We live in the age of enormous data generation, where our all gadgets, services and platforms are creating footprints in digital world in form of data. Facebook alone processes around 500+ terabytes of data every day. This data is processed and the fetched information is sent to product owners, helping them build a better product or improve the current one.
The above-mentioned process of extracting useful information from an accumulation of data, and making sense of it, is called Data Mining.
Data Mining is useful for dozens of business verticals in various ways. If you want to figure out how it works and why it is valuable for you, this article will help you out. We also have covered data mining tools and use cases thoroughly for providing detailed insights.
Also watch: Process Mining – A Value Discovery for Enterprises
How Data Mining works?
Data mining tools utilize various statistical, mathematical, and analytical procedures to monitor and analyze the enormous amount of data. In fact, the technology proves to be more helpful and valuable with larger data sets and with more user experience.
The outputs of data mining processes are patterns, grouped/separated data sets, relationships in data, trends, predictions, and so on. Together (or alone), this information can help organization in decision-making and business planning.
Data mining operation consists of the following elements:
- Define the Problem – Stakeholders should be know about internal & external data types that will be used for this exploration, alongside having figured out the problem area for this particular functional use case.
- Data Gathering – Assess, collect and understand the required data from various sources.
- Prepare & Pre-process – Extract the relevant data sets and cleanse the data as per requirement.
- Data Model – Select the proper algorithm and build predictive models.
- Train and Test – Train the data model with appropriate data and simulate.
- Verify and Deploy – Verify final data model, prepare visualization and deploy.
Why use data mining?
Whenever you have to work with a plethora of data in real-time and despite having the task as essential, you think of it as unreasonable, data mining can be your savior.
The key benefit of data mining is to identify hidden patterns and associations in huge volumes of data from multiple sources. With more data from diverse sources like social media platforms, remote sensors, and detailed reports – data mining offers the tools to explore Big Data and turn it into actionable insights.
The data mining process can help to detect astonishing and intriguing relations and patterns in apparently unrelated piece of information.
To summarize this section, the major benefits of Data Mining are:
- Helps create a searchable knowledge-base with reliable information;
- Streamline your production and operations with better insights in your industry;
- Avoid the use of costly and less precise statistical data applications;
- Improve business decision making and inventory trend prediction;
- Less costly technology that can be deployed on new as well as legacy systems;
- Correct predictions and study of hidden patterns, trends and data;
- Process massive business data in real-time without delays.
Data Mining Techniques
Data mining requires you to utilize a generic tool kit instead of following a pre-defined process. It is very productive process implied we select appropriate and accurate technique. However, the real challenge for the experts is to choose the most optimal techniques for specific scenarios considering the variety of options available for them.
Data Mining techniques that we have listed here are based on the data to be processed for finding trends, associations, intelligence, and business insights. Have a look at them:
1.Association
Goal of association technique is to link two apparently unrelated activities or events. Association as a data mining technique helps businesses to craft their marketing plans in better way.
2.Classification
As you can predict from its name, classification is a technique for analysis of data that first groups various data values as per their behavioral proximity with different classes. In the end, you will get classified data in multiple classes, while data in each class meeting a particular common criteria.
3.Clustering
Clustering is a bit different from classification technique, as it does the grouping of data but not on the basis of pre-defined classes. In this technique, common features form the basis of grouping. Clusters comprise similar data sets organized in proximity to each other in one frame without having clear boundaries between them.
4.Regression
Regression analysis predicts a value based on trends or patterns set by historic data. By doing this, it gives out the expected value of future events. The process is crafted to explore existing interaction between different variables. This process can calculate the possibility of a variable being derived from other existing variables. The goal of regression is to link between two distinct information pieces in one group.
5.Prediction
Prediction method looks into the output of various other data mining processes, for example – trends, clusters, classes, sequential patterns, analytics data, etc. Thereafter, it combines these data sets and creates future event predictions as per timeline inclinations.
