Explaining why the stock brokerage firm does not have a data scientist, Zerodha CEO Nithin Kamath said not only the data scientists do not provide useful insights but the brokerage firm also never upsell. ALSO READ: Want a secure retirement life? 5 tips by Zerodha co-founder Nithin Kamath you shouldn't ignore
"How useful is collecting & analysing tons of data is a question I ask my peers. We have no data team at Zerodha as we believe it doesn't give useful insights & also since we never upsell," Kamath tweeted backing his remark with an article titled "Big Data is dead."
In the article, a user contested that the real reason businesses struggle to gain actionable insights from their data is that they typically ignore what the data says. Wading through data, the user adds, requires a great deal of thought, which is in short supply. "Data Scientists are commonly disregarded by VPs in large corporations, despite the claims about being "data driven". Most corporate decision making is highly political, the needs of/what's best for the business is just one parameter in a complex equation," the post adds. ALSO READ: Zerodha co-founder Nikhil Kamath says Rupee at 82 ‘isn’t such a bad thing'
To this, another user said, “I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated.” The user says, nobody would question the data's accuracy or dependability. They would select an eye-catching metric, such as a percentage, and experiment with it. However, the user continues, whenever analysis contradicts, despite efforts to address any questions and concerns about the data quality, figures, “analysis would be tossed away as non-actionable/useless/etc”.
{{/usCountry}}To this, another user said, “I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated.” The user says, nobody would question the data's accuracy or dependability. They would select an eye-catching metric, such as a percentage, and experiment with it. However, the user continues, whenever analysis contradicts, despite efforts to address any questions and concerns about the data quality, figures, “analysis would be tossed away as non-actionable/useless/etc”.
{{/usCountry}}The user takes a dig saying “if you want to be a successful data scientist and receive praise from management, you must produce data analysis that supports management's ideas (no matter how wrong or ineffective they may be).”
"Data Scientist's job is to launder management's intuition using quantitative methods," it adds.