Big Data Challenges – Top challenges in big data analytics

There are multiple big data challenges that this great opportunity has thrown at us.

With the advent of the “Internet of things (IOT)”, efficient analytics and increased connectivity through new technology and software bring significant opportunities for companies.

However, we do see companies facing challenges in leveraging the value that data have to offer. Below are few of the major Big Data challenges:

Meeting the need for speed (Processing Capabilities)

How to match the processing speed with the speed at which the data is being generated.

How to extract useful information out of the heap, one possible solution is hardware.

Few customers use increased memory and powerful parallel processing to crunch large volumes of data quickly.

Another method is putting data in-memory. This allows organizations to explore huge data volumes and gain business insights in near-real time.

Understanding the data

This is one of the basic challenges to understand and prioritize the data coming from the variety of sources where ninety percent of data is noise.

We have to filter out the valuable data from noise. It requires the good understanding of data so that you can use visualization as part of data analysis.

One solution to this challenge is to have the proper domain expertise in place. The people who are analyzing the data should have a deep understanding of where the data comes from, what audience will be consuming the data and how that audience will interpret the information.

Addressing data quality and consistency

Even if you put the data in the proper context for the audience who will be consuming the information, the value of data for decision-making purposes will be jeopardized if the data is not accurate or timely.

Again the data visualization tools and techniques play an important role to assure data quality.

Data access and connectivity

Data access and connectivity can be another obstacle.

Companies often do not have the right platforms to aggregate and manage the data across the enterprise as the majority of data points are not yet connected.

In order to overcome this obstacle of growing volume of data which is not yet connected, companies like Accenture, Siemens formed a joint venture which focuses on solutions and services for system integration and data management.

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