Making your engineering team more data-driven



In December 2020, I was invited to lead a group discussion as part of Plato circles. The topic that I chose was “Making your engineering team more data-driven” and we had such a good discussion over 3 sessions as a group that I decided to open-source our notes. Please find them below.


In this post, we will explore why data-driven engineering is important and how to build a data-driven engineering culture within your teams and org.

Part #1: why is data-driven engineering important?

Part #2: learn about the build-measure-learn cycle and double-click on the “measure” component

Part #3: Tips on how to build a data-driven culture within the team and org

Part #1: Why is data-driven engineering important?

What is a data-driven culture?

Being data-driven means that you are using data in your tactical and strategic decision-making process. If you are not using data, then you are either using intuition/gut-feeling or usability studies to make the decision. Both intuition and usability aren’t the best ways to go about making decisions, here’s why 1) Intuition: In tech, intuition doesn’t scale because you are catering to a diverse user-base (usually millions of users) and you won’t get it right. A simple exercise is to vote for a design change and then run an A/B test. How many times does your intuition win? 2) Usability studies: usability studies are complementary to data-driven processes but shouldn’t be the only source for making decisions. This is because usability studies require a high-touch (1:1’s or focus group) approach, expensive and more importantly, it takes a while for the data to be gathered. As your product grows, it becomes unscalable to keep reaching out to your customers to help you with your decision making process. In the next section, let’s double click on why data-driven is important. 

Why is data-driven important? 

In tech, we have an unprecedented scale that we haven’t seen before and this rollercoaster hasn’t ended yet. With the unprecedented scale, the industry comes up with new buzzwords like “Big Data”. In simple terms, it means that we now have a small team that caters to billions of users (e.g. WhatsApp famously had just 50 people when Facebook acquired it. source) and so we are building and running products that generate a lot of data. This data is gold because it is the voice of customers at scale. If you want to be effective (doing the right things) and efficient (doing things right) then you need to listen to your customers, be obsessed about solving customer pain points and build the best solutions through your products. Big Data and being data-driven is your best mechanism to achieve your desired outcomes. 

What does it mean for an engineering team to be data-driven? 

For an engineering team to be data-driven, it means that you are using data to make tactical and strategic decisions. For e.g. (not exhaustive)

  1. Are you logging the right data at the right location with the right data quality bar so you can analyze your data later? 
  2. Are you running A/B tests to figure out which version of the product is performing against your goals? 
  3. Are you using metrics to figure out what you should be building next? 
  4. Are you using metrics to scale up/down your software development resourcing investments in existing product features? 
  5. Do you have alarms and monitors set up that alert if your product health metrics (latency, load time, etc) are spiking and causing detrimental customer experience? (Note: all engineering teams have alarms and monitors if the product goes down but few teams think about “health” metrics)
Who are the other partners that need to be data-driven for your engineering team to be successful at this approach? 

In tech, within a product team, you usually have multiple partners so it’s important to be data-driven and influence other partner teams to be data-driven too. These partners include business leaders, product managers, marketing managers, finance owners, data science & engineering owners, design owners, user research, and others. Below you will find a matrix of common “product” metrics and who should own them: 

Metric CategoryMetric DescriptionMetric ExampleSuggested Owner
HealthIs the product performing reliablyLatency, uptime, load timeEngineering
UsageHow are customers using the product# of Activities, # of Active users (Daily Active User – DAU)Engineering + Product + Design
New user AdoptionAre new users adopting the product?# of new usersMarketing
RetentionAre users coming back?# of retained usersEngineering + Product
OutcomeWhat is overall business result?RevenueBusiness leader + Finance 
SatisfactionWhat is the overall customer satisfaction?Net promoter scoreUser research
Engineering Productivity and HappinessWhat is the engineering productivity and their happiness?Cycle Time, Velocity, # of employees thumbs up, down, neutral last week/month Engineering

Note: your data science and data engineering team should build an underlying platform for you to track your metrics. Having strong data engineering and data science teams is a huge plus and a clear indicator that business leaders consider data-driven culture as a critical component for success.  

Part #2: Build-measure-learn cycle 

What is a build-measure-learn cycle?

