As IT departments and businesses across all sectors handle larger quantities of raw data, processes have been created to turn this data into useful information.
Data mining is the umbrella term for this process. Data mining has a rich history, and with the advancement of technology as a whole, has had many different definitions over the years. We’ve gathered information on the history of data mining, key use cases, as well as its future. Clearly data mining is a concept that is core to today’s digital transformation efforts.
What is Data Mining?
Data mining is the process of turning raw data into usable information for business. The most common way this is done is through various data mining software solutions that look for patterns in data.
There are a number of techniques that have been developed over the years to practically apply and practice data mining. Each technique is built on the fundamental idea of tracking patterns in a set of data. From here, you can hone in on your data mining methodology depending on your project’s focus and the depth of your research.
For example, you could use association to simply correlate multiple dependent variables. Conversely, you could dive deeper and leverage outlier and anomaly detection to sift through large data sets and spot any anomalies. Although these techniques are widely used today, it’s important to understand the history of data mining and how these techniques have changed.
History of Data Mining
Surprisingly enough, the concept of data mining stretches back all the way to the 1700s. Bayes’s theorem and regression analysis are both early examples of identifying patterns in raw data sets. Although these methods were used for centuries, the term “data mining” first appeared in 1983, in an article published by economist Michael C. Lovell.
Originally, Lovell and a slew of other economists viewed the practice in a negative light. They believed that modern data mining could lead economists and business leaders to falsely correlating data that is not necessarily relevant. Regardless, the phenomenon grew in popularity, and by the 1990s, data warehouse vendors leveraged the term for marketing purposes.
One of the most important events in the history of data mining is the Cross-Industry Standard Process for Data Mining (CRISP-DM). This was a standard created by a number of companies in 1996 to help standardize the process and prevent the issues brought up by Lovell and his peers. The process includes six critical steps:
- Business understanding
- Data understanding
- Data preparation
This model has continued to be iterated, and even received an updated version by IBM. As technological advances have been made, data mining has become a more complex and flexible process – and far more powerful as well. In today’s market, effective data mining is critical for competitive advantage.
The primary issue data mining aims to solve in the present day is analyzing the sheer abundance of data that is produced. Data mining has seen major advances – and in fact has strained the capacity of IT departments – with the growth of machine learning and artificial intelligence.
Data Mining Use Cases
Data mining has various use cases depending on the industry vertical it’s being applied in. Here are a few key examples.
Data mining can be used to uncover the relationships between diseases and treatment effectiveness. In a high-level sense, data mining can be used to identify new drugs and bring timely care for patients. One of the most effective data mining uses cases is to detect suitable treatments by comparing and contrasting symptoms.
It can also help with detecting fraud for healthcare companies. Users can leverage anomaly detection to identify outliers in medical claims, whether they’ve been made by physicians, labs, clinics, or others. You can also use outlier detection to track referrals and prescriptions that stray from the norm.
Data mining for fraud detection in the healthcare industry is frequently used. For instance, the Texas Medicaid Fraud and Abuse Detection System leveraged it to recover over $2 million in stolen funds and identify over 1,000 suspects.
Data mining can be very important for intelligence agencies to determine crimes at all levels. This includes money laundering and various forms of trafficking. This is yet another vertical that relies heavily on anomaly detection for its data mining purposes. This helps intelligence agencies such as the FBI to anticipate, prevent, and respond accordingly to various crimes.
In the same vein, data mining could be leveraged for cybersecurity purposes. Applications can detect anomalies and learn from data sets to prevent further attacks. There are many AI cybersecurity firms working on developing these sorts of tools currently.
Data mining is leveraged with enormous effectiveness for sales and marketing. The main use case is detecting hidden trends inside purchasing data. This can help marketing firms and companies plan and launch tailored campaigns.
Sales teams can leverage customer data to reach out to them through their preferred methods. Generally speaking, data mining in marketing is best used for a holistically more tailored user journey. This could either relate to the purchasing journey or customer support journey. Another example would be analyzing which products are often purchases in tandem. This helps businesses gain a greater understanding of what overall packages and user carts look like.
Similar to the marketing and sales examples, data mining can be leveraged by banks and financial institutions to learn from and predict customer behavior. You will usually see financial institutions utilizing data mining to increase their overall customer loyalty.
This can allow companies to release relevant services to customers. Similar to the way intelligence agencies use data mining, financial institutions can employ data mining to identify either fraudulent or non-fraudulent actions. Again, anomaly and outlier detection is a major factor in this pursuit.
Financial analysts can also use data mining to understand purchasing and sales trends. They can pull from sales data to track peaks and dips. Advanced data mining techniques that take outside factors into account, such as holidays or seasonal promotions; and more specifically, what factors drove these purchases.
The Future of Data Mining
As customer data continues to grow, the future of data mining is continually considered and planned for by business leaders. Here are a few key directions and paths data mining could go in the upcoming years.
Data Mining in the Healthcare Industry
The healthcare industry has historically been at the forefront of data mining, leveraging it to understand patients and treatments better. One of its most effective applications has been found in signal detection, which is the process of discerning valuable pattern inside random information.
With the arrival of the pandemic, advances in data mining techniques specifically for pharmaceutical testing and clinical trial processes were made. Expect data mining to find some of its most ambitious applications in this field, with some data scientists using it to analyze DNA sequences.
Integrated Data Mining
Data mining historically has been accomplished either through proprietary software or other external means. Now, data mining features are entering a number of CRM SaaS platforms for various purposes, the main one being cybersecurity.
This has become even more important during the age of digital transformation. As key stakeholders and business leaders understand the value of modernizing their business operations, software vendors are adapting and creating low-code solutions. These solutions, alongside the ongoing data democratization movement, are opening up data mining possibilities for the everyday user.
Integrated data mining has a few benefits. The first benefit is that it introduces data mining and data as a whole in a more user-friendly way for employees. Because of this exposure, businesses can expect a general upskilling of employees. The second benefit is the fact that businesses can now gain a wider breadth of data analysis. This is because integrated data mining opens up data analysis for departments outside of your IT team.
The concept of hyperautomation will surely include data mining processes alongside it. Hyperautomation describes the approach some businesses are taking in identifying and automating as many business and IT operations as possible. This means that businesses are rapidly adopting artificial intelligence, machine learning, and robotic process automation software.
Many data mining solutions already leverage machine learning and artificial intelligence to deal with and analyze mass quantities of data. In fact, this is one of the reasons why interest in the AIOps space is rapidly growing. Hyperautomation and AIOps could be the keys to one of the main issues facing data mining today: too much data for humans to handle without assistance. Certainly, data mining aided by AIOps will play a key role in this challenge.