“Information is the oil of the 21st century, and analytics is the combustion engine,” says Peter Sondergaard, former Global Head of Research at Gartner. And he has a point.
Given that the global big data market is forecast to be valued at $103 billion in 2027, it’s worth noticing. As the amount of data generated worldwide every day has exploded, so has the demand for efficient tools to analyze it.
In this article, we’ll take a look at data mining: what it is and how it’s done. We’ll also examine a few areas where data mining has proven its value.
Are you ready? Let’s dive in.
What Is Data Mining?
Data mining— or as it’s also known, Knowledge Discovery in Databases (KDD)— is the process of collecting and analyzing massive datasets to harvest helpful insights. Typically, it involves finding hitherto hidden patterns in the data that can be used to inform strategy. As you can imagine, for businesses, the benefits can be huge.
Whether you’re looking to optimize marketing spend or implement dynamic pricing, the knowledge gained via data mining can help. It’s used across multiple varied fields of business endeavors to inspire innovation and drive growth.
Speaking of growth, do you know what a zettabyte is? Well, it’s equivalent to one billion trillion bytes, which is the basic unit of data storage. According to Statista, the total volume of data created, captured, copied, and consumed worldwide will hit 181 zettabytes by 2025. That’s a lot of information!
However, it’s no good if you don’t know what to do with it though. The volumes of data companies gather in the modern era are so enormous that it can be challenging to know where to start. So, let’s have a look at a typical data mining process.
Applying the Data Mining Process
Whether you’re focusing on big data for manufacturing processes or advertising strategies, it all begins in the same place. It’s vital to sit down and define your goals.
Understanding Your Business and Setting Goals
As with any research project, it’s important to look at where you’re starting from first. Consider the current situation of your business and work out what the objectives for the project are. Be specific.
For example, if your company works in a business phone system install, one of your goals might be to discover why one specific product sells better than others in certain regions. Taking the time to set detailed targets will pay dividends later on.
Collecting Relevant Data
Once you know what you’re aiming to achieve, you can decide which datasets you’ll need. Gather the relevant data from all available sources. The more resources you can access, the better the final result will be.
Preparing and Cleaning Up Data
Once you have your data on hand, it will need to be prepared for the next stage. It could mean stripping out duplicate records or putting the data in a format suitable for analysis.
The heart of the project: this is where the magic happens. There is a wide range of statistical and analytical techniques that can be used here, from pattern tracking to MLOps with databricks (machine learning operations). Any kind of approach that seeks to establish patterns can be effective. A few examples would be:
- cluster analysis
- regression analysis
- outlier detection
All worthwhile research projects include an evaluation phase to assess what went right and what could have been improved. Data mining is no different. During the evaluation phase, you’ll think about how useful the model employed was in order to reach your business goals. If the process has not been as successful as you’d hoped, that’s OK. This stage allows you to refine the model so that it performs better next time.
Data Mining in Practice
It would probably be helpful to look at a few real-world applications of data mining. We’ll consider use cases from the world of business and several other areas.
Marketing and Advertising
The use of big data analysis in advertising and marketing, in general, has transformed the field. Marketers use data mining to improve their understanding of market segmentation. By studying large datasets of consumer information and segmenting them by traits such as age, gender, region, and so on, it’s possible to predict consumer behavior with ever greater accuracy.
This gives marketers vital insight into how to increase consumer engagement. It enables them to design more relevant ads tailored to each individual or to understand what drives people to unsubscribe from a service. With this knowledge in their back pocket, marketers can create highly effective campaigns.
Retail and Ecommerce
Retail got serious about analyzing big customer data as far back as the mid-1990s. In fact, when the customer analytics company Dunnhumby first presented its trial findings to the board of the UK supermarket Tesco in 1995, the chairman responded:
“Well, this really worries me because you seem to know more about my business in three months than I’ve learned in 30 years.”
Since then, retailers of all stripes have embraced data mining as fundamental to their business. Today’s supermarkets fine-tune their service by studying buying preferences and purchase history of their customers. These insights determine, for instance, the placement of products on the shelves in-store and the nature of the promotions and discounts offered. Online retailers such as Amazon also use data mining to inform product recommendations and cross-referencing.
Banking and Finance
The large financial institutions all use strategic IT policies to maximize profits. These help them assess risk and implement compliance— both essential in this highly regulated industry.
Analyzing unusual patterns in card transactions and purchasing behavior helps detect fraudulent activity. And when it comes to larger-scale financial crime, techniques such as clustering analysis, data visualization, and outlier analysis can assist in exposing suspicious behavior that may otherwise have gone undetected.
