One of the ultimate aims of any business is to make money. But the modern age of connectivity and endless piles of information means that companies have to take new approaches to make a profit. There is where data and data mining come in.
Data is now the most valuable resource in the world. It is no surprise that large corporations such as Facebook and Google are looking to extract as much data from people’s activities as possible. But to make it valuable, getting this data isn’t enough. You also need to be able to extract valuable insights from it.
Data mining is the process of turning raw data into useful information. This information can then influence business decisions by better understanding customers, discovering untapped markets, and optimizing market strategies.
If you are looking to grow your business, data mining could be your next step. There are several data mining techniques that you can deploy, which will be covered in this article.
Classification is one of the most essential data mining techniques and is the most common technique that data scientists mean when they say machine learning. Classification is the process of taking rows of data and getting a machine to put them into different classes.
For example, your business could have rows of data where each one represents another business that uses one of your products. Each row has information on the company, such as their size, industry, and how much they pay to use your product. You might want to know whether they are going to renew their subscription to your product.
Classification can be used to estimate which businesses will renew their subscription automatically. With classification, you will have two classes: “renew” and “not renew.” By using complex machine learning algorithms, a computer can make these predictions with high accuracy.
This allows your business to take decisive action to ensure that more customers renew their subscriptions with your product. The great thing about classification is that it can be applied to many different tasks. If your answer can be represented as separate classes, then you can probably perform classification.
The clustering technique can be thought of as a cousin of the classification technique. In this technique, you are still putting objects into different classes – the only difference is that you won’t know what the classes are before you begin.
In clustering, you group similar objects into clusters. Each cluster contains many objects which are very similar to each other. For example, if each object is a business customer, you might perform clustering to group them based upon how they interact with your product.
Analysis after the clustering might show that some customers use your product every day, some who use your product in a particular way, and some who pay for your product without even using it.
This data mining technique for your sales or product team can be very insightful. It can give you a better understanding of your customers, leads, or clients without even knowing the exact thing you are looking for.
Some companies have used clustering to detect fraudulent activity or to identify fake news. There are many real-world applications for this technique.
Regression is slightly different from the previous data mining techniques. With regression, you are not working with specific groups, classes, or clusters. Instead, you are looking at numerical values.
Regression is the process of estimating an output value based on several input values. The most basic example of this would be drawing a line of best fit in a math class at school.
With your existing numerical data, you can plot these points on a graph and draw a line that best fits through all of those points. If you later get some new numerical values, you can estimate the output by looking at the line.
Regression analysis in data mining adds many complicated techniques on top of this to make the line(s) more accurate in a wider variety of situations. This makes regression an excellent technique for predicting numerical values or understanding how your existing numbers are related.
Forecasting and financial analysis are popular uses for regression in the corporate world. Do you want to predict your sales figures for the next four quarters? Do you want to understand how your churn rate has changed over the past two years? Do you want to predict the effect of a marketing campaign on profits? Regression can help solve all of these problems.
Tools as simple as Microsoft Excel can be used for this type of analysis. But if your problem is too large to handle in Excel, many programs can perform regression analysis.
Anomaly or Outlier Detection
In business, anomalies are extremely important to spot. Anomaly detection is even the basis of fraud detection, as client behavior that is out of the ordinary (clients who are outliers) can be a good indication that fraud is occurring.
An anomaly is something that deviates away from the norm or something unexpected. For example, you can expect your customers to interact with your website in a certain way. They might arrive on the homepage, click on the menu, and browse your products. If someone visits your website, tries to log into the admin panel 20 times, and then orders $100 of products for just $10, that would be a worrying anomaly.
Anomaly detection is a fine art. You want to spot the outliers in your data, but you know that not everything can be an anomaly and so you can’t just flag every row of data. A working anomaly detection system can save your business thousands or even millions of dollars if used in the right way.
There are many statistical and machine learning techniques used within the field of data mining that can help with this task. However, building a perfect system is complex, and there are always trade-offs when detecting anomalies.
So you have your vast databases full of data, and data mining is supposed to help you derive meaningful insights from the data. Perhaps you only have a few minutes to spare, and so you want your insights to be summarized in an easy-to-understand way. That is where the summarization technique comes in.
