6 Features that make big data analytics vital for businesses

Businesses need to invest in big data analytics. Processing big data is an immense challenge, which few other tools can do in a timely mannner. On the other hand, tools for big data are explicitly designed for this and can process large amounts of data promptly. Not only are the tools equipped to handle terabytes of data, but the tools also come with several features that lead to higher quality insights, lower costs and better productivity. Here are 6 essential features of analytical tools for big data.

6 features of big data analytics

Scalability

If corporations are to glean any meaningful insights from this data they must have a data analytics model that processes data without seeing a significant increase in cloud service and hardware costs.

However, data scientists usually build data analytics models by experimenting with smaller data sets. They must scale the model from small to large, which can prove to be a considerable challenge. The ideal big data analytics model should have scalability built into it to make it easier for data scientists to go from small to large.

Version control

Some projects may require data scientists to make changes to the parameters of a data analytics model. However, making changes is risky because one change to the parameter can cause the entire system to breakdown. A system breakdown brings the entire project crashing to a halt. Results are prolonged and costs go up because the project is delayed beyond the expected deadline. However, big data analytics tools with version control can prevent this from happening. Version controls are the systems and processes that track different versions of the software.

With version control, it’s much easier to revert to a previous version of a big data analytics model if the system crashes. Thus, reducing delays and keeping the project within budget.

Simple integration process

Big data analytics tools integrate data from different sources like data warehouses, cloud apps and enterprise applications. A lot of time is spent customising the integrations to make sure third-party applications are properly connected, and that data processing is smooth. Analytics tools with a simple integration process can save a lot of time for data scientists allowing them to do more vital tasks such as optimising the data analytics models to generate better results.

Better data exploration

Data exploration is a discovery phase where data scientists ‘explore’ the big data they collected. The purpose is to discover connections buried within the data, understand the context surrounding a business problem and ask better analytical questions. Analytics tools that facilitate the process save a lot of time. The tools allow data scientists to test a hypothesis faster, identify weak data quickly and complete the process with ease. Some analytics tools even come with visualisation capabilities, which makes data exploration even quicker.

Identity management

Identity management is a system that contains all information connected to hardware, software and any other individual computer. An identity management system is a boon to businesses because it helps with data security and protection. This is because identity management systems can determine who has access to what information, thus restricting access to a handful of computers. Therefore, identity management is vital for keeping information safe.

Reporting features

Reporting capabilities of big data analytics include location-based insights, dashboard management and real-time reporting. These reporting features allow businesses to ‘remain on top’ of their data. Hence, if there are meaningful connections found in data or actionable insights discovered, the company will know about it instantly. Thus, business leaders are in a better position to quick action and handle critical situations in a timely manner.

Business leaders can take action quickly and handle critical situations well. Without reporting features, it would be difficult to understand what is being analysed, what the results are and what the overall progress of the project is.

Key takeaways

Big data analytics tools are essential for businesses wanting to make sense of their big data. The tools come with several features that make big data processing much easier to accomplish. However, without the right tools, it’s impossible to process data in a timely manner to get accurate results. Some of these features include better reporting, data exploration, version control, data integration and simple integration.

For more information on big data analytics tools and processes, visit Selerity.