What is Prescriptive Analytics?

Due to the sheer amount of data now available to companies, it’s easier than ever to leverage information collected to drive real business value. However, it can be tricky to identify the best way to analyze this data. 

Applying prescriptive analytics is one option that can assist your business in identifying data-driven strategic decisions and help you avoid the limitations of standard data analytics practices, including:

  • Exhausting valuable resources on housing data that does not inform business decisions
  • Spending time sifting through unutilized data sets
  • Missing out on unique revenue streams and insights 

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Get started by learning what prescriptive analytics actually is, and how it is different from descriptive and predictive analytics. Understanding how it supports business intelligence, how other companies are already using it, and how the cloud is driving it forward will give you all the tools you need to get the most out of your organization’s data.

What is prescriptive analytics?

Prescriptive analytics is a process that analyzes data and provides instant recommendations on how to optimize business practices to suit multiple predicted outcomes. In essence, prescriptive analytics takes the “what we know” (data), comprehensively understands that data to predict what could happen, and suggests the best steps forward based on informed simulations.

Prescriptive analytics is the third and final tier in modern, computerized data processing. These three tiers include:

  • Descriptive analytics: Descriptive analytics acts as an initial catalyst to clear and concise data analysis. It is the “what we know” (current user data, real-time data, previous engagement data, and big data).
  • Predictive analytics: Predictive analytics applies mathematical models to the current data to inform (predict) future behavior. It is the “what could happen."
  • Prescriptive analytics: Prescriptive analytics utilizes similar modeling structures to predict outcomes and then utilizes a combination of machine learning, business rules, artificial intelligence, and algorithms to simulate various approaches to these numerous outcomes. It then suggests the best possible actions to optimize business practices. It is the “what should happen.”

Prescriptive analytics is the natural progression from descriptive and predictive analytics procedures. It goes a step further to remove the guesswork out of data analytics. It also saves data scientists and marketers time in trying to understand what their data means and what dots can be connected to deliver a highly personalized and propitious user experience to their audiences.

 

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Benefits of prescriptive analytics

If you’re a senior executive, looking to further optimize the efficiency and success of your organization’s operations is always top of mind. Prescriptive analytics is the smartest and most efficient tool available to scaffold any organization’s business intelligence. Prescriptive analytics affords organizations the ability to:

  • Effortlessly map the path to success. Prescriptive analytic models are designed to pull together data and operations to produce the roadmap that tells you what to do and how to do it right the first time. Artificial intelligence takes the reins of business intelligence to apply simulated actions to a scenario to produce the steps necessary to avoid failure or achieve success.
  • Inform real-time and long-term business operations. Decision makers can view both real-time and forecasted data simultaneously to make decisions that support sustained growth and success. This streamlines decision making by offering specific recommendations.
  • Spend less time thinking and more time doing. The instant turnaround of data analysis and outcome prediction lets your team spend less time finding problems and more time designing the perfect solutions. Artificial intelligence can curate and process data better than your team of data engineers and in a fraction of the time.
  • Reduce human error or bias. Through more advanced algorithms and machine learning processes, predictive analytics provides an even more comprehensive and accurate form of data aggregation and analysis than descriptive analytics, predictive analytics, or even individuals. 

Examples of real companies winning with predictive and prescriptive analytics

Prescriptive analytics isn’t just a trend or buzzword. Nor is it an unattainable resource for non-enterprise level organizations. Find out how the following companies are creating better processes and customer experiences through the prescriptive insights provided by their analytics tools. 

CenterLight Healthcare provides better patient care 

CenterLight uses prescriptive analytics to reduce the element of surprise when it comes to patient care and scheduling. Prescriptive analytics helps CenterLight find the best possible times to schedule treatments and check-up appointments without over-burdening their patients, while also ensuring the utmost patient safety and health. Prescriptive analytics also empowers CenterLight to be just as proactive as their patients when any setbacks or surprises occur.

SideTrade predicts payment behavior to provide better customer service 

SideTrade uses prescriptive analytics to deepen their understanding of a client’s true payment behavior. Through prescriptive analytics, SideTrade is able to score clients based on their payment track-record. This creates transparency and accuracy so that SideTrade and its clients can better account for costly payment delays.

The cloud and the future of prescriptive analytics

In order to analyze data comprehensively, you need a robust and versatile location for data storage. Enter the cloud data warehouse. Cloud data warehouses make massive undertakings like understanding prescriptive analytics not only possible, but user-friendly. With its ability to house information while also supporting an endless selection of external tools and proprietary integrations, cloud data warehouses gives users an all-in-one solution to data analytics. 

Imagine if businesses currently using on-premises system data as the basis for their predictive and prescriptive analytics could harness the power of the cloud? Not only would they gain more data, they would gain more accurate, secure, and real-time data. For example, a manufacturing company could draw on more than company data. It could leverage both historical and customer industry trends and predictions, and general economic predictive analytics.

The power of the cloud is pushing prescriptive analytics into new, exciting possibilities every day.

 

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Getting started with prescriptive analytics

With prescriptive analytics, businesses spend less time poring over spreadsheets and more time using informed data to create the processes and messaging that will set them apart from competitors. Effective, cloud-based prescriptive data tools can help businesses achieve this benefit even quicker.

Talend Data Fabric is an all-in-one solution for managing and analyzing data any time and anywhere. As a single suite of data integration and data integrity applications, Talend Data Fabric is the quickest way to acquire trusted data for all of your reports, forecasting, and prescriptive modeling. 

If you’re a CFO, data engineer, or business analyst looking to have your data do more, try Talend Data Fabric today to begin integrating prescriptive analytics into your business.

| Last Updated: September 30th, 2019