Top 5 Challenges In Big Data & Analytics

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Top 5 Challenges In Big Data & Analytics


As big data getting used this day, there is some challenge for those managers who use big data on their business.

The most obvious challenge associated with big data is simply storing and analyzing all that information. In its Digital Universe report, IDC estimates that the amount of information stored in the world's IT systems is doubling about every two years. By 2020, the total amount will be enough to fill a stack of tablets that reaches from the earth to the moon 6.6 times. And enterprises have responsibility or liability for about 85 percent of that information.

Much of that data is unstructured, meaning that it doesn't reside in a database. Documents, photos, audio, videos and other unstructured data can be difficult to search and analyze.

It's no surprise, then, that the IDG report found, "Managing unstructured data is growing as a challenge – rising from 31 percent in 2015 to 45 percent in 2016."

In order to deal with data growth, organizations are turning to a number of different technologies. When it comes to storage, converged and hyperconverged infrastructure and software-defined storage can make it easier for companies to scale their hardware. And technologies like compression, deduplication and tiering can reduce the amount of space and the costs associated with big data storage.

On the management and analysis side, enterprises are using tools like NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence and machine learning to help them comb through their big data stores to find the insights their companies need.

Comments

  1. SafooJune 26, 2020 at 12:00 PM

    Form this blog, the biggest challenge associated with big data is simply storing and analyzing all that information.The most obvious challenge associated with big data is simply storing and analyzing all information. IDC estimates in its Digital Universe report that the amount of information stored in global IT systems will double approximately every two years. By 2020, there will be more than all information. So in response to data growth, organizations are turning to many different technologies. In terms of storage, converged and hyper-converged infrastructure and software-defined storage can make it easier for companies to expand their hardware. Technologies such as compression, repeated data delete and stratification can reduce the amount of space and costs associated with big data storage. Obviously Enterprises are using many kinds of tools to help them sort out big data storage to find the insights the company needs, but can it finally achieve its purpose? I'm very curious.

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