WEBVTT - How to put your data to work, plus tech under Trump

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<v Speaker 1>It is now clear that we've achieved the most incredible

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<v Speaker 1>political there.

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<v Speaker 2>Look what happened?

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<v Speaker 3>Is this crazy? But it's a political victory that.

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<v Speaker 1>Our country has never seen before, nothing like this.

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<v Speaker 2>Look what happened. Indeed, Donald Trump is heading back to

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<v Speaker 2>the White House after a stronger than expected showing in

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<v Speaker 2>the presidential election, with the Senate and the House of

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<v Speaker 2>Representatives destined to also be in Republican control.

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<v Speaker 3>Which has major implications for Trump's policy agenda, including a

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<v Speaker 3>host of tech created issues from AI regulation to cryptocurrencies,

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<v Speaker 3>data and privacy law reform, and the tech arms race

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<v Speaker 3>with China.

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<v Speaker 2>This week, on the Business of Tech, powered by Two

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<v Speaker 2>Degrees Business, we look at what a Trump administration means

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<v Speaker 2>for tech, rold at influential tech billionaires and platforms played

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<v Speaker 2>in the election campaign. I'm Peter Griffin and.

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<v Speaker 3>I'm Ben Moore. Coming up on the show as our

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<v Speaker 3>featured guest. Tony Shore, the New Zealand country manager for Snowflake,

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<v Speaker 3>a company that isn't as well known as Microsoft, AWS

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<v Speaker 3>or Google, but is working with those companies and a

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<v Speaker 3>rapidly growing roster of Kiwi companies to help them store

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<v Speaker 3>and manage their data, run data analytics and use AI.

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<v Speaker 2>Yeah, Tony has some great advice for companies eyeing up data, analytics,

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<v Speaker 2>machine learning, and AI, which is really about the importance

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<v Speaker 2>of getting your data house in order before you delve

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<v Speaker 2>into these things. So stick around for Ben's interview with Tony.

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<v Speaker 2>But first we need to sift through the ashes of

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<v Speaker 2>Wednesday night's election results. Ben, we weren't actually going to

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<v Speaker 2>talk about the election on this episode because normally we

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<v Speaker 2>record the podcast on a Tuesday afternoon, which was just

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<v Speaker 2>before the election. Terrible timing, but yes.

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<v Speaker 3>But I happen to you got quite sick on Tuesday night,

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<v Speaker 3>so we pushed the publication of the Business of Tech

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<v Speaker 3>out a day, which is why you're hearing this on a.

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<v Speaker 2>Friday, Yeah, which sort of means we can reflect on

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<v Speaker 2>what went down and look at what Trump may have

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<v Speaker 2>in store on the tech front. And you know, I

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<v Speaker 2>think before we get into that, we should really talk

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<v Speaker 2>about the influence that tech billionaires and their platforms have

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<v Speaker 2>had on this presidential election. I mean it's pretty clear

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<v Speaker 2>in his victory speech from Florida, Trump very much talking

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<v Speaker 2>about Elon Musk. You know, last time around, Elon Musk

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<v Speaker 2>was relatively close to Trump. I remember that iconic meeting

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<v Speaker 2>where he basically called in all the heads of the

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<v Speaker 2>tech companies to Trump Tower and sort of had a

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<v Speaker 2>chat with him. Peter Thiel was there, I think Tim

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<v Speaker 2>Cook from Apple was there. Elon Musk was there, so

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<v Speaker 2>it all seems sort of quite Trump appointed him to

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<v Speaker 2>a couple of advisory councils and Musk ended up quitting them,

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<v Speaker 2>so there was a bit of a falling out between

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<v Speaker 2>him and Trump. He's really rekindled that relationship, and you

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<v Speaker 2>know Elon Musk, I think he sees as being quite

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<v Speaker 2>key to the success that he's had, so he'll need

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<v Speaker 2>to repay Elon Musk. Just talk about Musk taking some

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<v Speaker 2>sort of role the Department of Government Efficiency DOGE, so

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<v Speaker 2>I think this will be pivotal. You've also got Musk

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<v Speaker 2>clearly has his fingers in so many different pis, so

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<v Speaker 2>many different companies, so lots of scope for conflicts of

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<v Speaker 2>interest there. And of course with X and I think

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<v Speaker 2>we saw this definitely the morphing of X in the

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<v Speaker 2>last year or so during that campaign really into a

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<v Speaker 2>conservative stronghold that's also been influential. So all of these

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<v Speaker 2>things are intertwining. I think in Trump's favor.

