What can data teams learn from the Toyota Production System (and manufacturing teams)? ⚡️
Product and software have been all the hype, but what if the answers to some of our problems can be found in teams and industries that have lasted centuries?
Data is one of the most unique functions inside any organization. It has very diverse people (e.g. data analysts, engineers, and scientists) collaborating to build something. There is also just as much diversity in these people’s tools and technology preferences.
In the past year, there’s been a lot of talk about data teams as product teams. There have been some great blog posts, conference talks, a McKinsey report — and even an entire dedicated article in Benn’s newsletter last week.
But here’s a thought — what if product teams are not the only teams that data teams can learn from? Yes, in the last decade, product and software have been all the hype, but what if the answers to some of our problems can be found in teams and industries that have lasted centuries? I’ve been recently fascinated by manufacturing and supply chain teams, who deal with variability in raw materials (just like we do with our source data), and still need to ensure on-time, no-defect production of high-quality products. Just like data teams that transform a heap of unruly raw materials from diverse sources into a finished product.
✨ Spotlight: What can data teams learn from the Toyota Production Systems and the manufacturing teams?
In the 1950s, in the shadow of World War II, the auto industry — and the world as a whole — was getting back on its feet. For car manufacturers everywhere, employees were overworked, orders were delayed, costs were high, and customers were unhappy.
To solve this, Toyota created the Toyota Production System, a framework for conserving resources by eliminating waste. It tried to answer the question, how can you deliver the highest quality goods with the lowest cost in the shortest time? One of its key ideas is to eliminate the eight types of waste in manufacturing wherever possible — from overproduction, waiting time, transportation, underutilized workers, and so on — without sacrificing quality.
The TPS was the precursor to Lean, one of the four fundamental principles of DataOps. Lean focused on the idea of Value Stream Mapping. What does a Value Stream Mapping actually look like? Let’s start with an example in the real world
Say that you own a cafe, and you want to improve how your customers order a cup of coffee. The first step is to map out everything that happens when a customer takes when they order a coffee: taking the order, accepting payment, making the coffee, handing it to the customer, etc. For each of these steps, you then explain what can go wrong and how long the step can take — for example, a customer having trouble locating where they should order, then spending up to 7 minutes waiting in line once they get there.
How does this idea apply to data teams? They both work with raw material (i.e. source data) until it becomes a product (i.e. the “data product”) and reaches customers (i.e. data consumers or end users).
The same principles can be applied to Data Value Stream Mapping for optimizing to eliminate waste (everything that’s not making the experience of the data consumers better) and make the data team more efficient.
Just like you would map a manufacturing line with the TPS, you map out a business activity in excruciating detail, identify waste, and optimize the process to maintain quality while eliminating waste. If a part of the process doesn’t add value to the customer, it is waste — and all waste should be eliminated.
P.S. I’ve recently been having a ton of fun curating material for our inaugural DataOps leaders cohort. If you’re interested in diving deeper, in this 5-minute video, I talk about how the principles of Lean from manufacturing teams can be applied to DataOps, and I also walk through the concept of Data Value Stream Mapping.
📚 More from my reading list
Data Analyst’s Guide in Handling Flooding Data Ad-hoc Requests by Olivia Tanuwidjaja
Craze or Trend? Decoding the Role of an Analytics Engineer by Marie Lefevre
The Many Layers of Data Lineage by Borja Vazquez
How I Learned to Stop Worrying and Love Being a Manager by Brittany Bennett
Organizing and Scaling an Effective Data Team by Rob Dearborn
I’ve also added some more resources to my data stack reading list. If you haven’t checked out the list yet, you can find and bookmark it here.
💙 Masterclass series for data leaders: Building a strong DataOps culture
Here’s the deal. There’s a ton of hype in the modern data stack about very cool-sounding trends like “data as a product” and DataOps! But what we’ve realized is that, in reality, very few practitioners know how to translate these theories into practical applications in their day-to-day lives.
This is why we are super excited to announce a new masterclass series by Emily Lazio (Data Product Architect, WeWork). Our first masterclass with WeWork a few months ago got such rave reviews that we’ve been working since then to design and curate this series with ready-to-implement resources and best practices from the real world.
Emily has led DataOps enablement, building a great DataOps culture with her 15-member data team at WeWork and scaling self-service for 1,500+ data users at WeWork. In this series, she will share her firsthand learnings and cover some core topics:
#Masterclass 1: Applying a data product mindset to design a DataOps program for diverse data users
#Masterclass 2: Applying information architecture principles to create a single source of truth
#Masterclass 3: Power of words: Laying the foundation for the data community
#Masterclass 4: Applying user-centered design principles to identify types of data personas
#Masterclass 5: Creating and rolling out a data university to boost data literacy across the organization
We’re running this as a closed-door session to keep it engaging and fun for a cohort of awesome data leaders!
This first masterclass is later this week, and I highly recommend it as an amazing way for data people to learn all about using user research principles, designing pulse surveys, and a ton of tactical advice to truly understand your data users.
If you are a regular Metadata Weekly reader, respond to this email and we’ll save you a spot! :)
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