Definition of Prescriptive Analytics and Examples

Definition of Prescriptive Analytics and Examples

Definition of Prescriptive Analytics and Examples

It’s easy to use the data available to companies to generate real business value, thanks to the abundance of data. It can be difficult to determine the best way to analyze these data.

Prescriptive analytics can be a great option to help your business identify data-driven strategic decisions. It will also help you avoid the limitations inherent in standard data analytics practices such as:

  • Responsibilities exhausted on housing data that is not useful in business decisions
  • Spend time sorting through data that isn’t being used
  • You are missing out on exclusive revenue streams and insights

Learn what prescriptive analysis is and how it differs from predictive and descriptive analytics to get started. It will help you understand how it can be used to support business intelligence and the ways that other companies use it. You’ll also learn how the cloud is helping it advance.

What is Prescriptive Analytics?

Prescriptive Analytics is a process that analyses data and offers instant recommendations to improve business practices to meet multiple predicted outcomes. Based on simulations and information, prescriptive analysis takes what we know (data) and combines it with the data to predict the future.

Prescriptive analytics is the final tier of modern, computerized data handling. These three tiers are:

  • Descriptive analysis: This is the first step towards clear and concise data analytics. It is “what we know”, which includes current user data, past engagement data, and big data.
  • Predictive Analytics: Predictive analysis applies mathematical models and data to predict future behavior. It’s the “what might happen.”
  • Prescriptive Analytics: Prescriptive analysis uses similar modeling structures to predict outcomes. It then utilizes a mixture of machine learning, business rules, and algorithms to simulate different approaches to these outcomes. The best actions can then be taken to improve business practices. It is “What should happen.”

Prescriptive analytics follows on from predictive and descriptive analytics. This takes the guesswork out of data analysis. It saves data scientists as well as marketers their time trying to figure out what data they have and how to connect them to provide a personalized and optimal user experience.

Also read: The Role of Data Analytics in Product Development R&D Process

Advantages of Prescriptive Analytics

Senior executives are always looking for ways to improve the efficiency and effectiveness of their organization’s operations. Prescriptive analytics can be the most effective and efficient way to build any company’s business intelligence. Organizations have the opportunity to:

  • Map the path to success: Prescriptive analytics models combine data and operations to create a roadmap that shows you what to do and how. Artificial intelligence can take control of business intelligence, apply simulations to a situation, and determine the best way to avoid failure or succeed.
  • Inform about real-time and long-term business operations: To make informed decisions that will support sustainable growth and success, decision-makers can simultaneously view forecasted and real-time data. This allows for faster decision-making by providing specific recommendations.
  • Spend less time pondering and more time doing: Your team will spend less time looking for problems and more time creating the best solutions because they can quickly analyze and predict data outcomes. Artificial intelligence is able to process and curate data quicker than your data engineers and does it in a fraction of the time.
  • Reducing human error and bias: Predictive analytics uses more advanced algorithms and machine-learning processes to provide a more complete and accurate form of data aggregation, analysis, and reporting than descriptive analytics, Predictive Analytics, or individuals.

Examples of Prescriptive Analytics

Prescriptive analytics is used by businesses to solve real-world problems. It can be used by analysts in many industries to improve their processes.

Marketing and sales

Sales agencies and marketing have access to large quantities of customer data, which can be used to help them determine the best marketing strategies. For example, they can see what products go well together and how to price them. Marketers and sales personnel can use predictive analytics to be more precise in their outreach and campaigns. They no longer need to rely on intuition or experience.

Transportation industry

For success in the transportation and package delivery industry, cost-effective delivery is key to profitability and success. You can save money and time by optimizing route planning and resolving logistical problems such as incorrect shipping addresses.

Shipping companies generate huge amounts of data. These businesses don’t need to employ armies of dispatchers and analysts to determine how best to operate. Instead, they can automate and create prescriptive models that provide recommendations.

Financial markets

To maximize returns, quantitative traders and researchers use statistical modeling. Similar techniques can be used by financial firms to manage profitability and risk.

Financial firms, for example, can create algorithms that analyze historical trading data in order to determine the risks associated with trades. These analytics can be used to help financial firms decide whether or not to place trades.

Also read: Best Tools You Need to do Data Analysis

Real Companies that have Won with Prescriptive and Predictive Analytics

Prescriptive analytics isn’t a trendy buzzword. It is also not impossible to access for organizations of all levels, even if they aren’t enterprise-level. The following companies have improved customer experience and processes through the use of prescriptive analytics software.

SideTrade uses payment patterns to improve customer service

SideTrade uses prescriptive analysis to gain a deeper understanding of the true payment behavior of clients. SideTrade can score clients based upon their payment history using prescriptive analytics. SideTrade’s clients are able to better understand and account for payment delays and can be more transparent.

The Future of Prescriptive Analytics in the Cloud

To analyze data in-depth, you will need to have a reliable and flexible location for data storage. Cloud storage is available. Cloud data warehouses make it possible to understand prescriptive analytics in large-scale and easy-to-use ways. Cloud data warehouses offer users a one-stop solution for data analytics. They can store information and also support a wide range of proprietary tools and external integrations.

Imagine what businesses could do if they used cloud-based data to power their prescriptive and predictive analytics. They would not only gain more data but also more accurate, secure, and real-time information. A manufacturing company might draw more than just company data. It could draw on both historical and future trends, as well as predictions and general economic forecasting analytics.

Prescriptive analytics is being pushed into new and exciting directions by the cloud.

Conclusion

Prescriptive analytics allows businesses to spend less time looking at spreadsheets and more time using data to develop processes and messaging that will differentiate them from their competitors. Businesses can achieve this benefit faster with cloud-based prescriptive analytics tools.

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