Prescriptive Analytics

Prescriptive Analytics

Prescriptive analytics is the newest and most advanced analytics system since descriptive and predictive analytics. It’s becoming more and more common in the business world. The three different existing analytic models may be defined as follows:

  • Descriptive analytics is the analysis of data to help show trends and patterns that emerged in the past. It tells us what happened.
  • Predictive analytics uses data, statistical algorithms, and machine learning techniques to make predictions about future events. It tells us what will happen.
  • Prescriptive analytics uses optimization and simulation algorithms to show companies the best actions to take to maximize profit and growth and determine options for the future. It tells us what we should do.

Prescriptive analytics reaches past what the future holds and tells companies what they should be doing to prepare for it. It provides different options for action and suggests which ones would be best. There is a certain measure of artificial intelligence built into its processes, in the sense that it can analyze optimization and simulation algorithms and then determine what the options are, moving forward. It can even recommend which would be the best option to take.

Compared to the other two models, prescriptive analytics is more difficult to administer than the other two analytical models. With this model, machine thinking and computational modeling are applied to several different data sets, such as historical data, transactional data, and real-time data feeds.

Prescriptive analytics uses algorithms that work on data with very few parameters. The algorithms are specially designed to conform to the changes in established parameters and are not subject to external human controls. The algorithms are free to be optimized automatically. As time progresses, their “learning” ability helps them to better predict future events.

Benifits of Prescriptive analytics

Prescriptive analytics is still fairly new, so many companies aren’t yet using it in their daily processes, although some of the larger corporations are already using it daily. The benefits are already beginning to manifest in many industries, especially supply chains, insurance, credit risk management, and healthcare.

Because it learns from transactions the customer made in the past, predictive analytics can, for example, prescribe the best time of day to contact the client, which marketing channel is likely to be more successful, and what product will be most appropriate. Much of this can be automated, such as by using email services and text messages.

Thus, a benefit is that the knowledge gained from the analysis of big data can be applied to a vast number of decisions that would usually take up too much time for people to manually decide upon.

Future of Prescriptive analytics

The future of prescriptive analytics looks bright. Figures show that in 2014, about 3% of companies were using prescriptive analytics. It was predicted that, by 2016, so-called “streaming analytics” would become mainstream. Streaming analytics is a form of analytics that gets applied in real-time as transactions occur.

Prescriptive analytics will become more and more important for cybersecurity, analyzing suspicious events as they happen, having great application in preventing, for example, terrorism events.

Prescriptive analytics is also set to become very big in lifestyle activities in 2016 onwards. This includes activities such as online shopping and home security.

The predictions for 2016 haven’t been entirely accurate, as it’s not yet used much in the home environment, but its use in the corporate world is taking off. However, even in businesses that use it, it seems that it’s used in some departments but not in others.

Google’s “Self-Driving Car”

Google made extensive use of prescriptive analytics when designing its self-driving car. Self-driving cars utilize machine learning to develop smarter ways of driving on the roads. In the vehicle, the machine, as opposed to a human driver, analyzes the real-time incoming and stored data to make decisions.

The vehicles house sensors and software that detects surrounding vehicles, other road users such as cyclists, and obstacles such as roadworks. It detects small movements such as hand signals given by other drivers and uses these to predict what the other driver is probably going to do. Based on this, the software takes action. It can even adjust to unexpected events, such as a child running across the road.

It started in 2009 when the challenge for Google was to drive over ten uninterrupted 100-mile routes completely without human driver intervention. By 2012, they had branched out onto the highways and busy city streets. By 2014, new prototype vehicles were being designed with no steering wheels or foot pedals. These prototypes hit the roads (safely) in 2015, with Google employees as testers. In 2016, the Google self-driving car project became an independent company dedicated to making self-driving technology a safe and affordable option for all drivers. (The company’s name is Waymo.)

The cars are a perfect metaphor for what prescriptive analytics can do for a business: the computer tells management what route to navigate based on its analysis of all the data.

Prescriptive Analytics in the Healthcare Industry

The healthcare industry is also becoming aware of how the cutting-edge technology that prescriptive analytics offers can improve their complex operational activities. It’s not good enough to just have piles and piles of information. This on its own will not give the industry insight. It’s only when data is put into context that it provides usable knowledge and can be translated into clinical action. The goal must always be to use historical patient data to improve current patient outcomes.

An example is in the context of patient readmissions. Predictive analytics can accurately forecast which patients are likely to return in the next month and can provide suggestions regarding associated costs, what medications are likely to be needed, and what patient education will probably be needed at the time of discharge.

