In addition to its critical tasks of refining requirements, Data Unicorn is also a star player in a few more areas:
When it comes to creating metrics, Data Unicorn is able to Look at the metrics formula and say if they make sense., without depending completely on its business partners. While business stakeholders are excellent in their own field, They may not have enough knowledge of the data to explain the logic of the metrics in precise pseudocode.especially when the source system stores data in a way that requires multiple joins and window functions to arrive at the latest unduplicated snapshot of a population.
When it comes to product onboarding, Data Unicorn can communicate the limitations of your data solutions in business languageand guide business stakeholders to use the product. Instead of simply accepting every request to improve solutions, They propose the best product package given your resource limitations.. Data Unicorn can avoid over-engineering and uncover the opportunity to maximize the value of existing solutions.
Finally, Data Unicorns help the data team stay relevant and influential by Communicate the impact in business-friendly terms.. Instead of saying: “The model has 15% accuracy for an audience of a thousand people”data scientist unicorn will say: “The model's audience of 1,000 customers is expected to produce 150 purchases, compared to only 5 purchases if the audience is selected randomly.” Read more about communicating as a data scientist here.
Where to find them?
Find existing Data Unicorns on your team
Assuming the people on your data team should already possess good technical skills, the best way to find data unicorns is search for information from commercial departments. You can survey some business colleagues and see who on your team interacts with them the most. You can also design an experiment, send your team out into the business world to present, gather requirements or gather information and see how they survive.
It is important to have a reasonable expectation when taking this test or survey. Between attitude and performance, my measure of success depends largely on attitude. I will look for people who are excited, comfortable or having courage walk into a room full of people who don't speak your language. After all, it's harder to change attitude than skill set, and all Data Unicorns need to be truly passionate about business problems.
Finding external data unicorns
Lea Pica briefly mentioned in her podcast that many Data Unicorns are already very high in the Digital Marketing and Analytics Agency. I'm not surprised. The marketing world has a very strong culture of customer centricity – it is the fundamental premise of the domain. At the same time, digital marketers can be technical to some extent, due to their day-to-day work of dealing with campaign optimization, funnel conversions, and funnel analysis. The field of performance marketing is based on a developed database, with many mature data sharing and analytics providers. So if you're looking for a Data Unicorn, try a customer facing solutions engineer in Digital Marketing or Results Marketing.
Generally, when interviewing a potential candidate, you can spend a portion of the time asking about their Past interactions with business stakeholdersand test how they can describe, deconstruct and analyze problems in non-technical terms. You also want to look for their ability to recommend technical solutions that address specific business problems. I suspect candidates with consulting experience will do well in this section – dig deep and apply concrete examples of whatever abstract concept (or buzzword) they refer to. It will be fun and rewarding.
In addition to background, job titles can also be a useful indicator. I think some roles tend to be more unicorn-like than others, like data analyst or analytics engineer. The responsibilities of these roles include many business-oriented activities.: requirements analysis, presentation, storytelling. While other roles have traditionally been designed to be more technical, such as a data engineer or especially a DevOps engineer. You can use the Data Unicorn quadrant below as a starting point:
Tool: The Data Unicorn Quadrant
Where to Find Data Unicorns: A Rough Argument to Guide Your Thinking. Author's image.
—While I believe every role here is in the correct quadrant, the exact position of a role in this plot is speculative and based on a very small sample. I would suggest using it only as a starting point and modifying the plot based on your observations.
How to raise them? Unlike the mystical nature of the name it is given, Data Unicorn is a stack of skills that can be acquired. It is possible to master both technological and business language. Difficult, but achievable. As a data leader, what you can do is to create the environmentfor your team to develop these skills. Create more touchpoints between technology and business. Send your team members on a field trip. Ask your engineers to shadow end users. A helpful exercise for your team is to recreate this customer journey map below.
(Surprise surprise, this map also comes from the Marketing department)
Tool: Data Customer Journey Map Map your data-driven customer journey from start to finish, their needs, actions and concerns. Author's image Another important thing to do is create the motivation to becomemore like a unicorn. Many technical contributors are more comfortable with the binary world than the human world. They have little desire to understand the problems of end users and are rather entertained by engineering challenges: their natural habitats. This mentality is usually due to a lack of awareness. With
encouragement and rewards for unicorn-like behaviors Data leaders can foster a culture where it's not only cool to be a tech whiz, but also to know why Mr. A in Finance doesn't find the new dashboard useful, or why Ms. Logistics, you need to update the data every hour. .Most importantly, the approach is not to focus on one or two individual individuals, but
build a Unicorn Data team
. Despite the extreme case of the genius engineer who is too interested in cracking the Enigma code to talk to the Finance department, most data professionals can, and should, be motivated to learn about business problems. This strategy will result in activities that reinforce each other. Each team member is motivated by both their leader and their colleagues, as business issues are now integrated into daily conversations within their team.
Conclusion Data unicorns are not imaginary creatures. Nor are they innate abilities granted only to a few. While it is rare that Data Unicorns can be found, they can also be raised in a encouraging environment. With a Unicorn team and its superb ability to integrate the business and technology worlds, you can maximize the fit of your product and solution and become a much more integral part of the organization.
