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Data Mining Definition and Applications

Data mining (DM) is a process that helps us find patterns within large sets of data with the help of specific software. Companies use this process in order to optimize their business strategies and increaser their profit margin.

Although it can be done manually, a company will use software to analyze databases and extract needed intelligence. Of course, mining data is just a small part of the process. It also involves data management, various processing, model and inference considerations, etc.

Mining process is nothing new; it has existed for centuries. However, with the advance of technology we are able to learn much more from extracted data. To better establish relationships between data sets and create our own database based on this.

Because of how easy it is to mine data nowadays, companies are using as a way of learning more about their customers, competitors’ products and services as well as general market trends.

Before we go into the real application of data mining, it is necessary to mention knowledge discovery in databases or KDD. KDD is the general process of acquiring knowledge by using big data; the process which data mining is part of.

Knowledge discovery in databases

Whether you’re using online data mining tools or manual techniques, you always have to follow these rules:

  • Start by selecting a data set. It will be used as a source during your work
  • Next is data cleaning and preprocessing. During this steps you will be using various methods for removing noise or outliers and to account for time sequence information. You will also have to make a decision regarding missing data fields
  • During data transformation you need to convert your current data from its original format to a new format so that a new system can view it
  • Next, we come to the actual data mining process. Here, the system will identify patterns and perform classification afterwards
  • Lastly, we have interpretation of patterns

There are misconceptions regarding KDD and data mining. Some people think that these two are synonyms which is flawed. Please bear in mind that knowledge discovery in databases is a field of computer science while data mining is just an individual step of KDD.

Although there are two main ways to perform DM, as a term it is usually used to explain a computerized process. Manual mining of data is usually referred to as scraping.

For example, you are free to search Facebook or Google manually but in order to get the best and most relevant results you will have to use some software.

Data mining tasks

Like knowledge discovery in databases, DM consists of individual methods:

  • Classification – As the name implies, classification focuses on placing data with similar characteristics into same groups
  • Regression – Regression is used to make predictions based on available data. By analyzing statistical data, you can use two or more sets of data to predict all possible outcomes
  • Clustering – During clustering, data is used to create a number of clusters. Clustering is important for determining similarities and differences between various sets as well as for determining where these sets intersect
  • Summarization – Summarization is used to provide a common description for a set of data
  • Dependency modeling – Here, algorithms try to find dependencies between variables
  • Change and deviation detection – Detects changes in comparison to previously measured data

Data mining is important for statistics but can be used for executive processes as well.

It is not a simple data extraction but there are a lot of different processes that are performed prior to that. That is the best way to support final data and to reduce margin of error.

Importance for website owners

Let’s first consider the impact of mining for data on web businesses.

If you are a site owner, there are a lot of reasons why you should perform this process to:

  • Extract names, various stats, social pages and email addresses of competitive websites (used during competitive analysis and outreach)
  • Access and store best performing content for particular queries (used during content analysis and keyword research)
  • Perform various SEO, PPC and SMM tasks
  • Create a potential customer, influencer or partner list
  • Analyze customer behavior and trends

Data mining is especially important for SEO, SMM and PPC as all these professions are heavily reliant on data. Although artificial intelligence is slowly taking over digital marketing, you still need to perform necessary measurements that will help you make a realistic assessment of your market position and adapt accordingly.

In fact, digital marketers are so reliant on these programs that their job would be impossible without them.

So, what kind of data is being extracting with digital marketing software?

  • Number and quality of links on a page or a website
  • User engagement data such as time spent on site, bounce rate, unique visitors, etc.
  • Tracking positions for various keywords as well as their fluctuations
  • Volume, difficulty, average bid for a keyword
  • Social mentions on various social platforms
  • Lists all authors who wrote on a topic
  • Checking health as well as other characteristics of sites
  • Various demographic checks allowing us to segregate both website customers and visitors
  • Provide us with conversion data such as impressions, clicks and revenue per visitor

Data mining is crucial for any internet business.

Not only does it show us current state of business, number of sales, profit and loss, but it also allows us to improve our online strategy and visibility.

Importance for brick and mortar businesses

Same way data mining is important for any online business, it is also commonly used for brick and mortar enterprises. Here are some of the industries that are heavily reliant on knowledge discovery in databases:

  • Finance
  • Marketing
  • Sales
  • Telecommunications
  • Health Care
  • Science
  • IT
  • Manufacturing

Almost any industry is open to data mining and can benefit from it. However, the usefulness is more notable when working with larger amount of data. The process is of analytical character and can be especially beneficial for organizations who have a lot of clients or a lot of products.

However, it is not only used for sales and client relationships.

Mining of data can be used for numerous things such as to:

  • Predict costs and optimize your processes (during product development)
  • Optimize teaching processes
  • Prevent security breaches and various programming errors
  • Optimize hospital treatments, etc.

The biggest issues with mining of data

Although this process is the future, there are potential issues that can arise in certain fields.

Given that the whole process is focused on software, our successful will also be based on quality of that software. Logically, the more variables there are, the harder it will be for algorithm to retrieve appropriate data. Sometimes, there will not be enough data or it will be hard for software to retrieve it.

Here are some of the major issues of data mining:

  • Poor quality of data
  • Inability to access data
  • Size of data sets
  • Inefficiency of algorithms
  • Inability to create or maintain quality software
  • Integrating conflicting data etc.

Nowadays, most industries rely on tools which are themselves reliant on data mining. Most of them have various options but not all of them are good for all these options. We’ve seen programs that are able to do one thing flawlessly while providing limited success for other things.

This means that you will likely have to combine several tools in order to get all the necessary features for your company.

Last thoughts

Data mining is a really wide topic; there are so many things you can do with this process.

It makes sense that companies today, more than ever, are reliant on software to help them with their daily tasks.

Still, if you’re new to a business, you need to be careful when getting some of these programs. The fact that something is popular or widely advertised doesn’t mean it will provide necessary results. In most cases you will have to go for trials and errors until you find program(s) that suit your needs.

Is your industry reliant on data mining software? How much does it help? Share your views in the comment section below!

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