Product Manager Guide : How to work with Data Analysts — Become Best Workplace Buddies!

Intro

There are times when the role of Data Analyst (DA) becomes ambiguous in product sprints.

As a Data Analyst, I experienced such ambiguity mainly when there wasn’t enough communication and collaboration with my Product Manager.

After chatting with couple of Product Managers over a cup of coffee to solve this problem,
I realized writing up a guide would be helpful.

More specifically, a guide that would explain when & for what purpose
a Product Manager and a Data Analyst should collaborate during product sprints.

There was already a consensus amongst everyone at LINER that the synergy between a Product Manager and a Data Analyst drives significant difference in the quality of sprint results and impact.

However, I also agreed that it was indeed difficult for a PM to put deep thinking into when would be a perfect time to communicate with his/her Data Analyst during such a tight sprint schedule without a proper guide.

I write this guide based on my personal experience in that hopes that the Data Analysts at LINER now or in the near future will be able to find their place in product building and see their skills be put to meaningful use.

I believe this article will be helpful to the people below.

• Product Manager who doesn’t know when or how to communicate with the Data Analysts in their team.
• Product Leaders wondering if their organization needs a Data Analyst today
• Data Analysts who feel like they are in an awkward place when working with their Product Manager.

Collaboration Guide

Dear Product Managers,

make your Data Analyst your best friend

& share all your thoughts on product strategy!

A proper collaboration between a Data Analyst and a Product Manger in a product sprint is a crucial component in setting up a clear product strategy and deciding on iteration cycles.

In LINER, one product sprint is managed in three sequential stages :
Shaping, Building, and Cooldown.

PMs collaborate with DAs the most during these product sprints composed of Shaping — Building — Cooldown phases.

• Shaping phase deals with deciding upon the direction and overall planning of the sprint
• Building phase refers to the product making process up to product launch, including engineering and QA.
• The Cooldown step is the step after product release where the product is being monitored and iteration cycles are discussed based on product data. Fixing minor bugs found after deployment or taking care of tasks that were not taken during the building phase is also done in Cooldown.

The expected outcome from PM & DA collaboration vary for each of the phases.
Here are the collaboration what & why during Shaping, Building, and Cooldown phases.

SHAPING — Choosing the Metric

Whether the DA is synced up with his/her PM during the Shaping phase determines whether the DA will be able to produce a powerful and actionable data driven insight in the Cooldown phase.

During the Shaping phase of a product sprint,
Product Managers need to sync up with their Data Analysts to the max.
Really understand each other, like a best friend!

1. PM and DA need to communicate the purpose of product sprint and the desired outcome or impact. (sync-up)
2. PM and DA need to discuss the KPI that determine whether the hypothesis is rejected or not and additional KPIs that quantitatively measure the product UX. Also determine if there needs to be product experimentation like AB tests.
3. DA can start drawing up a first draft of the KPI monitoring dashboard based on the metric conversation with the PM from #1 and #2.

BUILDING — Data Collection Process

1. When planning out the overall sprint with your engineers,
   PMs please remember to discuss the necessary data tagging request schedule with your DA.
   (ex : what are the new Amplitude or Firebase events that needs to be added along with the new feature release?)
   
   - When planning engineering schedule, data tagging should also be considered and communicated with the engineers in advance.
   (Don’t go to your engineer and ask to tag more data last minute before product deployment! Their hands are already full)
2. DAs need to design data event taxonomy that needs to be collected along with the product release based on the metric discussion.
   Request the engineers for data tagging based on the schedule fro #1.
3. After the engineers are done, PMs and DAs do not forget about data QA.
   
   - DAs, be responsible and thorough with data QA. Make sure that the data collected for follow-up data analysis will be reliable.
   
   - PMs, make sure that the data QA is complete before deploying the build.
   
   Deploying when data QA is not finished is a mistake that should not be made and can be very easily avoided with even one simple slack message.
   (all you need to do is ask!)

COOLDOWN — Following Up with Data

1. After deployment, PMs please check up on your product using the dashboard the DA made. Provide feedback and additional requests regarding the dashboard. (this is a must!)
   
   - Of course, The information you thought you wanted to see during the Shaping phase could have changed at this point. You might have found more value when actually getting the feel of the new released feature or company expectations upon the product sprint might have changed.
   
   - You might feel bad about asking for additional work to your DA, but there is nothing more disappointing for a DA than a PM not utilizing the dashboard the DA made for data-driven decision making.
   
   - The KPI monitoring dashboard is only good as long as the PM finds it actionable and insightful, but reaching that state requires attention from both the PM and the DA
   
   Therefore, PMs if your gut tells you that some dashboard changes are necessary, please ask for feedback or modification on the dashboard.

2. PMs, please gather with your sprint crew (designers, engineers, researchers…everyone!) after launch to discuss product performance and cooldown together. Go ahead and ask the DA to share the current status of the product and what kind of impact the sprint made using the updated dashboard.

- Chat with sprint crew while looking at the dashboard.

- Talk about what went well and which KPIs or funnels need better conversion rates. Gather different thoughts and it will help you draw up new hypothesis and if an iteration sprint is needed.

- Please be careful not to share data with personal bias.

Communicate the achievements and lessons learned clearly with your organization

3. (If necessary) PMs, please ask the DA to analyze data to discover insights & opportunities for further optimization

- You might need to request an analysis, without knowing what to expect or what to find from the DA’s data analysis. This is where the sync that you built with your DA during the Shaping phase sheds light.

- If your DA is synced up with you as to what you wanted to see from the product sprint, what the company expectation was, and why it was important the DA will most likely bring back data insights that you can act on with the time and resources you have.

4. PMs, feel free to share your product strategy concerns with your DA.

- After communicating with other DAs, I can confidently say that most DAs are more motivated and driven when working as a Data Strategists rather than like a query machine.

The more PM trusts the DA and show more willingness in collaboration, the DA will grow as your closest entourage in the hopes of brining out more meaningful change with data.

Outro

It’s a tremendous asset to have a teammate who will analyze and dig up insights that is needed for good product decision making.

Even if you are a data-savvy PM, the broader your product is the less time you will have to look at the data closely.

Collaborating with a trusted DA is essential for PMs to spend more on product strategies and customer problems.

When you work closely with the DA you synced up with,
you will feel that change in the magnitude of the impact and the quality of sprint follow-ups.

The impact of your product sprint won’t be determined by one KPI anymore, but you will be able to distinguish performance and important product lessons by hyper-utilizing your data. You will also be able to attend leadership meetings with more information about your product up your sleeve.

When PMs and DAs work together, product teams start moving in a data-driven direction and makers find more motivation in their work by fully understanding the impact they are making each day.

This is a collaboration guide based on LINER’s sprint structure, but I hope this guide helps PMs collaborate better with their DAs.

To any Product Manager that finds this article, I wish the very best for a trustworthy partnership with your Data Analysts!

Happy product building! 🚀