How Machine Learning can boost predictive analytics
IoT Machine Learning Product engineering services

How Machine Learning can boost predictive analytics

Machine Learning is a subset of Artificial Intelligence — the more prominent cognitive computing technology that has become the priority for more digital enterprises. Google, Facebook, Apple, Tesla, and Netflix, almost every significant enterprise globally, is betting enormous sums in AI and ML. 

 ML brings to the table a host of benefits that no business can turn a blind eye to. To begin with, it automates manual operations and accelerates the pace at which work is done. It also manages to improve the accuracy, which is usually compromised in a manual setting.

 Machine Learning also lends tremendous support to Predictive Analytics. Predictive analytics is a form of advanced analytics that uses past, real-time, and historical data to predict future outcomes. 

 For example, predictive analytics can skim through past and present weather patterns, match them with current weather patterns to predict the weather for today, tomorrow, next week, and so on. In a digital enterprise where a large amount of data is available, predictive analytics can make a huge difference. 

  1. Preventive maintenance
  2. Chatbot conversations
  3. IoT data analytics
  4. Optical character recognition

1. Preventive maintenance

We spoke about digital enterprises earlier. Did you know that manufacturing companies are also in the race to become digital enterprises? They have already kickstarted Manufacturing 4.0 activities that will turn assembly floors into data churning hotspots.

 The data so created can be put to extensive use for predictive analytics. Predictive analytics can help predict preventive maintenance activities that should be initiated to prevent downtime. 

Traditionally, the analytics was done manually with spreadsheets and sophisticated computing algorithms. Today, Machine Learning has become mature enough to dissect data and forecast outcomes on its own. It gives more power to predictive analytics by maintaining accuracy and efficiency in operations. 

2. Chatbot conversations

Chatbots are perhaps the most popular avatars of Artificial intelligence and Machine Learning. Market studies by Juniper suggest that chatbots will generate up to $8 Billion in global savings by 2022.

 The primary benefit of chatbots is that they can provide canned responses to FAQs that customers ask. This is a massive benefit for any kind of business since they don’t have to assign a person to handle the website and the inquiries that come in through it round the clock.

 Furthermore, there is another added advantage of chatbots. They give so much data about the conversations that transpire between users and the chatbot. This can provide a direction about customer expectations, product features that they are searching for, etc. In other words, it can give the business cues to pave the future roadmap that is more aligned to customer wants. 

 Now the true potential of analytics can be brought to life when chat conversation data is made subject to analytics using Machine Learning. Machine Learning can pick up patterns in conversations, attach labels to them, and simplify the entire process of disseminating data. This also helps in better visualization of the conversational data, ultimately leading to better decision-making. 

3. IoT Data Analytics

IoT (Internet of Things) represents a mesh of internet-connected devices that are powered by penny-sized BLE batteries. They are embedded with sensors of several types, including temperature, light, motion, humidity, and so on. When these IoT devices are attached to any surface, they can collect data and transmit them to cloud servers. Such data can then be downloaded into analytic data platforms for further analysis.

The unique nature of this data is that it comes in diverse forms and also in varying structures. With the help of Machine Learning, it is possible to analyze data easily even if they are unstructured. In a way, Machine Learning acts as an underlying technology that can make data analysis easier for any predictive analytics platform. This enhances the accuracy of the forecasts made by predictive analytics.

4. Optical character recognition

One of the obvious use cases of machine learning has been image or pattern recognition. Image recognition is a computer discipline where systems are trained to understand pictures and their meanings through visual analysis. Machine learning empowers them to continuously update their database of known images and thus recognize them easily as and when they recur. 

The predictive analysis goes further to unearth reasons why certain images keep repeating, the frequency of repetition, and also the time span during which the repetition is the highest. According to Russ Penlington, “Combining the nuances of pattern changes with the consistency of established pattern stability is the key to creating accurate forecasts in predictive analytics.”

Predictive analytics along with optical character recognition can be used in industries where there is a high volume of paperwork—for instance, the banking industry. 

A large volume of banking transactions still requires analog paperwork. With the help of image recognition and predictive analytics, it is possible to hasten the pace at which documents are processed and proceeded for task completion. It will result in an overall upliftment of productivity and also simplify workflows at scale. 

Is Machine Learning + Predictive Analytics a silver bullet?

Predictive analytics has been used by businesses on a basic level. Spreadsheets have macros that can forecast sales figures or net earnings based on given values. However, when predictive analytics is merged with machine learning, it creates a powerful combination. It makes it possible to dissect large volumes of data at scale and arrive at predictions that can turn around business fortunes. 

Machine learning development has come a long way from where it was a decade or even five years ago. Today, the availability of large volumes of structured and unstructured data makes it possible to unearth deep insights. They can tell a single tell-tale sign of where a business is headed, how users are using a product, what kind of issues create maximum downtime, and so on. 

In other words, Machine Learning + Predictive Analytics may not be a silver bullet, not yet. However, it is on its way to becoming one. Enterprises of all scale, startups, small and medium enterprises, and large-scale organizations reach a tipping point. 

They either move forward with machine learning development or get stuck with legacy systems that are slow and soon will become obsolete.