Summarization is what it says on the tin, the process of summarizing data. The measure your data is, the harder it will be to perform summarization. This technique also works better on numerical data as it is easier for computers to understand, and it is easy to summarize this type of data in charts and graphs.
Nevertheless, huge strides are being made in research for the summarization of text. This could be shortening a news article into a single paragraph or explaining a 10-minute speech in just 30 seconds.
Visualization is a technique that can be combined with almost any other data mining technique. It is the method of conveying information in an easy-to-understand and visually pleasing format.
Visualizations are often thought of as bar charts and line graphs. But they can also include word clouds, histograms, box plots, infographics, and animations. While these may not include as much complicated theory as other techniques, creating a good visualization is still a fine art.
There are multiple tools that can be used to create visualizations. Microsoft Excel allows you to create charts from spreadsheets. There are also purpose-built tools such as Tableau and Power BI for business intelligence. If you require programming alongside the visualizations, languages such as Python and R also have packages to assist you.
Decision Tree Learning
Techniques such as classification, clustering, and regression can help you to make predictions. But if you want to convince others to take action, it can be essential to know how you came to these predictions.
Decision trees are another form of predictive analysis that is based on having several understandable decision steps. Once you have made your prediction, you can work your way back through the decision tree to understand your computer’s data decisions to come to its conclusion.
Going back to one of our previous examples, you could be using classification to determine if your customers will renew their product subscriptions. Using decision tree learning, you can look at what pieces of data were used to make some of the predictions. Perhaps a business customer has seen a loss in revenue, meaning they are unlikely to renew. Maybe they responded poorly to a customer survey.
Decision tree learning can be a great data mining technique for sales teams to understand what is within their control and what isn’t. A customer might not renew due to a loss in revenue, but you cannot control that. But if they won’t renew because they are unhappy with some features, that is something your business can work on.
Once you begin to talk about neural networks, you are slowly leaving the realms of data mining and entering the computationally heavy worlds of machine learning and deep learning. Nevertheless, neural networks can still be considered a part of data mining – and they can help businesses grow at unbelievable rates.
Neural networks are computer programs that were initially designed to mimic the structure of the human brain, which has billions of neurons firing to allow humans to make decisions. Several neural network techniques have branched out of this for several different tasks.
The flexibility of neural networks means that they can be used in place of many other data mining techniques discussed in this article. This includes clustering, regression, classification, summarization, and anomaly detection.
They can also be used in more complicated tasks such as image recognition (extracting information from images) and text extraction. If you have enough data, the neural networks can structure themselves to solve whatever task you give it.
Unfortunately, there are two main downsides to neural networks that mean not every business can use them. Firstly, neural networks are complicated. Some companies will employ people whose sole responsibility is to build new neural networks, and these will be people who have spent years researching them. Small businesses may not be able to afford to employ teams who can do this.
The second downside is that they require a lot of computing power. This computing power is usually at the building stage, and it can be cheaper to run once built. Even so, some businesses need to rent computing power in the cloud from tech companies such as Amazon and Google just to build neural networks – and this can be very expensive.
If you are considering neural networks, then it is often best to try other techniques first. If those techniques cannot handle the task, then you turn to this mega-weapon.
Data Mining Techniques For Your Business
Data mining is the process of taking raw data and turning it into useful information. Since data is now the most valuable resource globally, data mining can be a great way to grow a business.
The first step to creating useful information is to gather your data. But in the modern world, data is everywhere. Every email, every survey, and every click has some data that you could use. This is why companies turn to data mining, machine learning, and artificial intelligence to understand it all.
In this article, we have looked at several data mining techniques, including:
- Regression Analysis
- Anomaly or Outlier Detection
- Decision Tree Learning
- Neural Networks
Each technique has its use. Data mining techniques for sales teams and product teams are growing, and these techniques can be incorporated into current working practices to improve efficiency and increase insights.
If you are looking to make sense of the world of data and grow your business, data mining is for you. Ready to put it to work? Grab a quick meeting on our calendar, and let’s talk about how Umbric can help you put data mining to use for your organization!
About Umbric Data Services
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