Organizations have long examined data and content to identify patterns and trends over time, but now tech professionals are interested in using data to look ahead and answer the question “How can we best leverage the data to help the business in the most valuable way?”

One technology that is gaining increasing attention is that of prescriptive analytics. Here are four ways that prescriptive technology can be impactful, and how IT leaders can best use the functionality to achieve core organizational objectives and empower business decision makers.

Advanced Analytics Aren’t Really “Advanced” Without a Strategy

Businesses today are drowning in data, but it’s a misperception that sheer volume of data alone will solve our problems. The overabundance of data does not necessarily mean it’s easy for us to draw the right conclusions and take the right actions.

 

Though at first it might seem like a good idea to collect and use as much information as possible, overindulgence can soon have some negative side effects. As noted in this Forbes article, “The problem is that we are rapidly approaching our breaking point with how much data a company can actually process, analyze and act upon. We may even have already passed that point of ‘peak data.’”

Forrester Research estimates that as much as 60 to 73 percent of the data collected by an organization is never successfully used for any strategic purpose.

To avoid becoming “drunk on data,” as I often call it, it is important to implement strategies for handling data and digitalization. Ultimately, everything you do should further the purpose of enabling more reliable and confident decision-making—not just at the executive level, but throughout the entire organization.

Implementing analytic tools around data allows IT professionals to make decisions without guessing or making assumptions. Encapsulating all of the data under a strategic umbrella increases transparency and validation while eliminating angst and uncertainty.

Consider Options and Tradeoffs Like Never Before

Descriptive analytics tell us what has happened; predictive analytics can tell us what might happen. And prescriptive analytics goes a little bit deeper, considering business objectives, constraints, and inputs to recommend the best action forward, showing the impact of each decision on relevant KPIs.

One potential value of prescriptive analytics is that you don’t necessarily need a ton of data to reach the best decision or outcome. Prescriptive analytics focuses the question you’re asking, and the decisions you’re trying to reach, to one tangible answer using a smart model of your business that is not dependent on the amount of data (how much or how little) that you have.

Predictive techniques and functionalities can be great at identifying a multitude of options through statistical modeling and forecasting, as long as you have the relevant data—but that’s precisely the problem. It’s difficult to process and synthesize numerous options and the nuanced differences among them to determine what you should actually do. How can you be sure that you’re making the best decision? How can you be sure of the impact it will have on your company?

Prescriptive analytics can involve hundreds of thousands of tradeoffs associated with a question you might have, and it uses the available data to identify the best decision and impact relative to the goal you’re trying to achieve. As you begin to better manage tradeoffs among conflicting goals, you’ll gain deep organizational knowledge that you could have never achieved with descriptive and predictive technologies alone.

You Don’t Need a Degree in Advanced Mathematics

Even with its advanced capabilities, a prescriptive analytics approach may not seem as obvious as predictive analytics, which is more widely used.

Predictive capabilities will allow you to teach functionalities like machine learning and artificial intelligence (AI) from reviewing the data patterns at your disposal. Prescriptive technology requires a bit of a different approach, because you’re trying to build an algorithmic representation that the data itself doesn’t adequately define.

The good news is that there have been wonderful advancements in the prescriptive world that now enable organizations to implement very complex algorithmic capabilities without the use of skilled data experts, who (by the way) are few and far between. Rather than employing an army of highly specialized programmers with brilliant mathematical backgrounds, organizations can now utilize approaches where AI can generate those algorithms.

Allowing AI to do the math empowers people within the organization to be able to interact with prescriptive technology in a way that doesn’t require tremendous levels of technical experience in obscure branches of mathematics. Prescriptive analytics allows you to put your data to a new and more valuable use.

Test, Fail, Repeat? Not so fast...

When talking about advanced analytics, I often use the analogy of firing a rocket. Predictive technology might allow you to record launch and flight data to see what happens after deploying the rocket, but you have to keep firing the rocket and recording the data with the hope of eventually figuring out what’s going wrong. Another alternative is to use mathematics and physics upfront, write down the equations, and consider the other factors involved (gravity, payload, and so on).

When you’re looking at how an organization works and where it wants to go, you need a smart model that is highly reliable but doesn’t depend on testing, failing, and trying again in order to interpret the data, like the rocket analogy. This is where the use of prescriptive analytics can be highly beneficial.

Rather than refiring the rocket hundreds or even thousands of times, a simple tweak to the equation can help you determine how far you want the rocket to go, what load it can bear, and other considerations. Prescriptive analytics can enable an organization to remain agile and confident that they’re producing optimal, feasible plans as the world around them changes.

Prescriptive analytics is the most advanced form of analytics available today. It has the potential to have the greatest impact on large-scale business objectives like profit, cost, risk, service levels and customer satisfaction, performance efficiency, and more.

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