Is Data Analytics really that complex?
Myths of Data Science
“You need a PHD to have a chance of becoming a data scientist. Two is even better” – This is one of the best ever argument put forth that needs to be quashed at its core. Misconceptions can hamper the growth of any organisation. Everything trending is mired in these myths and misconceptions and hence data analysis is not left out in this . In this article we will address the misconceptions that allows myths to gain mileage and dissuades businesses from jumping the bandwagon.
Data analytics is expensive and only large companies need analytics – This is a misconception which most businesses are caught in. They believe that implementing data analytics involves sophisticated hardware and large amounts of money which is rarely the case. Whereas, in reality, what is required is the right people to extract the right data to operate on and interpret in a manner that provides actionable business insights. The technology requirements may, at times, be met through open source tools that are easily available.
Data scientist are experts – Fancy degrees do not guarantee accurate or meaningful insights into data. Machine learning and deep learning with advanced statistics can help gather and decipher information.
It requires mass volumes of data – Large volumes do not guarantee accuracy of data. Most companies shy away from incorporating analysis for the fear that without large volumes of data any analysis is inaccurate. Rather it is the way in which data is analysed is what makes the difference.
Data analysis is a hype – Many believe it is a fad that will fade with time as bubble that is waiting to burst. However, as Clive Humby rightly said, “Data is the new oil”. This oil will need to be refined if it must add value and help companies and individuals take better decisions. As long is information is power, there will be a need for data analytics.
Data analysis takes a lot of time – Most businesses decide against data analytics with the misconception that the required metrices to focus on, will take a lot of time and leave little time to focus on important matters. The reality is that once those focal metrices are identified, it is fairly easy to navigate or work around them.
AI powered data analytics will eliminate human workforce – Since it is believed that AI and its algorithms will help formulate answers to our problems, human work force will find themselves redundant in the coming years. However, the fact of the matter is that routine and mundane tasks get eliminated so that human force and brain power can be utilised on complex roles that require the human touch and attention.
Organisations data is accurate and clean – Existing data is mired in errors and are inaccurate. Data needs to be scrutinised to weed out inaccurate and erroneous data so that the right data can be used for interpretations.
Bottomline: Data analytics is here to stay and is bound to create niche job areas that needs to be tapped into. It is time we look into analytics as a thread that can be woven to harness its immense potential in creating intricate innovative solutions.
Author: Jerrin Thomas; Co-author: Benila Jacob