What is predictive analytics and why does it matter more now than ever before? Predictive analytics uses applied algorithms and rules to structured data in order to predict outcomes. These predictions can be used to make operational decisions, which will result in the best course of action being selected to respond to a given set of circumstances
The practical applications these solutions provide can be significant to say the least. In the commercial space, they can allow for the identification of key consumer trends, drive deeper customer understanding, predict consumer behavior at the macro, micro and individual level, anticipate customer complaints and recommend actions to address all of these issues. In finance, such tools can predict market movements, identify potential fraud and recommend cross and up-sell opportunities based on spending patterns.
Arguably the most significant application of predictive tools is in the industrial sphere, where being able to predict the required maintenance needs of capital assets or equipment can drive huge cost savings across a multitude of industries. Predictive maintenance delivers insight on when maintenance, replacement and upgrades are actually needed as opposed to performing maintenance tasks on a predefined schedule (when it may not be necessary) or after a failure occurs (and the cost is much higher).
For example, in energy extraction industries, predictive analytics can help identify the best places to drill for oil, cutting exploration costs. For utilities, it can help anticipate potential line failures (reducing outages), improve utilization of maintenance labor and equipment (lowering energy costs to consumers). In transportation industries such as railways, predictive analytics can be used to analyze usage patterns of rolling stock and infrastructure. This will help advise where maintenance resources can be focused on areas under the most strain that require preventative maintenance actions, avoiding costly failures and service delays.
All of these capabilities and benefits of predictive analytics have existed for a number of years. However, recent trends are allowing them to be democratized and spread to smaller organizations and individual users, avoiding the need for large software budgets or armies of data scientists to create and analyze the insights generated. Increasing computer power, better understanding of how big data can be best utilized, simpler to use applications, and the availability of low-cost SaaS offerings are allowing the use of predictive analytics to spread into smaller organizations and even to individual business users within large enterprises.
This democratization is an exciting development in its own right, driving cost savings, providing environmental benefits, reducing waste and providing competitive advantage to an ever-growing user base. However, what is potentially even more exciting is the very near future, when such insights are combined with cognitive platforms. When this happens, the overall effectiveness of predictive decision making will be greatly expanded, moving solutions not just from providing recommendations, but being able to act on them in a manner far faster and more reliably than at present.
Cognitive platforms will be able to take the insight generated from the data analysis and predictive recommendations, and build those into a holistic view of a business or services needs. This means that actions taken through the recommendations of predictive maintenance solutions can be managed and executed in a manner that is not just appropriate for the equipment being monitored, but will be done in a way that best relates to the whole solution that it is a part of. When cognitive systems are able to manage these interactions in such an interconnected way on thousands or even millions of individual component parts of a complex solution, this will lead to huge cost potential cost savings and increases in efficiency and profitability
This sounds in many ways like what cognitive platforms are doing already, but currently these systems learn and recommend primarily based on historical data sources, not predictive ones. This shift in orientation will change the dynamic of what cognitive solutions will be able to do. When in the near future cognitive capabilities are added to ever more affordable predictive analytics tools the ability to drive this level of insight and execution will be extended.
The irony is that what predictive analytics can’t do is predict its own future (for now at least). Regardless, the combination of predictive and cognitive abilities is likely to be one of the most exciting developments in the emerging breed of predictive maintenance solutions.