algorithms have no bias. 

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Sales through lead scoring

Prescriptive analytics has a vital role in sales achieved through lead scoring. This is also called lead ranking. This is where point value is assigned to different actions in a sales funnel. This makes it possible for an algorithm or human to rank leads effectively depending on their conversion likelihood. Actions that can be assigned value include content engagement, page views, etc. 

Content curation

Anyone who has ever used a dating app or a social media platform has experienced this analytics method firsthand. Such sites work through content recommendations achieved through algorithms. The algorithms usually gather data according to engagement history on the platform. This is combined with past behaviors to inspire the release of a recommendation. For example, you get recommendations based on what you seem most interested in on a platform such as YouTube. Again, your history is used for this. 

Bottom line

There are many other areas where prescriptive analytics can be sued, including product management, marketing, and fraud detection. data science course in bangalorePrescriptive analytics has great potential, and since it works with data, it is one of the most important things for a data scientist to master. As such, prescriptive analytics needs to be covered by data science institutes in Bangalore. With such knowledge, trained data scientists are in a better position to bring tremendous value to the organizations they end up in. Training is essential, but it should also offer value and prepare the students for what lies ahead. 

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