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6 must-have features of Big Data Tools

With the Big Data boom an explosion in the available Big Data tools has also occurred. Because data analysis is obviously not a single process, but rather a collection of processes, it's important that the tools that you use can be widely deployed. In today's blog post, we discuss the six must-have features of Big Data tools.

1. Easy result options

Big Data is all about better decision-making, which means that you also want tools that will support you in the decision-making process. Therefore, with any Big Data tool, think carefully about the data output options that it offers. Consider, for example, the depiction of real time data in a way that allows conclusions to be drawn quickly. This is an important factor in deciding which tool is suitable for your company. Not all companies need the same data for decision making; therefore, it's essential to check what the options are before introducing a new tool.

Search for tools which have dashboards for your KPIs and ensure that these are fully adaptable. Today's tools (including the free tools) must offer at least real-time reporting, dashboard insights and location-based insights.

2. Raw data processing

Whichever tool you use, it's important that it can perform the analysis using raw data. This enables you to load data from different sources into the tool, which then converts it into orderly and organised data. Preferably, use tools with different visual outputs, so that data is easier to interpret. It’s also important that data can effectively be exported to, for example, pdf, Word or Excel and that it can be used within the company by people who don't have direct access to the tool.

3. Account management

Clearly, you don't want just anybody to be able to do anything they like with the data. To keep the data sets as clean as possible it's important to be able to create different profiles within the tools. For instance, there should be profiles for administrators, for analysts and for people who only want to view the output.

4. Data security

Against the backdrop of the GDPR it is essential, of course, that your data is properly secured. It’s also wise to look into the options for data encryption, data anonymisation where possible, the erasure of personal data and the deletion of data that you don't need (or not saving it in the first place). Because you can no longer simply keep every item of information about everyone, it's important that the tools you use offer multiple options.

5. Support for the most widely used tools and technologies

The tool must also, of course, support the most widely used tools and technologies. Consider, for example, A/B tests on websites and technical support and integration with Hadoop, where you should especially take into account the integration options with MapReduce, Hadoop Common, YARN and Distributed File System.

6. The tool must be scalable

Your current data requirements are not always representative of the future. With the growth in Big Data applications you will probably want to store and process increasing volumes of data. Therefore, be sure to invest in the right tools, tools which are ready to take on the future. Also bear in mind the number of accounts that you can create in a tool and analyses that you perhaps don't need yet but that you will want to perform in the future.

With this general list of must-have features you will have more insight into whether the software that you're contemplating today will actually do what you want it to, on into the future. When you take your first steps with Big Data as a company, it can be advisable to engage a specialist right from the start. They will help ensure that you make the right investments and that you don't spend too much or too little. Our consultants are always open to talking with you, with no obligation, so that you can decide whether a specialist is the right step for your company.