One ongoing debate in the world of work is whether it is better to be a specialist or a generalist. This is a question that has gotten more and more relevant as technological capabilities have changed workplace functions. Is it better to dive deep into a particular skill set and make yourself invaluable in a single area or to broaden your abilities in order to maximize flexibility and adaptability? Experts suggest that a middle road is the best bet:
“The best approach is actually a combination of both, or a T-shaped employee skill set. The top line of the T is a general skill set that gives an employee a broad range of understanding and capabilities, such as general marketing skills. This is where generalist tendencies come in. However, underneath that top line is the vertical line of the T that allows the employee to go deep into a specific expertise, such as social media or marketing analytics, and be a specialist in that area.”
Who will dominate the future of work? Specialists or Generalists? – Inc. – Jacob Morgan – March 2017
When adopting this “T-shaped” approach, there is a set of data skills that need to be in the top line of the T for virtually all employees, and that’s their ability to read, digest and interpret data.
Data Scientists Cannot Do it All
Gone are the days when a team of data analysts and scientists could be depended on to process, interpret, and disseminate the meaning from all of the collected data. The sheer volume of data alone makes this arrangement untenable for modern-day workplaces. Most companies are simply collecting too many data points from too many sources to expect the insights and applications desired to come solely from data experts.
On top of that, demand for data scientists outpaces availability, and the United States is facing a serious shortfall of eligible employees in the near future. While companies scramble to find candidates for these positions, rethinking just who does what with all that data may be a more fruitful approach in the long-run.
While data specialists certainly fill important roles, many of the tasks they’re currently asked to do can be alleviated by democratizing data analysis to include employees across different teams and departments.
The Benefits of Democratization
The first benefit of democratization is that it is a more efficient use of data scientist talent. In a competitive field where hiring and retaining data scientists will be difficult and expensive, shifting some of their assigned tasks to other employees frees them up to focus on the most important tasks to which they are better suited.
The second benefit of democratization (arguably even more important than the first) is that it allows for the insights and applications of data that would not be possible without this kind of breadth and scope. People who are trained in different specialties and who have different day-to-day roles within a business environment necessarily see the data through different lenses. That perspective shift is exactly what drives them to see new ways to use data that are practical, actionable, and truly revolutionary.
A third benefit is scale. The more minds that are asking and answering their own complex business questions, the better chance an organization has to unlock more value from their data. Put simply, they’re crowdsourcing insights. Data democratization means business SMEs (those with the questions) have the knowledge, tools, and access to get to quick insights without the middleman.
While this has been an idealistic goal for quite some time, the actual realization of that dream is fast becoming not just possible, but necessary. Data silos are breaking down as many data sets become publicly available and crowdsourcing eliminates gaps. At the same time, the tools that turn data into accessible, easy-to-understand and actionable insights are getting better and cheaper.
This perfect storm of content and accessibility means that any company that is not democratizing its data is missing out on a big opportunity to do more with the data it has.
James Nanscawen
As Director of Enterprise Analytics, James helped Thomson Reuters establish data management capabilities and an enterprise-wide analytics competency.
Latest posts by James Nanscawen (see all)
- Business Glossaries and Data Dictionaries: What’s the Difference and Why Should I Care? - February 18, 2020
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- See It to Believe It: Why Humans Need Data Visualization - December 10, 2019
