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11 Sep 2020
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Overview

In February 2020 we had a quick Q&A with Max. Here’s some of what he had to say:

 

Can you tell me more about the problem we’re trying to solve for our clients? 
  • The easiest way to describe it is that for-purpose organistions basically work on educated guesses and cross their fingers. They have no way to currently know what is or isn’t contributing towards positive OUTCOMES and CHANGE the problem of the beneficiaries they serve, or have the insights to be able to know why. (either individual, parts of programs, programs overall or between programs)
  • isgood enables evidence of outcomes. This results in the number of activities and the percentage of money spent on admin are no longer the only measures.
  • For the most part, this problem is completely hidden (as it’s stupidly embarrassing)
  • There’s a whole heap of issues around this, which we can positively affect.
  • For obvious reasons there are very few public studies/info admitting this.
    note: that while problem is known, as there is no solution, people/orgs are indoctrinated in terminology or approach that is “commercial” and evaluate impact at a dollar value 🙁
Is data the solution? I mean, programs fail to meet their goals for a variety of reasons that organisations already know about – better data & insights won’t change that…?  
  • If we can notify them in real time WHY, they have a chance of not failing … but, if they are completely incompetent, don’t change or the program is shown to not be effective, then they will still fail.
  • Meaningful and actionable insights, can save a program and lives.
Impact measurement is such a hot topic. People want this for a variety of reasons. Do we have a quick overview of how isgood meets this/ doesn’t meet these needs?
  • We do NOT measure, we are not a thermometer (or form tool)
  • We enable an organisation/program to optimise beneficiary outcomes. The platform helps to inform them of the data required, does a gap analysis and enables them to get started straight away, focussed on OUTCOMES, to be able to identify the evidence of what may be impacting the ability to create the change they wish.
  • This gap analysis is like a data maturity assessment, which enables them to get started while having a clear idea of how to improve their evidence base, so as to keep improving.
  • We can pull data from all current sources, with almost no need for change/work on those data sources, and have recommended measurement tools
  • We can pull in the data to then move beyond measurement, evaluation and management … to achieve improvement, optimisation and create systemic and sustainable change.
Is there a product roadmap ‘for dummies’ somewhere?
  • “product roadmap for dummies” is not yet available, but is on the list of “to do” items
I don’t need to understand the detail of how the tech works, nonetheless I’m still curious. But I’m far from technical. So…What makes this so complicated?
  • The millions of relationships across the millions of projects and millions of data points and millions of cause and effect relations in the world … and having to understand all of those and how they relate or affect each other towards the millions of human/social/humanitarian issues facing the world … and understanding the gaps and differences and the WHY of those, to be able to provide insight onto how we can improve beneficiary outcomes now … and also create systemic change in the future.
  • It’s like mapping the universe and understanding the relations and (seen, unseen, known, unknown) effect and relation between every single atom/planect/object to every other one, simultaneously.
How does it work to forecast/ predict outcomes? 
  • Nothing can predict the future.That’s sorta magic … but, we can sorta emulate magic
  • We can do evidence based pattern recognition and pr-event insights and notifications … the more projects and data we get, the more accurate this becomes (improves over time).
  • Once this time elapses and this gets better, we can run simulations (predictive and prescriptive modelling) of programs/initiatives and have high probably of “right fit for right place and problem” and basically play sim-earth on global humanitarian scale …. we are NOT there yet. 
Why haven’t others done this yet (apart from the fact that it’s hard to do)?
  • Until recently, wasn’t technically possible … and we even had to pause on fourth build as database/I theory was unable to handle our requirements (but we got it now).
  • Is in reverse to basically every other data, M&E and AI system … as we are not based on extractive econcomics/capitalsit theory  (needs to work in reverse to understand effect and value of input).
  • No one else has worked out how to do it … took me over 6 years R&D and onto 4th tech build .. and on the cusp of what is possible with cloud compute and AI.
What response have you had from NFPs re sharing data? And are they using comparable metrics?
  • We overcome issues in that NO data is shared, no data-source stored and no data-pre-processing, extra storage compilation or preparation required
  • None of the metrics/ frameworks are comparable in any way, they are all completely different (as is all the data impacts, etc)
  • That is part of our brain, in that the AI works out the relations (on a number of layers) of the metrics, data, standards, frameworks and research to understand how every one relates to every one.
  • All data becomes comparable to all data. all metrics becomes comparable to all metrics, all standards becomes comparable to all standards … and relation and cause and effect attribution of every single one, to every single other one is known and able to drive the insights of what is affecting what and why, so as to inform how to get better outcomes for the project. 
How is a geographic area determined for projects and data sharing?
  • It’s both in the project settings and the data 

An interview

In August 2020 Max was asked to explain how the isgood platform works to a non-tech audience. Here’s what he said:

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