Predictive analytics is one of the most promising technologies on the horizon for modern businesses. If optimized to its ideal form, it would be able to crunch a few hundred to a few thousand variables, and provide a detailed prediction for the future, whether it’s how your customers’ preferences will change over time or how pricing fluctuations could impact your company’s profitability.
Today’s predictive analytics tools are impressive, applied to healthcare, marketing, and other applications, but there are still several hurdles that need to be overcome before we can consider it at its full potential.
Despite decades of advancement, our best weather forecasting systems still aren’t great at predicting the weather. Why? Because all it takes is one tiny variation in temperature or pressure, magnified over time, to completely disrupt our vision for the future.
In the same way, a single variable can completely discount or disrupt an experiment, especially in a field as complicated as human health, or consumer purchasing decisions. No matter how much data we intend to gather, there’s no way to account for what we don’t know we don’t know, and that can skew even the most effective models.
Reliability is hard to establish, and even harder to prove—especially for large data sets over a long period of time. Let’s say you use an algorithm that predicts a spike in demand for one of your products, and that demand spike happens. Is this evidence that the algorithm will be reliable for future predictions? Or was it just a lucky shot? For marketers planning six- or seven-figure budgets, this is a vital consideration.
Thankfully, the cost of predictive analytics software has fallen in recent years, making it affordable for small businesses as well as large corporations. However, not all businesses can tolerate the full requirements for the investment. Choosing the right platform, integrating it with your other software products, training your staff, and constantly acquiring more data are all costly endeavors, and ones that can compromise the overall cost-effectiveness of the investment.
Accessibility for Non-Experts
Data analysis is a complex field, and one that has, historically, required years of education to master. Accordingly, demand for data analysts and data scientists has risen dramatically over the past few years. But the real power of predictive analytics is to simplify the most complex data problems, ideally making their solutions accessible even to non-experts.
Modern platforms are attempting to solve the accessibility problem with smoother, more intuitive user interfaces, and easily accessible display formats, like charts and graphs. However, there’s still more room to grow before predictive analytics are in the hands of the majority of employees. Until that transition happens, the potential of predictive analytics will still be limited.
Creativity and Recommendations
Predictive analytics may be able to forecast what your customers’ needs are, but it can’t yet tell you how to address those needs. It may be able to give you information you can use in your marketing and advertising materials, but it can’t write your advertisements for you. In the future, the automation of those recommendations is going to be imperative to streamlining operations.
In the meantime, predictive analytics has already grown significantly since its early stages of development. SAP’s predictive analytics platform is used by thousands of businesses to help them better understand their markets and make better decisions; if you’re interested in giving it a try, and seeing how far predictive analytics has come, sign up for a free trial today!