Over the past few years, the fields of data science and analytics have grown substantially. More and more companies and industries have hired quantitative professionals to harness their data for competitive insights.
As data science and analytics roles continue to proliferate, we’ve heard about the emergence of a new type of position: the data translator or storyteller. This role acts as an intermediary between the quantitative team and other business units and often helps facilitate the process of using the output from the analytics team for business-related decision-making.
Since we’ve been hearing that this role is becoming increasingly common, we decided to try and quantify the extent to which it has spread in the business world. To do so, we reached out to our existing network of data scientists and analytics professionals with two questions:
- Does your analytics or data science team have a person whose main responsibility is to act as a data translator, liaison, or storyteller?
- If yes, what is that person’s job title in your organization?
We then examined the results from respondents at 247 different companies to see how common this “data translator” role is, and noted its prevalence in various company sizes and industries.
Frequency of a Data Translator Role in Data Science & Analytics
We found that nearly a third of the companies in our sample did have a role fitting the data translator description, with two-thirds reporting that they did not. We then examined the various titles for this role that our sample had provided us.
There was no clear and consistent title for this type of role. It seems that each organization calls this position something different. Some respondents indicated that while the data translator does exist at their company, their title is the same as other data scientists or analysts.
Some of the more common names for the position were Business Analyst, Decision Scientist, Engagement Manager, and Product Manager. No responses referred to a “data translator” verbatim, however one response for the position’s title was “Data Storyteller.”
While the specific titles for this sort of role varied significantly, a clear trend we observed was that these types of positions tend to be more senior. Of the 77 companies who reported that their organization had a data translator, 71% reported the position had a title of manager or higher.
More specifically, the position was most commonly a more senior member of the data science team. This indicates that businesses expect data translators to have experience with data analysis themselves before becoming a translator, and that the translator must have proven themselves through prior work.
Prevalence of a Data Translator Role by Company Size
Using our sample, we also looked at the proportion of companies who had a data translator based on the size of the company.
The data show that larger companies (10,000+ employees) are significantly more likely to have a data translator sort of role than smaller companies (less than 1,000 employees.) Predictably, medium-sized companies, which had between 1,001 and 10,000 employees, were more likely than smaller companies to have a data translator but less likely than larger firms.
Larger companies generally have larger teams with more specialized individuals on each team, so a specialized role such as the data translator may be more common. Additionally, larger companies may have more individuals overall that need to interface with the analytics team, requiring frequent and effective communication that a data translator can help facilitate.
Frequency of a Data Translator Role by Company Industry
We also examined how responses varied by company industry. While most industries reported similar levels of the prevalence of a data translator as the overall sample, there were some notable differences.
For one, companies in the marketing/advertising services and consulting industries reported a noticeably higher prevalence of the data translator role than other industries. This is most likely because a translator-type role is especially important in industries that involve a lot of client-facing activity, such as these two.
Another interesting point was the relatively low pervasiveness of the data translator role in the financial services industry. We think that this discrepancy could be due in part to the tendency of larger financial companies to adopt new trends more slowly than other industries due to legacy systems and processes they have in place. For example, they were one of the last industries to move away from SAS to open source software.
As the data science and analytics space continues to evolve, we are curious to see what’s in store for the data translator role in the future. Since quantitative initiatives at many companies are continuing to expand, if we conduct a similar survey again in two years, what trends will we notice? Only time will tell.
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