Big Data and Data Scientists Today
The advent of big data produced a need for skilled roles that could take massive amounts of structured and unstructured data from numerous sources and extract more detailed meanings which are significant to the business. For companies that compete in a demanding global market with rapid-changing resources and events, big data offers the advantages of more in-depth insights. This advantage is so vital that global business will spend more than $200 billion by 2020 for big data and analytics, up from $130 billion in 2016, at a compound annual growth rate of almost 12%.1
The leading question, however, is how to manage big data when an estimated 60–73% of all enterprise data goes unused for analytics!2
Is the lack of big data use due to personnel or technology? The answers implicate both.
Enter the Data Scientist
Use of the buzzwords “data science” and “data scientist” has spiked in recent years. The term “data science,” however, appeared as early as 2001 when a Purdue University professor, William S. Cleveland, advocated for the merger of computer science and statistics. This specialized information is delivered by employees trained in mathematics and computer science who understand IT, but have limited knowledge of the day-to-day happenings of the business. These employees are known, either formally or informally, as data scientists. They may develop their skills by way of academia or as a career evolution from a data analyst or statistician, and these data scientists create algorithms that lead to discoveries for business improvements and opportunities. The creation of algorithms is what sets data scientists apart from other roles like a business analyst, for example, who delivers straightforward, statistical data. When faced with a problem, data scientists turn to their unmatched programming skills.
One of the challenges identified in numerous studies is that data scientists spend 70–80% of their time assembling data like SQL statements, which could be run by other, less specialized roles, like a data integration specialist.3 This reality is incongruent with the level of education and training of today’s data scientists, of whom more than 80% have master’s degrees, and 41% hold a PhD. This problem doesn’t bode well for the one million reported unfilled positions that exist in 2018.
Introducing the New Superhero: Citizen Data Scientists
If deep insights are the superpowers from big data and data scientists are the heroes with powers of their own (ability to create algorithms), think about the value a company can have if it opens the door to other hero-types — ones who don’t need to know to create algorithms but can interpret the results that out-of-the-box algorithms generate.
Research firm Gartner states that more than 40% of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists.4 Thanks to technology, employees with traditional business roles like marketing and sales can learn how to operate advanced analytics with built-in algorithms. With no coding or complete data required, more employees can participate. Virtually anyone with a good business understanding and minimal training can model scenarios, thereby allowing the advanced analytics technology to perform the heavy lifting and provide profound insights into the best possible outcomes.
While data scientists will always provide substantial benefits, citizen data scientists — or those who rely on technology with added intelligence — can offer many of the same results. Citizen data scientists can now respond to unexpected changes requiring fast shifts to plans and strategies. There will likely always be a need for data scientists in business, but the future should be focused on automating jobs when possible and transitioning the work to CDS-type roles. This opens up the opportunity for data scientists to tackle new, unique problems that they previously lacked bandwidth to address. With this type of strategy, companies are able to continue progressing their data and analytics capabilities.
To put it plainly, citizen data scientists create a league of their own.
This superhero ability to be a citizen data scientist seems plausible for the future. However, the good news is that it exists today. The power lies in companies granting technology access to a broader base of corporate citizens so they can become citizen data scientists, too.
Key takeaways:
- Big data continues to grow -- only 53% of companies are using it today.
- More than 40 percent of data science tasks will be automated by 2020, allowing citizen data scientists the ability to participate.4
- By 2019, citizen data scientists will surpass data scientists in the amount of advanced analysis produced.4
- The technology exists today that enables more corporate citizens to access more profound, actionable insights.
1”6 Predictions for the $203 Billion Big Data Analytics Market,” Forbes, January 20, 2017.
2”Hadoop Is Data’s Darling For A Reason,” Forrester, January 21, 2016.
3”How To Become A Data Scientist In 2017,” Forbes, October 21, 2016.
4”Gartner Says More Than 40 Percent of Data Science Tasks Will Be Automated by 2020,” Press Release, Gartner, January 16, 2017.