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<v Speaker 3>Yeah, it definitely has been a swell, a more public

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<v Speaker 3>swell of support for Donald Trump from that kind of

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<v Speaker 3>tech elite in the last over the selection. And I

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<v Speaker 3>think it speaks a little bit to the way maybe

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<v Speaker 3>that Trump does approach these these kinds of companies where

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<v Speaker 3>he wants their favor, He wants them to kind of

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<v Speaker 3>be on his side, and as in a return, he'll

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<v Speaker 3>be on their side. And we've seen him, We saw

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<v Speaker 3>him use these kind of anti monopoly laws to kind

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<v Speaker 3>of put pressure on the ones that maybe weren't as

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<v Speaker 3>vocal of support for him in the past. Not to

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<v Speaker 3>say that there's a direct link there, but it's hard

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<v Speaker 3>to not see some kind of correlation at least. So

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<v Speaker 3>Elon Musk, with his ambitions for Tesla and for SpaceX

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<v Speaker 3>and for a lot of the defense contracts, must be

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<v Speaker 3>really glad to be in Trump's good graces now, especially

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<v Speaker 3>because Trump has talked quite a lot about increasing the

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<v Speaker 3>amount of private companies contracting to defense and working and

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<v Speaker 3>spending a lot more money on defense, which will mean

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<v Speaker 3>more money in the pockets of these tech companies working

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<v Speaker 3>on defense technologies.

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<v Speaker 2>Yeah, it's got to be good for SpaceX, I mean Tesla.

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<v Speaker 2>Trump has been very anti electric vehicle, so will he

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<v Speaker 2>now pivot to suddenly being influenced by Elon Musk along

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<v Speaker 2>the lines of, well, no, EV's actually makes sense. I'm

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<v Speaker 2>keen to support them. Whether there'll be more subsidies for evs,

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<v Speaker 2>and likely given Trump's interest in the liquid gold, you know,

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<v Speaker 2>the energy industry, the fossil fuel industry, but definitely on SpaceX,

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<v Speaker 2>very much in bed with the US defense, so there'll

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<v Speaker 2>be synergies there. And as yea, his other business obviously

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<v Speaker 2>x AI and Grock building, you know, the biggest AI

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<v Speaker 2>centric supercomputer stuff like that. I can see Musk sort

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<v Speaker 2>of putting proposals to Trump about what the government should

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<v Speaker 2>be doing with AI, and I think on a lot

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<v Speaker 2>of these issues, Trump really he's not a tech guy.

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<v Speaker 2>He doesn't really get this sort of stuff, but he's

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<v Speaker 2>very influenced by the people around him. So you've got

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<v Speaker 2>Musk there as a trusted advisor. You've got jd Vance,

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<v Speaker 2>who has a previous life in venture capital, worked with

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<v Speaker 2>Silicon Valley to fund companies, so he's very much embedded

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<v Speaker 2>with some of that tech elite. You've got Peter Thiel

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<v Speaker 2>who's a big backer of Trump as well. New Zealand citizen,

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<v Speaker 2>founder of Pallenteer and AI company that does a lot

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<v Speaker 2>of work for US agencies, police force, and military. There's

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<v Speaker 2>share prices surging at the moment on the back of

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<v Speaker 2>their latest results, all driven by AI. We've got this

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<v Speaker 2>sort of cluster of people in Trump's orbit who have

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<v Speaker 2>strong ideas about where the tech world should go, and

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<v Speaker 2>he's listening to them and he owes them. A lot

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<v Speaker 2>of them put money into these super packs to get

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<v Speaker 2>him into power. They didn't raise as much money as

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<v Speaker 2>Kamala Harris did in the Democrats, so it was all

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<v Speaker 2>a bit of a waste of time and money. But

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<v Speaker 2>he now has a lot of bills falling due, and

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<v Speaker 2>what is that going to mean for the flavor of

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<v Speaker 2>his policies. That's going to be the big question.

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<v Speaker 3>Yeah, I think a lot of it is going to

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<v Speaker 3>central around deregulation. Personally, I think that's going to be

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<v Speaker 3>a big flag that Trump will be waiving is getting

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<v Speaker 3>out of the way of these particularly those Silicon Valley companies.

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<v Speaker 3>I think the likes of Google and Microsoft may see

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<v Speaker 3>some continuation of those anti trust kind of approaches. But

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<v Speaker 3>when it comes to the those on the cutting edge

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<v Speaker 3>with AI and the ones that are kind of more

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<v Speaker 3>and Donald Trump's in a circle, will start to see

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<v Speaker 3>that a lot of deregulation there seems to be pretty

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<v Speaker 3>explicit in what he's been saying. And the same goes

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<v Speaker 3>for cryptocurrency as well.

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<v Speaker 2>Well. It's interesting on crypto. You know, Trump has done

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<v Speaker 2>a bit of a U turn. He was quite sort

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<v Speaker 2>of hawkish against crypto a few years ago, and again

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<v Speaker 2>I think this is people getting to him, and probably Musk,

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<v Speaker 2>who you know is a big fan of dogecoin and

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<v Speaker 2>is a crypto advocate as well, basically saying to him, no,

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<v Speaker 2>you need to support this. He's done a U turn.

