Why is Product-led Growth the number one business trend to watch for data leaders? Plus Apache Pinot And Senior Director Data Products at Novo Nordisk
A trend that gives data leaders a seat at the table among senior leadership
The Product-Led Collective defines it as a business methodology in which user acquisition, expansion, conversion, and retention are all driven primarily by the product itself.
PLG creates company-wide alignment across teams—from engineering to sales and marketing—around the product as the largest source of sustainable, scalable business growth.
But why should data leaders have this trend on their radar?
1️⃣ Puts data closer to revenue.
PLG requires business functions to be fully enabled by data. Often at an event level.
Data leaders must be able to answer questions such as "What are the email addresses of users who visited our website three times this week and subscribed to our Newsletter?"
PLG requires data leaders to stop seeing the data function as a central function, provider of infrastructure and disconnected from the daily business.
The data leader must now work closely with revenue leaders to understand which queries they need to enable and how this data should look.
The new stakeholders and partners of the data function become sales, success, Growth and operations. Classic areas such as product and engineering take the back seat.
2️⃣ Requires proficiency in business systems.
Consequently, data leaders must develop a deeper understanding of business systems from the classics such as ERPs and CRMs to Helpdesk, CDP, Billing and more. Both as data sources, as well as destinations for the data.
They must become proficient with their APIs and quirks. But also, the data product changes. These systems do not expect raw data or are meant for visualisations. Instead, the data leader must provide aggregations with relevant insights for the business.
Some examples of those insights, often computed by data science, are customer scores, churn, segments, expected return time and similar.
These different views will also change depending on the business function. Whereas a GTM function will be interested in having a probability for conversion in the CRM, customer success will prefer a churn score in a tool such as Gainsight.
3️⃣ Enables a new set of roles for data-skilled business practitioners.
And the data leader must have a saying. Is someone creating pipelines between a data warehouse and a commercial system still a data engineer? Or is it already an analytics engineer? Or a business system engineer? And where are the direct and dotted lines for this profile?
The data leader must have an opinion and strategy for integrating and interacting with these hybrid roles. They have an understanding and intuition for data, but likely, they might be more on the business side and prefer tools with user interfaces over programming or command-line interfaces.
4️⃣ Makes event collection king in the data game.
One discipline in the data space that needs more attention from data leaders is event tracking and collection. Preparing a predictive model delivering insights for leaders in the revenue function is only possible with the relevant events on the website and app.
How do you create a user journey if the data function does not track users across all pertinent events of the respective funnel?
The need for high granularity at the user level of product usage data forces the data leader to put more attention to event collection, processing and storage.
Sampled data such as the one provided by Google Analytics is insufficient for the requirements of these data products.
The first step will be to set up a dedicated owner for tag manager and event tracking. The current situation at many companies looks very different.
This responsibility is distributed as a part-time occupation of multiple product management without much accountability.
5️⃣ Materialises the need for a semantic layer across the organisation.
The move to embrace PLG forces the business leader in adopting a strategy for keeping definitions consistent and relevant across functions.
Topics that until now felt like buzzwords, such as the semantic layer, start becoming a reality and mission-critical. When is a visitor updated as lead, user, recurrent user, paid user and so on in our systems? What is even a user? And are those definitions aligned across departments?
Acting around a unified set of definitions grants the data leader a strategic position within the company. Suddenly, the rollout of unique definitions is no longer purely a system and technical question but becomes strategic and gives the data leader a seat among senior leadership.
Suddenly, specific departments will look bad in their performance once definitions are updated, as they have been using others that suit them better. Other departments will start to be measured, and performance will become comparable.
Consequently, the data leader must also navigate the organisation's politics and identify necessary allies.
Product-Led Growth is a developing trend, and more so outside the US. Many companies have yet to hear of it and start to implement it. However, it is one that the data leader must pay attention to, especially if she wants a seat at the table among senior leadership.
Data Library of the Week
Because a data library a day keeps the boredom away!
Apache Pinot
🥜 In a nutshell, Pinot is a real-time distributed OLAP datastore designed to answer OLAP queries with low latency.
Apache Pinot is a real-time analytics system that helps make large amounts of data queryable quickly and at scale.
It covers BI dashboards, machine learning use cases, and user-facing analytics. Suppose you are building a product used by end users. In that case, you want to empower them with real-time analytics and actionable insights and give them the ability to take action and make decisions.
With Pinot, you have a system for ingestion, indexing in real-time and interactive querying to enable those use cases.
✅ Dedicated Docker image
✅ Comprehensive documentation
✅ Build instructions outlined in the README
❌ Extensive practical examples
✅ 10-min or less to get a Hello World
🌟4.3k+ stars on GitHub
🍴980+ forks
👏🏽 Xiaotian (Jackie) Jiang is the top contributor
🔗 https://pinot.apache.org/
🔗 GitHub: https://github.com/apache/pinot
The Fascinating Job of the Week
Senior Director Data Products at Novo Nordisk
Why do we like it?
The position is responsible for starting a new department responsible for data products.
What are the responsibilities?
Build and lead a highly talented team of data engineers
Create data products that meet the ongoing needs of colleagues through deep understanding of scientific use cases
Lead data engineering for the creation, management, and integration of data products
Define and implement data governance practices to ensure data products are of sufficient quality
Implement data use guidelines and policies for Novo Nordisk
What do they expect from me?
Minimum of 8 years of data engineering experience
Minimum of 5 years of leadership experience
Experience working with healthcare and/or life sciences data
Outstanding communication skills for working with colleagues at different levels in the company and in different disciplines
Where do I apply?
https://www.linkedin.com/jobs/view/3349313143
Have you already started to make your data function fit for product-led Growth? Which challenges have you faced?