«A lot of things break with scale.»

In our latest conversation with Mikkel Dengsøe, co-founder of SYNQ and former Head of Data, Ops and Financial Crimes at Monzo Bank, we explore the secrets behind effective scaling of data teams.

Mikkel reveals surprising statistics based on his analysis of over 10,000 LinkedIn data points and valuable insights from Monzo’s scaling journey, where the data team grew from 30 to over 100 people in just two years.

We discuss the critical balance between central data teams and domain experts, the importance of career paths for individual contributors (not just managers), and how data professionals can succeed by building relationships with stakeholders who involve them early in strategic processes.

Here are our key takeaways:

Data Teams

• There are some high-level questions you need to ask yourself when building, structuring or scaling a new data team
• This includes how big the team should be, also relatively too your organizations size and other teams, how it should be composed and structured, etc.
• A good idea is to collect data to create a benchmark.
• Benchmarks can be hard to combine and are a moving target, but they are nevertheless valueable.
• Most importantly, you need to ask yourself: WHY do we need to scale our data team?
• Involve people actively in setting the goals based on your WHY.
• Mikkel collected over 10.000 data points from companies on Linkedin. Here’s what he found:
  
  • Median % of data people in companies out of overall staff is 1-4%.
  • Data team relative to engineering team varies between 1 data person per 10 engineers to 1 in 3.
  • From the benchmark it is evident that data governance roles only appear in lager companies.
  • In marketplace companies the effect of data on the business value is easiest to track. Therefore they seem more willing to invest In data teams.
• Investment in data means investment in your business. The consequences of not investing in data will be tangible in your business.
• Find a risk based approach to data as well. At what level can you balance investment, outcome and risk?
• Be cautious of «pseudo-data teams» - teams in a Business unit that do kind-of data work, but are not aligned with the organization.
• Be clear on the skills and competencies you need. What is a data analyst? What does a data scientist do in your organization?
• It is important to have a clear and consistent internal career ladder. Make it visible and understandable what is expected from each role on your team and don’t change these expectations too often.
• Create pulse checks to understand what people are happy about and what not.

Scaling Data Teams

• «Golden Nugget Awards» to showcase good data work every month. These were added to a database, so every new employee could evaluate them to see what good looks like.
• Write down your progression framework to get clear about your ideas and how people excel in your organization.
• You can show open what work lead to promotions. That can be engaging for people to follow in these tracks.
• Hub-n-Spoke model, where people rotate in and out of the central team and the distributed teams.
• Citizen developer programs are a way for larger organizations to scale data work. But It bears risk related to data literacy.
• Don’t try to enable everyone, but enable those that are motivated.
• «You shouldn’t necessarily force people into management to progress.»
• Senior technical careers can ensure an advanced level of quality. Which is a different way of scaling your data team.
• You need a career ladder for professionals that is independent from management careers.
• Create rituals that make good work stand out.