Discover how predictive analytics/maintenance is the next big thing in the data discovery standpoint. Predictive analytics juggles with probabilities instead of facts and figures, helping enterprises to anticipate and offset risks.

Decision-making is and has always been critical in determining individual as well as organizational growth. We are now, at any given point of time, a step ahead of our age, thanks to the continuously spinning heads taking proactive decisions all this while! However, as we embark on our cognitive journey, most decisions cannot wait for data scientists’ skill set to evaluate potential options. This eagerness to bridge the gap between the present and the future has lead to predictive maintenance as one of the most imperative industrial solutions.

Predictive maintenance is a sensor driven technology that passes unambiguous information to intelligent systems loaded with AI algorithms. These algorithms then gather facts about the situation and compare them to the previous data set to decide the significance of the information. Further, the system scans through a set of possible actions and predicts which action will be the most successful.

Predictive Maintenance solutions aim at:
• Reducing downtime
• Reducing maintenance cost
• Ensuring workers’ safety
• Yielding improved productivity by forecasting future failures
• Proposing periodic maintenance schedules

A 2015 report titled The Internet of Things: Mapping the Value Beyond the Hype [1] by McKinsey Global Institute, estimates that predictive maintenance could reduce maintenance costs of factory equipment by 10 to 40 percent. The report also suggests that predictive maintenance using IoT can reduce equipment downtime by up to 50 percent and reduce equipment capital investment by 3 to 5 percent by extending the useful life of machinery. In manufacturing, these savings have a potential economic impact of nearly $630 billion per year in 2025.
This has been made possible by predictive analytics which follows the first phase of business analytics, the descriptive analytics. While the latter is about understanding the past behavior through data mining, the former is about answering ‘what’ and ‘when’ of a possible future occurrence. However, the answer to ‘why’ and ‘how’ do not come straight and this is where big data analytics plays a vital role. Data mining, machine learning and statistical techniques form the foundation of predictive analytics. Companies leverage the sophisticated predictive analytics software to decode data sets for critical information in order to predict future outcomes.

Commonly used predictive analytics software for enterprise [2]:
• RapidMiner
• IBM SPSS Statistics
• SAS Advanced Analytics

The IoT enabled intelligent systems generate enormous amount of information to help businesses predict and act accordingly. In times to follow, this intuitive technology will also be able to suggest beneficial actions from the predictions, indicating implications of each decision. Wouldn’t that be great! With such proactivity enterprises will soon be able to see the future rather than just predict it, thereby minimizing the risk to their businesses and managing disruptive competition.

It goes without saying; big data indeed is a big deal as it paves our path from insights to innovation through predictive analytics. Have you incorporated predictive analytics technology in your setup yet? If not, it is just about time for you to join the change because predictive analytics is the next big thing that will rapidly propel your business towards future.

Sources:
[1] – http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world
[2] – https://www.g2crowd.com/categories/predictive-analytics/top/enterprise/products

Author: Vivek Madathil, Chief Architect, Digital Transformation Services