What is the data driven organisation? What is robotics business, what does that involve, the evolution of robotics in business, what does that encompass, and where we’re at now. And if we can discuss from your perspective, what are the aspects you feel it necessary to explore?

From my perspective, our concern and some of the work that we’ve been doing is to 1) build data-driven organisations, and secondly to understand the drivers of trust and community building and that comes from a philosophy that basically says the challenge going forward is to build sufficiently sustainable human systems using the technology that we have at hand.

If you look at the explosion of data that digitisation has caused and the acceleration of that data transmission, that aperture, it’s accelerated so much and so fast that it’s effectively overwhelmed the filters that human systems created to derive authority, trust, belief, a whole range of things. So what’s happening at the moment is we’ve got a situation where there are a whole lot of people who don’t understand how the digital systems work and operate, and you find the trust being eroded in many of the areas so when we’re looking at that, we’re looking at OK how do we treat data with dignity? And ensure that there’s equity in the participation of how that data might be used or enriched so that people understand the choices that they’re making when they participate. That there shouldn’t just be a case of a one-sided view of that data. So we’re coming to it from a more humanist angle – we believe that there are a number of clear issues that are beginning to surface in the way humans exchange data and the usage of that data, and the value systems that are being affected by that.

There’s a lot of static in the air, and that comes from the fact that actually, to talk about becoming a data driven organisation, effectively, it means becoming a digital organisation and one of the things we’ve got to do is have the courage to change and part of that change is having the courage to sweat the little things. Data means, here are 10 things you can do something about, you actually have to look at all of those 10 things. The challenge with doing that properly is that the only way to do that effectively is to very rapidly broaden and open up the aperture of information processing through the organisation, which happens to be your people – that means pulling the next challenge forward which means having the courage to diversify your data set and information flow – so most clients pause right there, they can’t make that change so they focus on iterating process and really it’s not about the process. In our industry, where we come from, management systems have had to change from the traditional hierarchical drop-down model to a more collaboration-driven, agile, very rapidly twisting time model, so in that really you don’t have time to send it upstairs so someone can think about it, you’ve got to cut the shit and get on with it. There has to be a strong sense of trust in that environment and trust in the data flow so when you recurse that into society as a whole, we’re basically now struggling with a situation where some organisations are changing and adopting the markers of that change and others are negating

Most people don’t think about that – they think about the logistics of employing data scientists, building models…

The problem is that this comes from our own frustrations right? It’s like hey, don’t you see this pattern? The fundamental point of departure is and Arthur C Clarke put it best – Any sufficiently advanced technology is indistinguishable from magic, now here I come as a data scientist or a technologist or a data-driven individual and tell you that the data says that if you want to be an effective team, you got to throw away these prejudices you currently have in your organisation about who you think is effective and at what, now you better believe me and you better trust me because that is the only thing you have to go on because the rest of it you have no clue what I’m talking about – its’s indistinguishable from magic. And that’s our challenge as an industry, because actually we haven’t exactly covered ourselves in glory, when it comes to trust.

How do we go about building that trust? 

You’ve got to put some topics on the table, you know the things that should not be talked about that need to be talked about and that’s only going to come, particularly from the African perspective, from an emerging market perspective, from teams that are currently dealing with these realities and part of that is the willingness to listen when those far corners of the data pool say – here’s some revelation we can bring on the subject.

I’ll give you a very simple example, Facebook has a habit of testing algorithms in I guess what they would call ‘markets…” To me it looks like hey lets experiment in this 3rd world country, and then they take it larger than that but I guess they’re just thinking well lets just try this out in a data set – I think the data set in downtown Palo Alto is more diverse and denser than the entire country of Namibia – the thing is they don’t choose to run this test of their algorithm in downtown Paulo Alto – they run it in the country of Namibia. And you’ve got to ask yourself so – if this algorithm causes some kind of damage or lack of trust in communities because fake news starts getting injected and and and … As it turns out was the case, what about this human collateral damage you’re leaving behind? And who said you can do that anyway, actually? And that comes down to when we are modelling this data and when we are gathering this data, how we’re looking at this as people, as humans, first.

So its almost like there’s this whole other realm of government we haven’t even spoken about that will be needed going forward.

Absolutely – there are a few obvious things here right – you can debate it as much as you want but as data scientists, we can agree, 1 – if we can mesh into an open network of data sharing and modelling, it would exponentially explode the processing power and problem. You know what would happen for the next 25 years? … would argue that no such system exists. That’s what it is until the bell curve explodes so far to one side that…

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