big data analytics

How to build an intelligent big data analytics infrastructure

Businesses must choose the right software, hardware and security solutions for their big data analytics infrastructures

In today’s data-driven, digital economy, big data makes it possible to help gain insights and unearth patterns that inform decisions to improve customer service and fight cyber threats. Big data analytics makes it possible for businesses to quickly analyze and extract meaningful information from large volumes of data. It is, therefore, important that businesses choose the right software, hardware and security solution for their big data analytics infrastructures.

Big data is usually classified as structured, unstructured and semi-structured. Of these, structured data is the easiest to work with while unstructured data requires a lot of effort to make it fit for processing. Further, there are many challenges that big data analytics faces, which include: data collection, storage, analysis, visualization, querying, privacy and so forth.

To cleanse the data and make it fit for further processing, requires working with several technologies. According to Forrester Research, there is an ecosystem of 22 technologies and tools that work together to provide businesses with the real benefits of big data analytics. Apart from this ecosystem of technologies, Hadoop, MongoDB, Couchbase, KAFKA, Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Yarn, and MapReduce are some of the commonly used big data solutions.

Use smart data management solutions

To build data-driven, intelligent enterprises, businesses must use smart data management solutions—that include a balanced mix of hardware, software and support services. They must use hardware that is capable of scaling up given the ever-increasing volumes of data. Today, purpose-built hardware, especially for big data analytics, is easily available that supports high performance given their enormous storage capabilities for both raw and analyzed data.

The virtual infrastructure must be robust enough to support a wide spectrum of apps. Therefore, businesses must consider software solutions that can support managing data in hybrid and multi-cloud environments. There are both proprietary and open-source software solutions for big data analytics.

Address the security concerns

Businesses must adequately address data security concerns as big data is vulnerable to cyber threats. They must deploy solutions that can protect data from possible exposure. That said, ensuring security for a complex infrastructure with disparate platforms and environments can be an onerous task. Businesses must, therefore, look for solutions that allow them to secure all touchpoints with the same policy and incident management. Again, there are cloud-based security solutions available that enable businesses to implement an adaptive security strategy for long-term protection.

Train the team members

Lastly, given the shortage of skilled professionals in big data analytics, it is of utmost importance that businesses nurture and retain their skilled team members. This will help businesses become more productive and gain a competitive edge.

The article has been written by Neetu Katyal, Content and Marketing Consultant

She can be reached on LinkedIn.

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