DataOps for Government

A work in progress guide to applying DataOps principles for Governments:

Goal of DataOps for Government:

The continuous delivery of insight through the efficient use of data.

A working definition of DataOps for Government:

DataOps is a way to organize the people and processes involved with data that promotes communication between, and integration of, formerly siloed data, teams, and systems. It takes advantage of process change, organizational realignment, and technology to facilitate relationships between everyone who handles data. DataOps closely connects the people who collect and prepare the data, those who analyze the data, and those who put the findings from those analyses to good use. -Note: this definition was adapted from Ashish Thusoo's definition found here

DataOps Principles for Government:

In no particular order (and not exhaustive) - inspired by and borrowed from the DataOps Manifesto

• Everyone involved in the data pipeline must know how data are being used within the organization. The person collecting/entering data must know that it is being aggregated/analyzed down the road; why & for what purpose

• Data must be re-usable, easily

• Everyone is equally valuable – You can’t make that fancy dashboard without quality data

• It is interdisciplinary - working with colleagues from different backgrounds allows us harness the work in those fields. We can go farther together.

• Data/Analytics is a process not a product, DataOps must focus on process-thinking aimed at achieving continuous improvement both in terms of data quality, but also analytics quality; ultimately leading to organizational/functional improvements and outcomes

• Frequent, face-to-face communication is a requirement

• Deliver simple, incremental insight first – Don’t set out to develop a dashboard or reproduce a report. Answer simple questions first, then build on those. i.e How many inspections have been scheduled this week? What are the most common violations?

• Use existing tools first – There’s no need to purchase additional software or special tools until you’ve determined how they add value to your data analytics process

• Frequent reflection and feedback, to all team members’ is critical. Be positive when things go well, be constructive when things need improvement

• Be open: the decisions we make using data affect people. Thus we must, to the extent legally possible, make the data, analysis, and methods we used accessible and reproduce able.

Getting Started with DataOps for Government

• Form a DataOps Team

• Understand your current process

• Conduct some simple analysis

• Evaluate the "data pipeline"

• Answer simple questions easily and repeatedly

• Identify and address data quality issues

• Add more complexity to questions

Here are some freely available tools to help just in case you need them (work in progress)

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)