Advancing Your Career in Data and Analytics | Peter Fishman
Interviewing Peter Fishman: know your skills well and progress from data reporting to insight interpretation.
Welcome to my Data Analytics Journal, where I write about data science and analytics.
February was a month focused on metrics, covering topics from measuring data quality to tracking product adoption. This month, paid subscribers learned about:
How To Measure Data Quality - A consolidated list of the top metrics for measuring data governance ROI and data quality.
How To Measure New Feature Adoption - Types of A/B tests, ways to measure new feature rollouts, how to evaluate feature discovery vs. usage, and metrics for measuring product adoption.
Measuring Non-Cohorted Retention or Blended Churn - Cohorted vs. non-cohorted retention, the nuances of blended churn, the connection between churn and retention, and effective ways to report overall retention.
The world of data analytics is relatively small yet full of surprises. You might encounter
at a meetup in San Francisco, spot while boarding an airplane, or run into Peter Fishman at a conference.I have been following Peter Fishman for some time, so it was exciting to unexpectedly run into him at SaaStr in San Mateo last year. We discussed various topics related to data and analytics, including influencers that could be dropped into a volcano as a sacrifice. I asked Peter to do an interview for my newsletter, and he agreed! I can't wait to share it with all of you!
Peter Fishman is a data analyst, statistician, and economist. He has served as Head of Analytics, VP of Analytics, and Chief Strategy Officer and has led data and analytics teams at Microsoft, Yammer, Zenefits, Opendoor, Playdom, and Eaze. Today, he is the Co-Founder and CEO of Mozart Data, an all-in-one modern data platform for centralizing, organizing, and analyzing your data.
Peter has seen “from the inside” how analytics evolved over the decade, and now he is directly shaping its direction. I am excited to share his perspective and thoughts on my favorite topics - how to advance in analytics, what data leadership challenges are, how to prepare for the GenAI era and secure our jobs, and more in our interview below.
What are the must-have qualities of a strong analytics leader? What does developing, growing, and maintaining strong analytics entail today?
Building data teams at technology companies used to involve identifying data talent in adjacent fields – academics, operators, consultants, backend engineers, etc. A strong data leader would lightly guide a talented team of individuals to understand the business and the key business questions.
Strong analytics is no different today than decades ago:
The ability to deeply understand signals of business health.
Identify what areas might be sub-optimized and can be actioned on.
Track and analyze the relevant data.
Provide consumable, pragmatic recommendations.
What are common mistakes data and analytics leaders make?
Two common mistakes are over-building and antagonizing operators.
There are plenty of reasons why, but it’s often a struggle to implement analytics wins. There’s a tension between the metric’s owner and the owner of the metrics (despite working under the same roof).
The best leaders build efficiently, just like we’d like product leaders to do (think MVI, minimum viable insight), but also, the product isn’t just shipping the insight but capitalizing on the win by collaborating with the operator.
and I have remarked on this quite a bit – our greatest impact came at a time when tools were well behind today. A couple of reasons might be that we’ve made marginal progress on the tools that matter or that the bar for valuable data insight has climbed (faster than the tools have).Recently, you mentioned something that stuck with me: over 10 years ago, we had bad tools, and most of the work was done manually. Now we have this amazing tech, yet the time to value hasn’t changed. The productivity output didn’t change.
Why do you think this is the case?
I think both things are true. In baseball, it’s no longer an inefficiency to find players with good OBP (an insight from 2000, it is now obsolete), we need to comb through different data sets or take new approaches to find similar inefficiencies; the same likely holds true for the businesses that the modern data tools try to optimize.
But I think more importantly, while the tools were always important and did speed up the analyst work, the bulk of the work is to understand the business context and opportunities, and we haven’t gotten much better as a profession at that. We’ve focused on speeding up our own technical output, assuming (possibly incorrectly) that we’re already capable of the first.
Looking at the data landscape today, there are many data storage and management tools. However, it seems there isn't much focus on visualizations and analytics. Why is BI lagging behind?
