Real world examples of data mining

In this article, I will try to give you some of the real-world examples of data mining.

·     Data mining and its data functionalities

Datamining, predictive analysis or knowledge discovery all of these terms are used in different places by different people but all of these terms mean one and the same.

Let’s try to understand it in a simpler word, these terms refer to a set of techniques for discovering hidden patterns or insights from a large dataset. These patterns help in creating a predictive model to stay on top of future behaviors.

Today, almost all of the organizations irrespective of whatever their domain is looking to capitalize on their BigData and are hence favoring using sophisticated analytical methods to derive some hidden insights from the data which can help the organization to stand-in this competitive world. As the consumption of Big Data grew, so did the need for data mining.

Today, we can see applications of data mining everywhere around us in our day to day activities.

Let’s see some of the real-life examples of data mining.

Machine Learning and Artificial Intelligence

MachineLearning and Artificial Intelligence both are gaining a lot of popularity in the world today, and why they are becoming so popular? We can simply say the credit goes to Data Mining.

One of the most common examples of AI and Machine Learning that you most likely come across every day is the beloved recommendation systems. Has it ever happened that after watching a video on YouTube, you’re shown a list of recommended videos, and you end up watching some of them? How did YouTube do this? By thoroughly studying and analyzing your past data and behaviors. Using your behavioral trends, YouTube can categories products depending on the probability of your purchasing the product. While YouTube and other online streaming websites use AI to show videos recommendations, product and music streaming platforms like Amazon, Flipkart and Netflix use the same to better curate your playlists and provide better customer services.

The examples which I have mentioned above uses Artificial Intelligence on top of the mined data. However, reverse usage is also possible, i.e., you can develop theories and then use data mining to strengthen your theory. For example, if a self-driving car sees a red Alto car over speeding by twice the speed limit, it might develop a theory that all red Alto car over speed. This AI can then use Data Mining methods to strengthen or weaken the theory.

Crime Prevention Agencies:

The use of Data Mining and Analytics is not just restricted to corporate applications or education and technology. The list goes to prove the same. Beyond corporate organizations, many of the crime prevention agencies also use data analytics to find trends across myriads of data present with them. This data includes information including details of all the major criminal activities that have happened till date.
Mining this data and thoroughly studying and understanding patterns and trends allows these crime prevention agencies to predict the future events with much better accuracy.

With the help of Data Mining and analytics, these agencies can find out everything from where to deploy maximum police manpower (where is the next crime most likely to happen and when?), who to search at a border crossing (based on type or age of the vehicle, number or age of occupants, or border crossing history), to even which intelligence to take seriously in counter-terrorism activities.

Supermarkets and retails stores

Data mining allows the supermarket and retails stores owners to know your choices and preferences even better than yourself. If you don’t believe us, you’ll be amazed.
Following the purchase history and behaviors of one customer, one of the supermarkets correctly concluded that the customer is pregnant. And let me tell you – this was even before the woman herself knew. You can now get to how much power data have.
In general, these retail stores divide the customers into what they call “recency, frequency, monetary” (RFM) groups and specific groups with different campaigns and strategies. So, when a customer who spends a lot but infrequently will be dealt differently than a customer who spends little but often. The latter kind may receive loyalty, upsell, or cross-sell offers, coupons, whereas the former might be offered a win-back deal.

Service providers

Nowadays, Service providers have been using Data Mining to retain their customers for a very long time now. Using the techniques of Business Intelligence and Data Mining allows these service providers to predict the “churn” which means when a customer leaves them for another service provider.

Today, every service provider has terabytes of data on their customers. This data includes things like your billing information, customer services interactions, website visits, and such. Using data mining and data analysis, the service providers assign a probability score to each customer. This probability represents how likely you are of switching the vendors. Then, these companies target the people at a higher risk by providing incentives and personalized attention, to retain the customers.

As we see some of the example of data mining above but now it’s becoming a limitless technique, which every small or big companies trying to implements in their business model so that they can face this competitive world of due to technologies advancement.

I hope after reading this article, finally, you came to know about some of the real-life examples of data mining?

Insideaiml is one of the best platforms where you can learn Python, Data Science, Machine Learning, Artificial Intelligence & showcase your knowledge to the outside world.

#insideaiml  #artificialintelligence  #machinelearning  #deeplearning  #datascience  #realexamples