Prescriptive Analytics: The Next Leap

Analytics is the discovery of meaningful patterns in data. While it is not new, we’re going through a renaissance in data science and technology in business analytics. What started as Descriptive Analytics, using data to build reports and dashboards to present it in a more consumable way, grew to Investigative Analytics to answer the ‘why’ question, has matured into Predictive Analytics, with historical data, models and algorithms to predict the future outcomes. The next leap is Prescriptive Analytics which not only predicts the outcomes but also suggest or prescribe a solution in order to influence the future in the desired way. It encompasses all spheres of traditional cause and effect analytics with new age machine learning and artificial intelligence techniques to suggest the best route to obtain the desired result. For example, prescriptive analytics can help in the processes and decisions related to oil and gas exploration to suggest ‘where to drill’ and ‘how to drill’ to minimize costs and impact on environment.

How is it different from other Analytics?

Though Prescriptive and Predictive Analytics are the buzz words around the BI and Analytics space, yet 80% percent of the analytics done in organizations is descriptive. Descriptive answers “What happened?” It is a deductive logic which looks at the overall trends in the business. On the other hand, prescriptive analytics is considered to be the combination of all the analytics (descriptive, investigative and predictive), however it is even more – It answers the question “So what?” integrated with – Business Rules, Feedback Mechanism and Adaptive System. How would a prescriptive system know whether its prescriptions are being used? The action (or inaction) with respect to a prescription is fed back to the system to adapt the future prescriptions accordingly making it more inductive than deductive. However, a prescriptive system would take entitle its user to take actions, artificial intelligence systems on the other hand which take actions on their own categorize under automated systems.

I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

Applications of Prescriptive Analytics

Prescriptive Analytics is a sophisticated technique and it is still evolving. However, it finds many applications in some of the niche areas:

Crime Analytics

Crime analytics is a growing field and has vast potential because of the very nature and stakes involved. With prescriptive systems in place, it is now possible to detect, prevent and fight a crime even before it has happened. The prescriptive analytics ingests historical crime data with several data points like crime date, location, type of convict, nature of convict, spatial data, real time surveillance information, satellite imagery, audio and visual feeds and many more. The first step is Predictive, it calculates the probability of crime happening at a location, then based on the historical data as well as real time information it suggest the best possible way to fight the probable crime. Some of the analytics products which offer solutions in this space include Alteryx, IBM, Command Central, Crime Reports and Verisk.

E-Commerce

Ecommerce giant Amazon recently announced that it would be shipping the products even before the customer buys it. Strange, how can Amazon know who would buy which product at what time? Amazon uses Predictive analytics blended with descriptive analytics (trends, patterns, exceptions) of customers’ historical shopping data to predict the probability of a customer to buy a product with the date-time information. The system prescribes Amazon to ship the product to the customer with an expected order date. Amazon then packages and dispatches the product to a local warehouse until a real order has arrived. As soon as the order arrives, the product is delivered to the customer – almost instantaneously. Many other players also use analytics for customer experience management including Flipkart, eBay, makemytrip, BigRock, HomeShop18 etc.

Oil and Gas

Another area where Prescriptive Analytics find huge applications is Oil and Gas exploration. Explorers are using prescriptive analytics to discover natural resources hundreds of kilometers beneath the earth’s surface. It takes into account several variables like earth’s sedimentation characteristics, temperature, pressure, type of soil, depth, chemical composition, molecular structures, seismic activity, machine data and others to prescribe the best possible location to drill for resources. Not only it saves the effort and cost associated with the operations but also unveils many obscure facts about the earth’s resources. Pricing is another area where prescriptive analytics help companies to figure out the right pricing by foreseeing a multitude of factors to hedge the financial risks beforehand.

Health Care

When it comes to healthcare, there are multiple factors involved which affect the business and processes whether it’s a patient, clinics, doctors or pharmaceutical companies which accounts for the inherited uncertainty. Prescriptive analytics can help doctors to recommend medicines to patients based on the medical history, historical data of patients with similar conditions, allergies, environmental conditions, stage of cure etc. Prescriptive analytics is helping healthcare providers in maximizing the value delivered to the stakeholders (patients, doctors, clinics, pharmaceutical companies) – at a time when the market is gradually shifting towards fee-for-performance and value based model – by leveraging operational and usage data combined with other data like population density, economic data, demographic trends and health trends.

Education

Prescriptive Analytics is well suited to cater to education sector because of the very fact that it generates large amount of high quality data. It can be used for recommending courses to a student using the internal (records, marks, grades) and external data (social, big data, interest, hobbies) – not only that but also on the institutional side – for student enrolment, retention, aspirant analysis, improving learning systems and curriculum planning. Prescriptive analytics benefits both the institution and the student by aligning their interests on the same platform.

The above text lists only a few applications of prescriptive analytics. As the field of data science is growing, the analytics techniques and tools are also scaling up with the growing volume, velocity and variety of data. The next generation of business analytics and visualization tools like Alteryx and Tableau powered by advanced analytics help the business owners solve these problems. While different business problems might require different levels of analytics, with the growing amount of data, need for optimized information and advanced analytics tools, possibilities are endless – one just needs to envision them.

About Grazitti^™ Alteryx Services