Data is definitely priceless, today is probably the biggest asset companies can have. However, the process of obtaining value from the data collected is not a simple or inexpensive process.

Data Mining is the process of discovering patterns between large sets of data to transform them into effective information. This technique is based on the use of specific Algorithms, Statistical Analysis, Artificial Intelligence and Database Systems in order to extract information from large datasets and convert them into valuable information.

Exponential growth

With increasing data volumes and companies needing useful information to make better business decisions and formulate concise strategies, the market for data mining tools is growing rapidly.

These tools are reshaping the landscape by enabling Digital Transformation in industries such as banking, manufacturing, professional services, government or business processes.

Due to its great potential, the data mining tools market is expected to reach $1.039 billion by 2023, at a compound annual growth rate of 11.9%.

The growing trend of BYOD in multiple organizations and the growth of mobile devices and applications, has also directly impacted on a massive increase in the amount of data generated, which in turn has led to an increase in the demand for data mining tools.

In addition, the rapid demand for data mining tools by IT and e-commerce organizations to solve problems with agility in order to provide a higher level of satisfaction at a reduced cost is one of the important factors driving the growth of this market.

Fundamental characteristics of Data Mining tools

To take advantage of large data, companies must have strategies. And that’s exactly where data analysis tools come in. As discussed above, they help companies identify trends, point out patterns, and gain valuable ideas that decision makers can use in the future to guide the brand through a particular path.

It is important to highlight the main characteristics that any data analysis tool should contain.

  • Analytical Capabilities: Different Big Data analysis tools come with different types of analytical capabilities such as Predictive Mining, Neural Networks, Time Series, etc.
  • Integration: sometimes, companies require additional programming languages and statistical tools to perform different forms of customized analysis. Therefore, Big Data analysis tools are required to be equipped with it.
  • Scalability: the data will not always be the same and will grow as a business grows. With the scalability feature of data analysis tools, it is always easy to scale as soon as the company captures new information.
  • Version Control: Most Big Data analysis tools are involved in the parameter adjustment of data analysis models. Version control helps improve capabilities to track possible changes.
  • Identity Management: Identity management is a necessary feature for all effective Big Data analysis tools. They must be able to access all systems and all related information that may be associated with the Software, Hardware or any other individual device.
  • Security Features: Data security must be paramount to any successful business. The data analysis tools used must come with security features to protect the data collected. In addition, data encryption is an essential feature that Big Data’s analysis tools should offer.
  • Visualization: This feature of Big Data’s analysis tools allows professionals to display data in a graphical format, making it more usable.
  • Collaboration: While analysis can sometimes be a solitary exercise, it often involves collaboration and therefore this feature is necessary.

From data analysis to total understanding

The importance of data for companies to improve their capabilities has been repeatedly highlighted, however, Data Mining tools do not always provide 100% understandable information for company managers.

This is when another branch of Big Data technology enters the scene, specifically the one known as Data Visualization. Nowadays, when data teams present findings that differ from executives’ intuition, 90% of them ask for more data instead of relying on the data they have just been given.

Transparency and good communication are key to any business decision, and the visualisation of data allows decision-makers to extract insight from all of them, rather than receiving just one simplification.

This is a booming market, which together with prior analysis has grown steadily in recent years. According to the most recent reports, the data visualization market will have its maximum exponent until 2026, generating more than 6,400 million dollars globally.

Specifically, this boom will be experienced more intensely in North America, although Europe will not be left behind and will also be a lucrative market for data visualization and mining.

The control panels

A step beyond the simple visualization of data, captured in a graphic or infographics, are the control panels. Control panels take visualization to the next level by creating a context around a single image (and minimizing some of the potential for accidental misviewing).

Panels are excellent elements for collecting related visualizations that allow for a more complete understanding (as long as graphics are updated frequently). However, control panels can quickly become messy and confusing, so it is important to evaluate which visualizations will actually be useful for generating understanding.

The main difference between basic data visualization and dashboards revolves around how often data is updated. While data visualizations are only generated from the data, the dashboards are periodically updated according to changes in the data set. In addition, the panels allow easier comparisons between models controlled by databases of very different sizes and types.

Panels have the added benefit of helping data team members collaborate, and business-oriented users play with data and filter charts to help broaden their understanding.


It is important to understand that large data are of no use without a prior analysis of the information captured, which is responsible for making sense of the data. In order to do this, the Data Mining tools offer different capacities designed to provide the greatest possible value to the different companies.

Given the complexity of the data, it is recommended that these data analysis tools be accompanied by an efficient and clear visualization of the information, which can even be reflected in control panels, through which decision-makers can visualize this data in a familiar context and interpret them in a more correct way.