Data Science

By 2020, IDC/EMC estimates that the digital universe will be 40 trillion gigabytes, up from 130 billion in 2005. To put that in proper perspective, that’s about 80 billion laptops. That’s nearly impossible to fathom, let alone process, organize, and analyze, and that is where data science enters the picture.

Data Scientists will need to tame these trillions of gigabytes of data.

Scientific method, data analysis, software engineering, statistics, and visualization have all come together and data science has emerged as its own multi-layered discipline. A data scientist is proficient in each of these areas and combines them with business acumen to turn massive amounts of electronic information into actionable insights.

Data Science Is Big Business

Data scientists can identify remarkable trends and analytics from streams of data. Corporations looking for a competitive advantage and researchers and scientists looking to break new ground will come together via data science to unlock value in organizations’ data.

Companies can capitalize on such data findings by being more accurate in their long-range business planning and spotting consumer trends earlier than ever before. The rapidly growing field of data science has enormous implications for the way business, science, research, government, and health care can be carried out.

The Data Scientist Is Here to Stay

The rise of data scientists represents a distinct shift in modern business. For large companies mining massive amounts of data while immersed in global competition, traditional data analysis is no longer enough. Businesses need more than just someone with the computer science, math, and statistical skills to solve problems; they need analysts with the business acumen to identify the right problems. Moreover, these data scientists will need to combine data from unrelated datasets and design experiments that can help answer a company’s toughest questions.

A good data scientist will identify the questions that when answered can bring the biggest return on investment. While a traditional data analyst collects and reports on data, the data scientist takes a deeper dive:

  • Sifting through raw data
  • Examining data from unrelated sources (both structured and unstructured)
  • Asking pertinent questions
  • Questioning assumptions
  • Reviewing organizational processes
  • Studying patterns and identifying variances

The data scientist aims to uncover a previously hidden insight or weakness that in turn can provide a competitive advantage or address a pressing business problem. Equipped with data and analytics, the data scientist will communicate persuasively their expert findings and recommendations to business leaders and across company lines.

“A data scientist is somebody who is inquisitive, who can stare at data and spot trends,” Anjul Bhambhri, vice president of Big Data Products at IBM, says. “It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”