Big data tools are all software and applications that allow companies to maximize the value of their big data. There are many types of big data tools and each of them covers a different business need in relation to the management, analysis or governance of your information assets. As important as the choice of these instruments that will allow extracting value from the data is to have the talent of trained professionals to take advantage of all the opportunities they offer to the organization. In the following lines, we will discuss types of big data tools, their characteristics. We will also discuss the most important failures that occur in relation to their use and how to avoid them.
Characteristics and types of big data tools
In terms of the level of sophistication and market strategy, Big Data tools are divided into three levels, which are the following:
Big data open source tools They offer basic infrastructure, servers and storage.
Big data platforms It is a superior layer, where all the applications with more advanced functionalities are included.
Specific vertical applications They are those that focus on the specific needs of each industry offering collusions that make companies more competitive.
In addition to this classification, big data tools can be differentiated according to the purpose for which they were designed.
Thus, we could differentiate between the following:
- Big data storage tools.
- Big data management tools.
- Big data visualization tools.
- Big data mining tools.
- Quality big data tools.
- Big data analysis tools.
- Big data security tools.
Each company must decide what type of big data tools it needs to incorporate into its big data management to optimize results.
The best big data tools
The determination of the best big data tools for the organization will depend on their internal capabilities and their needs. The big data tools that can work very well for a company, just do not deliver such good results in another for strategic or talent.
When choosing big data tools, in addition to paying attention to the budget and internal capabilities, we must take into account:
- The present and future needs of the organization.
- The data with which the company and its sources count.
- The objective of investment in technology.
- The difficulty of implementation.
- The profitability of the decision.
For example, for many, the best big data tools are open source. But committing to the development of open source implies a great effort in time and resources that end up becoming a more expensive economic bet than any supplier offer would have been.
The same happens with certain types of tools, such as visualization, in which in some businesses a short learning curve and a friendly interface will be prioritized, while in others the focus will be on benefits.
Big data tools: errors to avoid
The effectiveness of big data tools depends not only on the provider, nor on its functionalities. There are 3 errors that companies can commit, limiting their efficiency and the accuracy of the results they provide, are the following:
1. Do not choose the right visualization tools
The selection of big data tools often focuses on the technical level, leaving aside everything that is not directly related to the analysis. Acting in this way involves ending up implementing solutions whose visualization potential is not as broad.
When this happens, the consequences do not take long to appear:
- Causing difficulties in understanding the data.
- Subtracting agility to the process of extraction and sharing of knowledge within the organization.
- Increasing latencies in the taking of action.
- Being able to even divert the decision making that would lose effectiveness.
A crucial step in solving any problem in data science is to obtain an idea of the data, representing it through rich images that can form the basis for the analysis and model it.
To avoid these consequences, you must set the objective of the visualization as the first step. All that is needed is an effective visualization of the results to understand the difference between a data pattern and reality, a knowledge that will allow to apply business intelligence to the business and yield results in a convincing way.
2. Do not give the quality of data the attention it deserves
The quality of the data plays a crucial role in determining its effectiveness. Not all data are created in the same way and each one has a different origin, hence its heterogeneity.
No matter how powerful and complete the big data tools available to the organization are, insufficient or incomplete data can often lead data scientists to conclusions that may not be entirely correct and could, therefore, adversely affect to the business.
The effectiveness of big data in an analysis process depends on the accuracy, consistency, relevance, integrity, completeness and updating of the data used. In the absence of any of these factors, the data analysis is no longer reliable.
3. Leave everything in the hands of big data tools
Although the advances already allow to trust in the technology and in spite of the fact that when companies have problems on a large scale, they often resort to big data as the way to solve it; do not forget that, often the big data can only solve one aspect of the issue to be evaluated, leaving a larger problem ignored and unresolved.
It is not enough to have the best big data tools on the market, but data scientists must use their creativity to identify the underlying issue and find solutions. Big Data is a very complete tool, but it only delivers the expected results when guided by the right hands and applied to the right problems.
Sometimes, without the business vision, the conclusion that big data tools provide is only part of the solution.
Recommendations for the use of big data tools
Errors when using big data tools are more common than would be desirable. These failures begin to occur before their implementation and deployment, in the phase of choosing big data tools, when you lose sight of a very important aspect.
Once immersed in the work, the IT staff can fail in the analysis, not because of the big data tools or their lack of technical experience or preparation, but because of issues related to the data itself and one of its most relevant attributes.
Having extracted the analytical conclusions that the big data tools allow to arrive, sometimes it is verified that the issues that were launched and that gave rise to the construction of the data model were not well directed. It is a problem of approach, which ends up subtracting effectiveness from one of the most powerful instruments available to organizations to extract the full value of their data assets.
There are ethical questions about the use of data and standards that ensure the protection of information. None of them can be ignored and both should be kept in mind when choosing big data tools.
While it may be true that, the greater the data collection, the more correlations can be found, do not forget the context, not only applied to purely data issues but to the vision about how the analysis can benefit the organization.