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TREATING DATA AS A PRODUCT“Our mindset across the company is to make data available toeveryone. We also hold internal training for team leads forMetabase so they can get data themselves.” – TourlanePromoting a culture of self-serve data is also a core focus for Auto Trader’scentral data platform. Auto Trader has an experienced and capable team,made up of data engineers, developers, analysts and data scientists, but theyalso stress the importance of empowering other teams to help themselvesand preventing a bottleneck.“Our teams are empowered to care about the analytics theirproducts are generating and the insights they want to drive.”– Auto TraderBut Auto Trader’s data team is not one dimensional. By creating an agile‘project team’, data engineers can get in the trenches alongside developers,analysts, and data scientists to build product features together. In one suchproject, Auto Trader’s data team is working on a cross-functional project toenhance customer performance. For Auto Trader, as with many other datateams, it is important to strike the right balance between making themselvesavailable to others while maintaining focus on core data projects.23
TREATING DATA AS A PRODUCTA balancing actThe centralized data team is not without its challenges. As the first port of callfor any data-related queries from the rest of the business, it’s easy for a datateam to be pulled in so many different directions that it cannot focus on itsown tasks.At Peak Labs, the data team faced exactly that challenge. Inundated withdemands from internal stakeholders, such the product team, Peak’s datateam were so busy with requests that they were forced to compromise ontheir own endeavors.People are habitual creatures, and despite efforts to limit outsidedistractions, employees from other teams simply got used to approachingindividuals in the data team. Those approaches meant the team had to beconstantly code-switching.“We were all becoming less productive because of the contextswitching we were having to do multiple times per day.Sometimes per hour!” – PeakTo tackle the issue, Dr. Emma Walker, Lead Data Scientist at Peak, drew upnew communication rules around data. She set up public Slack channels witheach team or project managers, and encouraged other teams to use thosechannels as their first point of contact, rather than messaging individuals.She also established ‘office hours’, when a member of the team would hostan hour-long data clinic in the company kitchen for employees to ask anydata-related question. These questions would range from finding userinformation, how to track new features, determining the success of amarketing campaign or even questions about GDPR.24
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TREATING DATA AS A PRODUCT
A balancing act
The centralized data team is not without its challenges. As the first port of call
for any data-related queries from the rest of the business, it’s easy for a data
team to be pulled in so many different directions that it cannot focus on its
own tasks.
At Peak Labs, the data team faced exactly that challenge. Inundated with
demands from internal stakeholders, such the product team, Peak’s data
team were so busy with requests that they were forced to compromise on
their own endeavors.
People are habitual creatures, and despite efforts to limit outside
distractions, employees from other teams simply got used to approaching
individuals in the data team. Those approaches meant the team had to be
constantly code-switching.
“We were all becoming less productive because of the context
switching we were having to do multiple times per day.
Sometimes per hour!” – Peak
To tackle the issue, Dr. Emma Walker, Lead Data Scientist at Peak, drew up
new communication rules around data. She set up public Slack channels with
each team or project managers, and encouraged other teams to use those
channels as their first point of contact, rather than messaging individuals.
She also established ‘office hours’, when a member of the team would host
an hour-long data clinic in the company kitchen for employees to ask any
data-related question. These questions would range from finding user
information, how to track new features, determining the success of a
marketing campaign or even questions about GDPR.
24
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