Data Mining How To: A Brief Guide to Technology

We live in an era of big data. Despite the Internet being around for over 30 years, 90% of all the data available online was created since 2016. IDC predicts that by 2025, we’ll be generating 463 billion gigabytes of data per day. However, all those petabytes of data are like a dead weight, useless unless we don’t do something with it. 

In this article, we’ll talk about data mining and how it can help us and our businesses benefit from the information available.

What is data mining?

We’re all familiar with the idea of mining for minerals, diamonds, and metals hidden deep beneath the earth’s surface. What is data mining and how does it work? Data mining term stems from the same concept – sorting the available mass of data to find something useful for business or research purposes.

Data Mining: the process of sorting the available mass of data to find something useful for business or research purposes.

We’ll get to the business applications of data mining a bit later, but let me illustrate the point with the help of Minecraft Data Mining. Even if you haven’t played this game, you know about it or have seen the cubed figurines. Anyway, the surface area of each world in Minecraft is 8 times that of the Earth, the planet we live on. The Minecraft game world spans over 4 billion square kilometers. 

Well, it’s easy to lose something even in the 40 sq. feet, and it’s no surprise there are things in Minecraft that no one will find unless they look for them very specifically. Anyway, closer to the point of data mining. One player, Matt B., used this technology to find written signs left by other players.

With the help of two Java programs (BookReader.jar and SignReader.jar), he reviewed the maps to see if anything was interesting. As it turned out, he uncovered many signs that tell sad stories of people going their separate ways and missing each other. There are many other notes as well that found a home in a subreddit. 

Why is this case important to the benefits of data mining? With the help of these tools, people can look for those who are desperately needing help and offer timely support. Suicide prevention is one of the major tasks right now due to the rising number of people around the world suffering from depression and mental illnesses. Minecraft’s sign project is just one of many applications of data mining.

So, let’s get back to the technical side of our talk. 

The two main tasks of data mining are:

1. Processing data
2. Getting insights out of this processed data

The insights, as a result, can be used for several purposes: 

• Outcome prediction
• Trend detection
• Target audience modeling
• Data collection about the usage of a given service or product

You might also be interested: How pattern recognition works.

How does data mining work?

The process of data mining consists of several steps:

1. Definition of the expected results. This step helps you to get rid of the possible data noise in the long run. 
2. Target dataset creation. Once you’ve defined what kind of results you need, you can identify the kind of data that would help you reach those results and build your datasets based on this information. 
3. Data exploration. This step is also known as preprocessing, where you explore what kind of data is available in general that can help you in your operations. 
4. Data preparation. Here you create the rules for data segmentation, clean the noise and possible anomalies from the data, etc. 90% of data created is unstructured, so to work with it, you have to first prepare it.
5. Data mining. Finally, the actual data mining starts, when your machine learning algorithms set to work and start digging through the layers of information. 

Data mining software and methods

What are data mining techniques? Seven main methods are used for data mining. Most of the time, they use specifically trained machine learning algorithms since the amounts of data usually are tremendous and require specific data mining tools.

1. Classification. This data mining method goes through the dataset and identifies information about this data that creates a foundation for further analysis. For example, “This is A,” “This is B”, and “This is C.”
2. Clustering. It is a bit similar to the classification, but besides simply identifying the information about the data (pardon the tautology), it looks for similar items and groups them into clusters. For example, “This is A, it goes with the other As,” “This looks like B, so it goes with the Bs,” etc. 
3. Regression. This data mining method helps to identify relationships between various data points. For example, the probability of A, given the amount of B and C. 
4. Outer Detection. This method, also known as Outlier Mining or Outlier Analysis, is an important tool used for fraud, fault, anomaly detection, intrusions, etc. It analyses the data and focuses on the data points that don’t fit the norm.
5. Association Rules. This method is used for the discovery of hidden patterns and associations between two or more items. 
6. Sequential Patterns. The method deals with the identification or discovery of trends and patterns in transaction data over a certain period. In a way, it is similar to Association Rules, but this one is more global.
7. Prediction. Last, but not least, the data mining method is a combination of other techniques. It uses the information gleaned from trend analysis, sequential patterns, clustering, etc. about the events in the past to make predictions about how things would work in the future.  

