Crystal Widjaja is former SVP Growth and Business Intelligence at Gojek, one of the largest super apps in Southeast Asia. Crystal helped Gojek scale from 20K orders per day to 5M. In a recent Reforge article, she talked about the most common questions around data in tech companies. Things like:
Should I hire a data engineer or a data analyst?
Do I need a data scientist right now?
What kind of analysis should the data team be doing?
Is the PM or data team responsible for data collection?
Should I be using Looker (an advanced data transformation and visualization tool)?
What’s the right ratio of analysts to PMs?
Where do analysts report into?
She says at the core of these questions is one root problem. It views data as a team to hire or set of tools to implement. If you take this approach, you almost guarantee getting to the wrong answer. Instead, you need to approach data questions from perspective that data is a strategic lever of growth.
Strategy → Stage → Team → Tools
Viewing data as a strategic lever of growth involves going through four areas:
Strategy - What are your points of leverage? How does data improve those points of leverage?
Stage - What stage of maturity is our product in? What stage of maturity is our Data in?
Team - What people do we need to achieve the data strategy? Are they set up for success internally?
Tools - What tools do we need to adopt to facilitate the team's impact?
Data Informed → Data Driven → Data Led
Crystal breaks this out into three major stages. Know which stage you are in informs which capabilities you should be focused on:
Stage 1: Data Informed. These companies are focused on building the business and getting to product-market-fit (stable user retention rates). The key business need is for data to provide operational visibility.
Stage 2: Data Driven. These companies have reached product-market-fit and are actively optimizing for specific users, behaviors, and experiences in the product at the feature-level. The key business need is for data to support the organization’s growth with scalable tooling, data products, and deep-dive insights.
Stage 3: Data Led. These companies are operationally run by data products, infrastructure, and services. The key business need is the “productization” of data services that unlock Product and Data Science teams, allowing them to automate operational decision-making and user product experiences.
Get all the details on this framework in her full post: Scaling Data: from Data Informed to Data Led.
Related Posts: The Data Wheel of Death, Why Analytics Fail, How To Drive Insights For Growth
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