6.Sequential Patterns
This technique requires a lot of transaction data with timestamps. After analysis, it groups similar patterns and create trends as per the recurring events.
Data mining tools
- MonkeyLearn – No-code text mining tools that utilize machine learning
- Apache Mahout – Ideal for complex and large-scale data mining
- Oracle Data Mining – Lets developers predictive data mining models for their applications
- RapidMiner – A data science platform for workflow visualization through data mining in Python.
- IBM SPSS Modeler – A predictive analytics solution to allow data scientists work with data assets and modern applications without much programming
- Weka – Open-source software for knowledge analysis through multiple data mining techniques
- Knime – This platform has pre-built analytics, integration and reporting components for your data mining projects.
- H2O – The open-source AI hybrid cloud for building models and applications using data mining in Python
- Orange – A powerful open-source data mining toolbox for analysis and visualization of data
- SAS Enterprise Miner – Solve business problems with data mining
Data mining Challenges
Large Dataset
In current times, data is being generated through multiple channels in any organization very rapidly – giving data mining processes a better chance to search through it. However, the same, i.e. Big Data is a bit problematic for existing systems considering its high volume, high speed of transactions of it, a number of data structures being used in it, and a lot of unstructured data.
Quality & availability of data
With enormous information or raw data there is incomplete, incorrect, ambiguous, fraudulent, and useless data. Data Mining tools and platforms can help to sort such data, but the users must provide full details about the source of the data. Also, it is your responsibility to check the credibility and reliability of input data sets to get precise results.
User Competency
While the purpose of data mining tools is to gain insights about the underlying data and give analysis reports, the task is not that easy. Modern tools now require design and user-friendly interface so that all kinds of users could use it. This way, we can reduce the efforts developers put during user training. Only the fully-aware users with a good understanding of business context, processes to be executed as per the use case, and data utility can make the best use of a data mining tool.
Data Mining Use Cases
| Domain | Use Case |
| Manufacturing | Using data mining manufacturers can track quality trends, overhaul data, production rates, and product performance data to identify production gaps. It helps to identify possible process upgrades that would improve quality, save time and cost, and improve product performance. |
| E-Commerce | E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites by using common buying patterns of their customers or using search patterns. |
| Insurance | Price prediction for their products, choosing new or existing customers to their pitch new offers, competitor analysis, etc. can be done using data mining tools. |
| Education | Data mining techniques benefits instructors to view student data, predict accomplishment levels, analyze students’ performance, and discover students (or student groups) who need mentorship. |
| Banking | Banks use data mining for understanding of market risks and meet the regulatory compliance obligations that change very often in this industry. Data mining techniques commonly applied to credit ratings and to intelligent anti-fraud systems to analyses transactions, card transactions, purchasing patterns and customer financial data. |
| Retail | From the site location or new branch location suggestions. Data Mining can help retail stores, grocery stores and malls pick more profitable locations for their shops. Product selection, inventory trends, pitching of certain products to certain customers as per their purchase history, etc. can be handled through this technology. |
| Service Providers | Customer retaining as well as reasons of clients’ going away are studied and classified through data mining in the utilities sector where services are the major offerings of an organization. For example – Mobile operators look for the billing data, service related calls/interactions, grievance management for their tokens, quality of associated product, incentives offered and maintenance cost, in order to prevent and thereby reduce the rate of customers leaving. |
Summary
As organizations continue to be flooded with massive amounts of internal and external data, they need the ability to extract that raw material down to actionable insights at the speed their business requires. Data Mining, being one of the fundamental techniques of modern business operation, helps to get the actionable insights and lend the needed help for your requirements. It is one of the pieces for the bigger picture that can be accomplished by working with larger data sets, Big Data or Smart Data.
Data Mining approaches are continually evolving and getting more efficient in digging out the insights from enormous amount of data from various sources. With Data Mining professionals at your service, knowledge discovery through this process and the further utilization of the precious knowledge becomes easy for your mission-critical needs. So, consider consulting the experts to make the most out of it.