Harvinder Atwal: What Happened When MoneySupermarket Embraced DataOps

Harvinder Atwal, Group Data Director at MoneySupermarket, shares
how DataOps principles have dramatically enhanced the productivity
of the group’s data function

Adopting DataOps practices has helped MoneySupermarket’s data
function drive significant productivity gains in recent years.
The results have been so good, MoneySupermarket Group Data
Director Harvinder Atwal decided to chronicle his experiences in
his book, Practical DataOps: Delivering Agile Data Science at
Scale.


In this episode of the Business of Data podcast, he outlines the
key principles that define DataOps and shares how adopting a
‘data products’ mindset is helping his team drive business
results more effectively.


“For us, DataOps is data analytics – in its broadest sense,
including data science and AI – combined with ‘lean thinking’,”
he explains. “The creation of data products is key.”
What DataOps and DevOps Have in Common

There are two strands to the DataOps concept of ‘lean thinking’.
One is about looking at processes, making them more efficient and
adapting to change. The other is DevOps.


Atwal explains that DevOps has its roots in the historic tension
between software developers and operations professionals. While
developers want to innovate and improve applications, this can
create challenges for operations people, who need to make sure
things run in production reliably.


“The challenge was that you get to a place where the operations
people are maintaining a really brittle product in production,”
he explains. “DevOps is there to make sure that these things
[don’t] happen.”


Instead of developing apps as giant ‘monoliths’, DevOps breaks
them down into independent constituent parts. These can then be
iterated rapidly to incrementally improve their performance.


Harvinder notes that a key step in applying ‘lean thinking’
principles to data and analytics is making the switch from a
‘project’ to a ‘product’ mindset. Rather than starting with data
and trawling it for insights, data teams should start with a
‘desired outcome’ and go from there.


“Traditionally, the way people have approached using data is to
think about actionable insights,” he says. “ So, ‘What can we
find in the data that will produce some insights and create a
recommendation?’”


“It’s about flipping everything on its head,” he continues.
“We’ll take an outcome and say, ‘What kind of data product can we
build that will deliver that outcome?’”
Key Takeaways

· Adopt a ‘data products’ mindset. Data teams
should start with a business challenge and design a data product
that achieves a predefined desired outcome


· Streamline the data product pipeline. Use
‘lean thinking’ principles to find bottlenecks in existing
business processes and find ways to make the data pipeline more
efficient


· Continuously integrate; continuously develop.
Rapidly iterate data products to add in new features, reduce
model scoring latency and drive better business outcomes