Understanding Data Mining in 2019

The real reason behind why digital personalization and customer experience (CX) is soaring every year is because experts have been able to hack into the heart of their main challenge, and that is – data. Yes. Data is everywhere today. Every big enterprise is scrambling to tell us about their big data, data analytics, data privacy etc, and amidst that data mania there is data mining.    Tweet This! A process that involves sifting through large amount of raw data to extract the valuable ones in an effort to predict the future outcomes. But that’s not all; data mining techniques are also used to build machine learning (ML) models that power modern artificial intelligence (AI) applications such as search engine algorithms and recommendation systems. 

There are a lot of benefits of data mining. When done right it can reduce time and also the risk of human error, resulting into more efficiency and room for humans to instead focus on all the other aspects of work. This eventually impacts the decision-making by providing more accurate forecasting to improve the overall customer experience (CX) as well as potential revenue stream.

How Are the Industries Using Data Mining?

If you think that data mining process is for tech professionals only then think again. As it turns out, there are many other industries that are reaping benefits for implementing data mining into their systems. And the process comprises of six main steps:-

Understanding the Business

The first step is to establish what the goals of the project are and how data mining can help them accomplish that goal. In accordance with that, a plan should be put in place which includes timelines, role assignments and actions. 

Understanding of Data

Data visualization tools are used to explore the properties of data to ensure that it will help in accomplishing the business goals. This is done by collecting all the relevant and valuable data from the applicable sources.

Preparation of Data

This process involves cleansing of data in order to find and include the missing data and ensure that it’s ready to be minded. The distributed systems are included in the modern database management systems (DBMS) for improving the speed of the process. This is done to avoid the tedious and time consuming amount of work that can affect the productivity.

Having a DBMS also keeps the data secured which is not usually the case while storing all the data in a single warehouse. Additionally, it’s important to include failsafe measures in the data manipulation stage to prevent permanent loss of the data. 

Data Modeling

An important skill for data scientist, data modeling is built during the analysis and design phase of a project in order to ensure that the requirements for a new application are understood. It is basically used to find patterns in the data using sophisticated tools.

Evaluation

The findings are then measured, evaluated and compared to marketing objectives for determining whether they should be deployed. 

Deployment

To ensure a smooth deployment strategy the findings of data mining are shared across everyday business operations. To do this, an enterprise business intelligence platform can be used for providing a single source for self-service data discovery.

What Are the Benefits of Data Mining?

  • Data mining helps organizations to continually analyze data by automating both routine and critical decisions that’s devoid of any human errors and biases. Thanks to data mining now banks can instantly and easily detect fraudulent transactions, request for verification and also protect the personal information of customers to avoid identity theft. When deployed within a firm’s operational algorithms, these models collect, analyze and independently act on data to streamline decision-making and impact the overall process of any organization. 
  • Data mining eases planning and provides managers with dependable forecasts based on previous trends and also the current situations.
  • The use of data mining means getting accurate forecast with minimum costs. For example, Delta, an Italian airline company, imbeds RFID chips in passengers’ checked-in baggages to identify holes in their process and reduce the number of bags that could be mishandled. This eventually results in an improved customer satisfaction and reduces the likelihood of searching for and re-routing lost baggage.
  • Offering the best customer experience is a top priority for any business these days, and leveraging data mining can definitely help achieve that. Data mining is used to understand customers’ preferences and choices better to give them an enhanced and more personalized experience. 

Even the most advanced and unique inventions can have their flaws and data mining is no exception to that. Below are some of the challenges that may hinder the process of data mining:

  • The increasing demand for storage requirement of data has forced a lot of firms to rely upon cloud computing and storage. The popularity of cloud computing may have empowered many businesses and organizations but the nature of service does create significant privacy and security threats. In an age where almost every work gets done digitally with millions of sensitive individual data floating around, it has become very important to be aware of malicious threats and maintain the trust with partners and customers alike.
  • With big data comes even bigger challenges and they are – volume, velocity, variety and veracity. 
  • Volume is the challenge of processing and storing an enormous amount of data that’s been collected by businesses and organizations. Not only, is it difficult to find the correct data but it also slows down the processing speed of data mining tools. 
  • Velocity on the other hand is the challenge that occurs when data generation increases rapidly.
  • Variety refers to the different kinds of data being collected and stored. Inability to focus an analysis on both structured and unstructured data hinders the data mining efforts. 
  • Data can be extremely messy, biased, incomplete, inaccurate etc. the quicker data is collected chances are the more errors it will have. The challenge of veracity is to retain the quality of data within its quantity.
  • As data velocity will have a direct impact on volume and variety, companies must scale their models and apply them across the entire organization. To experience the full benefits of data mining, companies must purchase and maintain powerful computers, software and servers designed to handle and take care of the company’s database.
  • The over-fitted models can often times be very complex and utilize an excess of independent variables to generate a prediction. Hence the risk of that happening is heightened by the increase in volume and data. Because too few variables will make the model irrelevant, whereas too many can restrict the model to the known sample data. The challenge here is to moderate the number of variables used in data mining models and balancing its predictive power with accuracy. 

Data mining opens a whole lot of opportunities for firms and organizations alike, yet that ease of use can lead people astray. While it makes businesses soar and the customers happy with highly personalized experience, the idea of having millions of user data falling into the wrong hands can be really frightening. Which is why we have strict laws like the GDPR and CASL coming into the picture to give people control over how their data is being used. Hence, to make the most out of data mining, not only the CX, client churn and profitability should be kept in mind, but organizations at large should be more insistent upon data privacy. 

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