Product Ops: Managing Data Analysts
Data analysts provide the eyes and ears for Product Leaders. A few simple tips can maximize this relationship.
When you are the manager of someone who is NOT in your career path, you want to be mindful of their unique career path needs.
First, many analysts do financial forecasting for the CFO and the sales team as a way to help the executives understand the whole company's future.
To get a read on current job performance, go to the folks who most frequently request and consume the analyst's reports and see how it's going…from a timeliness and quality of work point of view. Aggregate the feedback and help your analyst improve.
Second, co-create with the analyst a set of Product success metrics to be collected and analyzed…usage, adoption and end results achieved with the technology your org produces.
Product success metrics are leading indicators that inform your Product org whether you’ll achieve the company goals that the execs are looking at.
You might get sucked into being a traffic cop for all the requests to this analyst. Try to avoid that by setting up a ticket system (Jira) or some other transparent request system (Baserow, Airtable, etc). Or coach the analyst to manage their workflow and backlog.
Third, ask your analyst about their career and skills goals.
My analysts/data scientists went through this trajectory (albeit simplified):
Receive data from others. Use Excel/Google Sheets for visualization. Build dashboards manually.
Learn SQL to pull data
Learn Python to pull data (necessary for those non-SQL DBs out there)
Teach non-analysts to access and analyze their own data to offload simple tasks
Level up to data scientist by taking courses and learning statistical analysis (box plots, R, regression, etc).
Create case studies and do deeper multivariable analysis
Suggest ways to improve data collection to support better analysis
Use Python for analysis
Build self-updating dashboards
Learn and use machine learning techniques (supervised, unsupervised, etc)
Level up to data science engineer by productionizing insight generation with scripts and code
Regularly incorporate insights into user-facing products
They found this path and pushed to make it happen.
If you have more questions about managing data analysts or analytics in general, reach out to me.
Jim coaches Product Management organizations in startups, scale ups and Fortune 100s.
He's a Silicon Valley entrepreneur with over two decades of experience including an IPO ($450 million) and a buyout ($168 million). These days, he coaches Product leaders and teams to find product-market fit and accelerate growth across a variety of industries and business models.
Jim graduated from Stanford University with a BS in Computer Science and currently lectures at UC Berkeley in Product Management.