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<v Speaker 2>He wants minimal crypto regulation. He'll probably limit the rather

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<v Speaker 2>hawkish moves by the SEC in the US to regulate

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<v Speaker 2>cryptocurrencies and those digital asset markets. So that's all well

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<v Speaker 2>and good, but is that going to lead to more

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<v Speaker 2>of the sort of FTX style implosions that we've seen.

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<v Speaker 2>He won't want that either. On deregulation, sure, last time

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<v Speaker 2>he cut tax and red tape. Businesses love that. It

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<v Speaker 2>means they can spend more money on R and D

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<v Speaker 2>and return more money to shareholders. So any company, particularly

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<v Speaker 2>those big tech companies that make a lot of profit,

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<v Speaker 2>they'll love that. But it was Trump after all, that

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<v Speaker 2>kicked off a lot of that antitrust stuff a few

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<v Speaker 2>years ago, So yeah, will he continue that and see

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<v Speaker 2>the breakup of Google and others. But I agree with you.

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<v Speaker 2>I think you know, he clearly doesn't like a monopoly.

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<v Speaker 2>He's a free market guy, that's his philosophy on this.

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<v Speaker 2>But he does want to see all of the red

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<v Speaker 2>tape and the restrictions removed from really innovative companies, and

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<v Speaker 2>at the moment they're the AI one. So I don't

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<v Speaker 2>see him carrying forward some of those executive orders around

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<v Speaker 2>AI that Biden put in place. I think we he'll

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<v Speaker 2>dial that back sognificantly.

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<v Speaker 3>Yeah. I think the other area where we're going to

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<v Speaker 3>see a big retraction in the US at least is

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<v Speaker 3>green tech. So if there were you know, we've had

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<v Speaker 3>a lot of eggs in the green tech basket here

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<v Speaker 3>in New Zealand with our startups, and that may indicate

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<v Speaker 3>that the US is no longer a viable entry point

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<v Speaker 3>for these companies to really scale. So maybe refocusing more

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<v Speaker 3>on the EU. If the US was kind of a

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<v Speaker 3>big part of your strategy.

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<v Speaker 2>Yeah, and you know there have been as part of

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<v Speaker 2>the Big Reconstruction Act that Biden passed after COVID, there

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<v Speaker 2>was green tech funding in there. So whether that will continue.

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<v Speaker 2>One area that will continue which Trump and the Democrats

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<v Speaker 2>are on the same page on as the semiconductor industry,

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<v Speaker 2>the Chips Act. So Trump is very much of the

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<v Speaker 2>view that we need more local production in the US

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<v Speaker 2>of semiconductors, the really high end important stuff that runs

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<v Speaker 2>ai to reduce reliance in the global supply chain on Taiwan,

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<v Speaker 2>which is very vulnerable to attack from China. So he'll

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<v Speaker 2>carry on that sort of stuff, things like five G.

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<v Speaker 2>You know, he's expressed his dismay that a lot of

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<v Speaker 2>that technology is provided by European companies. So again Biden

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<v Speaker 2>was on the same page. And I think for Trump

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<v Speaker 2>what it really all is about is taking on China

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<v Speaker 2>and that continuing sort of pressure, whether it's through the

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<v Speaker 2>form of taris or big tariffs on stuff coming from

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<v Speaker 2>China into the US sixty percent tariffs potentially, which is

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<v Speaker 2>quite staggering, but really that polarization of technology between the

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<v Speaker 2>Western world and the Chinese world, and We've seen China

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<v Speaker 2>in the intervening few years since Trump was out of office,

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<v Speaker 2>building its own operating systems, trying to generate higher capacity

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<v Speaker 2>semiconductors to go into phones and AI devices. Trump will

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<v Speaker 2>basically accelerate that further by putting more export controls on

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<v Speaker 2>the exports of high technology to China, tariffs and local productions.

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<v Speaker 2>So I think we'll just see an acceleration of that.

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<v Speaker 3>And that's also going to roll over to New Zealand

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<v Speaker 3>a little bit in terms of trade agreements. He's talked

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<v Speaker 3>about getting rid of the Indo Pacific Partnership trade Agreement,

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<v Speaker 3>which would impact New Zealand. So how that will interact

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<v Speaker 3>with New Zealand's tech exports to the US would not

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<v Speaker 3>one hundred percent clear at the moment, but you know

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<v Speaker 3>there is potentially some impact there.

