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Storage Resources Are Not the Only Issues for Big Data Featured

Storage Resources Are Not the Only Issues for Big Data JOSHUA COLEMAN

Data has become a new goldmine for organizations. In the current age, no organization can function without data. With the huge amounts of data emanating from various sources every second from business transactions, social media, customer logs, and stakeholders, data has become the fuel without which companies cannot move. All the data is piled up in a large data set called Big Data. The big data needs to be analyzed to help in decision-making. Big Data has various challenges, including handling it in the best possible way. This involves processing, storing, analyzing massive amounts of data on various data stores. Here are some of the challenges that come into play while dealing with big data.

  1. Lack of understanding of big data

 While there are many talks about big data, not everyone understands what it is all about. The same can be said about organizations. Companies fail in their big data initiatives because they lack adequate knowledge. Employees may not be fully aware of what data is, the right storage, processing, and importance to business decision-making. While professionals may know what is going on, others may not have the right picture about it. If employees fail to understand this, they might not backup sensitive data. Furthermore, they may not use databases correctly. For this to be solved, big data training, seminars and workshops need to be held in companies for all employees to understand the value of data.

  1. Confusion in the selection of data

For many companies, selecting the best tool for the analysis and storage of big data is confusing. The challenge can also come from choosing the best technology to store data, such as Cassandra or HBase, or the best analytics and technology between MapReduce and Spark. This bothers companies, who are in some instances unable to find answers. They, therefore, end up making poor decisions and selecting the right technology. In the end, money, time and effort are wasted. Therefore, professional help should be sought as the best way to go around this issue. You can think of hiring experienced people that understand different tools.

  1. Inadequate data professionals

Data alone is not enough for decision-making in an organization. Rather, you will need qualified professionals and skilled personnel to use big data tools. The professionals will range from data engineers, scientists and analysts who work together to make sense of massive datasets emanating from different sources. With the huge demand and fewer professionals in the markets, finding qualified people for the job has become a challenge. The evolution of data handling tools have evolved rapidly has made this even more difficult. Companies should invest more money in recruiting skilled professionals. Training programs should also be offered to the existing staff to get the most out of them.

Data security

Securing huge data sets has become a daunting task for organizations. Although companies give their all in understanding, storing, and analyzing datasets in their possession, they sometimes forget their security, which is one of the most critical components. This is always a danger because unprotected data repositories become easy targets for hackers. An attack can lead to losses of millions of dollars. A solution to this involves recruiting more cybersecurity professionals to protect their data. Companies should also carry out data encryption, segregation, identity and access control. Other measures include real-time security monitoring, using big data security tools and implementing big data security monitoring.

As an organization, begin by understanding the challenges that face your big data repositories before implementing and improving their use in your business.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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