Journey to Data Science

As the Big Data phenomenon continues to grow, we’re seeing demand for trained and talented data scientists far exceeding the supply. In the past, there were individual specialists that handled pieces of what today we call data science, with three or four different people with different skill sets touching your company’s data. There might be someone who’s really good at working with databases and writing SQL, and someone else who is a business analyst and easily understands problems and domain areas. Now, these once siloed specialties are all coming together under the role of data scientist.

The basic underpinnings of data science are statistics and mathematics, but now these skills are converging, and people are realizing that if their skills expand to incorporate a broader perceptive and more sophisticated analytics, they are able to gain greater insight from the whole than from the individual parts. In other words, instead of just knowing one piece of a puzzle and then handing it over to someone else who may not understand the entire problem, a data scientist is able to see the process very holistically and as a result, is able to derive more value for the business more quickly.

Additionally, when dealing with Big Data, human skills such as creativity, data visualization and communication are becoming more valuable than ever. When you have people who are strong lateral thinkers, they can connect these disparate disciplines and come up with new insights and new methods more effectively than someone who digs deeply in one particular area.

We recently launched a new course at EMC in Data Science and Big Data Analytics to educate not just the industry, but also to help shape what is becoming a new profession. We have received great feedback from the first participants of the training who are either taking the course on their own accord to develop more advanced skills or are participating because their companies have realized that traditional business intelligence (BI) skills are not enough to maximize the Big Data opportunity.

While BI looks at historical data, data science enables organizations to look at disparate data sets in real time and draw conclusions that can help better predict future patterns of events. The largest-ever global study of data scientists, sponsored by EMC, revealed that one of the biggest obstacles to data science adoption within an organization is a lack of employees with the right skills or training.

EMC is helping to define this new discipline the same way we did many years for storage administrators and data center architects. Through this Data Science and Big Data Analytics course, we’re giving people an introduction into how to reframe a business challenge as an analytics challenge; how to operationalize Big Data projects within their own organizations; how to analyze unstructured data; how to classify information based on historical events; how to make predictions on future behaviors; and ultimately, provide the building blocks of how to turn data into true business value. So, as organizations are under more and more pressure to increase the speed and effectiveness of their decision making, we are enabling and equipping individuals to start on their journey to becoming data scientists and working more effectively with Big Data.

About the Author: Tom Clancy