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<v Speaker 2>Yeah. And the other sort of local angle I guess

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<v Speaker 2>is orcus. You know, this agreement this packed between Australia,

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<v Speaker 2>the US and the UK really about submarines, but you've

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<v Speaker 2>got orcust Pillar two, which is about other advanced technologies

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<v Speaker 2>like AI, like quantum computing, stealth technologies, you know, high

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<v Speaker 2>end military stuff. And I've been quite supportive of the

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<v Speaker 2>idea of New Zealand being involved in Pillar two. Not

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<v Speaker 2>necessarily around nuclear submarines or anything like that, but Pillar two,

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<v Speaker 2>these advanced technologies, we should have a hand with our

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<v Speaker 2>allies and developing those. And I think, you know, Orcust

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<v Speaker 2>has run into some trouble. I mean, this submarine deal

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<v Speaker 2>is so vastly expensive. Whether it will actually come to

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<v Speaker 2>fruition is anyone's guess. I think the Australians are starting

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<v Speaker 2>to realize what they've signed up for is massive. But

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<v Speaker 2>the other you know, you've got South Korean others Japan

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<v Speaker 2>are saying, hey, we want it on Pillar two because

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<v Speaker 2>they're starting to see some of the stuff that the Americans,

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<v Speaker 2>the Brits and the Aussies are working on and saying,

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<v Speaker 2>you know, we want to see that the table in

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<v Speaker 2>developing that stuff because there is a greater threat from China,

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<v Speaker 2>so let's work on this together. Trump will just carry

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<v Speaker 2>on thinking, I think around orcus he sees that as

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<v Speaker 2>a way to shore up support military support among allies

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<v Speaker 2>in the Pacific. Whether that will encourage New Zealand to

0:14:12.320 --> 0:14:15.840
<v Speaker 2>join or maybe will there be more pressure with a

0:14:15.880 --> 0:14:21.360
<v Speaker 2>new you right leaning ambassador in this country, Will there

0:14:21.400 --> 0:14:23.920
<v Speaker 2>be more pressure for New Zealand to actually put its

0:14:23.960 --> 0:14:26.360
<v Speaker 2>cards on the table and join Orcus. I think that's

0:14:26.400 --> 0:14:27.120
<v Speaker 2>a possibility.

0:14:29.440 --> 0:14:32.400
<v Speaker 3>It's hard to see exactly where we're going in terms

0:14:32.440 --> 0:14:36.080
<v Speaker 3>of the ramifications. You know, with the potential for a

0:14:36.120 --> 0:14:37.960
<v Speaker 3>Harris government, it was a lot more of the same,

0:14:38.920 --> 0:14:43.000
<v Speaker 3>but a Trump government because his rhetoric can be quite inconsistent.

0:14:43.320 --> 0:14:46.720
<v Speaker 3>You know, there is some stuff that we can guess about,

0:14:46.920 --> 0:14:48.840
<v Speaker 3>but at the end of the day, it's really going

0:14:48.920 --> 0:14:53.360
<v Speaker 3>to be just reacting as things happen. So it's going

0:14:53.440 --> 0:14:55.760
<v Speaker 3>to be really important to actually pay attention, I think,

0:14:55.840 --> 0:14:59.400
<v Speaker 3>to what is actually happening rather than what is being

0:14:59.520 --> 0:15:02.000
<v Speaker 3>said through Trump presidency, And if.

0:15:02.800 --> 0:15:07.320
<v Speaker 2>His last stint as president has anything to go by,

0:15:07.840 --> 0:15:10.920
<v Speaker 2>it'll be those key personalities around him because he really

0:15:11.000 --> 0:15:13.600
<v Speaker 2>is a bit of an empty vessel in terms of

0:15:13.640 --> 0:15:17.200
<v Speaker 2>his thinking on some of these issues, particularly around technology. Now,

0:15:17.320 --> 0:15:22.000
<v Speaker 2>just listening to him explain on election night, you know,

0:15:22.240 --> 0:15:26.560
<v Speaker 2>the starship returning to Earth, you know, when he was

0:15:26.600 --> 0:15:29.440
<v Speaker 2>praising you on Muscus, just clear he doesn't really understand

0:15:29.760 --> 0:15:32.840
<v Speaker 2>this stuff at all, which is fine, but it's the

0:15:32.840 --> 0:15:35.720
<v Speaker 2>people around him and the worry I think in the

0:15:35.800 --> 0:15:39.400
<v Speaker 2>US at the moment is you know, this paranoia about

0:15:39.440 --> 0:15:43.800
<v Speaker 2>the deep state in the US, this shadowy sort of

0:15:44.000 --> 0:15:48.280
<v Speaker 2>left leaning cabal that runs America that he's been trying

0:15:48.280 --> 0:15:52.160
<v Speaker 2>to root out. Is he just going to replace people

0:15:52.200 --> 0:15:57.000
<v Speaker 2>at the SEC, his top tech advisors, people responsible for

