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Το περιεχόμενο παρέχεται από το Sam Knowles. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Sam Knowles ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.
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How is it possible to understand everything that the world’s Governments want to buy? With Ian Makgill

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Manage episode 407526790 series 3562888
Το περιεχόμενο παρέχεται από το Sam Knowles. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Sam Knowles ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.

In this episode of the Data Malarkey podcast, data storyteller Sam Knowles is joined by Ian Makgill, the Founder of Spend Network. Ian and his company are on a mission to improve the global public sector procurement market. Spend Network’s website boldly claims that it can help users to “Unlock the $13 trillion global procurement market through the world’s leading tender, contract, spend and grant data”. That’s about 13% of the total global economy.

Throughout his career – building databases for 20 years and working with AI for six – Ian has been a passionate believer that data can shape our world for the better. While it often feels as if data is used to point at bad stuff that has happened or show where everyone is failing, Ian is committed to telling stories of how his organisation is using data to shape the future.

Our conversation was recorded remotely, via the medium of Riverside.fm, on 29 November 2023.

Thanks to Joe Hickey for production support.

Podcast artwork by Shatter Media.

Voice over by Samantha Boffin.

As the driving force behind Spend Network, Ian’s ambition is to level the playing field of Government procurement – from “haircuts in Mexican prisons to airports in China”. As a consequence, every moment of his every working day is steeped in data. Unruly, different, misaligned, fundamentally different data that very definitely is not “apples with apples”. At least when the Spend Network team get their hands on it, bringing together more than 700 diverse sources each day.

“All data is bad; all data is dirty!” observes Ian, “though most of it can be made to be useful”. His sentiment echoing the maxim from the British statistician, George Box, that “All models are wrong; some are useful.” Ian also has elements of the forensic scientist about him, with his observation that “the absence of data is a data point in himself”, bringing to mind our 25 October guest, Professor Angela Gallop, and her encouragement to go looking “when the dogs DON’T bark”.

Spend Network has so far analysed, cleaned, augmented, validated, and verified 220m lines of spend data from hundreds of Government departments around the world. And he and his data wranglers don’t just apply data science smarts to their heavyweight data. They’ve been using AI since 2017.

For Ian, The Financial Times’ John Burn-Murdoch – the paper’s Chief Data Reporter – is a hero of data storytelling and data visualisation, skills that he honed during the pandemic. Burn-Murdoch was the first to conceptualise and visualise excess mortality as the key indicator of Government success (and otherwise) in measures to tackle COVID. Jacob Rees-Mogg is his data devil, thanks to the politician’s imperial measures consultation that provided no option to object (reported here in The Guardian).

EXTERNAL LINKS

Ian’s LinkedIn profile – https://www.linkedin.com/in/ianmakgill/

Spend Network – https://spendnetwork.com

OpenOpps – https://openopps.com

Spend Network on Twitter / X – https://twitter.com/SpendNetwork

To find out what kind of data storyteller you are, complete our data storytelling scorecard at https://data-storytelling.scoreapp.com. It takes just two minutes, and we’ll send you your own personalised scorecard which tells you what kind of data storyteller you are.

  continue reading

40 επεισόδια

Artwork
iconΜοίρασέ το
 
Manage episode 407526790 series 3562888
Το περιεχόμενο παρέχεται από το Sam Knowles. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Sam Knowles ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.

In this episode of the Data Malarkey podcast, data storyteller Sam Knowles is joined by Ian Makgill, the Founder of Spend Network. Ian and his company are on a mission to improve the global public sector procurement market. Spend Network’s website boldly claims that it can help users to “Unlock the $13 trillion global procurement market through the world’s leading tender, contract, spend and grant data”. That’s about 13% of the total global economy.

Throughout his career – building databases for 20 years and working with AI for six – Ian has been a passionate believer that data can shape our world for the better. While it often feels as if data is used to point at bad stuff that has happened or show where everyone is failing, Ian is committed to telling stories of how his organisation is using data to shape the future.

Our conversation was recorded remotely, via the medium of Riverside.fm, on 29 November 2023.

Thanks to Joe Hickey for production support.

Podcast artwork by Shatter Media.

Voice over by Samantha Boffin.

As the driving force behind Spend Network, Ian’s ambition is to level the playing field of Government procurement – from “haircuts in Mexican prisons to airports in China”. As a consequence, every moment of his every working day is steeped in data. Unruly, different, misaligned, fundamentally different data that very definitely is not “apples with apples”. At least when the Spend Network team get their hands on it, bringing together more than 700 diverse sources each day.

“All data is bad; all data is dirty!” observes Ian, “though most of it can be made to be useful”. His sentiment echoing the maxim from the British statistician, George Box, that “All models are wrong; some are useful.” Ian also has elements of the forensic scientist about him, with his observation that “the absence of data is a data point in himself”, bringing to mind our 25 October guest, Professor Angela Gallop, and her encouragement to go looking “when the dogs DON’T bark”.

Spend Network has so far analysed, cleaned, augmented, validated, and verified 220m lines of spend data from hundreds of Government departments around the world. And he and his data wranglers don’t just apply data science smarts to their heavyweight data. They’ve been using AI since 2017.

For Ian, The Financial Times’ John Burn-Murdoch – the paper’s Chief Data Reporter – is a hero of data storytelling and data visualisation, skills that he honed during the pandemic. Burn-Murdoch was the first to conceptualise and visualise excess mortality as the key indicator of Government success (and otherwise) in measures to tackle COVID. Jacob Rees-Mogg is his data devil, thanks to the politician’s imperial measures consultation that provided no option to object (reported here in The Guardian).

EXTERNAL LINKS

Ian’s LinkedIn profile – https://www.linkedin.com/in/ianmakgill/

Spend Network – https://spendnetwork.com

OpenOpps – https://openopps.com

Spend Network on Twitter / X – https://twitter.com/SpendNetwork

To find out what kind of data storyteller you are, complete our data storytelling scorecard at https://data-storytelling.scoreapp.com. It takes just two minutes, and we’ll send you your own personalised scorecard which tells you what kind of data storyteller you are.

  continue reading

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