Today, every second spent on the internet or a gadget produces enormous chunks of data or information. This data then goes back to the service or product owner who uses it to create a better experience. This process of collecting data and scrutinising it to make sense of it is called data mining.
But how does it help the owner create a better product for the user? This article answers the compelling question and explores the various aspects that make data mining a predominant modern solution that professionals use to magnify their businesses.
What is the Meaning of Data Mining?
In layman terms, mining means the extraction process of valuable minerals. There are no prizes for guessing; the most expensive mineral of the 21st century is ‘data’. So, data mining is the extraction of useful and valuable data from a raw data set.
Two major highlights of Data mining are:
- Processing the raw data
- Extracting valuable information to identify patterns and relationships
Different industries have long used this process to dig through the data to find hidden connections and predict future trends. The plethora of information in this data helps predict consumer behaviour and guide the companies to adapt themselves to provide the best services.
Popular as knowledge discovery in databases, data mining is a complex process that involves complete data warehousing using powerful and dynamic computational technologies.
Why is Data Mining Important?
The information that data mining churns out is a game-changer for businesses across the globe. The valuable data it receives can be used in advanced analytics applications and business intelligence (BI). It helps the professionals develop competitive business strategies and manage operations effectively, including marketing, finance, customer support, HR, and other areas. It assists in risk management, fraud detection and cyber security planning, among other significant advantages.
How Does Data Mining Work?
Data mining consists of the following elements and the stage-wise operations::
- Data Cleaning: Removing the repetitive data and irregularities to make it noise-free.
- Data Integration: Combining the multiple data sources into one.
- Data Selection: Extracting data from the database.
- Data Transformation: Transforming the data to execute summary analysis and aggregatory operations.
- Data Mining: Extracting useful data from the existing data.
- Pattern Evaluation: Analysing the patterns existing in the data.
- Knowledge Representation: Representing the knowledge to the user through graphs, tables, trees, and matrices.
Data Mining Techniques
Data mining uses various techniques to convert extensive data into valuable insights. Some of the methods are:
Association Rules
A rule-based method helps look for relationships between different variables in the given data set. Association rules help companies get an insight into the relationship between various products.
Neural Networks
Most commonly used for deep learning applications, neural networks function by processing training data. This is achieved by replicating the intrinsic network of the human brain via a complicated network of nodes. Every node comprises a threshold, inputs, weights, and outputs. If the output data is more than the threshold, it signals the node to pass the data to the next node layer of the network. Neural networks adapt to this mapping function with the help of supervised learning and adjustment based on loss function during the steepest descent. We can be assured of the model’s efficiency and accuracy if the cost function is near or at 0.
Decision Tree
This technique uses regression or classification methods to predict or classify the potential outcomes established by a set of decisions. The name suggests that it represents the possible outcomes of these decisions via tree-like visualisation.
K-Nearest Neighbour (KNN)
Also known as the KNN algorithm, this non-parametric algorithm classifies the data points based on their association and proximity to the other data. The assumption is that similar data points can be found alongside each other.
Benefits of Data Mining
The advantages of data mining can be best achieved through predictive analysis and conventional data analysis. Unravelling the hidden patterns, trends, anomalies and correlations in data sets helps the businesses in their true sense. Given below are some of the benefits of data mining:
Effective marketing and sales
Databases help study consumer behaviour, generating targeted advertising and marketing campaigns. Sales teams can also use the same to offer different products to the existing customers by analysing their needs.
Improved Customer Service
Companies can identify the issues with customer service and prompt them immediately to improve their efficiency in chats and calls.
Better Management of Supply Chain
Organisations can predict the demand and supply patterns. This will eventually help manage their inventories, distribution, warehousing and logistics operations more effectively.
Risk Management
Data mining reduces the risks involved in business operations by helping the managers assess their legal, financial and cybersecurity.
Lower Costs
Data mining eventually helps in cost savings through enhanced competence in operations. It reduces waste and redundancy in corporate spending.
So Who’s Using It?
Data mining is being used by a variety of disciplines and industries throughout the world, as it is a cornerstone of analytics.
Telecom, Technology and Media
In a world where competition has taken centre stage, the strategies to keep the customers intact can be driven through consumer data. Technology, telecom and media industries can use analytic models to scrutinise the overabundance of consumer data to predict consumer behaviour and produce relevant and targeted campaigns.
Insurance
With the help of the analytic know-how, insurance companies can resolve grave problems related to compliance, fraud, customer attrition, and risk management. Data mining has been an integral part of this industry’s strategy to price products effectively and offer competitive products to keep a clasp on their existing consumer base.
Manufacturing
Keeping check of the demand forecasts to align the supply plans is as essential for a manufacturer as assuring quality, detecting problems and investing in brand equity. Data mining helps manufacturers achieve this effect with ease.
Banking
Automated algorithms through data mining help banks check on their customer base and the billions of transactions taking place intrinsically in the financial system.
Retail
Data mining helps the retail sector produce targeted campaigns directly impacting the consumer base. This result is accomplished through the massive customer databases, which helps improve customer satisfaction and relationships.
Impact of Data Mining in Real Estate
Brokers, agents and financial institutions all have a particular interest in consumer behaviour. Marital status, employment history, the recent sale of homes and property values create a consumer database that is a mine of gold for the real estate companies. But, the real estate sector has yet to tap the potential of this robust database.
The majority of the professionals in the real estate sector do not know how to make use of the data mining technologies to their benefit. At the same time, this revolution has already occurred in many industries throughout. The gains in this sector can be harnessed through:
- Property Price Indices
- Automated Valuation Models
- Time Series Forecasting
- Cluster Analysis
- Geographic Information System
Using the proper techniques and weaving through the huge raw database will help companies in real estate to understand the past, present and future submarket performance of the sector. This will aid them in making superior business and investment decisions.
Challenges in Data Mining
There is no denying that this is increasing manifold with each second. Comprehensively organising them is a task, and this challenge is threefold:
- Segmentation of the data
- Filtering out the noise
- Activating or integrating the valuable data
The primary concerns are:
Privacy and Security
Data mining opens the doors to personally identifiable information and can breach the security of any business/organisation. Since data mining can also be synonymously used with surveillance, this technology can be used positively and negatively. It becomes important to keep the data safe from the hands of the mischievous, which can be achieved through encryption mechanisms, security audits etc.
Data Accuracy
The data gathered must be accurate, reliable, and complete to reap the required benefits. It also gravely affects the decision-making process.
Data Noise
Noise in data means all the unwanted information that does not add value to your business or the data which was not your end goal. Professionals also have to deal with missing and corrupt attribute values while mining data. Both affect the quality of the data and throw off the entire operation.
Data Training Set
An adequate training data set is required for the algorithm to work efficiently. However, it can be affected due to several factors, some of which are:
- The data set is not representative
- Boundary cases or a detailed distinction between two aspects is missing
- Not enough information
Future of Data Mining
Utilising the ‘Big Data is the future of data mining. Using data mining to improve businesses has been adopted worldwide and is not an exception anymore. It is one of the essential assets of the current business operations and is a key to a competitive edge for future endeavours. The approaches and technologies related to data mining are evolving continuously, and its inexplicable potential is what the future beholds.