0:15:57.040 --> 0:15:59.400
<v Speaker 2>climate change policy. Is he just going to replace them

0:15:59.400 --> 0:16:03.640
<v Speaker 2>with political appointees who don't really care about the evidence

0:16:03.760 --> 0:16:07.200
<v Speaker 2>or the science or what technical advisors suggest is the

0:16:07.280 --> 0:16:09.600
<v Speaker 2>right thing to do. And he's just going to take

0:16:09.640 --> 0:16:13.480
<v Speaker 2>the advice off a small group of very wealthy, right

0:16:13.560 --> 0:16:17.640
<v Speaker 2>leaning tech elites who he owes big time because they

0:16:17.640 --> 0:16:21.640
<v Speaker 2>helped get him into office, you know, judging by past performance,

0:16:21.920 --> 0:16:24.120
<v Speaker 2>that's what he tends to do. He surrounds himself with

0:16:24.200 --> 0:16:29.040
<v Speaker 2>people who are loyal, but people he also relied on

0:16:29.120 --> 0:16:31.120
<v Speaker 2>to get into office first time round. I think we'll

0:16:31.120 --> 0:16:32.480
<v Speaker 2>see a lot more of that unfortunately.

0:16:32.880 --> 0:16:37.320
<v Speaker 3>Yeah, it really is about an exchange of wealth and

0:16:37.360 --> 0:16:41.600
<v Speaker 3>favors and keys and power and deals. It really is

0:16:41.640 --> 0:16:42.600
<v Speaker 3>all about the deals.

0:16:42.880 --> 0:16:46.800
<v Speaker 2>It's transactional with Trump, and people have advised you know,

0:16:46.840 --> 0:16:48.560
<v Speaker 2>if he does get in, you've got to treat it

0:16:48.960 --> 0:16:52.240
<v Speaker 2>as a transaction with whether you're negotiating what to do

0:16:52.320 --> 0:16:58.880
<v Speaker 2>with Ukraine or a trade deal with a country. It's transactional.

0:16:58.960 --> 0:17:02.080
<v Speaker 2>You need to be in that mindset dealing with this guy.

0:17:02.480 --> 0:17:04.719
<v Speaker 2>So maybe that's the approach that maybe we should take

0:17:04.760 --> 0:17:14.560
<v Speaker 2>as well. Absolutely, so clearly it's going to be an

0:17:14.560 --> 0:17:18.040
<v Speaker 2>interesting year. Head We'll keep you posted and give our

0:17:18.119 --> 0:17:22.080
<v Speaker 2>analysis on everything related to tech as the Trump administration

0:17:22.359 --> 0:17:26.879
<v Speaker 2>settles in. But Ben, whenever we interview companies around some

0:17:26.920 --> 0:17:29.600
<v Speaker 2>of these issues like AI, predictive analytics, and all the

0:17:29.600 --> 0:17:33.320
<v Speaker 2>cool things businesses can technically do these days with the

0:17:33.400 --> 0:17:37.639
<v Speaker 2>data generated by the businesses, we get the same surprising response.

0:17:38.080 --> 0:17:40.639
<v Speaker 3>Yeah, the conversation typically grinds to a halt, and we

0:17:40.680 --> 0:17:43.119
<v Speaker 3>are told that a lot of our businesses just don't

0:17:43.119 --> 0:17:45.840
<v Speaker 3>have their data in the right places, in the right

0:17:45.920 --> 0:17:47.399
<v Speaker 3>formats to do any of that.

0:17:47.920 --> 0:17:49.679
<v Speaker 2>So talking about it's a bit of a waste of

0:17:49.720 --> 0:17:52.240
<v Speaker 2>time if the basics really aren't done well.

0:17:52.160 --> 0:17:54.480
<v Speaker 3>Which is why we're hearing a lot more from companies

0:17:54.560 --> 0:17:57.560
<v Speaker 3>like Snowflake and data Bricks, companies that have emerged in

0:17:57.640 --> 0:18:01.080
<v Speaker 3>recent years to help organizations manage that data.

0:18:01.520 --> 0:18:05.720
<v Speaker 2>They're basically data warehousing and analytics platforms that try to

0:18:05.760 --> 0:18:08.480
<v Speaker 2>get all your data in one place, process it in

0:18:08.520 --> 0:18:12.600
<v Speaker 2>a uniform and secure way and interact with the various

0:18:12.640 --> 0:18:16.160
<v Speaker 2>applications you're using to run your business. I was actually

0:18:16.160 --> 0:18:18.360
<v Speaker 2>staying at a hotel in Awkant recently and found myself

0:18:18.480 --> 0:18:22.280
<v Speaker 2>walking into the middle of a Snowflake conference. It was

0:18:22.400 --> 0:18:23.679
<v Speaker 2>actually quite a big affair.

