Data science is having a moment. Since being named “the sexiest job of the 21st century,” data scientists have become every industry’s must-have hire. Strong math and computer science skills, combined with the ability to extract and understand data and trends, is a skillset that’s going to get almost any company to “swipe right.”
But high demand can make data scientists prohibitively expensive for midsize companies. And in a marketplace where data is the new oil, businesses that lack people who can drive and execute data-driven strategies will lose the ability to compete and innovate.
Cue the “citizen data scientist.” It’s an informal designation of a power user who can use today’s automated solutions to gain analytical insights, a task that in the past required the statistics or analytics skills of a traditional data scientist.
Here’s how Gartner defines a citizen data scientist:
“Citizen data scientists are ‘power users’ who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Today, they provide a complementary role to expert data scientists. They do not typically have coding skills but can build models using visual drag-and-drop tools and run prebuilt models and data pipelines." (Gartner, "Maximize the Value of Your Data Science Efforts by Empowering Citizen Data Scientists," June 2018.)
In other words, citizen data scientists bridge the gap between business intelligence solutions and formal data science training.
And the best part is, mid-market organizations already have potential citizen data scientists on their staff—it’s just a matter of tapping those with potential and interest in the work, and cultivating an analytical mindset across their workforce. Then, those who serve as citizen data scientists can grow their own skillsets, all the while being active players in their companies’ ability to tap the value of big data and drive transformation.
CFOs and finance leaders say data science is the most important expertise for finance teams of the future. According to a global survey commissioned by Workday, finance executives believe the ability to interpret and act upon data will be the most valuable skill to their function over the next five years. However, leveraging the data science discipline for insights can require ninja-like spreadsheet skills and expertise in database and programming languages. Without these skills in your workforce, organizations can feel like they’re being dropped in the middle of the ocean—data, data everywhere and not a drop to drink.
That’s where advancements in augmented analytics is driving change. Augmented analytics are making it possible for people to blossom in their citizen data scientist roles.
Augmented analytics, according to Gartner, “is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”
Simply put, augmented analytics removes the heavy lifting of extracting value from data, and transforms manual tasks like data cleansing, insight generation, and ad-hoc modelling into automated processes.
Let’s say you’re leading a midsize company, and your latest income statement shows that while the company is on track to meet its year-end revenue goals, that’s not yet a sure thing. How can you keep the momentum going?
Here’s where augmented analytics makes a difference. Using business intelligence tools, users can streamline and analyze disparate sets of data in a matter of clicks. Leaders can go beyond simple questions like, “What's my current revenue booked for this quarter?,” and approach data with strategic curiosity: “What's my current revenue booked, why do my pipelines looks so wildly different in these two regions, and why are these newer sales people doing better than the more experienced ones in this particular office?”
Once you have those answers, you’ll have the insight you need to make better business decisions. Traditionally, data analysis took weeks of gathering and reconciling data—if there was anyone available to do that. These conditions often pushed many teams to make decisions without the backing of data. Now, cloud-based analytics tools have sped up the data-crunching process, shortening it from days to seconds, all without manual data gathering.
Technology is clearly the catalyst for the rise of data science in organizations. But even with self-service analytics tools, companies can still miss out on the full potential of data. Strategic insights are only as strong as their ability to analyze information.
“Our research shows a troubling gap for midsize enterprises between demand for analytics capabilities and the skills needed to address that demand,” says Gartner. “Most midsize enterprises encounter challenges in finding and retaining the personnel necessary to perform typical IT-related BI and analytics tasks, such as data preparation and modeling, data exploration and analysis, interpreting results, and making insights actionable.”
Here’s where citizen data scientists can help. Consider how organizations using business intelligence tools have seen many in their workforce transform into data-savvy professionals.
In the webinar “Driving Digital Change through Finance,” John Hugo, vice president of financial products and go-to-market at Workday, said he saw the transformation at Life Time, where he had previously served as a senior vice president and corporate controller.
Hugo recalled when Life Time underwent digital transformation, including moving its technology systems to the cloud, the company had an accounts payable representative who had spent much of her time on repetitive tasks. But with the shift, the employee, who demonstrated technical curiosity, was able to fit into a new role as a systems administrator who worked with analytics.
In the same webinar, TMX Group expressed a similar shift as part of its digital transformation. Hiring practices previously focused on core skillsets related to finance or accounting. Then in 2018, the company had an objective to increase self-service tools so that employees could provide more insights. Ever since, “we’re looking more at softer skills around understanding how technology and business intersect,” says Frank Di Liso, vice president of corporate finance and administration at TMX Group. In addition, hiring managers started asking about candidates’ experience with data sets and machine learning, and “how do they embed technology in their daily routine in terms of an automated feature set,” Di Liso says.
Advances in technology and the availability of data naturally give rise to the development of citizen data scientists, but company leaders can do more to strategically leverage these budding data-savvy professionals. Leaders should encourage these employees to apply their business intelligence learnings beyond their departments. The collaboration makes data a greater aspect of a company’s decision-making culture.
Gartner offered a handful of recommendations that foster employees as citizen data scientists:
Gleaning insights from data has been hampered by the need to have formal data science training. More and more data has only made this problem more challenging. Now, there’s a happy medium: Midsize companies can bridge the data skills gap in their workforce by augmenting current roles with self-service analytics and discovery tools. These citizen data scientists show that data-driven insights aren’t only for organizations that can afford formally trained experts—anyone can put their (analytical) mind to it.