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5 Data Analytics Misconceptions Only Best Trainers Can Deal With

by Soft2share.com

According to a very reliable IT industry research report, the global software and hardware industry is undergoing a massive transformation with the coming of age of new emerging techniques, especially with Data Science and AI-based automation. The global Big Data market has opened up new avenues for data analytics training courses that are alone expected to contribute 100+ billion US dollars in revenue by 2028. While data analytics training remains a popular specialization in the IT domain, offering a competitive edge over other data science applications, there are certain misconceptions too that could significantly hamper your growth in the industry.

In this article, we have identified the common misconceptions in data science and how best trainers try to neutralize these with verifiable answers.

So, let’s begin…

Data Equality: All Data will be Equal if they come from the Same Source

The anomalies of data management have revealed that no data points are equal in volume, variety, and value — meaning, each data point has to be treated separately and analytics results may or may not vary depending on the algorithms you have used to collect and analyze these.

The data equality anomaly has been so deeply studied in the last 3-4 years that analysts have begun to debunk the theories related to “Good data / bad data” concepts. According to leading data analysts, good data may not be still good enough to drive results, unless the problem is studied purely on the basis of data’s merit.

Investments are Huge and often Unproductive

Another myth surrounding the adoption of Big Data analytics is related to the cost of its acquisition, analysis, and processing. For the most part of the last decade, analysts believed that to become a good trainer, you ought to deploy high-cost AI hardware and software tools and platforms. Meaning, only large enterprise companies are capable of running data analytics.

In today’s remote workplace and collaboration space, IT teams have learned that it’s possible to run data analytics with basic IT and networking support, even as it means investing in only virtualized desktops and GPUs.

Quantity versus Quality of Data

Data analytics misconception rise from the fact that you need tons of data to analyze and segment them in a proper way to drive meaningful results. That’s not true anymore! Analysts are focusing on acquiring quality data over large data sets. When it comes to acquiring Big Data databases, things can spiral out of control as working with Big Data means shelling thousands of dollars into the acquisition, storage, and processing. But, if you focus on a good data database, you can have an upper hand on price and volume control– which eventually saves you dollars and tons of resources.

Data is good– but analytics still doesn’t work for me? Why?

A common misconception about data analytics arises from analyst’s own belief systems– “I can’t do it because I am not a Ph.D. in Data Engineering or I am not QUALIFIED Data Scientist!”

You must trust your analytical skills when results are drying up. Trust us, analytics works and it all depends on your persistence and ability to secure good data and segment them in a reliable manner.

There are many Machine Learning models that can help you find the value of each data point lying underneath tons of information. You can either work with Business analysts or citizen data scientists who leverage Open Source DevOps community forums to charter a new avenue for every kind of data pushed for analytics engines in the mainstream data analytics training.

Once I have AI Force, I Don’t Need to Work More

This is a common misconception that has prolonged the adoption of AIOps in Data Science projects. Most analytics with certifications develop AI ML models to nurture data and pull out insights that they think is accurate because it’s automated to machine learning techniques.

That’s far from the truth — 90% of the Artificial Intelligence and Machine Learning based data analyses are a failure! Reason: Complete dependence on faulty Machine Learning models.

Yes, AI certainly removes certain parts of the work but to let it completely replace data scientist work is a spell for failure in the making.

Conclusion

We have been hearing that AI will take away analysts’ jobs and render them jobless — this is far from the truth as we are witnessing a more collaborative culture developing in the industry — thanks to very well planned data analytics training programs across India.

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