The-four-priorities-for-an-analytics-team-of-one-l
The-four-priorities-for-an-analytics-team-of-one-l #
At the start of his tenure at Lola, Sagar laid out his analytics strategy, a 10-page document that described his plan to make Lola a “lean, fact-driven organization.” His plan described an approach that started with enabling business users to self-serve in the near-term–meeting the data needs of the organization today–while he implemented an analytics engineering workflow that would scale to meet future data needs.
Priority #1: Enable business users to understand what is happening in their department today #
Sagar took inspiration from Carl Anderson’s book, Creating a Data Driven Organization, which outlines the six types of questions that can be answered using analytics: ! Instead of spending 80% of my time cleaning data, I spend my time building tools that enable business users to do it themselves, and generating real insights that can help scale the business.” So instead of churning out monthly Excel reports, Sagar’s job is now to: 1.
Priority #3: Empower business users to do their own data exploration #
In Sagar’s strategy doc he wrote: “Eventually, the organization will need to explore data and deliver insights at a rate that isn’t possible if all queries must filter through a centralized analytics team. Looker is currently the best business intelligence platform on the market that enables analysts to build data tools for business users, and enable them to support themselves.” In Sagar’s view, it makes sense for analysts to build some reporting for business users, but long term, “Our goal is to have each vice president be able to do their own data work. “As an analyst, this is a huge relief.”
Priority #4: Plan for scale #
Even with best-in-class technology and code-based processes, the analytics needs of the organization will eventually grow to a point where “it becomes important to optimize for cost and efficiency.” When Lola reaches that stage, Sagar says that’s when it makes sense to hire data engineers, data scientists, and other business intelligence professionals. A few tips:
- Staging and mart models: Sagar follows the convention of using staging models for data cleaning, and marts for storing the business logic of a given business function.