Breaking Down the Data Language Barrier
Breaking Down the Data Language Barrier
Over the last twenty years, data has replaced accounting as the universal language of business. Across industries and departments, teams rely on data to drive decision-making and optimize day-to-day operations.
When companies commit to a data-driven approach, it increases internal alignment, helps teams and departments collaborate, and allows everyone to see how their efforts are moving the needle and contributing to organizational objectives. As Ben Yoskovitz, founding partner at Highline Beta explains, “Data not only helps us figure out what to do, but it helps us understand why we’re doing it.”
Even more valuable are the powerful insights data unlocks. When companies are able to decipher trends, patterns and behaviors in their data, they’re able to develop winning ideas, strategies, and plans that catapult the business forward. In fact, insight-driven companies are growing 8x faster than global GDP.
But surfacing these transformational insights requires the collaboration and cooperation of two very different groups of people — those with data expertise and those with business domain expertise.
- Data teams: For data to be useful, it must be collected, organized, and analyzed. Databases are complicated technical systems that require deep SQL knowledge. Companies employ teams of data engineers, data scientists, and data analysts to prepare data, explore it, and begin finding insights.
- Domain experts: Accessing, manipulating and analyzing data is only half the answer. Data can only solve business challenges when teams deeply understand the problems and opportunities at play. Business domain experts have a fundamental grasp of these specific issues and are intimately familiar with their nuances due to years of experience in their respective fields.
Nobody knows it all, and it’s naive to think one group can successfully solve business problems and surface game-changing data insights without help from the other. Yet despite the obvious symbiotic relationship between these two groups, a data language barrier prevents them from working together effectively. The results are wasted time and resources, few to zero meaningful data insights, and missed opportunities.
In this survey report, you’ll discover why data teams and domain experts struggle with collaboration, the reasons the data language barrier exists, and what companies can do to harness the full value of their data.
About This Survey
Sigma commissioned Atomik Research, an independent creative market research field agency, to conduct an online survey of 801 data experts and 800 line of business employees from companies with between 201-10,000 employees within the United States. The margin of error fell within +/- 3 percentage points with a confidence interval of 95%. The fieldwork took place between March 2nd and March 9th, 2020.
Note: total percentages may not add up to 100% due to rounding
Gen Z (18-22)
Gen X (39-54)
Baby Boomer (55-73)
VP or C-level
Manager/ Sr. Manager
Data experts and business domain experts both claim to be data driven but have different definitions of what that means
More than three quarters (76%) of survey respondents say their companies are somewhat to very data driven. When asked to define what being “data driven” means, 39% of domain experts admit they’re “not totally sure,” while only 6% of data experts feel the same.
Despite feeling confident about what it means to be data driven, data experts feel insecure about the results they’re able to deliver. Nearly half (45%) say they struggle to interpret domain experts’ data questions or needs. Domain experts seem to agree that the deliverables data experts provide are missing the mark. A fifth (20%) of domain experts report they rarely or never feel as though their data needs are adequately met by their data team.
Data experts believe they aren’t completely to blame for this. More than a third (36%) surveyed believe domain experts do not understand what can and can’t be done with data.
The data language barrier prevents companies from extracting the full value of their data
Communication gaps between data teams and domain experts are slowing down the organization’s ability to make use of data when it’s still fresh and at its most valuable.
More than half (55%) of data experts admit the average turnaround time for a data request is 1-4 weeks. When asked what causes these bottlenecks, 29% say it’s because too few data analysts support too many business teams, 28% claim C-level requests are given precedence over others and create a backlog, and 27% blame the bottlenecks on large data sets that are difficult and time consuming to curate and analyze.
Stuck waiting for data that doesn’t always address their needs, domain experts are quick to abandon their pursuit. A quarter (25%) admit they have given up on getting an answer they needed because the data analysis took too long.
Lack of confidence and low data literacy prevent teams from being data driven
Business domain experts lack basic data skills but are uncomfortable turning to data experts for answers. Some 34% reveal they are not confident in their ability to articulate data questions or needs to their data teams, and 30% admit feeling embarrassed by their lack of data knowledge.
Despite their shortcomings, the majority of domain experts recognize the value data can bring to their roles. Nearly three-quarters (71%) express a desire to improve their ability to understand and analyze data, and almost half (49%) think this could help them become 50-100% more effective at their jobs.
Unfortunately, domain experts feel their companies don’t support them in their quest to become more data driven. Almost half (48%) of all surveyed employees report their companies do not offer or do not make it clear that they offer any sort of data training.
Meanwhile, “fear of messing it up” is preventing 29% of domain experts from exploring data even if the tools to do so are available.
Data experts feel unappreciated and unfulfilled
More than a quarter (27%) of data experts report feeling unfulfilled or very unfulfilled in their roles. A major contributor to these feelings is the high volume of low-value ad hoc reporting requests, which take up a large portion of their time. Three-quarters (76%) of data experts say up to half (49%) of their time is spent preparing ad hoc reports for business teams.
These reports often grow in scope and spiral into never-ending projects. Over half (53%) of data experts reveal they receive up to 4 follow up questions for each fulfilled data request. After all the time and resources that go into preparing these reports, data experts feel their work is under-appreciated by company leadership, with 34% sharing they’ve felt unable to convey the value of an analysis to an executive or key decision maker.
Despite these challenges, data experts and domain experts want to collaborate more effectively
More than three quarters (79%) of data experts and well over half (64%) of business experts desire to work together more closely. For them to collaborate more effectively, organizations need to provide a common tool that allows both groups to make the best use of their expertise and participate in the data conversation.
Modern cloud analytics and business intelligence (A&BI) tools bridge the gap between data experts and domain experts. Connecting directly to cloud data warehouses, these tools offer visual, near real-time analysis of billions of data rows without the need to manually write SQL. A familiar and accessible interface, like a spreadsheet, serves as the common language and transforms A&BI into an iterative, community-driven process that allows both groups to explore data together, build on each other’s work, and surface powerful data insights.
Want to keep reading?
Learn how Sigma can help you break down the data language barrier.