Previously, we wrote about machine learning algorithms and you can read that article here. 

Data mining tools

It’s all good, but what instruments do you need to mine data or what is data mining software? It’s not like you can use a pickaxe for this job. Well, thankfully, two excellent tools are widely used in the industry. 

• Oracle Data Mining (often goes by the abbreviation of ODM) is a popular data mining module for the Oracle Advanced Analytics Database. It can analyze data to generate insights and make predictions based on these insights. ODM is used, for example, to predict customer behavior, identify cross-selling opportunities, as well as create customer profiles. Learn more about it on Oracle’s official website. 
• If you’re looking for an open-source tool, R language is your helper in this case. It offers a wide variety of statistical tests, classification, and time-series analysis, among other features. You can learn more about the R project on its website. 

Practical uses of data mining in business

Since there is software that can be used across industries as well as industry-specific solutions, we’ll divide the practical uses into two categories: 

• Solution-oriented data mining applications (can be used in businesses of different industries, including but not limited to those mentioned in the table below)
• Industry-oriented data mining applications (how data mining can be applied in a specific industry)

Solution-oriented data mining applications

Solution	Industries	Purpose
Customer Relationship Management (CRM)	This solution helps to group certain items based on various rules, create targeted customer lists (based on previous purchases), etc. The business intelligence from this solution helps to adapt goods and services to match the expectations.	Data mining’s purpose for CRM is simple: you glean insights about your customers that build a foundation for customer attraction and retention. Based on these decisions, you can further adapt your marketing approaches and available services to the true needs of your clients. Data mining algorithms look for patterns in the data and predict the possible outcomes.
Fraud & Anomaly Detection	Advertising Technology (AdTech), Banking and Financial Services, E-commerce, Marketing, Healthcare, etc.	Thanks to the outlier analysis and combination of other methods, businesses can not only benefit from finding patterns in the data but also from analyzing those data points that don’t fit the general behavior. In the financial sector, data mining can expose manipulations and credit card fraud. In healthcare, it can help to detect and identify the possibility of various diseases at an early stage due to some health factors being outside of the norm.
Customer Segmentation	E-commerce, Marketing, etc.	People react the best at targeted messages (it doesn’t have to be a positive reaction, but they do react if you “hit home.”) If a child sees an ad for a dishwasher, s/he probably won’t even think about it. If it’s one of his/her parents, however, they might get interested. Therefore, in order for marketing specialists to do their job well, user modeling and customer segmentation are among the top tasks before anything else can be done. Therefore, data mining can help with identifying clients’ interests, preferences, needs, habits, and generic behavior patterns (for example, they prefer to read lengthy blog posts on Tuesdays.)
Research Analysis	Healthcare, etc.	Data mining can be used for research analysis to build, for example, a timeline of disease outbreaks and their progression. In this case, the technology is used to process the data, clean it from the noise (or detect anomalies), and present the big-picture results.
Market Basket Analysis	E-commerce, etc.	This solution helps to group certain items together based on various rules, create targeted customer lists (based on previous purchases), etc. The business intelligence from this solution helps to adapt goods and services to match the expectations.
Forecasting / Predictive Analytics	Business Analytics, Marketing, etc.	This is critical for the decision-making process when you have numerous factors to take into account (which is, pretty much, always.) Predictive analytics is great for calculating the outcomes and planning various advertising campaigns or changes in the process.
Risk Management	Business Analytics, Human Resource Analytics, etc.	Risk management is an important factor in a successful business. All risks cannot (and should not) be averted, but data mining allows us to remain proactive instead of reactive in terms of managing the possible options. For example, in HR, it allows you to see the employee churn and predict the factors that might affect the turnover negatively. Risk management is one of the most appropriate answers to questions like “What is BI data mining?” because business intelligence (BI) in this case brings the most value.

