Fundamentals of Prescriptive Analytics

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Prescriptive analytics uses historical data to find the best course of action for the future. In a way, prescriptive analytics combines elements from both descriptive and predictive analytics to arrive at actual solutions. The increased preoccupation with everything “data” is now a mainstream trend. The analytics market is expected to reach $190 billion in 2023, growing at an annual rate of 11.1%. Meanwhile, the business analytics market is projected to reach $60 billion by 2024.

In 2023, prescriptive analytics will be at the forefront of business analytics. Using predictive and descriptive analytics to analyze historical data and provide descriptive outcomes will still be important. However, prescriptive analytics will take analytics to a new level by providing specific recommendations given the current circumstances. 

Newer data technologies such as the Internet of Things (IoT), real-time analytics, and sensor-driven business operations have recently bolstered the craft of advanced data analytics. Businesses are no longer satisfied with simply providing accurate descriptions or making accurate predictions.

Now, businesses want more – they want to know which solution best suits a business problem. In other words, they seek a doctor’s prescription for a particular problem. This is prescriptive analytics in a nutshell. This final stage of analysis sets it apart from both predictive and descriptive analytics, as it provides outcomes that can guide decision-making based on the future potential of different paths. For example, a retail company can use prescriptive analytics to take sales data, trends, patterns, and predictions into account when making weighted decisions that will bring about better business outcomes. 

To simplify complex concepts, the ultimate goal of prescriptive analytics is to find the best version of the truth and optimize the business process end to end. Newcomers may be more curious to know why businesses need prescriptive analytics rather than the hows of this field of advanced analytics.

Why Do You Need Prescriptive Analytics?

Most business executives prefer to get ready-made, data-driven business solutions to run their operations better, but they may not have the time or skill to pursue Data Science

While business operators understand their domain well and can assist in providing the needed data for analytics, they want seasoned data professionals to step in and conduct advanced prescriptive analytics to arrive at definite solutions to particular problems. The prescriptive quality of advanced data analytics is particularly appealing to already-stressed business executives who need immediate solutions to problems.

By understanding predictive data and market trends, companies can craft better business strategies and increase their profits. In 2023, predictive and prescriptive analytics will be critical for any business looking to stay ahead of the competition. Predictive analytics will help companies identify potential customer behavior and anticipate market shifts that could affect their bottom line. Predictive analytics will then provide recommendations on which decisions to make to capitalize on these insights. 

In 2023, a comprehensive analytics approach to business growth will require an understanding of descriptive analytics processes and the ability to utilize prescriptive analytics. Companies must use business analysis and intelligence reporting together to gain actionable insights and develop sales strategies that are both forward-looking and data-driven. Predictive models should inform decision-making, and business leaders should analyze past revenue and customer behavior to create a comprehensive strategy for future growth.

Analytics can assist companies in doing just that. Predictive analytics uses artificial intelligence (AI) and machine learning (ML) to analyze customer statistics, social engagement counts, and sales numbers to predict future outcomes. Descriptive analytics takes that information and describes what happened in the past and provides insights into current behavior patterns. Prescriptive analytics then looks at those patterns to provide recommendations on how best to optimize operations for the highest success rate possible. With these three types of analytics working together, businesses can use AI-driven predictive models to anticipate customer preferences, identify trends early on, better allocate resources, and increase sales by keeping salespeople informed of their performance against goals.

Prescriptive Analytics Use Cases

In 2023, fundamental principles of prescriptive analytics will be the core of business decision-making processes. Prescriptive analytics models leverage mathematical algorithms, heuristic decisions, and key decisions to support decision-making. Time adjustments can be applied to the data types to identify the best possible course of action and provide a support system for prescriptive decisions. With each passing year, businesses are becoming increasingly reliant on prescriptive analytics models in order to make informed decisions that maximize their returns.

Prescriptive Analytics Use Case: Health Care

A Health Catalyst article cites the advantages of prescriptive analytics in health care over predictive analytics. This article indicates that predictions alone cannot solve patient care problems. An additional step, which provides interpretation of associated data along with predictions, and also probable treatment procedures makes the analysis useful.

This additional step includes prescriptive analytics where specific, evidence-backed reasons behind predictions are cited along with probable treatment procedures. This approach to analytics offers immediate benefits to the medical practitioner, who may be a health care expert but not adequately skilled at data technologies to arrive at quick and immediate solutions. The prescriptive part of the analytics in the case of health care acts as an agent for prescribing specific treatment procedures, which would otherwise take medical practitioners a long time to figure out.

In another article titled Data Science in Healthcarethe author claims that in order to understand the outcome of predictions and probable course of actions, the medical practitioners should experience the advanced data analytics process first hand. The author hints about the usefulness of a data warehouse in medical data analytics, which can expose experts and analysts alike to a larger sample size than siloed data repositories.

The Infographic titled 10 Use Cases for Prescriptive Analytics in Healthcare is worth reviewing, as it supplements the common knowledge available among the health care and patient communities.

Prescriptive Analytics Use Case: Sales and Marketing 

In the retail sales and marketing operations, prescriptive analytics is widely used to optimize products and prices, identify micro markets, manage the supply chain, and design targeted campaigns, to name a few. The primary difference between predictive and prescriptive analytics is that while predictive tools simply signal future sales or marketing trends, prescriptive tools can actually provide the means to achieve the trends. The blog post titled Predictive Analytics in Marketing describes how prescriptive analytics systems and tools help optimize sales and marketing efforts.

Prescriptive Analytics Use Case: Risk Assessment

The risk mitigation use cases described in an insightful blog post help readers understand how risk is assessed, mitigated, and managed to today’s business landscape, especially with the use of the latest technologies. Another article highlights the top predictive analytics use cases in the insurance business.

What Is Next-Gen Analytics?

In the article titled Next-Generation Analytics Is Doing Tremendous Things, the author makes a lot of futuristic claims in favor of next-gen prescriptive analytics, but actual practitioners can only tell how much of the available literature is relevant in today’s advanced analytics landscape.

Meanwhile, an article about the impact of machine learning in supply chain businesses explains that supply chain businesses rely on three pillars of sustenance: data, machine learning algorithms, and actionability. Though data in conjunction with the superior algorithms can identify risks and potential problems, without actionability, the intended outcomes cannot be achieved. Here, actionability refers to prescriptive analytics, which delivers clear, definitive data technology solutions to business users at the right time.

For any business hoping to succeed in 2023, prescriptive analytics applications provide valuable tools by allowing access to predictive models that can help users better understand customer behavior while simultaneously predicting future outcomes with accuracy and efficiency. By utilizing analytics software, users can gain insights into data and processes in order to make decisions that drive better performance.

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