The build-measure-learn cycle was popularized by Eric Reis in his book “The Lean Startup”. It provides a mental model to effectively approach startup development. There are three steps in this cycle:

Image source

  1. Build: You “build” your product. It could be an MVP (minimum viable product), MLP (minimum lovable product), experiment, or iteration on your existing product.  
  1. Measure: The measure part should be kicked off on day 1 when you start architecting/designing your system. You should figure out a simple and robust data infrastructure since day 1. Your software developers should work backward from figuring out a system where it takes one line of code to send an event to the data infrastructure and one line of code to query the event. You don’t need to build your own solution here since there are plug-and-play options available. For e.g. if you are launching on the web, Google Analytics is a good solution. If you want to capture mobile events and get something a little more advanced, maybe look into Mixpanel or Amplitude. When you search for these vendors, you’ll get a list of 20 other tools. It’s not that critical on which tool you end up picking but more important that you have an architecture in place and are able to query your events and unlock the “measure” part of this cycle — since without this, you won’t be able to “learn” quickly.   
  1. Learn: We “learned” (pun intended) in the last session that using intuition isn’t always the effective way and using usability studies is a time-consuming process so if you want to make decisions fast, move fast, learn fast and iterate — you need to be data-driven! The Learning step here means that you are using the measuring step above to figure things that are not working so you can scale back and things that you are working on so you can double down. Sometimes, data might also help you figure out when to Pivot if needed. For e.g. Airbnb learned through data that listings that had a professional photo were getting booked by 2.5x times (source) compared to listings that didn’t have a professional photo so they quickly doubled down on that and skyrocketed their growth! 

Image Source (Airbnb growth after starting professional photographs)

Why is it useful for engineering teams?

The most important thing for engineering teams to note here is that the build-measure-learn mental model is a cycle vs a one-off occurrence. As an engineering team, you’ll need to keep going through these cycles and spin this wheel as fast as you can and should. If you are unstoppable at this in building software by iterating and learning very quickly then you will eventually build a product that your customers LOVE! 

How do you implement the “measure” part? 

Deciding that you and your team will adopt this mental model was the hardest part. The easier part is to now figure out how to implement it. Few ideas and questions to think about (not exhaustive since you will need to build-measure-learn this on your own for what works best for you):

  1. Do you know your success metrics? (e.g. Are you optimizing for Daily Active Users and engagement rates?) 
  2. Do you understand customer behavior? Do you understand the common paths that the customers will take in your product? Based on that, do you know the events that you want to capture? Do you have a prioritized list of events that you should capture? 
  3. Do you know your system architecture on how to log these events? How big is your team and how many users will you have? If the product will have multiple millions of users soon, have you considered how your events logging solution will scale? Do you have a data engineer on the team?
  4. Are you aware that most Analytics projects fail? So it’s important to start, iterate and fail fast. (Read the “why most analytics fail” blog linked in the appendix after this session) 
  5. Do you have a data-store where you will store the events? (e.g. Redshift, PostgreSQL, etc)
  6. Do you have a tool to visualize your events data? (e.g. Tableau, Excel, Quicksight, etc.)
  7. Are you planning to just go with a vendor that gives you end-to-end analytics capabilities? Have you run a proof of concept? Have you talked about other peers who might have experience with this? Are you aware that it’s hard to switch analytics vendors so you need to choose wisely? (e.g. Google Analytics, Amplitude, Mixpanel)
  8. Have you thought about rapid A/B testing tooling? (e.g. In-house, Google Analytics, Visual Website Optimizer, Optimizely, Amplitude, etc.) 
  9. How do you plan to address complex use-cases that your engineers can’t analyze? Developers should be very good at analyzing one event at a time? What if you need to analyze entire customer journeys and funnels? That will require a Data Scientist or Data Analyst on the team? Do you have one? 

Note that you should come up with the architecture here for the measure part that helps you and your engineer team keep moving fast. Your architecture should solve simple use-cases without external help as often as possible. 

Part #3: How to build a data-driven culture?

What is Data Culture?

First, let’s define what is culture: “The set of shared values, goals, and practices that characterizes a group of people” – source; Now building on top of that for defining data culture, What are sets of shared values? Decisions will be made based on insights generated through data. And also, a group of people represent all decision makers in the organization. So in other words:”An org that has a great data culture will have a group of decision makers that uses data & insights to make decisions”

What are the ingredients for a successful data culture?

It’s 3 P’s: Platform, Process and People and continuously iterating and improving each of the P’s to improve data culture.

How to build data culture?

Here’s a mental model for a leader within an org:

  1. Understand data needs and prioritize
  2. Hire the right people
  3. Set team goals and define success
  4. Build something that people use
  5. Iterate on the data product and make it better
  6. Launch and communicate broadly
  7. Provide Training & Support
  8. Celebrate wins and communicate progress against goals
  9. Continue to build and identify next set of data needs


Book recommendation: 
  1. Lean Analytics: Use Data to Build a Better Startup Faster; Free Video companion: 
  2. Measure what matters: 
  3. An elegant Puzzle: 
  4. The Lean Startup: 
  1. Why does most analytics fail?
  2. Airbnb growth story:  
  3. Building data driven companies: 
  4. Data culture mental model: 
What is Plato’s circle?

1-on-1 mentorship is valuable but often not scalable. We value your time and we’d love for you to help multiple people at once (plus it’s a chance for participants to meet their peers). In a Circle, people connect virtually so they can (a) learn from you and (b) be in a safe space where they can share experiences, pain points, & successes. Typically, a Circle happens over 4 weeks over Zoom for 45 min per session.

