指南：创建仓库的优先数据分析堆栈堆栈.pdf
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Guide: Creating a
Warehouse-First Data
Analytics Stack
Suhail Doshi, founder of Mixpanel, once said: "Most of the world will make decisions by either
guessing or using their gut. They will be either lucky or wrong." For organizations to make
intelligent business decisions, like deciding what new business opportunities to pursue or how to
reduce customer churn, they need data.
But, according to Gartner's 2020 Analytics Survey, the reality is that, even with data, most
companies are still unable to derive meaningful business insights from their analysis. The reason?
Very few companies correctly combine disparate sources of data from marketing, sales, product,
and finance teams and rarely apply the business context to sampled data. However, adopting a
warehouse-first data analytics approach overcomes these issues by centralizing all of your
company's data in one location, which allows you to have complete control and access to all your
data.
WHAT EXACTLY DOES “WAREHOUSE-FIRST” DATA ANALYTICS MEAN?
A warehouse-first data analytics stack is an analytics stack that has a single data repository
(otherwise known as the data warehouse) that all customer data is fed into. Analytics tools (e.g.,
Google Analytics) and other customer tools (e.g., Salesforce) used within organizations create
data silos and cannot communicate with each other. This makes it challenging for organizations
to access all the data they need to make complex decisions.
By gathering all of your company's data into a single location, you're able to build efficient
analytics on large, diverse, high-quality datasets to answer questions that your analytics tools are
not able to answer on their own.
For example, suppose your company is a startup in its early stages. In that case, you're probably
just trying to understand how customers perceive or interact with your product, so you likely have
questions like:
● How many active users did we have this month?
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