Analytics has transformed from basic Data collection, to framing a Predictive Analysis Model from where periodic insights can be drawn from. After Big Data entered the industries, the ways to manipulate the data became a major focus. Hadoop and NoSQL was a boon to the professionals who worked night and day with data.
Analytics has been around since a long, long time. Long before the industrial revolution. Consider, even small shop owners will have some analytics system running in their head that tells them when to expect more customers, and around what time of the year. You may bet that when it comes to inventory and sales, not all items get sold all around the year. In the end, what this makes evident is that taking decisions based on information is the most common-sensical thing to do, rather than being whimsical about it.
And sometimes, our ideas may not always match with what the data has to say.
There is one thing that analytics does, and that is make data CLEAR-ER. It is making sense out of data, and to put in a form that is transferable. It is ‘surprising’ how momentary most forms of technology are. Not long before, Big Data was big, now the term makes more sense with how it can be used. Now that we are able to process loads and loads of data with the advent of advanced analytics using machine learning, the traditional, inaccurate and sometimes biased decision-making is facing a dead-end.
Analytics Finding a Place in Stocks, Betting and Customer Relations
Money-making prospects that involved predictions, such as betting has been about favoritism, present conditions, and visions. Now analytics encroaches, and these areas are now Data-Centric.
By far, the toughest predictive analysis applications are betting and customer-relationship analysis. This is due to the dynamic nature of human behaviour. Isaac Newton, once lost a ton of investment money in England’s stock share market bubble to famously quote: “I can predict the motions of the heavenly bodies, but not the madness of people”.
Yet, modern analytics techniques like crowd sentiment analysis and others are being founded to keep track of how people’s perceptions change on material matters. Unlike the times of Newton, the world is a more open place, and somewhere a bunch of people will share a common sentiment on trade. This information triggers preventive measures that can soften the blow in any future case.
In short, human behavior is dynamic, and mostly tend to follow trends. Sometimes it’s better to be able to know the future by closely following such trends, rather than adopt later when you can no longer gain substantially.
Simply put, letting a machine go through records, clean it up, pick the label categories, and make a prediction for you. It’s a lot more convenient, can evade human errors, and eventually the most time saving strategy of all.
Remember when 3-dimensional analysis was impressive, and new? With Machine Learning, there is no limit to how many dimensions the data can take.
Common Predictive Analysis Applications and Cases:
Promotions Impact
Retail Markets, whether B&M (Brick and Mortar), or Online depend on seasonal and festive promotional offers, to keep their supplies off the godowns. Sold is better than sold for a great margin. Ads on social media, SMSes, and emails can then be easily tracked for conversion. The impact is then measured in various categories, to easily rid of low impact promotions, and device better promotions.
Scenario Planning
Sometimes, following what others are doing is not what you would want to do for your business, and sometimes you wanna try out what the others are doing. In this ‘scenario’, you have a lot of questions, and doubt to whether such an idea will pay off. Scenario planning does exactly that where you apply uncertainties to the axes of matrices, to gather some possible scenarios where you can optimally implement a case.
Forecasts
Forecasting might be the best ‘shoot in the dark’ format in predictive analysis. Yet, it is the best night vision goggle you can procure to do just that. There are minor impact forecasts, that are better accurate. Then, there are forecasts that are more precarious.
The minor ones maybe like Google’s type-in suggestions, GPS time tracking, or sleep tracking on your health band. The more challenging ones would be weather forecasts, market predictions, and customer relations management. As with any other form of predictive analysis, accuracy in these categories depend hugely of data collection over time. The more time and data that is fed on individual devices, location, or markets, the more accurate a model becomes. So, patience is key.
Fraud detection
There are people out there who try to take the easy way to reach their goal, and end up giving a difficult time to others. All your efforts may get wiped out with the malicious intent of some such folks. In fact, companies spend more money trying to build security features into the products they have build than they do on anything else. The logic seems screwed, but history has shown how disastrous it is to compromise on security and fraud detection.
Predictive Analysis with Machine Learning Across Industries
There is an incredible potential to Predictive Analysis, because we like to know what happens in the future if we take a decision on something. With Machine Learning thrown-in, analytics look like they come from the future. From predicting the next recession to predicting a customer’s buying pattern… with data, predictions can be made and tested for accuracy. Now analytics is being strategically applied in sports, and other betting arenas, even betting on employees (that is if they should be retained or not, and how to retain the valuable ones).
Though every industry has touched analytics, modern analytics is yet to penetrate all geographical locations on earth, and smaller enterprises are able to gain access to better analytics. Right now in developing countries, industries such as e-commerce, banking, and insurance are finding value in improving their analytics processes.
While other industries are slowly adopting analytics, the race is only about to get tighter. These factors create a demand for specialists and experts that can bring Machine Learning to the mix. The demand is hard to meet, so why not make it easy to do with minimal skills? Our company focuses on Predictive Analysis with Machine Learning, in an effort to make things easier for the many professionals out there. Most professionals are bombarded with projects, and their contractors or employers expect them to finish a project sooner than they can.
I think that making Predictive Analytics more accessible will drive inclusiveness, in a world that is largely divided. Progress should not be confined to the most affluent, but should be available to the less-skilled, and less-resourceful.
-Shashank Kripa
About
About
At Mate Labs we have built Mateverse, a Machine Learning Platform, where you can build customized ML models in minutes without writing a single line of code. Our platform enables everyone to easily build and train Machine Learning models, without writing a single line of code. Feel free to reach us out at mate@matelabs.in
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