People ask Google other questions regarding data mining as well and we’ll try to highlight the answers here: 

• What is text data mining? As Marti Hearst from Berkeley puts it, text mining is using the computer to discover new information that was previously unknown by automatically extracting data from different written resources. 
• What is game data mining? Game data mining is using technology to find patterns and explore the properties of the datasets, which include the players’ information on how they play (session duration, for example) or how they react to other players, etc.
• What is financial data mining? Hidden patterns and predicting the possible future outcomes of certain actions are made possible with financial data mining. Financial sector businesses are often big data projects and the stakes are high, so analytics have to be done in a very precise and quick way. 
• What is email data mining? Along with data analytics tools, e-mail data mining helps businesses explore a large number of emails with less effort and use this BI to open their market potential. 
• What is clinical data mining? Clinical data mining focuses on exploring the datasets of clinical research information to detect patterns and find information that was previously not known in correlation to other factors. Combined with multimedia data mining, it can be used for automatic 3D delineation of highly aggressive brain tumors or analysis of X-rays, MRIs, ECGs, etc.
• What is multimedia data mining? Multimedia data mining uses the technology to extract knowledge from datasets that contain static (text or image) or dynamic (video or audio) multimedia. This is useful, for example, for Natural Language Processing (NLP) and subsequent analysis for call centers where the audio data is examined to rate the quality of service.
• What is sales data mining? B2B sales managers and business developers can mine their CRMs and ERP sales data for insights to understand the trends and patterns of potential clients’ behavior. 

Industry-oriented data mining applications

Industries	How data mining can enhance processes?
Banking	In the banking and financial services sector, data mining technology can help with assessing market risks, managing compliance with regulations, detecting fraud, and making decisions on whether to issue loans, credit cards, or similar actions.
Bioinformatics & Healthcare	Retail malls and grocery stores can identify and arrange the most sellable items in the areas that get the most attention from the visitors. As a result, the owners can create offers that encourage customers to increase their spending at the store with the help of data mining and analytics.
Communications, Advertising, & Marketing	Data mining techniques help to predict customer behavior in response to advertising given various conditions (thus helping to make the ads more tailored to the target audience and cut costs on ineffective ads.) In addition, with the help of the outlier detection data mining method, AdTech businesses can save a lot of money by detecting fraudulent activities and blocking them.
Crime Investigation	For customs and police departments (and other services like that), data mining helps in understanding where a police patrol should be deployed (for example, areas where crime is most likely to happen and at what time), whom to search additionally at the border crossing, and other useful analytics like this.
E-commerce	Mined data is useful for e-commerce businesses to offer cross-sells and upsells via the website. For example, Amazon uses data mining to research the market basket of a customer and offer relevant additional products.
Education	Retail malls and grocery stores can identify and arrange the most sellable items in the areas that get the most attention from visitors. As a result, the owners can create offers that encourage customers to increase their spending at the store with the help of data mining and analytics.
Insurance	Insurance companies benefit from the implementation of data mining technologies to set attractive prices for their products and services and to promote new offers to current or potential customers.
Manufacturing	Production assets’ wear and tear is a critical factor to manage. Data mining helps to forecast the depreciation of the equipment and schedule timely maintenance to reduce downtime.
Retail	Retail malls and grocery stores can identify and arrange most sellable items in the areas that get the most attention from the visitors. As a result, the owners can create offers that encourage customers to increase their spending at the store with the help of data mining and analytics.
Service Providers	Utility and telecom service providers (for example), can use data mining to understand the reasons a client might decide to switch to another company. By analyzing the billing details, interactions with customer service, complaints, etc., the company can assign a probability score to each customer and offer specific incentives.

Benefits of data mining

Data mining is like a treasure chest hidden within your data. It can uncover valuable insights that help you make smarter decisions and improve your business.