Thanks to Plato Circle Attendees:

Julius (VP of Engineering @ Hubble), Sridhar (Director of engineering @ BlackHawk network), Ken (Software @ AES – EdTech), Danny Philayvanh (Director of Engineering @ Rakuten), Royce (Product @ Plaid), Daniel (Founder @ Canadian Entrepreneurs), Brian (Staff Research Scientist @ SambaTV), Yahia (Lead Staff Engineer @ Argo AI), Sky (Tech Lead @ Mindvalley), Minna (Senior software engineer @ Fable Tech Labs).

Data Engineering and Data Science Newsletter #4


The purpose of this Insight Extractor’s newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following articles made the cut for today’s newsletter.

1. What does a Business Intelligence Engineer (BIE) do in Amazon?

Have you wondered what Analytics professionals at Top tech companies work on? Are you job hunting and wondering what data roles (data engineer, data science, or Bi engineer) at Amazon are a great fit for your profile? If so, read Jamie Zhang’s (Sr Business Intelligence Engineer at Amazon) post here

2. What are the 2 Data & Analytics Maturity models that you should absolutely know about?

If you have read my blog, you know that I am a fan of mental models. So, here are 2 mental models (frameworks) shared by Greg Coquillo that are worth reading/digesting here

3. Using Machine Learning to Predict Value of Homes On Airbnb

Really good case study by Airbnb Data scientist Robert Chang here

4. How Netflix measures product succes?

Really good post on how to define metrics to prove or disprove your hypotheses and measure progress in a quick and simple manner. To do this, the author, Gibson Biddle, shares a mechanism of proxy metrics and it’s a really good approach. You can read the post here

Once you read the post above, also suggest learning about leading vs lagging indicators. It’s a similar approach and something that all data teams should strive to build for their customers.

5. Leading vs lagging indicators

Kieran Flanagan and Brian Balfour talk about why your north star metric should be a leading indicator and if it’s not then how to think about it. Read about it here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Data Maturity Mental Model Screenshot:

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The purpose of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. Following articles made the cut for today’s newsletter:

1.What I love about Scrum for Data Sciene.

I love the Scrum mechanism for all data roles: data engineering, data analytics and data science. The author (Eugene) shares his perspective based on his experiences. I love that the below quote from the blog and you can read the full post here

Better to move in the right direction, albeit slower, than fast on the wrong path.


2. Building Analytics at 500px:

One of the best article on end to end anayltics journey at a startup by Samson Hu. Must read! Go here (Note that the analytics architectures have changed since this post which was published in 2015 but read through the mental model instead of exact tech tools that were mentioned in the post)

3. GO-FAST: The Data Behind Ramadan:

A great example of data storytelling from Go-Jek BI team lead Crysal Widjaja. Read here

4. Why Robinhood uses Airflow:

Airflow is a popular data engineering tool out there and this post provides really good context on it’s benefits and how it stacks up against other tools. Read here

5. Are dashboards dead?

Every new presentation layer format in the data field can lead to experts questioning the value of dashboards. With the rise of Jupyter notebooks, most vendors have now added the “notebooks” functionality and with that comes the follow-up question on if dashboards are dead? Here’s one such article. Read here

I am not still personally convinced that dashboards are “dead” but it should complement other presentation formats that are out there. The post does have good points against dashboards (e.g data is going portrait mode) and you should be aware about those to ensure that you are picking the right format for your customers. The author is also biased since they work for a data vendor that is betting big on notebooks and so you might want to account for that bias while reading this. Also, I had written about “Are dashboards dead?” in context of chat-bots in 2016 and that hypothesis turned out to be true; you can read that here

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Data is going portrait mode! Source

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Insight Extractor’s Data Engineering and Science Newsletter #2


The purpose of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. Following articles made the cut for today’s newsletter:

  1. Amazing data storytelling example from Ben Evans. Ben starts from a basic premise around “Amazon is not profitable” that a lot of people argue about. He then goes on a data storytelling journey with publicly available data-sets around his chosen premise. Must read! here
  2. What kind of data scientist am I? Elena Greval from Airbnb wrote this excellent article in 2018 but it’s still relevant to understand 3 different flavors of data scientist. Read here
  3. What does it mean to be a data science leader or manager? Eric Weber’s short post on Linkedin on what does it mean to be a leader. IC’s should exhibit these traits for faster career growth especially if you are the sole data person in a decentralized structure. Read here
  4. Functional data engineering: In the blog post here, Maxime Beauchemin explains how to apply functional programming concepts to data engineering.
  5. Interested in growth analytics? Think about this interview question from Andrew Chen: How would you 10x the growth of Product X? LinkedIn post here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

3 types of data scientist
3 Types of Data Scientist (Source)