It is crucial to have lots of data for the kind of usage the modern companies challenged by the business. But, it is vital to have clean and relevant data to arrive at conclusions.
Note: If you did not get to read the previous post in this series click the link: Series 1 Part 6 https://www.abigdatablog.com/post/series-1-part-6-big-data-formats-structured-unstructured-and-semi-structured
Introduction to Big data Challenges:
Big data opened up to a world of data. One end industry is figuring out how to store and utilize the data; the other end, the industry realized there are multiple challenges with the vast amount of data.
So, let us see what challenges the industry is facing with the discovery of Big data in regards to Security, Quality, Storage, Analysis, Talent, and Discovery.
Challenges with Big Data:
Data Quality:
The data quality problem challenges from multiple ends, even without Big data. Now with Big data, the problem multiplied due to the Veracity. The data here is very messy, inconsistent, and incomplete. Dirty data cost $600 billion to companies every year in the United States.
Data Discovery:
Big data sources and stored petabytes of data and finding insight challenges the people working with the data. Big Data is like finding a needle in a haystack. So, analyzing the petabytes of data using powerful algorithms to find patterns and insights are very difficult.
Data Storage:
How to store data, always a challenge; Big Data brings massive amounts of data, and storing the data will make things worse in terms of storage. The more data an organization has, the more complex the problem. So, How to store the data? Where to store the data? And the type of storage system? As well, Big Data asks for scale up or down on-demand.
Data Analytics:
In the case of Big Data, most of the time, we are unaware of the kind of data we are dealing with one end. We are not sure about the reliability and relevance of the source of the data. Most of the external sources come with messy data, and cleaning such data becomes the priority before usage. So, performing the right analysis is a much tricky challenge.
Data Security:
Since the data is enormous, keeping it secure and sage challenges the security team, providing user access and user authentication, restricting access based on a user, recording data access histories, proper use of data encryption, etc.
Data Professionals:
There are a lot of Big Data projects in significant organizations. However, a sophisticated team of developers, data scientists, and analysts who also have a sufficient amount of domain knowledge is still a challenge.
Conclusion:
There are multiple challenges in the world of Big Data, but the industry made strides from problems and moving in a positive direction. Companies are handling challenges learning through experience and reaping the benefits.
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