What Is Data Mining and Role of data mining in business processes

5 min readAug 26, 2020

In this technological era, every business is continually looking for a competitive advantage, which will allow them to deliver high-quality services at a lower cost, at a faster speed than its competitors.

Data drives almost everything into business such as manufacturing to supply chain, pre-sales marketing, and customer experience to post-sales marketing and customer experience. It helps to make the process more efficient, cheaper, productive, and to generate more profit margins.

Hence data is important, and so is data mining!!

Data mining in business is essential as it helps to generate valuable data insights by analysis of its pattern.

So, What is data mining?

Data mining is a process that helps to analyze large masses of data stored in repositories such as databases and storage devices, to identify business intelligence to solve company issues, mitigate risks, and seize new opportunities. Along with machine learning, artificial intelligence, and natural language processing (NLP), data mining in business plays a significant role in advanced technologies.

Data mining enables a series of statistical techniques for analyzing data in different ways, which traditionally were too time-consuming to resolve manually. If it is done meticulously, it can enable you to develop effective marketing strategies, learn more about customers, increase revenue, and decrease costs.

Steps of data mining -

Define your business proposal or hypothesis
Identify all data related to the interpretation.
Picking the data points that need to be analyzed
Extract the relevant details for data by using correlation analysis to test statistical models
identify the key value for the picked data
Interpret and report the result

Key Data Mining Commands -

An array of tools and techniques can help to achieve the best results from data mining in business.

Commonly used data mining techniques are -

Data cleansing and preparation — It transforms data for more analysis and processing by identifying and removing missing data and errors.
Pattern detection — It involves simple bar charts, and line graphs to represent a deviation in data on specific time intervals.
Artificial intelligence (AI) — AI performs analytical activities such as learning, planning, and problem-solving, associated with human intelligence.
Clustering — It helps to identify clusters in your database and do a partitioning into a set of meaningful subclasses. It allows users to understand the natural grouping or structure in the data.
Association rule learning — This technique helps to track relationships among variables in a dataset, such as if any customer is purchasing mobile, they tend to purchase mobile cases and earphones too. And this technique helps to determine which products are typically purchased together.
Classification — It helps to categorize items in different categories such as”low margin” or “high margin” to accurately predict the target class for each case in the data.
Machine learning — It is a computer programming technique that has the ability to learn without being explicitly programmed.
Data analytics — Use this technique to evaluate digital information into useful business intelligence.
Data warehousing — It is a large collection of business data; it helps an organization to make correct decisions.
Prediction — It helps data mining technology to forecast a module that can predict how independent variables can change in the future.
Regression- Based on a specific data set, this technique helps to assume numeric values such as sales or stock prices.

Data mining importance for any businesses -

Just being a data-driven business is not an option now; the success of your business depends on how quickly to discover insights from data and how you effectively incorporate them into business decisions and processes. As an example, on the basis of customers’ past behavior, data mining in business can enable you to see which prospects are interested in your services. And you can utilize this information to increase your revenue by making the right decision and providing offers to only those who are likely to become valuable customers.

Data mining importance that can add value to your business are -

Optimize marketing campaigns — Data mining in business helps an organization to understand which marketing strategies can help them to generate more engagements and can optimize marketing spend.
Make beneficial business decisions — Rather than relying on assumptions and experience, data mining enables you with your own reliable data that can help you to make better decisions. For example, you can assume that a few of your products are not performing well in the market, but the truth is, they have not been appropriately exposed and have been distributed on fewer channels. Data mining gives you an accurate insight into your business.

Apart from these, other benefits of data mining are -

Increasing revenue
Detecting fraud
Retaining customers and in gaining their trust
Acquiring new customers.
Acquiring customers needs and preferences
Identifying credit risks
In Monitoring operational performance

Data mining decisions can be based on real business intelligence so that it can deliver consistent results to keep a business ahead.

What business should do to gain maximum benefits of data mining -

Know about your business core goal, including tactical and strategic needs which data mining can address
Including the BI systems, determine the applications with data mining tools must interoperate.
Evaluate the data sources which data mining tools used to determine accuracy and relevancy
Use standard data mining techniques that match the needs of IT, data scientists, analysts, and visualize the needs of business users.

How Data Mining in business Works -

Generally, a data mining working process includes six steps -

Business understanding; Data mining helps to understand the core business objectives.
Data understanding: It helps to determine the problem and gather all relevant information from available devices.
Data preparation: Assembling data in a proper format helps a business to fix any data quality problems such as fraud or duplicate data.
Modeling: Use algorithms to identify data patterns.
Evaluation: Evaluate which module can help to achieve the business goal.
Deployment: Deploy the best approach to the project.

Latest Data mining trends and developments -

A few latest data mining trends which used to make a critical business decision are -

Multimedia Data Mining — Cleaning and extracting data which received from different kinds of multimedia sources such as files, images, hypertext, videos, etc. requires the latest data mining tools such as speech analytics software, etc.
Security and privacy concerns — Data mining captures sensitive customer information that needs to be protected. Regulations such as GDPR reduce the risk of data glitch.
Distributed Data Mining — It involves the mining of huge amounts of information. Sophisticated algorithms help to gather different organizational data from several locations to generate reports.
Ubiquitous Data Mining — It involves gathering the customer’s mobile data, which especially helps in studying human-computer interactions.
Spatial and Geographic Data Mining — This data mining technique involves capturing customer geographical location, astronomical data to reveal various aspects such as distance and topology to use in geographic information systems, and application navigation.