What is data mining? (With definitions and examples)

By Indeed Editorial Team

Published 8 June 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

When companies use it properly, data can be a valuable resource to provide a deeper understanding of a market, product or customer base. Being able to analyse data is a valuable skill for any employee and can inform future decision making, directly impacting the success of a business. Learning about data mining can be helpful, as all businesses have some form of data they use in their strategic planning. In this article, we answer the question, 'What is data mining? ' discuss its uses and provide examples of how companies can use it in their day-to-day operations.

What is data mining?

Businesses and their employees may wonder, 'What is data mining?' Data mining is a method of examining raw data to find patterns, repetition or useful information for use in a business strategy. An employee who works in a role that includes data mining may use computer software to quickly analyse, evaluate and sort data and to provide useful results for different applications across a business.

It's particularly useful in a transactional business—where customers provide data around a product, service or element of a business—although many other businesses can have a bank of raw data that may benefit from analysis.

Benefits of data mining

Data mining can help a business reveal niche information that may not be immediately apparent, such as how long a customer spends on your website before buying a product or the efficiency of a certain task within a much larger automated operation. Other common benefits of data mining are:

  • discovering more about customer intent to inform new products or services

  • making data-led decisions around operational procedures or steps that improve output

  • understanding how to source new customers based on existing customer behaviour

  • evaluating the performance of an educational platform for students or employees

  • rooting out anomalies that may hinder efficiency

Why is data mining important?

As businesses increasingly move into the digital space, the amount of raw data that they accumulate can grow at a rapid pace. The evolution of data mining has also quickly accelerated over time, bringing new techniques and applications into the business world. Modern businesses are often incredibly agile, especially in fast-moving industries, which is where data can offer a distinctive edge. If a business can understand its current customer base and why they behave in the way they do, it can make informed forecasts around business strategy.

Through the use of data analysis, data mining has directly improved many organisations' decision-making processes. Senior business leaders can now, at a glance, understand more about their long-term business strategy. Similarly, machine learning algorithms increasingly use data to quickly pull up the most important information so they can provide more accurate forecasting models. This can lead to immediate, actionable results, such as those involving bottlenecks, operational challenges or even potential niches to explore. All this can allow a company to gain a competitive advantage, as it can stay up to date with current consumer or operational trends.

Related: What does a machine learning engineer do? (With skills)

What are common data-mining techniques?

Regarding this type of analysis, there are a number of common techniques that a business can utilise:

Data association

This is a basic method of setting rules to distinguish relationships or patterns between certain categories across a dataset. Market researchers or consumer behaviour analysts frequently use this method, as it allows a business to understand the habits of its customer base and their relationship with either the brand or certain products. An example of this may be a retailer discovering that a certain product line sells better during a specific season, which identifies opportunities for scaling or even new products. Understanding these consumer habits can allow a business to develop new cross-selling or up-selling strategies.

Neural networks

Neural networks are most common in machine-learning or 'deep-learning' algorithms, and they use layers of algorithms to replicate the structure of the human brain and process data incredibly quickly. Under the supervision of an engineer, machine learning can identify patterns or 'train' itself to understand certain rulesets. Analysts can then apply this knowledge to future datasets, with a higher probability of finding relevant, usable patterns. Machine learning is particularly useful because it's highly automated, extremely efficient and typically very reliable, with applications across multiple industries and business types.

Decision tree

Based on a 'branch' of decisions, this technique utilises classification and regression models to predict future outcomes. The technique uses branches to visualise data, which gives the method its name. Software using this method can quickly form a range of potential outcomes and maximise the number of positive actions.

Related: How to become a data analyst

What is the data-mining process?

The data-mining process typically follows four simple steps, starting with planning and ending with the analysis of results. Throughout the process, the individual performing the data mining may integrate visualisation techniques to better report on their results. The four main steps of data mining are:

1. Setting objectives and collecting data

While this can be a challenging part of the process, it can set the tone for the entire operation. For this step, key stakeholders define the issue they want to solve or the objectives that the data can assist with. These decisions inform the questions or rulesets that apply to the data and the parameters surrounding a project. After establishing the rules, analysts collect the relevant data and store it in a temporary location, such as a data warehouse, so it's ready for mining.

2. Preparing the data

Once the business has identified the objectives of the data-mining exercise and understands which datasets can help answer its questions, it cleans the data. This involves removing duplicates, errors, inconsistencies or anomalies. It's important to do this early on, as it can reduce the operational strain of the data-mining process and ensure that the final output is as accurate as possible. Analysts also decide on the best way to manage and organise the data at this stage, depending on the wider objectives or what might deliver the most meaningful insights.

Related: Analytical skills: definitions and examples

3. Sorting the data

This is where the bulk of the data mining occurs, although it typically requires the lowest amount of human interaction. The analyst communicates the ruleset with the software, which then starts automatically sorting the data. Depending on what data-mining techniques a software utilises, the time to complete this process may vary. Once the stage is complete, the analysts can review the data and start building actionable insights.

4. Evaluating and implementing insights

At this stage, depending on the results, analysts may either present their findings to management or refine their mining techniques in an effort to reach the desired result. It's important for the results to be valid, reasonable and understandable. This is particularly important when reporting to internal and external stakeholders, especially if they require a concise, actionable breakdown. When presenting the data, analysts typically communicate it visually, as this can create a more compelling and engaging report. These visualisations generally take the form of tables, graphs or infographics that provide pertinent statistics in an easy-to-read format.

Data-mining examples

Below are two examples of data mining within a business setting:

Data mining for insights on customer behaviour

Shoewear Ltd has an interest in understanding more about its customers' purchasing habits within its dedicated app over the course of the year. Every time someone downloads the app, Shoewear Ltd receives introductory information to build an initial profile. As the customer uses the app, including browsing and purchasing, the profile becomes more complete. Shoewear Ltd begins to understand their specific taste, the brands they enjoy and when best to target them with flash sales and news about clothing releases.

The company is going to use this data to better understand which brands its 18- to 24-year-old customers most engage with during the busy summer period. To gain this specific information, the analysts at Shoewear Ltd sift through the yearly data to discover how many of their customers aged 18 to 24 bought products between June and August and the specific brand they gravitated to. In a visual presentation, the analysts provide their findings to the marketing team, who can then create new promotions and social-marketing materials around that brand.

Data mining for insights on market retargeting

InvestCo wants to understand how it can re-engage with prospective investors interested in cryptocurrency. On its website, InvestCo currently has a range of new cryptocurrencies, including Calerium, which it believes may be a key emerging market. The primary objective for InvestCo is to find out how many of its customers have an interest in Calerium, along with their age, location and previous assets.

The company is going to use data from website traffic and existing customers to discover these insights. Analysts at InvestCo use the stored cookies of website visitors, along with existing database information, to identify how many people have visited the Calerium page on its website or purchased that particular cryptocurrency. They can then use this data in their retargeting programmes to attract new investors with similar buyer profiles.

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