It’s important to remember that even when clinical event prediction is specific and accurate, this information will only be useful if the proper infrastructure, staff, and other resources are available when the predicted events occur. If the clinical intervention doesn’t happen, no matter how good the predictors were, they will not be utilized to the full. Decision-makers must therefore not be too far removed from the point of decision. However, when it is used correctly, predictive analytics can help control costs and improve patient care, which is probably the main goal of any healthcare organization.

Prescriptive analytics can suggest which treatment would be the best for a specific patient’s needs. This helps streamline the diagnostic process for medical practitioners. Hours can be saved because the many options for any medical condition can be narrowed down by the software for the doctor to then make a final decision.

Prescriptive analytics can help the pharmaceutical industry as well by streamlining new drug development and minimizing the time it takes to get the medicines to the market. This would reduce expenditure on drug research. Drug simulations could shorten the time it takes to perfect new drugs.

As we can see from looking at the use of prescriptive analytics by the industries above, among the many benefits provided by prescriptive analysis, the most important seems to be reduced risk as decisions are data-driven and therefore more accurate, increased revenue because processes are optimized, thereby minimizing cost and maximizing profit; increased efficiency as processes are streamlined and improved.

Prescriptive Analytics in the Oil and Gas Industry

The oil and gas industry uses predictive analytics in many different ways to ensure efficient, safe, and clean extraction, processing, and delivery of their product. While shale oil and gas are abundant in the US, they are difficult to find and extract safely. Horizontal drilling and fracking are expensive and possibly cause environmental damage. They are also relatively inefficient. As a result, some of the biggest oil and gas corporations are using prescriptive analytics to help deal with and minimize these problems.

The processes surrounding oil and gas exploration and extraction generate huge amounts of data, which are set to double in the next couple of years. It’s easy to see how this will be possible when one considers that there are about 1 million oil wells currently in production in the US alone. The focus needs to be on ways of using all this data to automate small decisions and guide big ones, thus reducing risk, improving productivity, and lessening the environmental impact.

Companies can now look at a combination of structured and unstructured data, giving a better picture of problems and opportunities that may arise, and providing ideas for the best actions to take to solve these. This combination will mix machine learning, pattern recognition, computer vision, and image processing. The blend of all of these results in the ability to produce better recommendations of where and how to drill, and of how to solve problems that may crop up.

Types of data that are looked at include graphics from good logs and seismic reports, videos from cameras in the actual wells, fiber optic sensor sound recordings of fracking, and production figures. Geologists take data from existing wells to give information about the rocks forming the area and the nature of the ground below the surface. Prescriptive analytics is then used to interpret this information and predict what the ground maybe like between wells, enabling the rest of the ground to be visualized with some accuracy. The best course of action is then suggested, using these observations.

Prescriptive analytics should enable oil and gas companies to predict the future of the wells in a given oil field and know where to drill and where not to. Data is not only collected regarding the actual oil field and wells, but also about drilling equipment and other machinery. This is useful as it can be predicted when maintenance will be necessary, and the correct intervention can be prescribed. It can also be suggested when the old machinery will be likely to need replacing. The predictive analysis may predict corrosion in pipelines, using data collected by robotic devices in the pipelines, and then suggest preventative measures.

In this way, technology helps the oil companies to extract the oil and gas safely and efficiently, as well as deliver it to the market in the most environmentally friendly manner. The US is quickly becoming an energy superpower and was set to overtake Saudi Arabia in 2016 as the world’s biggest oil producer. Prescriptive analytics can only help in this endeavor.

Prescriptive Analytics and the Travel Industry

Predicting the future in any industry is mainly to do with finding patterns in the large quantities of available data so that we can gain insights from it. By looking at what customers have done in the past, industries can make predictions about what they’re likely to do in the future and prescribe what to do about it. They can suggest the perfect product, specifically tailored to the needs of the customer, such as holiday destinations, hotel recommendations, or the best flight routes, all within a fraction of a second.

As a traveler, this would likely work for you as follows: say you’re traveling to Denver for a four-day work conference, plus you want to spend a few days in the mountains straight after. You’d begin with an online search for flights into Denver using an online travel agency. Because of predictive analysis, you’d straight away receive a special offer from the airline you fly with most often, for the type of route you’d prefer (perhaps early morning with no stopovers.) You’d receive some information about some good restaurants in the city near to where you’ve booked your hotel, as well as some offers for mountain cabins and guided hikes. All this would be possible because using big data specialists to help them, travel companies are getting good at working out their customers’ needs based on previous travel patterns.

The travel industry has been gathering data much faster than it could use in recent decades from airlines, hotels, and car rental companies. Now that analytics tools and computer storage capacity are bigger, more powerful, and more affordable than ever before, this data can now be made sense of and utilized.

In the travel industry, predictive analytics promises to bring greater profits for suppliers and better travel experience for customers.

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Era Innovator

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