With the advancement of technology and data being generated every day, there is an increment in the amount of data being generated per second. Terms like ‘Big Data’ are becoming quite common. It is forecasted that soon our world will converge into a ‘Global village’ with data ruling over the world. To get useful information out of this huge set of data, Data mining process is here to help.

But, what exactly is Data mining?

Data mining is the process of looking at large banks of information to generate new information. It is about extrapolating patterns and new knowledge from the data you’ve already collected to predict outcomes.

Using a broad range of techniques, you can use this information to accelerate the pace of making informed decisions, increase revenues, reduce risks, improve customer relationships, cut costs and much more.

Who's using it?

Data mining is at the heart of analytics efforts across a variety of industries and disciplines. Some industries examples are:

• Retail

Retailers use data mining to better understand their customers. This allows them to better segment market groups and tailor and customise promotions to different consumers. Predictive consumer behaviour modelling is now a core focus of many organisations and viewed as essential to compete. Companies like Amazon built their own proprietary data mining models to forecast demand and enhance the customer experience across all touchpoints.

• Banking

Data mining helps financial services companies get a better view of market risks, detect fraud faster, manage regulatory compliance obligations and get optimal returns on their marketing investments. Banks deploy data mining models to predict a borrower’s ability to take on and repay debt. Using a variety of demographic and personal information, these models automatically select an interest rate based on the level of risk assigned to the client. Applicants with better credit scores generally receive lower interest rates since the model uses this score as a factor in its assessment.

• Insurance

With data mining, insurance companies can solve complex problems concerning fraud, compliance, risk management and customer attrition. Companies have used data mining techniques to price products more effectively across business lines and find new ways to offer competitive products to their existing customer base.

• Healthcare

Healthcare professionals use statistical models to predict a patient’s likelihood for different health conditions based on risk factors. Demographic, family, and genetic data can be modelled to help patients make changes to prevent or mediate the onset of negative health conditions.

• Education

With data-driven views of student progress, teachers can predict student performance before they set foot in the classroom – and develop intervention strategies to keep them on course. Data mining helps educators access student data, predict achievement levels and pinpoint students or groups of students in need of extra attention.

Top 3 Data Mining techniques

Data mining is highly effective, so long as it draws upon one or more of these techniques:

• Descriptive Modelling: it uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorising customers by product preferences or sentiment.

• Predictive Modelling: this modelling goes deeper to classify events in the future or estimate unknown outcomes – for example, using credit scoring to determine an individual's likelihood of repaying a loan. Predictive modelling also helps uncover insights for things like customer churn, campaign response or credit defaults.

• Prescriptive Modelling: with the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.

Top 4 Benefits of Data Mining

1. Automated Decision-Making

Data Mining allows organisations to continually analyse data and automate both routine and critical decisions without the delay of human judgement. For example, banks can instantly detect fraudulent transactions, request verification, and even secure personal information to protect customers against identity theft.

2. Accurate Prediction and Forecasting

Planning is a critical process within every organisation. Data mining facilitates planning and provides managers with reliable forecasts based on past trends and current conditions. 

3. Cost Reduction

Data mining allows for more efficient use and allocation of resources. Organisations can plan and make automated decisions with accurate forecasts that will result in maximum cost reduction.

4. Customer Insights

Companies deploy data mining models from customer data to uncover key characteristics and differences among their customers. Data mining can be used to create personas and personalise each touchpoint to improve overall customer experience.

Conclusion

However you approach it, data mining is the best collection of techniques you have for making the most out of the data you’ve already gathered. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionise your business.

GUEST BLOGGER AUTHOR:

CLAUDIA TINNIRELLO