Data Sciences as we see it today encompasses a vast scope & touches all facets of modern life. Recording data is as primitive as counting. What’s new is the way Data Science looks at this big data and scientifically skims the meaning from the mess.
Data today is often being referred as the new oil & the essential raw material for any business. This paradigm shift has made Data Scientists the new rock-stars in the world of big data. From small corporate entities to big Fortune 500 companies, everyone is jumping on the data analytics bandwagon in order to improvise their existing decision-making capabilities and be more productive.
“Data Scientist” is the sexiest job of the 21st century claimed by The Harvard Business Review. But, it is often observed that the sexiness associated with this role has taken a downward stream. It is all solecism that is utterly misleading as Data Scientists are today expected to be nothing more than ‘Skeptical Statisticians’ which is far different from the long-lived data science that once involved an interdisciplinary approach to unifying statistics, statistical methods and quantitative research, data mining, and arbitrary use of data.
According to Applied Statistician Nate Silver,
In the recent years, this reality has come to surface as data scientists are today expected to perform the key responsibilities of what we call a data statistician. Companies are happy to hire someone with technical and analytical abilities as prerequisites; who can sort and organize massive data sets with a major focus on data patterns, data engineering, probe for insights, and refining algorithms to keep up with evolving complexity in the 21st-century Business Ecosystem.
“Only around 10% of data science is science, the rest of your time will be spent cleaning the data – sucking it out of whatever stupid format it comes in, and subjecting it to a sanity check so that it’s actually usable.” – declares Dominic Connor, a veteran headhunter in London.
Data scientists are expected to help most organizations with Data Management, which is the most crucial approach to Research & Development pertaining to the collection of more accurate and detailed performance information to make better management decisions.
This way, they portray a strong role in improving a company’s potential to mitigate risks, control operational costs, and generate maximum revenue. They just find new data sets, evaluate the information and make everything accessible without giving any significant consideration to “Data Wrangling” – the most crucial step in the much-hyped Data Science for value creation.
Data Scientists has emerged as an elite bunch of ‘quant-focused Ph.D. business analytics’ who are expected to perform multivariate statistical analysis, quantitative research, time-series analysis and cleaning dirty dumping piles of data over and over again.
Most companies are demanding these professionals to be an eclectic mix of mathematicians, physicists, economists and digital analysts with a deep contextual understanding. They must expertise in applying advanced statistical modeling techniques and solving complex algorithms along with mining large sets of structured and unstructured data in order to break through all kinds of big data bottlenecks, which is a plentiful messy affair.
Organisations must realize that the yawning gap between the data scientist’s aspirations and the industry reality can create a massive disconnect.
Instead of getting Data Scientists involved in non-productive activities like data cleaning and data preparation at the workplace, companies must give them automated governance where they can invest all their time and energy in making valuable contributions.