Banks also need to predict how many loans are likely to go bad. Mining the big databases available to them allows them to do this with far greater accuracy than using more traditional methods.
Companies such as mobile phone or broadband service providers work within industries where customer churn is challenging. They use a multifaceted approach to try to deal with the issue. For example, there’s a strong focus on optimizing their websites’ UX for competitive advantage. A smooth online experience is the minimum their customers expect.
It’s an ever-present problem, though. So, one priority is to learn which factors are important in motivating existing customers to move on. A better understanding of this can inspire innovative customer service improvements and increase engagement.
One common technique is to analyze customer interactions, complaints, and bill payments to generate a “danger of leaving” score for each customer. Appropriate incentives to remain can then be targeted at suitable candidates.
Real-time data mining can be effective for enabling personalized experiences. TV networks broadcasting online (IPTV) use in-the-moment data to learn as much as possible about viewing habits. This enables them to create a detailed recommendations model.
The companies take a particular interest in how viewers spend their online time and how it changes throughout the day. It can help predict customers’ click behavior and give some clues about search intent, which are helpful for customer retention.
The range of applications for data mining in healthcare is enormous. It has proven its worth in medical research and everyday healthcare management.
In February 2020, the medical research journal Cell published a paper describing a major breakthrough. The researchers, led by synthetic biologist Jim Collins at the Massachusetts Institute of Technology, had successfully used machine learning techniques to discover a new antibiotic called halicin.
It was the first time an AI had discovered a new antibiotic without depending on human assumptions. It found halicin by itself, in other words, by looking for patterns in large datasets.
Of course, not every day on the job can be as exciting as the one where you discover a new wonder drug. But even for those working in healthcare at a more ordinary level, data mining is a huge boon. For one thing, it makes for more accurate diagnostics. Being able to predict that some demographic groups are at higher risk for certain diseases, for example, is a key factor in improving healthcare outcomes.
Teaching staff are often undervalued. Sometimes it seems a little mysterious why such a pivotal role doesn’t command greater respect. Who decides what is minimum wage work and which jobs should pay double?
While teaching can be a vocation for many teaching professionals, drowning in paperwork is their least favorite part of the day. It can be that there doesn’t seem to be enough time to pay sufficient attention to the students— which can lead to problems, especially if a student is struggling silently.
Modern data mining techniques can help identify which students need more help in specific areas. Early intervention measures can then be put in place to ensure that the student catches up. The ability to do this is partly thanks to recent advances in educational tech.
The widespread uptake of remote learning during the pandemic changed the game by enabling the collection of copious amounts of student data. For example, the adoption of math apps now allows a more detailed analysis of students’ click actions in fluency-oriented math games. So, it’s easier than ever to establish which students require more support.
Our final example of real-world use cases for data mining is all about criminal investigation. Of course, this is not surprising to anyone who’s a fan of TV detective dramas. A typical application in this field however, involves modeling crime detection problems.
Crimes cost society, and finding techniques to speed up solving them is of paramount importance. Clustering algorithms can be used to detect patterns in various attributes related to crimes that have been committed. In turn, this helps develop more effective processes for investigation, which ultimately saves time.
Benefits of Data Mining
In any modern business there are a million things for staff to think about every working day. From nailing those monthly sales targets to implementing policies for boosting diversity in the workforce, everything eats up time and energy.
Data mining offers an efficient route to solve various business challenges without breaking the bank. That’s because generally speaking, the datasets themselves already exist. It’s just a question of finding ways to use them to discern trends and spot patterns.
Maybe you’re facing a tricky policy decision over a cloud vs. on-premises comparison for software implementation. Or possibly you’re trying to establish what kinds of promotions to offer and which customers to offer them. Data mining techniques can make solving these problems much more straightforward. Indeed, they can help you achieve all sorts of business goals, such as:
- devising more efficient operations and processes
- increasing profitability
- building more accurate risk models
- predicting consumer behavior and increasing engagement
- more upselling
- customer acquisition
- detecting fraud
- cost-effectiveness gains in marketing
A well-planned data mining research project will involve improving data governance procedures wherever possible. The evaluation stage tends to be an iterative process too— with results feeding into the new, improved models. With solid principles in place, there’s no reason why you can’t create a virtuous circle of improved data mining and analysis processes leading to better customer satisfaction and vice versa.
Find Your Own Goldmine!
Almost every modern business generates a massive amount of its data every day. Often, there’s so much of it that it can be tough to know how best to make use of it all.
But by using up-to-date data mining techniques, you could turn this big pile of cluttered information into a cutting-edge advantage for your business. Maybe today’s the day to go and find your own goldmine of data and start digging!