What is Data Mining and Why Are Data Mining Tools so Important?
Data mining tools are some of the most sophisticated programs that analytics practitioners are using today. Data Mining is the process of looking for potentially meaningful patterns and information that would otherwise be hidden, within big data sets. The process usually involves the analysis of huge amounts of data to find valuable and statistically significant correlations and patterns.
These correlations and patterns can lead to inferences which can ultimately lead to business enhancements. When used in business, these enhancements create concrete value.
Data mining is also used to predict future outcomes, as a precursor to both predictive analytics and prescriptive analytics. It helps in providing insights that can be used in fraud detection, scientific discovery, process improvement, sales performance, and marketing optimization.
Types of Data Mining Tools: Open-source vs. Proprietary
There are dozens upon dozens of Data Mining Tools in the market at the moment. These tools typically fall into 1 of 2 categories: 1) Open-source, and 2) Proprietary Software. Open-source data mining tools are platforms that purposefully expose their internal code to the development community so they can collectively take advantage of crowdsourced improvements, evolution, and innovation.
These tools are almost always free and as a result, they gain rapid adoption. On the down-side, they come with very little formal support and it is often up to the user to diagnose and solve their issues by visiting development forums and consulting with others.
Proprietary software platforms, on the other hand, are tools and technologies developed for sale by software companies looking to gain user-adoption of their product, and to generate profit from those who are willing to pay for it. They typically make their code impossible to access. However, their solutions can be quite developed and almost always come with robust support. That development can come at the expense of flexibility though.
The Many Tools, Technologies, Platforms, and Solutions
The 30+ most commonly used data mining tools and platforms today are as follows (in alphabetical order): AdvancedMiner, Alteryx Analytics, Analytic Solver, Angoss Predictive Analytics, Civis Platform, Dataiku DSS, FICO Data Management Solutions, GhostMiner, HP Vertica Advanced Analytics, IBM SPSS Modeler, KNIME Analytics Platform, Lavastorm Analytics Engine, LIONoso, Microsoft SQL Server Integration Services, Neural Designer, OpenText Big Data Analytics, Oracle Data Mining ODM, Periscope Data, Portrait Predictive Analytics, PolyAnalyst, Portrait Predictive Analytics, pSeven, QIWare, Rapid Insight, RapidMiner Studio, Rialto, R-Studio, Salford Systems SPM, SAS Enterprise Miner, STATISTICA, Teradata Warehouse Miner, Think Enterprise Data Miner, TIBCO Spotfire, TIMi Suite, Valo, Veera, Viscovery Software Suite.
Today we have a myriad of different options on the market. Some are fantastic, and many are terrible, but they are all different.
What Can be Done with these Tools?
When it comes to data mining, many different techniques are being applied to data sets, depending completely upon what the goals are. Here are the top uses for data mining tools today:
- This mining method is used to classify and organize data to be mined in different categories. It can be used to get important and valuable information about the data such as the structure, shape, and hierarchies of the data.
- This technique is used to identify data points that are similar to one another. It helps in understanding the commonalities and differences between the data and often reveals patterns.
- This process is used to analyze and identify the relationships between data variables (and usually over a time-series). It is used to show the likelihood of different variables or attributes occurring in the presence of other variables or attributes and can be a very helpful technique for prediction.
- Association Rules
- This technique is used to determine the association between two items. It is very often used to discover hidden relationships between data-points. A great example of this is creating conditional or “if-then” statements that help to show the probability of relationships between records or data points. Association rules have numerous applications and are frequently used to discover correlations in transactional sales data.
- Outlier Detection
- This technique is used to refer to observations of rare events, anomalies, or as the term implies “outliers.” These are typically data points that do not match an expected behavior or pattern and are considered to be abnormal. The technique can be used to detect things like fraud. For example, unusually high dollar-amount orders, orders from unexpected customers, or even duplicate accounting entries.
- Sequential Patterns
- This technique is used to identify and discover similar trends in data for specific periods. This can include anything from measuring seasonality of sales to the best customer path to purchase.
- This technique involves leveraging past and present data, to forecast and predict future data. These techniques usually take data mining and turn those insights into predictive models. They leverage past event data to come up with the most likely prediction for the future.
