This article is an extract from Our Community Matters
Galaxy formation? That’s nothing. Try understanding the impact of grants.
Dr Paola Oliva-Altamirano, an astrophysicist, astronomer, and specialist in the creation of galaxies over billions of years, has turned her attention to something harder – data science for the social sector.
Just days after her appointment to Our Community’s Melbourne-based Innovation Lab, Dr Oliva-Altamirano’s name was put forward for a tricky new mission: to decipher the impact of nearly 300,000 grant applications.
Only eight data scientists would be selected for the prestigious six-month Uptake.Org Data Fellows program in Chicago, an all-expenses-paid exploration of some of the toughest data quests in the social sector.
Uptake, a technology pioneer that rocketed from start-up to US$2 billion firm, is working with data leaders in not-for-profits and similar organisations through Uptake.Org – its philanthropic and civic innovation arm – to help workers to hone “hard skills in data science”. The company provides invaluable coaching and connections for those fellows.
Dr Oliva-Altamirano’s proposal, which was one of 130 applications from data scientists across the globe, came in two parts:
The Innovation Lab’s proposal rose above the pack, partly as a result of Dr Oliva-Altamirano’s experience as a dedicated number cruncher, coder and problem solver.
The lab’s need to deal with a huge stack of data, and the potential benefits for the sector in extending Our Community’s CLASSIE taxonomy (the Classification System for Australian Social Sector Initiatives and Entities), also helped convince Uptake to support the lab's plan.
Our Community's project will be the only Australian one on Uptake's books. While the fellowship covers the costs of flights to the US, accommodation and more, the value of the input of three expert consultants who will help the plan come to life is much harder to quantify.
Dr Oliva-Altamirano says while her move from the vast gulf of space to the tricky field of grants management might seem unusual, her background in physics, computing, coding and problem solving required the same tools used in social data science.
“In the end, galaxies are a series of dots out there in space … a series of numbers. So too, grants are all about the numbers, and of course modelling.
“My thesis was looking into how a galaxy grows through certain characteristics – age, mass, the metals present … These are all numbers that create a pattern that you can use to predict.
“You can also apply similar methods to ‘people data’ and use patterns to predict and test your model.”
Dr Oliva-Altamirano says she has already learnt a great deal from a trio of highly qualified mentors based in the Uptake labs, including its top data scientist, Jay Qi; a software engineer specialising in handling big databases, Birchard Hayes; and social data academic and algorithm expert Momin Malik.
These experts selected her project, she was pleased to find out, because they thought they could help and learn at the same time.
And she says everyone's expectations are quite open.
“What they really want from us is that we learn something totally new.”
But Uptake also expects the work should help Our Community to increase the power of its social impact, and to improve its ability to develop these kinds of projects.
Dr Oliva-Altamirano says the social sector lags technologically behind other sectors in the US, in much the same way it does here.
But her mentors and fellows have helped her realise what can be achieved by community organisations equipped with the latest data models, methods and computing software.
She hopes the fellowship will lead to a product that can be used by anyone in the grants sector.
Her study will also delve into algorithmic “bias”, where data has been skewed by wrong assumptions, such as gender bias, she says.
Dr Oliva-Altamirano expects her latest mission to be much tougher than trying to unravel the secrets of the universe.
“With galaxies you can be plus or minus a billion years in your calculations. You certainly can’t afford making anything like those errors with this data."
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