The Invasion of the Data Scientists is here. Driven by an annual growth rate of 40%, the digital universe will reach 44 trillion gigabytes by 2020 – that amounts to more bits of digital data than there are stars in the sky, according to IDC,*1. A clarion call is sounding for statistical literacy, technical acumen, and sheer mathematical muscle within both private and public sectors, across all industries, and throughout enterprise-wide functional areas. Earlier this year, even the White House hired its first-ever US Chief Data Scientist to help the government harness the power of innovation and big data to better serve the American people.
As consumers become increasingly connected to their smart phones, tablets and laptops, these technologies are generating vast amounts of digital data and changing the ways in which consumer brands analyze, understand and engage with their customers. The promise of big data is rapidly becoming reality and increasing numbers of forward thinking companies are recognizing the competitive imperative to embrace data analytics. Business as usual is no longer business as usual. As today’s customer-centric brands struggle to transform into nimble, data-driven organizations, overwhelmingly marketing is proving to be ground zero for these changes. Traditional marketing functions from the CMO on down are undergoing fundamental transformations, which in the process are creating an unprecedented demand for the unique expertise that data scientists provide.
Data Science Virtuosity in High Demand
According to a Gartner report, in 2015, 81 percent of companies making more than $500 million in annual revenue have chief marketing technologists on their payroll. That figure is up from 71 percent in 2014 and is expected to increase to more than 87 percent in 2017,*2. Data scientists are in high demand and command a premium compensation. According to Glassdoor, the average salary for a data scientist is $118,709 as compared to a skilled programmer, whose average compensation is $64,537. A McKinsey study predicts that by 2018, the United States will face a shortage of 140,000 to 190,000 people with analytical expertise and a 1.5 million shortage of managers with the skills to understand and make decisions based on analysis of big data,*3. For the foreseeable future, it is a “sellers’ market” for data scientists.
Data Scientists: Roles and Responsibilities
The roles and responsibilities of data scientists practice data science vary greatly across organizations. A data scientist’s business card may indicate chief data officer, marketing technologist, chief information officer, VP of analytics and insights, or a variety of other titles. With the expanding role of the chief marketing officer, data scientist are increasing under the umbrella of marketing departments. According to Gartner research, 71 percent of chief marketing technologists report directly to a senior marketing executive, while the remaining 29 percent report to IT,*2. Their primary responsibilities include:
- Aligning marketing technology with business goals
- Facilitating projects and communications between marketing and internal IT
- Evaluating, selecting and managing the funding for marketing technology providers
- Developing business models
Data Science Requires Deep SkillsA big reason why data scientist are in such great demand, is that they are highly educated. According to a leading executive recruiting firm, 88 percent have at least a Master’s degree and 46 percent have PhDs –Their most common fields of study are Mathematics and Statistics (32 percent), followed by Computer Science (19 percent) and Engineering (16 percent),*4. Data scientists much be able to work with a wide variety of unstructured data, and typically their have expertise in:
- Analytical tools such as SAAS and/or R
- Python, Java, Perl, or C/C++
- Hadoop, Hive or Pig
- Cloud tools such as Amazon S3
- SQL or other database programs
Beyond extensive technical skills, a successful data scientists must possess strong business acumen, which includes an in depth knowledge of their business and industry in which they work. A solid understanding of marketing principles and various media models, including digital media, email and social media. Without this knowledge, the data scientist will be unable to understand important marketing issues facing the organization or to discern technology-bases solutions required to analyze and address these issues. For instance, mining insights from large databases of unstructured social media and other digital data, requires a fundamental understanding of the digital media channels and related models.
Finally, the data scientist must have strong communications skills in order to translate the statistical methodologies, technical capabilities, analytical techniques, and the related output into meaningful and actionable insight. As a critical consult to the highest levels of marketing and executive leadership within an organization, it is incumbent that the data scientist understand a broad range of business situations and marketing scenarios, bring to bear an extensive set of data technologies, and cogently articulate the valuable insights contained within the underlying data.
*1. “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things”, IDC, April 2014,http://www.emc.com/leadership/digital-universe/2014iview/internet-of-things.htm
*2. “Big Data, the Next Frontier of Innovation, Competition, and Productivity”, McKinsey Global Institute, May 2011, http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
*3. “81% of big firms now have a chief marketing technologist”, Scott Brinker, ChiefMarTec.com, January 2014, http://chiefmartec.com/2014/01/81-big-firms-now-chief-marketing-technologist/
*4. “The Burtchworks Study, Data Science Salary Study”, Burchworks Executive Recruiting, April 2014, http://www.burtchworks.com/big-data-analyst-salary/big-data-career-tips/