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Fundamentals of Prescriptive Analytics

By   /  November 14, 2017  /  No Comments

Prescriptive AnalyticsPrescriptive Analytics seeks to find the best course of action, based on past records, for the future. In a way, Prescriptive Analytics combines elements from both Descriptive Analytics and Predictive Analytics to arrive at actual solutions. The increased preoccupation with everything “data” was a natural outcome of the mainstreaming of the theory of probability, which had hitherto remained behind the closed doors of advanced statistics.

In recent times, newer data technologies such as Big Data, Internet of Things (IoT), Real-time Analytics, or sensor-driven business operations have bolstered the craft of advanced Data Analytics, where businesses are not just satisfied with providing accurate descriptions or making accurate predictions.

Now, businesses want more – they need to know which solution is best suited for a given business problem. In other words, they are looking for a doctor’s prescription for a particular problem. This is Prescriptive Analytics in a nutshell.

According to the global analytics and advisory firm Quantzig, Prescriptive Analytics is one of the hottest Data Technology trends of 2017 and 2018.  A good starting point for beginners to Prescriptive Analytics may be Forrester’s blog post titled What Exactly the Heck Are Prescriptive Analytics? This post introduces the benefits of Prescriptive Analytics through the looking glass of the global business community.

Although not strictly technical, the write-up can still persuade the sharp, technical minds to explore this field of advanced Data Analytics. To simplify complex concepts, the ultimate goal of Prescriptive Analytics is to find the best version of truth and optimize the business process end to end. The newcomers may be more curious to know why businesses need Prescriptive Analytics rather than the how’s of this field of Advanced Analytics.

Why do Businesses Need Prescriptive Analytics?

An average business today has a digital footprint, which forces the business owner or operator to collect, ingest, analyze, and present the data to gain competitive intelligence. As business owners or operators are typically very busy folks running their day-to-day business operations, they do not have the time and leisure to pursue data technologies or more specifically, advanced business analytics for increased profit.

However, they need the profit margins to remain healthy for future sustenance. For most business owners like these, either a Data Center or an advanced Data Analytics team or an out-sourced data service provider has to step in to handle and manage all data technology tasks.

A previous executive survey indicated that most business executives prefer to get ready-made business solutions in times of need. In an emergency situation, the business executives need the data-driven intelligence or data-driven solutions to better run their operations, but they do not have the time or skill to pursue Data Science. This is where Prescriptive Analytics come handy.

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 the 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.

Prescriptive Analytics Use Case: Healthcare

A Health Catalyst article cites the advantages of Prescriptive Analytics in healthcare  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 healthcare expert but not adequately skilled at data technologies to arrive at quick and immediate solutions. The prescriptive part of the analytics in case of healthcare 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 4 Essential Lessons for Adoption: Predictive Analytics in Healthcare, the 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 siloéd 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 healthcare and patient communities.

Prescriptive Analytics Use Case: Sales & Marketing 

In the retail Sales and Marketing operations, Prescriptive Analytics is widely used to optimize products and prices, to identify micro markets, to manage the supply chain, and to 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 means to achieve the trends. The River Logic blog post titled 5+1 Use Cases for Prescriptive Analytics in Sales & Marketing describes how Prescriptive Analytics systems and tools help optimize sales and marketing efforts.

Prescriptive Analytics Use Case: Risk Assessment

You can read the following article to understand how UniCredit utilizes Prescriptive Analytics tools to manage client risks: UniCredit Uses FICO to Apply Prescriptive Analytics Risk Management

In the Dataconomy article titled The Surge of Prescriptive Analytics, three other use cases of Prescriptive Analytics have been described in great details. The use cases cited here are Sales & Operations Planning (S&OP), integrated healthcare delivery network, and enterprise optimization in Oil and Gas industry (O&G). In each of these use cases, you will come across the use of Prescriptive Analytics strategies for reducing operational cost, increasing operational efficiency, and optimizing the business process. These use cases prove that Prescriptive Analytics can help optimize all facets of a business operations beginning with planning and ending with implementation.


What is Next Gen Prescriptive Analytics?

In the next generation of advanced Data Analytics, business users can expect transformational systems known as enterprise optimization. These advanced Data Analytics systems comprising Prescriptive Analytics will not just benefit fragmented business functions but the entire enterprise. These systems typically exploit sophisticated features such as visual programming, embedded knowledge, packaged financials, and built-in logic. Also read the Information Week feature article titled 8 Smart Ways to Use Prescriptive Analytics to understand how the hosted, Analytics-as-a-Service platforms of the future will provide Prescriptive Analytics in an eco-friendly way.

The DATAVERSITY® article titled Putting Focus on Prescriptive Analytics indicates that supply chain businesses reply on three pillars of sustenance, which are 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.

 

Photo Credit: chombosan/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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