Data Mining Tools: The What, The Why And The How

Data Mining Tools: The What, The Why And The How

With Big Data becoming more prevalent than ever, the demand for mining tools is growing. It’s becoming vital to know exactly what tools are capable of successfully dealing with huge amounts of data. In this article, we will discuss the complex prospecting algorithms and data visualization libraries that will be your primary tools in building your lead generation platform.

Data Processing

Before we dive deeper into the details, first we need a clear vision of how a very large amount of data transforms from a huge amount of unorganized information into an organized and structured set of lists, ready to be used by sales, marketers or even HRs.

Common data processing looks like this:

  1. Find a lead data source. This is the primary place from where all your data will be mined. This can be a popular social media platform like Facebook, LinkedIn, and Twitter. Now we have bulk data, most of which is no use to us.
  2. Target the relevant data. Here we define the targeted data type and source, suited for our purposes. We can have multiple associative data types, as well as several sub-sources to extract data from.
  3. Preprocess raw data for future processing. This part of the data mining process involves altering the data from a raw format into one that’s acceptable for further interactions.
  4. Convert preprocessed data into a readable format. Your original data language will be determined and transformed into one your system is able to process.
  5. Create Data Patterns/Models. Based on the data you have, you can determine common relationships between the subtypes of data and identify patterns, or create sets of tables connected by data relationships.

Data Visualization

With the relational data patterns identified, we are able to build all sorts of meaningful infographics and visualize them using third-party services or libraries. These third-party solutions don’t have a high learning curve, however analyzing the libraries directly would require the assistance of a developer who is familiar with the languages used in any given library. Here you can see the list of the most commonly used 3rd party tools for data visualization:

  • Tableau (big data tool for corporate use)
  • Infogram (simple tool for big data)
  • Datawrapper (data tool for journalists and news publishers)
  • D3.js (JavaScript library for displaying data on web platforms)
  • Google Charts (user-friendly library based on HTML5 and SVG for Android, iOS, and browsers)

With these tools, we can create infographics that will show all the data we need for our sales and marketing departments to create a successful marketing campaign. Moreover, collected data can be used for outreach to potential prospects. Lead generation cannot exist without a solid data foundation. If you want to generate leads – generate data.

So, what is data mining, why do we need it and how can we use it to generate enriched lead data? Let’s explore, starting with what data mining actually is.

What is data mining?

Data mining is the process of analyzing bulk data to find new unknown patterns and hidden correlations. With data mining enterprises we can use these models and patterns to generate quality leads.

Data mining was created to work on the following tasks:

  • Predict. Have an ability to foresee undefined or future values in one or another feature of your data.
  • Descriptive. Make your data understandably organized through user-friendly patterns and models.

Within these tasks are several techniques essential to the data mining process that can’t be neglected:

Descriptive techniques

  • Association. Data is being generated by analyzing the association between items in a given data set. This technique is often used by sales to determine which products customers buy together.
  • Clustering. Here data is treated like an object which is stored in automatically defined classes. To make it clearer, data is kept in clusters, with particular similarities between them.

Prediction techniques

  • Classification. This technique breaks data into relative classes and groups. With it you can classify leads into separate groups, like who is more likely to become your sales lead or who has no potential whatsoever.
  • Regression. Used to predict a range of numeric values in a precise data object. With regression, you can predict the flow of leads to your platform.

It’s important to know about these techniques, even if you don’t know how to properly use them. This is where the data mining tools come in handy for performing the analyses of your data. These tools have different features and ways of implementing them.

Some of them are more complex and take significantly more time to implement. It all boils down to the goals you are trying to achieve. You might ask if it’s so complex, why should I care? Well, let’s jump into the next section and explore why.

Why are data mining tools so useful?

Data is the oil of the 21st century, and oil equals money. Data mining tools will help you generate more revenue by creating informational assets, used both by sales and marketing departments. They can study the behavior of your clients, their location, position and create solid marketing strategies.

Enterprises thrive on the features of data mining tools, with them they can get detailed business intel, plan their business decisions and cut costs drastically. They can also help you detect anomalies inside your models and patterns to prevent your system from being exploited by third persons.

With all those features on board, you won’t need to implement complex algorithms from the ground up. Moreover, you can adjust those features with some additional tweaking to the code base (if it’s an open source tool), as your demands grow.

Overall, data mining tools were created to define and achieve numerous objectives, helping you generate more profit in the end. Now you see why these tools are genuinely useful. Let’s end this with the last but not least important question – how.

How can we implement them?

Different tools require different approaches. Some require zero to no coding experience, others would most likely demand some programming skills depending on the coding used. These tools are generally open-source and don’t have any paid plans.

Here is a list of the most commonly used data mining tools. Starting from entry level to enterprise-grade businesses:

RapidMiner

It’s an open source ready to use tool that requires no programming knowledge, with numerous features for data analytics. Thanks to built-in template frameworks, this tool speeds up the work of the data miner and cuts the number of errors during the runtime. This tool is written in Java and has multiple mining options like pre-processing, converting and prediction techniques. It can be used with other tools like WEKA and R-tool to give models written in the code of those two. Existing patterns, models and algorithms can be enhanced by the following programming languages:

  • R – a programming language used for data mining, extraction, exploration, and analytical tasks;
  • Python – a programming language used for rapid prototyping of software solutions.

They are well suited for rapid prototyping and data manipulation.  

RapidMiner has all the data analysis features from the simplest to the most advanced ones. With plugins from Rapidminer Marketplace, they extend the already vast functionality. Moreover, developers and data analysts can use the marketplace for publishing their plugins or algorithms.   

WEKA

WEKA contains a selection of algorithms, visualization tools for machine learning and data analytics. You can use this tool directly on your sets of data. With WEKA you can perform numerous data tasks, regression, clustering, classification, visualization and data processing. The main advantages of this software are:

  • Completely free
  • Portable, can be used on multiple platforms
  • Compilation of numerous machine learning and data mining algorithms
  • Compelling user experience with graphical user interface

Besides, this tool can be used for creating various machine learning schemes.

Orange

Orange is a Python library with a component-based structure for machine learning, data mining, analysis, and visualization. These components are also called widgets, they help not only with simple tasks like data preprocessing and visualization but also with creating complex algorithms and prediction models.

Orange has visual programming implemented into it for creating a solid workflow by linking user-made widgets. It can also be used as a Python library to change widgets and manipulate data.

R

R is both a free programming language and an environment for manipulating data and statistical computing. Thanks to the numerous packages R is commonly used for data mining and creating statistics by data scientists and analysts. These packages include community-created libraries for data manipulation.

What we’ve learned

Data mining tools are an essential part of enriching your leads. With these tools at your disposal, you can create patterns based on the user’s behavior and apply it to your marketing strategies. These patterns can also be used to enrich your leads with new data. There are various techniques to describe data by associations or split it into separate clusters, to predict the changes in data by classifying it or using regression.

Overall, data mining tools help us enrich our leads and make our lead generation campaigns more successful.

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