Making it Easy to Access Data
Analytics transformation requires behavior change.
BJ Fogg, PhD, a Stanford behavioral science researcher and author of Tiny Habits, has created a simple, yet powerful model of how to change behavior.
If users are more motivated and the desired action is easier to do then the behavior change is more likely to happen.
I’ve applied his Behavior Model to data analysis transformation.
When leaders make data easier to access and motivate their teams to analyze data then more decisions will be based on data analysis.
[ This is Part 3 of the Analytics Transformation series ]
Before you can understand why a user does something you must know what they are doing.
This starts with collecting the right data and making access to this data easy.
If you always need an engineer or a data analyst to unlock the secrets of your data, it is not easy to access your data.
When possible, use off-the-shelf tools. Teams that DIY their analytics will collect data but I rarely see them dedicate engineering time to export and analyze the data.
Concrete steps to make data access easy:
Collect. Collect. Collect.
Data collection should be a part of each launch
New products, even MVPs should launch simultaneously with analytics tagging
Provide self-service access
Use an off-the-shelf tool for collection, storage and analysis of web/mobile user activity (Google Analytics, Heap, Amplitude)
Use an off-the-shelf tool for collection, storage and analysis of satisfaction metrics (Delighted, Qualtrics, Survey Monkey, Typeform)
Use an off-the-shelf for replaying individual sessions (FullStory, MouseFlow, Pendo, WalkMe)
Create an analysis-only database for business metrics
As necessary, anonymize customer personal information
Aggregate multiple data sources into a single location (data warehouse)
Purchase and evangelize tools to access business metrics (Snowflake, holistics.io, PowerBI, Tableau, SQL, Excel/Google Sheets)
Give out credentials to the core discovery team so they can self-serve data and analysis (PM, Designer, Lead Engineer)
Get help
Train teams to use these tools
Specify one engineer to be the connection to the data
Find analytics-savvy team members that can be “data ambassadors” to evangelize data analysis across the organization
Hire a data analyst(s) dedicated to the Product organization
Borrow time from a financial analyst in the company to train team members on introductory analytical skills
Leaders can use these techniques to change behavior and avoid the “analytics-free” environment.
Jim coaches Product Management organizations in startups, scale ups and Fortune 100s.
He's a Silicon Valley entrepreneur with over two decades of experience including an IPO ($450 million) and a buyout ($168 million). These days, he coaches Product leaders and teams to find product-market fit and accelerate growth across a variety of industries and business models.
Jim graduated from Stanford University with a BS in Computer Science and currently lectures at UC Berkeley in Product Management.