0:18:24.240 --> 0:18:27.040
<v Speaker 3>Well, data is a big affair now, it's big business

0:18:27.080 --> 0:18:30.800
<v Speaker 3>and Snowflake has around two hundred customers in New Zealand

0:18:30.800 --> 0:18:33.760
<v Speaker 3>to date. It did around one hundred million dollars in

0:18:33.880 --> 0:18:37.920
<v Speaker 3>revenue last year just across Australia and New Zealand, according

0:18:37.960 --> 0:18:40.760
<v Speaker 3>to its financial accounts filed with the company's office.

0:18:41.040 --> 0:18:44.840
<v Speaker 2>And spending on data, warehousing and platforms is really growing fast.

0:18:45.080 --> 0:18:48.000
<v Speaker 2>So Ben, this is a timely interview with Tony Shaw,

0:18:48.080 --> 0:18:50.280
<v Speaker 2>who's been around a tech industry for a long time

0:18:50.480 --> 0:18:56.480
<v Speaker 2>since at NCR, dell, IBM, MuleSoft as well. Let's listen

0:18:56.480 --> 0:18:58.560
<v Speaker 2>to your interview with Tony Shaw and come back for

0:18:58.640 --> 0:19:02.240
<v Speaker 2>some thoughts on the back end.

0:19:03.520 --> 0:19:05.639
<v Speaker 3>Thank you so much, Tony for joining us on the

0:19:05.680 --> 0:19:08.160
<v Speaker 3>Business of Tech podcast. It's really great to have you here.

0:19:08.640 --> 0:19:10.280
<v Speaker 3>Why don't we start with just a little bit of

0:19:10.400 --> 0:19:12.560
<v Speaker 3>background about who you are and what you do.

0:19:12.840 --> 0:19:14.840
<v Speaker 1>Oh fantastic, Heyn, Thank you so much for having us

0:19:14.840 --> 0:19:17.119
<v Speaker 1>on board today. My name is Tony Shaw. I'm the

0:19:17.119 --> 0:19:20.000
<v Speaker 1>country manager for Snowflake in New Zealand. I've been with

0:19:20.040 --> 0:19:22.679
<v Speaker 1>the company just coming up to six years now, so

0:19:22.920 --> 0:19:26.520
<v Speaker 1>quite a long time to be with one organization. But

0:19:26.560 --> 0:19:30.200
<v Speaker 1>I've always been in tech and for a long time

0:19:30.280 --> 0:19:34.520
<v Speaker 1>in analytics. I originally started my career working for NCR

0:19:34.560 --> 0:19:37.680
<v Speaker 1>as a financial analyst and pricing and planning and using

0:19:37.760 --> 0:19:40.880
<v Speaker 1>data and realizing how important it can be to make

0:19:40.920 --> 0:19:45.120
<v Speaker 1>financial decisions. And then from there I moved into more

0:19:45.160 --> 0:19:47.680
<v Speaker 1>of the sales and business development side of things, both

0:19:47.720 --> 0:19:49.879
<v Speaker 1>in New Zealand and I had a long time in London,

0:19:51.040 --> 0:19:54.000
<v Speaker 1>and then predominantly in the data and analytics side of things.

0:19:54.160 --> 0:19:57.840
<v Speaker 3>Do you want to share maybe the perception of data

0:19:58.080 --> 0:20:01.240
<v Speaker 3>maybe pre your Snowflake time, and then how it's changed

0:20:01.280 --> 0:20:01.800
<v Speaker 3>since then?

0:20:02.160 --> 0:20:06.040
<v Speaker 1>Yeah, no problem. I think data's always been important. Organizations

0:20:06.040 --> 0:20:09.080
<v Speaker 1>have always had the aspiration to be using data better

0:20:09.359 --> 0:20:12.640
<v Speaker 1>to make better and informed decisions. But what's happened in

0:20:12.680 --> 0:20:17.359
<v Speaker 1>the last five to ten years is the accessibility of

0:20:17.400 --> 0:20:20.879
<v Speaker 1>the information has become so much easier. The cost to

0:20:20.960 --> 0:20:24.399
<v Speaker 1>get that data and analyze it has dropped significantly, and

0:20:24.440 --> 0:20:28.280
<v Speaker 1>that's opened up massive opportunities because it allows organizations to

0:20:28.320 --> 0:20:31.080
<v Speaker 1>bring all of their data from all of their disparate

0:20:31.119 --> 0:20:35.200
<v Speaker 1>systems into one environment where that structured data unstructured data,

0:20:35.280 --> 0:20:39.000
<v Speaker 1>and then can imply analytics to that. Historically, it used

0:20:39.040 --> 0:20:41.159
<v Speaker 1>to be a lot of backwards looking, a lot of

