The purpose of prescriptive analytics is to tell decision makers what will happen in the future.

How can you take all the data your business collects and use it for good? Data analytics offer a myriad of ways to transform numbers into actionable insights. From descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics, data analysis methods are used to assess both the past and predict the future so that businesses can make the most informed and rational decisions to protect their best interests. 

While descriptive analytics looks at actions that have already happened, predictive analytics helps inform what could happen across a set of potential decisions and their following outcome. This means that it provides your organisation with the ability to take advantage of future opportunities while mitigating risks by depicting the results of each decision before they happen. It moves predictive analytics to the next level by assessing the consequences of actions, therefore, providing the ability to decrease risk. 

Prescriptive analytics is a relatively new form of data analytics that businesses are now quickly integrating into their business intelligence toolstack to take advantage of its offerings. As the final state of business analytics methods, prescriptive analytics is transforming how organisations function. 

But, it is imperative to note that prescriptive analytics are based on assumptions, which means that businesses need to be strategic in determining accurate inputs if they want to receive actual outputs. 

What is Prescriptive Analytics

As the name implies, prescriptive analytics prescribes the next best step or course of action for a business to take. It helps to determine the best outcome by utilising information gleaned from descriptive analytics (which answers what happened by using past data) and predictive analytics (which surmises what will happen based on forecasting and modelling). 

With known parameters, prescriptive analytics not only can anticipate what will happen and when, but it also explains why it will happen. It can automatically improve prediction accuracy and inform the best next step because it can continually take in new data to re-predict and re-prescribe. As it functions, prescriptive analytics allows companies to assess several possible outcomes to answer “What should we do?” and provide advice. 

How It Works 

Grounded in statistics, prescriptive analytics utilises simulation algorithms, machine learning, business rules, computational modelling, neural networks and optimization to work. With different data sources, both historical and transactional, it works with big data and in real-time to best predict outcomes of decisions. 

This means that before you make a risky decision, you can take a look at the possible outcomes and mitigate risk. 

Benefits of Prescriptive Analytics for Businesses

Knowing what will happen before it happens is like having a superpower. With prescriptive analytics, organisations can take advantage of the following benefits: 

• Helps make decisions for the future before decisions have to be made 
• Can assist in mitigating risk
• Continuously processes new data to give better options 
• Improve operations - optimise planning, reduce inefficiencies, etc. 
• Optimise production 
• Schedule inventory and optimise supply chain 
  

These are all broad case benefits, but let’s dive deeper into some use cases, as they mainly relate to the financial industry. 

Use Cases: Financial Industry 

Here is a look at just a few ways that prescriptive analytics can help financial organisations optimise their future endeavours. 

• Product Management: To offer innovative financial solutions for customers, banks and financial institutions can use prescriptive analytics to create the best product mix and loan offerings to meet clients’ needs. 
  

• Decrease Processing Time: Financial exchanges and trading firms benefit significantly from quick processing times. As such, prescriptive analytics can help predict the best business practices to achieve optimal processing time before any changes are made or new technologies are adopted. 
  

• Reduce Compliance Risk: Financial institutions are under strict regulations. For example, banks in the UK and America have to pass a “stress test” to comply with the Bank of England or Federal Reserve and remain in business. Some banks have instituted prescriptive analytics to simulate the stress test in advance and ensure its operations meet the standards. 
  

Overall, prescriptive analytics can be used to mitigate risks naturally. Since risk is the unknown result of an action, prescriptive analytics gives you insight into what the result can be before taking a step. Therefore, organisations can reduce financial risk, compliance risk, market risk and more by utilising this form of big data analytics. 

Predictive Analytics Vs. Prescriptive Analytics

Since both predictive and prescriptive analytics are used to determine what will happen in the future, they are easily confused. However, they serve different functions, albeit they are closely related. 

Predictive analytics forecasts what could happen in the future. It uses decision analysis, predictive modelling and transactional profiling to predict opportunities and interpret risks. It can be used to maximise efficiency, as well as pinpoint potential issues and anomalies. 

Prescriptive analytics, on the other hand, takes predictive analytics to the next level by suggesting a range of options and outcomes. While they work together, prescriptive analytics is powering revolutionary changes in the world. To illustrate, prescriptive analytics is behind autonomous driving as it is used to help the car decide when to slow down, speed up or make a turn. 

Prescriptive Analytics is a CFO’s Secret Weapon 

Prescriptive analytics determines what should happen next. For Chief Financial Officers, it moves them beyond balance sheets and income statements to be informed decision-makers that can dramatically shape a company’s future. 

Before any action is taken, prescriptive analytics gives you the outcome to quantify a choice. It’s changing how businesses work because data once could only look to the past. But, now, technology has given rise to forward-looking, bold and accurate predictions that lead to improved performance and efficiencies. 

With the rise of such tools, businesses are taking a must adopt approach rather than seeing big data analytics as extra work. Working with descriptive, predictive and diagnostic analytics, a company can incorporate prescriptive analytics to have a complete overview of what has happened, why it happened, what could happen and the outcomes of each probable situation. 

Garner has predicted that the industry of prescriptive analytics alone will reach $1.1 billion in 2019. Although the percentage of businesses currently using predictive analytics is much higher than those using prescriptive, the adoption rate is high because of its benefits and the increasing ability and decreasing cost of mass data storage. 

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 

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. 

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.

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.