Most Common Examples of Data Mining

Talk about extracting knowledge from large datasets, talk about data mining!

Data mining, knowledge discovery, or predictive analysis – all of these terms mean one and the same. Broken down into simpler words, these terms refer to a set of techniques for discovering patterns in a large dataset. These patterns help in creating a predictive model to stay on top of the future behaviours in data science.
Today, most of the organisations – irrespective of their domain – are looking to capitalize on their Big Data and are hence using sophisticated analytical methods. As the consumption of Big Data grew, so did the need for data mining. Today, we can see examples of data mining everywhere around us.

Let’s look at some such examples of Data Mining that you come across frequently in your day-to-day life:

  1. Artificial Intelligence and Machine Learning

Both Artificial Intelligence and Machine Learning are gaining a lot of relevance in the world today, and the credit goes to Data Mining. How else do you make a system “artificially intelligent” without feeding it with relevant data and patterns? And, how do you extract relevant patterns if not by 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 buying a product from Amazon, you’re shown a list of recommended products, and you end up buying one of those in a blink of an eye? How did Amazon accomplish this? By thoroughly studying and analyzing your past data and behaviours. Using your behavioural trends, Amazon can categorise products depending on the probability of your purchasing the product. While Amazon and other e-commerce websites use AI to show product recommendations, video and music streaming platforms like Spotify and Netflix use the same to better curate your playlists.

The examples mentioned above use 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 Maruti overspeeding by twice the speed limit, it might develop a theory that all red Marutis over speed. This AI can then use Data Mining methods to strengthen or weaken the theory.
Who is a Data Scientist, a Data Analyst and a Data Engineer?

  1. Service Providers

Service providers have been using Data Mining to retain customers for a very long time now. Using the techniques of Business Intelligence and Data Mining allows these service providers to predict the “churn” – a term used for 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 mining and analysis of this data, the service providers assign a probability score to each customer. This probability score is a reflection of how likely you are of switching the vendors. Then, these companies target the people at a higher risk by providing incentives and personalised attention, to retain the customers.
Key Concepts of Data Warehousing: An Overview

  1. Supermarkets and Retail Stores

Data mining allows the supermarket owners to know your choices and preferences even better than yourself. If you don’t believe us, you’ll be amazed by what Target did a few years back.
Following the purchase history and behaviours of one of their female customers, Target correctly concluded that she is pregnant. Oh, and let’s tell you – this was even before the woman herself knew. Such is the power of data, patterns, and analysis.

Read: Data Mining Projects in India

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, 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, whereas the former might be offered a win-back deal, just for instance.
Data Visualisation: The What, The Why, and The How!

  1. Science, Engineering, and Education:

The areas of science and engineering have seen a massive overhaul ever since the application of data mining techniques. Let’s look at some specific fields that make use of Data Mining techniques:

    • Sequence mining finds extensive use in the study of human genetics. It helps in understanding the relationship between the variations in DNA sequence and the variability in susceptibility to diseases. Simply put, it aims to find out how the changes in DNA correspond to the risk of developing common diseases, which will aid significantly in improving methods of diagnosing, preventing, and treating these diseases.

 

    • Data mining is used in the field of educational research to understand the factors leading students to engage in behaviours which reduce their learning and efficiency.

 

  • In the area of electrical power engineering, data mining methods have been widely used for performing condition monitoring on high voltage electrical equipment. The aim of this is to obtain valuable information on various safety-related parameters like the status of insulation, and such, to avoid any mishaps.
The What’s What of Data Warehousing and Data Mining
  1. Crime Prevention Agencies:

The use of Data Mining and Analytics is not just restricted to corporate applications or education and technology, and the last example on this list goes to prove the same. Beyond corporate organisations, crime prevention agencies also use data analytics to spot trends across myriads of data. This data includes information including details of all the major criminal activities that have happened.
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.

Data Manipulation: How Can You Spot Data Lies?

What we’ve discussed above are just a few of the many examples of Data Mining. If this article has left you fascinated and wanting for more, we recommend you dive deeper into concepts like data mining, data analytics, business intelligence, and artificial intelligence. This will broaden your knowledge-base, and also help you make a more informed career choice – if you’re looking to jump ships to Data.
Business Intelligence is the present and the future and Data Mining forms the base of everything to quite an extent. So, make sure you’re thorough with your basics of the same if you’re looking for a rewarding and a fulfilling career!

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

What are the different sectors where Data Mining is applied?

Data mining is a broad field with practical applications in a variety of fields, and it has the potential to expand any business even further. Some sectors where Data Mining is frequently utilized include:

1. Healthcare : Mining can be used to forecast patient volume in any category. Processes are being built to ensure that patients receive adequate care at the right time and place.
2. Education : An institution can employ data mining to make informed decisions and anticipate student outcomes. As a result of the findings, the institution can concentrate on what to teach and how to teach it.
3. CRM : Customer Relationship Management entails attracting and maintaining customers, as well as increasing customer loyalty and executing customer-centric tactics. The collected data can be used for analysis using data mining methods. Rather than wondering where to focus in order to keep customers, solution seekers receive filtered results.

Why is Data Mining important for business?

Businesses that use data mining get a competitive advantage, have a better understanding of their customers, have more control over their operations, enhance client acquisition, and discover new business prospects. Data analytics will aid diverse industries in different ways. Some industries are seeking for new ways to attract new customers, while others are attempting to improve current systems. Data mining provides organizations with the tools and knowledge they need to make informed decisions, analyze data, and move forward.

What are the benefits of Data Mining?

Data mining provides significant benefits in terms of data collection, storage, and processing. Data mining advantages include:

1. It assists businesses in gathering accurate data.
2. In comparison to other data applications, it is a cost-effective and efficient option.
3. It assists businesses in making profitable production and operational changes.
4. Data mining makes use of both new and old systems.
5. It can be used by businesses in making well-informed decisions.
6. It helps in the detection of fraud and credit risks.
7. It enables Data Scientists to quickly evaluate large amounts of data.
8. Data Mining can be used by data scientists to detect fraud, create risk models, and improve product safety.
9. It helps Data Scientists not only to discover hidden patterns but also to initiate automated predictions of behaviours and trends.

Master The Technology of the Future - Data Science

Leave a comment

Your email address will not be published.

×
Let’s do it!
No, thanks.