Data Mining How To: A Brief Guide to Technology

Data Mining How To: A Brief Guide to Technology

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

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.

Data mining is… what?

We’re all familiar with the idea of mining for minerals, diamonds, and metals that are hidden deep beneath the surface of the earth. What is data mining and how does it work? Data mining term stems from the same concept – it’s the process of 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 went over 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

Minecraft hidden messages
minecraft hidden messages board

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 firs
  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? There are seven main methods that 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 together 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, there are two excellent tools that 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

Customer Relationship Management (CRM)E-commerce, Banking, and Financial Services, Marketing, etc. 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 DetectionAdvertising 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 SegmentationE-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 AnalysisHealthcare, 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 AnalysisE-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 AnalyticsBusiness 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 ManagementBusiness 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.
Solution-oriented data mining applications

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 to explore a large number of emails with less effort and use this BI for opening their market potential. 
  • What is clinical data mining? Clinical data mining focuses on exploring the datasets of clinical research information with the purpose of detecting patterns and finding 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 in order to understand the trends and patterns of potential client’s behavior. 

Industry-oriented data mining applications

IndustriesHow data mining can enhance processes?
BankingIn 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 & HealthcareMining biological data from massive datasets collected during the numerous biological studies and medical research with the help of data mining methods speed up the process and has the potential to save lives.
Communications, Advertising, & MarketingData 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 InvestigationFor 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-commerceMined 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. 
EducationRetail 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. 
InsuranceInsurance 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. 
ManufacturingProduction 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.
RetailRetail 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 ProvidersUtility 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

There are many benefits to data mining in our data-driven world. As you saw above, this technology is applicable across industries. Here are a few key value-added things:

  • It helps companies to get information that is based on real knowledge-based data. 
  • It helps to make adjustments in business and production processes that would affect profits positively.
  • It streamlines the decision-making process because you can base your actions on insights. 
  • It facilitates automated trend predictions and helps to discover hidden patterns in customer behavior. 
  • It can be implemented on both existing systems as well as brand new ones, which gets rid of the barrier of “Our systems are too old for this ****.” 
  • It helps you sort through the terabytes of data your business is getting, by preparing the foundation for further business intelligence analysis. 

Data mining challenges & ethics

There are always some challenges in each technology and you have to be aware of them in order to tackle them successfully. Here are a few: 

  • Data security and protection. Personal data is – and should remain – personal. There are many stories online about hackers getting into databases and using the information for their own benefit. Make sure your data centers are properly secured. (We should also add, there are stories about companies that sell useful information about their customers to others for money. For example, eBay, MasterCard, and AmEx do this.)
  • Using data mining software requires advanced training, therefore it’s not like you can easily start using it without any previous experience.
  • When you’re choosing the data mining tools to use, remember that different software works differently because their algorithms differ. 
  • Use caution when using the data mining results because there is always a chance of data bias, which might lead to wrong decisions. Trust, but always double-check.  

What can I do with data mining?

As you can see from this article, there’s a lot of what data mining can do. Basically, it builds the foundation for further data analysis and business intelligence insights, which are critical for success.

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