Overview of Proposed Work

High Level Goals:

● Resolve Data Chaos or Bottleneck in Analytics

Brief on Strategies:

● Start by collecting stories instead of requirements● Identify the common denominator across org. needs● Standardize the modeling layer, changing the physical layer as needed● Empower people through training and POC discoveries● Work with departmental leads to introduce newly centralized data into their

self-service workflow

Sample technical recommendations and case studies

1/12by Caura, Inc.

A Few Words on Assumptions

2/12

“Existing SQL queries and summarized emails communicate more about org. metrics than newly gathered requirements: how people actually use data vs. how one imagines data to be used”

PEOPLE CLOSEST TO DATA ARE MOST KNOWLEDGEABLE ABOUTS ITS MEANING

“Data warehouse and model designs should be driven by end user use cases, not by the messy reality of production databases”

DATA WAREHOUSES ARE FOR ANALYSTS - NOT FOR PRODUCT ENGINEERS

“Data discovery and reporting must be incorporated into existing workflows - be that IM, email, dashboards, or Excel”

PEOPLE MAKE DATA-DRIVEN DECISIONS ONLY WHEN LEADERSHIP USES THE SAME DATA

by Caura, Inc.