Data mining, the transformation of data sets into useful business information, can deliver big advantages to companies that understand how to do it right.
In fact, the ability to mine data successfully may become a major differentiator between digital leaders – the highest-performing organizations in the digital economy – and digital laggards.
Below, we’ll learn what data mining is, why it is so essential to success, and what it looks like in practice.
What Is Data Mining?
Data mining is the transformation of large data sets into useful information and actionable insights.
The precise steps involved can vary depending on who you ask, but data mining typically revolves around:
- The collection of massive quantities of data
- The combing of that data with digital software
- The extraction of patterns and useful information
- The application of those insights to decision-making
According to IBM, when a business implements a data mining project, it is important to follow four steps:
- Setting business objectives and defining the project scope
- Preparing the data
- Building models and mining patterns from the data
- Evaluating results and implementing that information
Since data mining relies on the use of big data – or extremely large data sets – it is impossible for humans to extract patterns from that data.
Instead, businesses often rely on artificial intelligence (AI) to do the pattern recognition and modeling of that data.
Data Sources
Data can come from any number of sources.
A business, for instance, can mine a large range of sources, including but not limited to:
- Internal customer data
- External customer data
- Web scraping
- Website usage
- APIs
- Physical sensors
In short, there are countless sources of data, and the more that technology evolves, the more data we have at our fingertips. The integration of physical sensors with the internet, for instance, will fuel the development of IoT. The new oceans of data created by this megatrend, in turn, will fuel a significant increase in the adoption of data mining and other technologies that rely upon big data.
Those new to this field, however, may wonder what the value of this approach is and how it differs from the normal use of data.
Business Benefits of Data Mining
Data-driven methods can add value to any area of business, since they offer insights that can be quantified objectively. This takes the guesswork out of many business processes, allowing decisions to be made on real-world facts and patterns, rather than upon feelings or subjective opinions.
For instance, data mining can reveal patterns in customer behavior that are based upon statistics, instead of “gut instincts” or the feelings of professionals within the business. The outcome: better decisions, better business performance, and more appropriate business actions.
Other benefits of data mining include:
- Improved ROI on business efforts
- Better predictions and decisions about future business activities
- A deeper understanding of the data being examined
- Cost savings
- Insights that would not otherwise be possible without the use of big data
In short, data can add an entirely new dimension to the business by enhancing the organization’s insight, hindsight, and foresight. The potential value – and the potential use cases – of this approach to data is virtually endless.
Use Cases of Data Mining
Big data insights can be applied in a wide variety of areas, from finance to operations to marketing to competitive intelligence.
Here are a few examples of how data mining can be applied in the real world:
- Finance professionals can use big data to model potential potential investments more accurately
- Supply chain managers can analyze their supply chain data to improve logistics, increase agility, boost efficiency, and more
- Large enterprises can analyze employee behavior, such as software utilization, to understand learning needs and, consequently, develop better employee training programs
- Marketing professionals can analyze customer data, web data, and other online data to gain deeper insights into the psyche of their audience and, as a result, design better marketing experiences
- Cybersecurity professionals can analyze patterns in their IT systems to check for anomalies that could be indicative of security threats
In short, wherever there is big data, there is the potential to mine and use that data to improve business processes – or even perform new types of activities that weren’t possible before the advent of AI.
For example, without AI, cybersecurity professionals simply wouldn’t be able to keep up with and analyze the vast amounts of information they need to in order to keep their organizations secure. But since AI can “crunch” all of that data quickly and extract patterns, it can perform cybersecurity tasks, such as threat detection, that would otherwise be impossible for humans.
Where to Go From Here
Should your organization invest in big data and data mining?
The answer, of course, will depend on many factors, including:
- Your organization’s digital maturity level
- The size of your organization
- Your budget
- Your company’s industry
- How your company currently uses data
- Your company’s digital transformation agenda
The answer, in other words, depends entirely upon your company’s goals.
That being said, however, it is critical to recognize that “data is the new oil” and success in tomorrow’s economy will depend, in part, upon how effectively your organization can adopt new technology, mine data, and use data-driven methods.
