Question: May i please get a positive respond about the comment below. After reading Data Analytics Implementation Barriers, the barriers of data analytics include technology, people,

May i please get a positive respond about the comment below.

After reading Data Analytics Implementation Barriers, the barriers of data analytics include technology, people, processes, analysis paralysis, drowning in data and misaligned metrics. Operations are faced with many barriers in implementing Supply Chain Management (SCM). Due to the globalization, advance technology and increase customer demand, organizations need to raise their bar of performance continuously. Recently, big data has attracted researchers due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular as it helps gain insights and make decisions based on big data.

The most obvious challenge associated with big data is storing and analyzing all that information. Often companies fail to know even the basics: what big data is, what its benefits are, what infrastructure is needed etc. Without a clear understanding, companies can waste lots of time and resources on things they dont know how to use.

There are many other challenges faced by big data analytics. Big data has many challenges because a lot of data is risky and can easily get out of control. While big data continues to expand; new businesses and inventions are being created daily. Which makes technology a challenge/barrier because companies must figure out which technology works best for them without new threats or issues being implemented.

When it comes to human barrier, big data is a growing and complex field. Not many people understand the difficulty and complex existence of this field. Companies often lack talent in the skills necessary to interpret big data.

Also, companies are investing significant amounts in gathering data instead of integrating, merging and analyzing it. Organizations are still gathering data without analyzing it because they are still locked into old ways of working with data.

Its not just big data, its data everywhere. Its how an individual use data big and small. Its not about large files, its about changing culture to a more analytical culture.

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