How to Cross the Analytic Divide and Democratize Data
How can agencies close this divide between the people who can and cannot analyze data?
Posts Tagged: Alteryx
How Automated Analytics Can Fuel Digital Transformation
“We’re helping states rebuild critical unemployment systems with analytic automation, enabling agencies to deliver on their mission and get critical support to citizens.”
What Agencies Achieved by Automating Data Processes
By automating analytics processes, agencies could conduct more efficient audits, identify water leaks faster, save over $1 million in duplicate payments and afford a faster recovery after a natural disaster.
Time, Staff and Skills Among Challenges Facing Gov Data Departments
Central data offices across government are outnumbered in staff, time and budget. Here’s what they’re trying to do about it.
What Do You Do When You Don’t Have Data Unicorns?
A data unicorn is a mathematician, data scientist and storyteller — someone who is mathematically strong, technically learned and narratively inclined. It’s exceedingly rare to come across them.
6 Ways Analytics Accelerated Communities’ COVID-19 Responses
Six use cases for how agencies can use self-service analytics to empower their teams and accelerate the COVID-19 response and recovery.
6 Ways Analytics Can Accelerate Your Community’s COVID-19 Response
Analytics should be a key part of the pandemic response, from tracking pandemic surveillance to verifying the availability of critical services.
It’s a Data Jungle Out There
Agencies might want to take the data problem head-on, but with so much data and so few dedicated staffers, where can they begin?
Don’t Start With Data, Start With Questions
Asking the right questions — strategic questions — is an essential starting point for agencies to be data-driven in a mission-driven way everyday.
The Value of Self-Service Analytics
The emergence of advanced self-service analytics platforms like Alteryx means people now have access to greater information and the power of insight, ultimately giving data workers more time to analyze than to prep.