Soft Skills for Data Professionals: Seven Skills to Build
In a 2024 AI Connect session, Kroger's Morgan Miller argued that soft skills separate good data scientists from great ones. She cited Google research and offered exercises for communication, collaboration, empathy, adaptability, critical thinking, detail orientation and curiosity.
By The AI Enterprise Magazine Team3 min read
Key takeaways
- Soft skills lead. Miller said Google's research found its top seven team-success skills were all soft skills, with coding last.
- Seven skills. Communication, collaboration, empathy, adaptability, critical thinking, attention to detail and curiosity.
- Communication tops employer lists. She cited 92% of employers naming it the most important soft skill.
- Practice is simple. Storytelling, volunteering, puzzles, reading and the five Ws build the skills.
- Ask more questions. Miller said there is no such thing as asking too many.
In a 2024 session at the AI Connect K-12 Data Science Conference, Morgan Miller, eCommerce Assortment Strategy Manager at Kroger, told students and early-career data practitioners that the skills that set people apart in data work are often not technical. Miller walked through seven soft skills and practical exercises for building each one.
Her opening argument: hard skills such as programming, machine learning and visualization can be measured, while soft skills cover how people communicate, adapt, lead and work in teams. In data, she said, "the difference between being a good data scientist and a great data scientist" comes down to the second group.
Why do soft skills matter for data careers?
Miller said data people tend to be technical and often introverted, which makes interpersonal skills easy to neglect. To make the case, she cited research Google published in 2017 from two internal studies, Project Oxygen and Project Aristotle. According to Miller, the top seven skills that predicted successful teams were all soft skills, and technical skills such as coding came last.
Her takeaway: if one of the best-known data and engineering companies says how people interact with teammates matters most, aspiring practitioners should take note. She recommended a personal inventory of strengths and weaknesses, and asking parents, teachers, coaches and friends for honest feedback.
What are the seven soft skills data practitioners need?
Miller named communication, collaboration, empathy, adaptability, critical thinking, being detail oriented and curiosity. She said some skills are universal and others vary by role and person.
How can you improve communication and collaboration?
Communication covers speaking, writing and listening, Miller said, and she cited a figure that 92% of employers name it the most important soft skill. Her tips: "practice telling stories," since data work depends on storytelling, practice public speaking in front of two people or 200, write clear emails and papers, and socialize with people you know and those you don't to sharpen listening.
On collaboration, she said teamwork is unavoidable in data regardless of industry. She suggested joining academic, athletic or artistic teams, taking part in volunteer projects and embracing group work, even when a group is frustrating.
Why do empathy and adaptability matter in data work?
Empathy, which she also described as emotional intelligence, matters wherever decisions affect people, Miller said. Because so much communication happens through email, text or online, she warned, people forget a person is on the other end. She recommended acts of kindness, conversations with people from different backgrounds and cultures, and some interaction without technology.
Adaptability matters because tools, systems and problems change quickly, she said. To build it, she suggested stepping outside your comfort zone, such as speaking in front of a crowd, and mindfulness activities such as meditation, which she said can make people more receptive to change.
How do critical thinking, attention to detail and curiosity help?
Data practitioners may look at thousands or millions of data points, Miller said, so they must look past the surface. She recommended puzzles, logic games such as sudoku, and a habit of questioning: "never take things at face value." She added that "there's no such thing as asking too many questions."
For attention to detail, she suggested reading often and noticing small details that change meaning, and visiting places with the phone put away while asking who, what, where, why and when. For curiosity, she recommended setting a goal to learn one new thing a day, even a fun fact, and sustaining one or two areas of interest.
Where can learners build soft skills?
Miller listed resources, including a free Google certificate on soft skills, TED talks on empathy and adaptability, and a Harvard article on improving communication. She said the slides would be shared later and invited questions on LinkedIn.
More from AI Connect
AI Connect continues to run free live webinars. The 2027 schedule includes 24 free live webinars across four series: the AI x Enterprise Panel Series, AI Demo Days, Agentic AI Showcases and the Plug & Play Series. Details are at /webinars/. Registration for 2027 events is coming soon.
Frequently asked questions
What soft skills do data scientists need?
Miller named communication, collaboration, empathy, adaptability, critical thinking, attention to detail and curiosity.
Why are soft skills important in data careers?
She cited Google research in which the top seven predictors of team success were soft skills and coding ranked last.
How can students improve communication skills?
She suggested practicing storytelling and public speaking, writing clear emails and papers, and socializing to build listening.
This article is based on remarks by Morgan Miller, eCommerce Assortment Strategy Manager, Kroger at AI Connect K-12 Data Science Conference 2024 on July 25, 2024. More coverage: More AI Connect Events.
Filed under: People, Culture & Leadership
