The immense reach and influence of Big Data over almost all the industry verticals is not unknown. With Big Data, seemingly massive and complex chain of communication, comments, and brand mentions is analyzed at a granular level. The purpose of this exercise is to unlock insights that may have hitherto remained hidden from the views of the decision makers of a company. Take the case of American Express. The card company giant wanted to bring more than just trailing indicators to take its aggressive growth plans ahead. This led AmEx to invest into building a complex yet powerful predictive models that take in as many as 115 variables. The purpose of the exercise? To look at ways to enhance brand loyalty among customers and bring down customer churn with the help of Big Data.
This predictive analysis is one of the forms of Data Science – the field that helps extract knowledge or insights from Big Data (both structured and unstructured). Some other implementations of data science include statistical analysis, data mining, data engineering, probability models, visualizations, and machine learning. Data science is a part of the bigger domain of competitive intelligence, which also includes data analysis and data mining.
A look at propelling the productivity of next- gen data scientists
IBM’s Big Data Evangelist, James Kobielus had produced an interesting article that highlighted the different ways in which the productivity of next generation data scientists can be enhanced. This can, in turn, impact the fortunes of the global economy, finance, and society.
He has acknowledged the mission critical role played by data scientists in providing value to the always-on business environment. Their value spans different repeatable solution integration to help analyze the data and generate meaningful insights to help stakeholders with their decision-making process.
Why boosting the data scientists’ productivity is essential
Data scientists perform a host of varied roles and responsibilities within the entire big data ecosystem. These include tasks such as –
As is evident, these tasks call for a set of human capital expertise that cannot be found in one single individual. A team of people who are experts in different niches has to be built. More importantly, they have to be aligned such that the business objective of having a team of data scientists is met amicably and without any politics. And this can be achieved by having a robust set of processes and protocols that need to be followed by every single one within the team.
However setting up and enforcing these protocols doesn’t necessarily mean a dip in the productivity of the data scientists. James takes a look at the real life examples where different processes have been set up to ensure optimum productivity of the data scientists within complex team environments. One instance that he has specifically mentioned in this context is Ben Lorica from O’Reilly. This article seeks to offer the below advantages in productivity to the data scientists:
It also talks about designing, clearly defining, and setting up error bounds to help check the efficacy of the machine learning projects. With the help of this effort, the actual performance can be measured against pre-defined benchmarks. In addition, it can help in fine- tuning the model if there is a significant diversion of the actual performance of the model from the expected outcomes.
This is one example of the efforts going on worldwide in different organizations to catapult the productivity of data scientists. With these efforts they perform their roles within deeply complex environments that touches multiple personnel, processes, protocols, and expectations.
James then goes on to highlight the ways in which data scientists can rally excel at their jobs and do remarkably well with the data analytics and visualization niche. There are two aspects – one is the technology itself (in the form of solutions like Hadoop, R, Python and Spark) and the other is the team of experts that form touchpoints for data scientists (data application developers, modelers, data engineers, senior management, and ETL experts). Both of them should work in tandem to provide an environment that fosters higher productivity for the data scientists. James has listed quite a few ways to achieve this.
With these helpful pointers, James brings out the ways in which enhancing the value of Data Scientists in the Big Data ecosystem can be made possible.
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