The BI tools themselves are perhaps the oldest of the stack. We’ve been using Tableau (and Excel) for decades. Yes, a wave of BI and viz tools came and brought improvements to the space. There are network effects to knowing how to use a tool, which makes disruption very difficult (and costly). But there are so many flavors of BI tools (Snowflake works with hundreds of them).
But, I don’t think “BI” (at least what we think of as BI) is where a lot of the value gets created – it’s not the visualizations, it’s the data that powers it and the impact of the folks who share it that matters.
As a person who works on tools that make BI tools successful, it’s a bit heretical, but I think what we’ve learned is that a data team contributes most by making data present in an organization and changing the typical workflows. It’s not any specific alert or spotted trend, but rather, the mere presence of a data team gets data (and reasoned thinking) to be more considered in conversations across product and go-to-market.
There are so many exciting topics and themes within analytics. With your impressive experience and depth of understanding analytics, why Mozart data? Aren’t there enough ETL and data transformation tools?
How about building the best analytics platform ever?
There are a lot of tools, especially ones that collectively do what Mozart Data does - we work as partners, technology consumers, or sit adjacent to many (if not all) of the tools in the space. But IT hasn’t happened. In this case, “IT” means teams universally leveraging the Modern Data Stack.
Sure, Data Twitter and a bunch of tech companies have adopted these tools, but for the most part, it is still the case that most of the world does a lot of manual and slow data manipulation to get insight. There’s a better way, and we’ve known about it for years. And finally, the toolset is mature enough and consumable enough for a mass audience.
What is next for Mozart data?
Mozart is still working to simplify data accessibility. This is in line with our goals to break down barriers to entry into data for technical operators and enable collaboration across technical and non-technical operational teams (pretty consistent with my thoughts above about the field). This includes improvements to our Sonata (free) plan , dbt Core integration, and Snowflake & Fivetran experience improvements.
It wouldn’t be 2024 without mentioning we’re working on some GenAI features. Though many are trying to use AI to master going from English to SQL, we’re really taking advantage of the other direction, including automating the documentation and cataloging processes.
In this new era of GenAI, how do you see it will transform analytics, and how can analysts prepare now to secure their jobs and continue bringing value?
We’ve built automated data engineers and data analysts at Mozart Data. At least for the analysts, it’s not there – we’ve got plenty of time. But some things are certainly true – know a skill well. Low-level work (e.g., basic counts, sums, and joins) will be automated. You can’t just report the data, you need to interpret and add value through that interpretation.
The best thing to do is get reps or practice – by completing problems and noticing the nuances that arise (why does something that seems right not work, what biases exist in the interpretation of a viz), your work up-levels, and that is the only permanent job security.
You are at so many different places at the same time - conferences, podcasts, talks, and webinars. Do you still remember SQL?
I love to say – “still got it!” Though I occasionally discover a new syntax that didn’t exist when I was learning SQL that makes something annoying much easier – though I tend to do it the inefficient way.
Do you miss the good old days with VLOOKUPs and doing 9-5 analytics? And sleeping?
Sometimes, a 9-5 job can feel so much more energy-consuming than a job that’s clearly taking up more of the calendar. There are many applicable cliches, but I’ve found them true.
Is there anything else you want to share to encourage or inspire people to learn data?
Though data has become a profession, it’s better seen as a lens for the world. It’s a tough one to master, as it takes technical chops and real-world intelligence to make or assess a prediction.
If you are someone who loves to make predictions and prove or disprove them – having a deep skill for assessing evidence (and thinking of that as analysis as opposed to writing SQL) is what we do as a profession.
So much of my personal life mirrors this – when I make decisions about who to start on my fantasy football team or what route to take to get to work – it’s all data. You’ll quickly find that developing these skills applies to both work life and outside-of-work life, even if you don’t use a spreadsheet or database for that.
Thank you, Peter!
Connect with Peter:
LinkedIn: Peter Fishman
Email: peter@mozartdata.com
Try Mozart Sonata: https://app.mozartdata.com/signup
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Heyy Olga I enjoyed this read especially he mentioning fantasy football, it's a thing of joy 😂🔥