Industries Using These Tools Today
When manufacturers use data mining in their operational engineering data (like data from machine sensors), they are often able to detect faulty equipment. They can also determine the optimal control parameters of the equipment. For example, the use of data mining may be used to accurately determine the range of speed at which a production line can safely operate.
Agencies and Retail
Marketing strategy requires intense knowledge of previous data to predict the future. The use of data mining helps in predicting future marketing trends based on previous market trends.
Data mining tools can also be used to determine the marketing campaigns and how best to reach the target market. Also, data mining and data mining tools can help retail stores know when to offer discounts on some of the products to attract more customers and even when to staff stores with more or fewer employees.
Controlling government finances can be extremely data intensive. For this reason, data mining can be used to analyze and uncover the financial records and transactions of large institutions with massive amounts of information, like governments. Everything from the Internal Revenue Service (IRS) building patterns to detect money laundering, to leverage census data for centralized planning, to the Federal Reserve deciding interest rates.
Financial Institutions & Consumer Credit
Data mining allows financial institutions to make highly accurate and informed credit decisions on potential clients. This includes extensive credit report history and detailed loan information.
The credit reporting bureaus themselves create scores from your historical data, and they can determine your overall credit-worthiness. Banks also often use this information to protect you against credit card fraud.
The Future Of Data Mining Tools
Data mining is growing with time and has made incredible strides. However, it can still be difficult to predict the future of certain events like health conditions years into the future. Regardless, when you use previous data, you can usually predict the patterns that history has followed.
This helps us become better planners, decision-makers, and risk-takers. If you are looking to expand your business, you can predict your target market by using previous data to see how your market grows. People have learned to trust the use of data mining to control their business and finances.
The 7 Most Important Things To Know With Data Mining Tools
Have Clearly Defined Objectives In Mind
The data mining process is very time-consuming and difficult. If you don’t have clear objectives of what you want to accomplish, you may end up wasting a lot of time. Have clear objectives for the project at hand while keeping the end goal in mind.
Anticipate Messy and Disorganized Data
Many different datasets are incomplete, unhygienic, and blatantly incorrect. As a data practitioner, it is incredibly important to recognize these things. Knowing which parts of your data require additional cleansing before being used, can mean the difference between genuine and valuable insights, and being led down a rabbit hole.
Ask The Right Questions
Ask empowering and insight-driving questions like “What is this data telling me about what happened?” To make your work easier, try to connect the data you’re working with to the real-life events that your data was generated from. Get straight to the point and connect your data to reality. This will immediately drive a better understanding of your data and allow you to “reality check” your assumptions.
Simplify Your Solutions
Occam’s Razor. Sometimes the simplest techniques are the best. You should always simplify your solutions and take the simplest path to generate your inferences. Not only does this ensure that data mining does not take up all of your time, but it almost always leads to elegant analysis and a clearer understanding of your data.
Don’t Rely Solely on Default Model Accuracy Metrics
Default model accuracy metrics are used to determine errors in data and the degrees of those errors. But they are often very specific metrics and are NOT “one size fits all.” When data mining, you will find that bulk data is highly prone to generating large errors. Take these errors with a grain of salt and use them only when appropriate. To ensure you get the correct outputs, you may opt to avoid using the default model accuracy metrics of your tools.
Backup Your Data and Document All Modeling Steps
First off, always ensure that the dataset you’re manipulating is not production data. Always pull data from the source, and duplicate it before mining. This ensures you don’t wipe out the only records that exist. Additionally, always document your steps. If you discover you made a mistake, you can easily retrace your steps and correct any incorrect logic.
Have Great Presentation And Communication Skills
Once you’ve determined your insights, you will need to be able to explain them to others. Remember that even the best analysis is worthless if other people can’t understand it and don’t know what to do with it.
Data mining tools are used to explore and understand big data sets. There are many platforms, methods, and uses for data mining and the different tools that come with it. But data mining can be incredibly valuable and the data mining tools required for this line of work help to uncover highly valuable information. So whether you want to analyze previous data to uncover history and patterns, detect unlikely or abnormal events, or even predict future performance; you should know your tools, master your techniques, and always look to drive incremental value.