0:20:41.240 --> 0:20:44.520
<v Speaker 1>reporting what did happen? And now where we're seeing is

0:20:44.560 --> 0:20:49.080
<v Speaker 1>a lot more predictive analytics, opening up the information to

0:20:49.640 --> 0:20:52.639
<v Speaker 1>a lot more of the business users and allowing that

0:20:52.760 --> 0:20:54.840
<v Speaker 1>decision making to be a lot more in the front

0:20:54.840 --> 0:20:57.800
<v Speaker 1>line rather than just the back office. So we're seeing

0:20:58.400 --> 0:21:03.479
<v Speaker 1>that dissemination of information across multiple channels, multiple users, and

0:21:03.520 --> 0:21:06.399
<v Speaker 1>the ease of use. So it's no longer just a

0:21:06.440 --> 0:21:09.800
<v Speaker 1>back office function as lines of business making decisions every

0:21:09.840 --> 0:21:12.440
<v Speaker 1>day which are moving the dial within those organizations.

0:21:13.320 --> 0:21:17.320
<v Speaker 3>Right. And you know, traditionally when we think of data,

0:21:17.400 --> 0:21:20.159
<v Speaker 3>we think big data, right especially these days, and we

0:21:20.200 --> 0:21:24.960
<v Speaker 3>think big companies. But that's increasingly changing as well. I

0:21:25.000 --> 0:21:28.520
<v Speaker 3>would imagine, like you say, as the accessibility, the affordability

0:21:28.560 --> 0:21:32.520
<v Speaker 3>of data and data analytics tools are starting to shift

0:21:32.520 --> 0:21:36.879
<v Speaker 3>a little bit, are you starting to see smaller companies,

0:21:37.119 --> 0:21:40.200
<v Speaker 3>you know, not necessarily your one person companies, but maybe

0:21:40.240 --> 0:21:45.200
<v Speaker 3>your medium size businesses gaining a better understanding of how

0:21:45.200 --> 0:21:46.800
<v Speaker 3>to utilize their data.

0:21:47.160 --> 0:21:49.879
<v Speaker 1>Yeah, it's been remarkable. Since we started the business in

0:21:49.880 --> 0:21:54.000
<v Speaker 1>New Zealand in twenty nineteen, we were looking at what

0:21:54.080 --> 0:21:56.680
<v Speaker 1>is the segments and what is the segmentation and customers

0:21:56.680 --> 0:21:59.080
<v Speaker 1>that we're going to look to try and require. We

0:21:59.160 --> 0:22:01.480
<v Speaker 1>had two stomers in New Zealand when we started the

0:22:01.480 --> 0:22:04.880
<v Speaker 1>business here and now we've got north of two hundred,

0:22:05.520 --> 0:22:08.040
<v Speaker 1>and it was really interesting. We started to think around

0:22:08.080 --> 0:22:10.360
<v Speaker 1>that segmentation and we thought it might be some mid

0:22:10.400 --> 0:22:13.160
<v Speaker 1>tier customers and then you know, maybe we can work

0:22:13.160 --> 0:22:15.399
<v Speaker 1>our way up or down across the different spectrum of

0:22:15.440 --> 0:22:19.480
<v Speaker 1>size and scale and complexity. But what happened was we've

0:22:19.480 --> 0:22:23.199
<v Speaker 1>got organizations of all size and scale very early. And

0:22:23.240 --> 0:22:25.640
<v Speaker 1>I think one of the things with Snowflake, and one

0:22:25.640 --> 0:22:28.320
<v Speaker 1>of the reasons why we had such fantastic adoption, was

0:22:28.640 --> 0:22:32.200
<v Speaker 1>it's the ability to scale down to New Zealand size companies,

0:22:32.560 --> 0:22:35.000
<v Speaker 1>not just being able to scale up. So there's the

0:22:35.000 --> 0:22:37.639
<v Speaker 1>global organizations, you know, there's the capital ones and the

0:22:37.640 --> 0:22:41.880
<v Speaker 1>sinespres etc. But within New Zealand because the platform scales

0:22:41.920 --> 0:22:44.159
<v Speaker 1>down and you only pay for what you use on

0:22:44.200 --> 0:22:46.879
<v Speaker 1>a true consumption basis. We've been able to scale to

0:22:47.000 --> 0:22:51.800
<v Speaker 1>organizations that are getting enterprise enterprise grade capability, but they're

0:22:51.840 --> 0:22:54.399
<v Speaker 1>only paying for what they use based on the size

0:22:54.400 --> 0:22:56.879
<v Speaker 1>of the organization or how much they actually need to

0:22:56.960 --> 0:22:57.760
<v Speaker 1>use of the platform.