Let’s explore some of the ways it can benefit you:

	Knowledge is Power	Stop guessing, start knowing: Data mining turns raw data into actionable knowledge, helping you move from assumptions to informed decisions. Solve problems like a detective: By identifying trends and patterns, you can pinpoint issues and find creative solutions.
	Optimize Your Business	Streamline processes: Find bottlenecks and inefficiencies in your operations and make them smoother. Predict the future: Forecast trends and make proactive adjustments to stay ahead of the curve.
	Make decisions with confidence	Evidence-based choices: Data mining provides the evidence you need to make sound decisions and reduce risks. Faster results: Automate data analysis and get insights quicker, so you can act faster.
	Discover hidden gems	Understand your customers: Segment customers to tailor your marketing and products. Predict what they’ll do next: Forecast customer behavior and stay ahead of the competition.
	Works with any data	Old or new data: Data mining can integrate seamlessly with your existing systems, regardless of their age or complexity. Big or small data: It can handle massive datasets, making it suitable for businesses of all sizes.
	Get ahead of the competition	Innovate with data: Use data-driven insights to develop new products, services, and marketing strategies. Delight your customers: Personalize experiences and improve customer satisfaction.

In short, data mining is like having a magic wand that turns your data into gold. It helps you make smarter decisions, optimize your business, and unlock new opportunities.

Data mining challenges & ethics

Data mining is a powerful tool, but like any tool, it comes with its own set of challenges. Let’s explore some of the common pitfalls and how to avoid them:

1. Security First:

Example: A healthcare provider accidentally exposes patient records online, leading to a widespread data breach and identity theft.

• Protect your data: Data breaches are a real threat. Make sure your systems are locked down tight to prevent hackers from getting their hands on sensitive information.
• Know the rules: Be aware of data privacy laws and regulations. Don’t use data in ways you’re not allowed.

2. Training is Key:

Example: A company invests in a new data mining tool but struggles to implement it effectively due to a lack of skilled personnel.

• Learn the ropes: Data mining tools can be complex. Invest in training to get the most out of them.
• Practice makes perfect: The more you use data mining tools, the better you’ll become at using them effectively.

3. Choose the Right Tools:

Example: A company selects a data mining tool that is not compatible with its existing database system, leading to integration challenges and delays.

• Different tools for different jobs: Not all data mining tools are created equal. Choose the right tool for the job based on your specific needs.
• Compatibility matters: Make sure your chosen tool works well with your existing systems.

4. Beware of Bias:

Example: A company relies heavily on historical sales data to predict future trends, but fails to account for seasonal fluctuations, leading to inaccurate forecasts.

• Garbage in, garbage out: If your data is biased, your results will be biased. Always check for errors and inconsistencies.
• Don’t overinterpret: Data mining can reveal patterns, but it’s up to you to interpret them correctly. Don’t jump to conclusions.

By being aware of these challenges and taking proactive steps to address them, you can harness the power of data mining while minimizing risks.

What can you do with data mining?

1. Predicting Patient Risk:

• Heart disease prediction: Hospitals can use data mining to identify patients at high risk for heart disease based on factors like age, family history, lifestyle, and medical records. This allows for early intervention and preventive care.
• Diabetes risk assessment: Data mining can help predict the likelihood of developing diabetes based on patient demographics, lifestyle, and genetic factors. This enables targeted screening and prevention efforts.

2. Disease Outbreak Detection:

• Flu surveillance: By analyzing data from electronic health records and other sources, healthcare organizations can track the spread of influenza and other infectious diseases in real-time.
• Outlier detection: Data mining algorithms can identify unusual patterns in patient data that may indicate a disease outbreak, allowing for early detection and response.

3. Personalized Treatment Plans:

• Precision medicine: Data mining can be used to identify genetic markers associated with specific diseases or drug responses, enabling personalized treatment plans.
• Treatment optimization: By analyzing patient data, healthcare providers can adjust treatment plans to maximize effectiveness and minimize side effects.

4. Fraud Detection:

• Anomaly detection: Data mining techniques can identify unusual patterns in claims data that may indicate fraudulent activity.
• Risk assessment: By analyzing historical data, healthcare organizations can assess the risk of fraud and implement preventive measures.

5. Drug Discovery:

• Target identification: Data mining can be used to identify new drug targets by analyzing genetic data and identifying molecular pathways associated with diseases.
• Clinical trial optimization: Data mining can help optimize clinical trials by identifying patient populations most likely to benefit from a particular drug.

These are just a few examples of how data mining is revolutionizing healthcare. As technology continues to advance, we can expect to see even more innovative applications of data mining in the years to come.