Recently, I was working on a data analytics project and needed to get confirmation on some of the data I was working on. I contacted my subject matter expert and we needed to exchange data. He was using one kind of software and I was using another. It took a few minutes to figure out how to export my data into a format that he could use. Luckily, data formats are more easily exchangeable than in years gone by, but it highlights the ongoing issues of sharing data and the hurdles we need to go through to share our data.
Sharing data is really important to the value of data. Data is like money - in many ways data is the new currency - the value of data goes up the more it circulates. If I have a data set that works for me - that is good. However, the value of that data goes up exponentially the more it is shared and referenced. This is a crucial component of creating impact, especially in public sector science based organizations.
In the areas of climate change research, public health, food safety, and so many more, the ability to collect, analyse, publish and share data is a key success factor to increasing the impact of data to contribute to positive outcomes.
The COVID-19 pandemic has been a perfect example of leveraging data and increasing the value. Never before have so many scientists and health experts gathered so much data and shared it so quickly to be able to hasten the response to COVID-19. The ability to rapidly develop vaccines was due to the rapid sharing of the virus genome. The ability to rapidly enact social measures to limit spread was due to the ability to gather and share infection data and trend analysis. As it seems that COVID-19 mutates and evolves quite rapidly - this ability to gather, analyse and share data will be essential in the ongoing management of the public health response over time.
What are the key success factors that made the pandemic response so effective;
- data was gathered quickly
- data was shared in repositories that made it easy to access, download and use
- data was shared in open formats that allowed ease of use in different analytical packages and processes
- analysis results were also shared in open repositories for use by others
- data analytical processes were also shared in open repositories and the analytical methods were easily copied and replicated for use with local data
All these open data approaches continues to contribute to an enhanced ability to make data informed decisions and contribute to data driven impact.
Imagine if we took the same open data approaches and applied them to the data we use every day in our organizations and with stakeholders. The value of our outputs and the value of our impact would increase tremendously. What's stopping you from sharing your data?
Let me know how you use these key success factors in the comments below, and any additional key success factors you have observed.
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