In Part 1 of our interview we discussed common challenges and best practices to integrate data scientists into workplace teams successfully.
We continue our conversation with Tom and Sebastien, unpacking the next part of the question: What is possible when our workplace data professionals are unleashed?
For data scientists, establishing a solid foundation of reliable data quality and establishing initial relationships with business stakeholders is just the beginning of an organization’s workplace data journey. The next step should be ensuring that data specialists can do their most valuable work and begin prospecting transformational insights.
Getting Creative and Valuable
While conventionally viewed as a monochromatic function, both Tom and Sebastien propose that, in its fullest expression, data science is actually a creative and iterative process.
Why is this important? Because creativity is where data scientists generate the most value for their teams and workplaces. When data science and analytics are given the space and encouragement to get creative, workplace teams can do three things better than before:
- Identify and solve unknown or previously hidden issues
- Reveal hidden dependencies and connections within the business
- Expand use cases for data-driven decisions
Sebastien emphasizes that data scientists develop their most valuable and actionable insights when they discover methods to connect and correlate data sets accurately: "[That’s] where we can understand [how] certain job roles, certain employee groups [and] certain tenure levels behavior differently."
"The most value that [data scientists] generate comes from the cross-system analyses where we start linking attendance or occupancy of certain areas to employee information."
Altering the Perception of Data Science: a Creative Process that requires Space
Unleashing the creativity of a data scientist requires reimagining the process and shifting expectations around the work.
What does this look like practically? Tom and Sebastien explain that creativity requires leadership and stakeholders to allow space and time for exploration as well as grace for experimentation and failure.
Tom says, “If you look at your data team as if they were artists— you would give these people space to think, to wrangle through the data, to be creative, to try new things, to experiment.”
According to Sebastien, this view contrasts with traditional roles, where specialists sit down and do the work, and their output is mostly measured in turnaround time.
Sebastian says Booking.com’s workforce support team has cultivated a culture that empowers him in creative data work that supports 7,000 employees at the company’s headquarters in Amsterdam.
“The Booking.com culture is really focused around experimentation,” he says. “When we have a certain issue, and we need to come up with some research topics, my manager and team really understand that it is an iterative and creative process, so I need to sit down and think first.”
Sebastian explains that efficiency in the creative process is less about fast delivery, and instead emphasizes that it is about making incremental and well thought-out improvements to the business through small interventions, check-ups, and even failure.
The Balance Between Creative Exploration and Defined Boundaries
While allowing space for creativity empowers data scientists to generate the most value, keeping creative workflows focused and on target can be challenging. Overly broad requests and unrealistic expectations about what will be ultimately delivered can easily smother any possibility for creative work. If goals and priorities are not clearly defined, creativity can misfire on the wrong targets, leading to inefficiencies and missed opportunities.
Overwhelming Vision & Use Case
One of the easiest ways to derail data science from making a meaningful impact is by imposing unrealistic, too strict, or too broad requests on data scientists without giving them an opportunity to shape the scope. Stakeholders not always privy to the data science process, might package their requests in a “shopping basket” approach. This often manifests in “asking for everything” or pointing to tools and features they’ve seen before, assuming development is simple.
“On an impulse, you may ask for a dashboard with 80 figures on it, but really only a couple make an impact.”
Tom says, “It takes a long time to design these little pieces of information — to make sure they’re high-quality, to make sure they’re accessible, to ensure the data is safe, private, and secure, and to ensure the governance of that data is fair as well.”
Stakeholders may also not know exactly what they need, complicating the data science process. “On an impulse, you may ask for a dashboard with 80 figures on it, but really only a couple make an impact,” Tom says.
Sebastien adds that data scientists need to be honest about their limitations and candid about trade-offs. “Many times we just have to say no. It’s important to prioritize what you want to invest your time into. Each data source has significant costs in terms of acquisition, integration, cleaning, and then eventual reporting,” he says.
Sebastian says that if requests are overly ambitious, he tries to be upfront about realistic timelines while also providing alternative routes.
Gone with the wind
On the flip side, giving data scientists too little guidance and too much liberty can also be detrimental. Without clearly defined objectives and targets, creativity can lack focus and direction, leading to inefficiencies and missed opportunities.
Creativity in data science is similar to a kite. A kite is excellent at gathering wind, but if it is not tethered down by a string, it won’t stay in the air. Likewise, without clearly defined targets anchoring the data process, creativity can get carried away due to a lack of focus and direction.
At Booking.com, Sebastien helps shape and prioritize requests by always assessing them against strategic priorities and the company’s missions and values.
“The company culture at Booking heavily influences what we focus on. That’s really key because it helps us convince our stakeholders and collaborate with them,” he says.
Tom notes that this type of clarity requires foundational allyships, where there is synergy with stakeholders, and they can communicate these priorities and visions. “That means you have to have a vision in advance of what your office wants to be and what [they] want the culture to be,” he says.
Sebastien explains that data science work should begin with clearly identifying a use case and vision to know what needs to be covered.
“There’s an infinite number of things you can choose to measure — infinite varieties and details you can keep drilling down into. What’s really key is there is a decision you’re trying to drive, and you need to know who that stakeholder is, who the owners are,” he says. “From there, you can decide which data sources can support that.”
"There is a decision you’re trying to drive, and you need to know who that stakeholder is, who the owners are.”
Sebastien stresses the importance of an iterative process, where constant checkups and adjustments are essential. “Is it still addressing the needs like it used to? There are still iterations, checkups, and failure is part of that,” he says. This iterative approach is crucial because too strict directives can lead data research into tunnel vision, potentially missing important indicators and signals.
Conclusion: Unleashing Creativity in Data Science for Maximum Value
Unlocking the full potential of data science requires recognizing it as a creative and iterative process. By fostering an environment that encourages exploration, experimentation, and even failure, organizations can enable data scientists to generate the most valuable insights.
While this approach may involve growing pains and challenges, it is essential for revealing deeper, more actionable stories within the data. Leaders like Tom and Sebastien are pioneering this creative standard, emphasizing the importance of clear objectives and strategic alignment to guide the creative process effectively.
Are you ready to unleash the creative power of your data science team? Mapiq Insights can help you transform complex data into compelling, actionable insights, driving significant value for your organization. Embrace the creative journey and discover the transformative impact of data science in your workplace.
