Data mining definition

Data Mining: Definition, Benefits, + Adoption Tips

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In this post, we’ll cover the ins and outs of data mining – definition, benefits, adoption tips, and more.

Data Mining: Definition, Benefits, + Adoption Tips

Data has become the “new oil,” as the saying goes. Effective use of data can grant companies a competitive advantage, so learning how to adopt and make use of data is not an option.


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Below, we’ll look at data mining specifically, since it is one of the most useful and valuable data-driven methods in the modern workplace.

A Definition of Data Mining

Data mining means analyzing and extracting useful information from large sets of data. In business, this definition often also includes making use of that data for the business.

Examples include:

These are just a few of the many possible ways data can be mined and used to add value to a business. 

Benefits of Data Mining

Information is knowledge, after all, and knowledge can be used to:

  • Gain insights into various areas of the business, from customer journeys to the employee experience to business processes
  • Accurately make predictions around events and trends that matter to the business
  • Inform decision-making and improve the accuracy of those decisions

Ultimately, information is a valuable resource that can be used to improve business efficiency, effectiveness, profitability, and much more.

The Data Mining Process

Every organization will model the data mining process slightly differently, but they tend to follow the same general steps.

These include:

  • Defining the business problem
  • Cleaning and preparing data
  • Building models and extracting patterns
  • Evaluating and representing results

When implementing this process, data teams will typically work together with other business units, such as IT teams, subject matter experts, and those providing the data sources.

Defining Data Maturity

Some organizations have become fully data-driven organizations and they leverage data to its fullest extent. Others, however, have yet to make use of data at all.

This spectrum of “data maturity” could be thought of as a data maturity scale. These scales would measure an organization’s sophistication and capabilities when it comes to data, beginning with the least sophisticated and ending with the most.

A five-level scale, for example, would look similar to the CMMI model:

  • Initial – Processes are unpredictable and uncontrolled
  • Managed – Processes are reactive
  • Defined – Processes are proactive
  • Quantitatively Managed – Processes are measured and controlled
  • Optimizing – Focuses on process improvement

There are other ways of defining data maturity, but this example offers a good illustration of the concept of data maturity.

The key takeaway here is that data maturity correlates with the benefits derived from that data – the more mature an organization’s data management function, the greater the impact that data will have on the organization’s effectiveness.

Challenges of Adopting Data-Driven Methods

Building data maturity is by no means an easy task.

As with any other business transformation, building data maturity requires an investment of time, money, and effort. 

Obstacles that could stand in the way of building data maturity could include:

  • Executive sponsorship and buy-in
  • Employee resistance and organizational inertia
  • Costs

Overcoming these challenges may not always be easy, but it can be done and it will usually be worth the ffort.

Data Mining: Adoption Tips

Digital transformation of any kind requires careful planning, change management, and a robust digital strategy, among other things. 

Here are a few pointers that can help during the adoption of data mining and other data-driven methods:

  • Assess the current state of the organization and compare that to the desired data maturity level using tools such as gap analyses
  • Use other assessment methods, such as business impact analyses and technology acceptance model questionnaires, to identify potential obstacles
  • Consider embedding data-driven thinking into the organizational culture, which can help shift employee attitudes in favor of data-driven methods
  • Mitigate or prevent potential employee resistance through effective change management communication, employee training, and upskilling efforts
  • Democratize data to help accelerate digital transformation and ensure that data becomes embedded within employee mindsets
  • Embed data-driven methods into the organization’s operating model to maximize the value of that data
  • Make a strong business case for the value of data-driven methods, then use that case to obtain buy-in from business leaders

Every organization will begin at a different data maturity level and they will have their own unique obstacles to overcome. 

For this reason, the very first point covered above, assessment, may be the most important. From that assessment can come the guidance needed to steer digital adoption and transformation efforts.

Data Mining vs. Other Data-Driven Methods

Data mining is only of several data-driven methods used to generate insights and intelligence.

Others include:

  • Managing data sources
  • Data preprocessing
  • Analyzing and exploring data
  • Data representation

Data mining would fit in the center of this list, showing why it is necessary not only to adopt data mining, but also to implement other data-driven methods – and to integrate these within the organization’s operating model.

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