0:22:57.840 --> 0:23:01.160
<v Speaker 3>To what extent a New Zealand companies really using all

0:23:01.200 --> 0:23:04.359
<v Speaker 3>of the capabilities of Snowflake. Are we up there in

0:23:04.400 --> 0:23:07.480
<v Speaker 3>the most advanced users or are we kind of just

0:23:08.119 --> 0:23:11.119
<v Speaker 3>using the very basics because we're smaller.

0:23:10.600 --> 0:23:14.119
<v Speaker 1>And yeah, absolutely. We just had a conference last week.

0:23:14.640 --> 0:23:16.840
<v Speaker 1>It was unbelievable. We had over a thousand people there,

0:23:17.520 --> 0:23:19.760
<v Speaker 1>which makes it the largest data and analytics event in

0:23:19.760 --> 0:23:22.840
<v Speaker 1>New Zealand, and we had some fabulous customers. So we'd

0:23:22.920 --> 0:23:26.639
<v Speaker 1>organizations like in New Zealand, Tavado, Aura in zed Health,

0:23:27.200 --> 0:23:30.840
<v Speaker 1>in zet, super one, end, z MITA ten, Spark, shares

0:23:30.840 --> 0:23:33.080
<v Speaker 1>e'se the kind of list goes on and it was

0:23:33.080 --> 0:23:37.320
<v Speaker 1>a really great opportunity for organizations to share what they're

0:23:37.320 --> 0:23:40.199
<v Speaker 1>doing and how they deliver value from the platform, and

0:23:40.240 --> 0:23:43.280
<v Speaker 1>also to build that community so organizations can network with

0:23:43.320 --> 0:23:45.880
<v Speaker 1>their peers and learn from each other. But in terms

0:23:45.920 --> 0:23:48.960
<v Speaker 1>of taking on the global stage, shares Y's is one

0:23:48.960 --> 0:23:52.640
<v Speaker 1>of our fantastic customers. They actually recently won the APJA

0:23:52.840 --> 0:23:55.440
<v Speaker 1>Data Driver Award for powered by So what that means

0:23:55.520 --> 0:24:00.560
<v Speaker 1>is they're powering their application using Snowflake to help drive

0:24:00.600 --> 0:24:03.920
<v Speaker 1>the adoption and understand their customer behaviors in order to

0:24:03.960 --> 0:24:07.000
<v Speaker 1>deliver a better service. And they've just had phenomenal growth.

0:24:07.000 --> 0:24:09.720
<v Speaker 1>So you know, they've got seven hundred thousand customers. So

0:24:09.760 --> 0:24:12.520
<v Speaker 1>we're taking on the world. Shares is being successful across

0:24:12.560 --> 0:24:16.240
<v Speaker 1>here and across in Australia, and we've got a number

0:24:16.240 --> 0:24:18.960
<v Speaker 1>of tech startups that we're working with who are winning

0:24:19.000 --> 0:24:23.320
<v Speaker 1>awards and delivering really fantastic results for their business on

0:24:23.359 --> 0:24:24.159
<v Speaker 1>a global scale.

0:24:25.000 --> 0:24:26.920
<v Speaker 3>Fantastic. Yeah, so it sounds like you've got some real

0:24:26.960 --> 0:24:28.320
<v Speaker 3>power users. Then that's what you're.

0:24:28.160 --> 0:24:32.959
<v Speaker 1>Saying, unbelievable. It's both the business users. So we had

0:24:33.000 --> 0:24:36.960
<v Speaker 1>the co CEOs presenting around how that's driving value. Data

0:24:37.000 --> 0:24:40.879
<v Speaker 1>analysts we have technical capabilities. It's the ability to work

0:24:40.960 --> 0:24:44.360
<v Speaker 1>with all of the different personas across an organization, not

0:24:44.440 --> 0:24:46.960
<v Speaker 1>just the technical people though they love the platform, so

0:24:47.119 --> 0:24:51.240
<v Speaker 1>the architects, the engineers, the really deep data people, but

0:24:51.280 --> 0:24:54.560
<v Speaker 1>then also the people who are consuming it, so technically

0:24:54.640 --> 0:24:59.439
<v Speaker 1>literate business analysts people just writing natural language questions in English,

0:24:59.520 --> 0:25:03.560
<v Speaker 1>executive writing. Sorry, just analyzing what's happened and what's going

0:25:03.600 --> 0:25:07.280
<v Speaker 1>to happen and their business. That's across the board, those

0:25:07.320 --> 0:25:10.920
<v Speaker 1>different personas that all use data in a slightly different nuance,

0:25:11.320 --> 0:25:14.400
<v Speaker 1>but they want consistency of information, they want high quality,

0:25:14.480 --> 0:25:18.359
<v Speaker 1>they want real time, they want accurate information so they

0:25:18.359 --> 0:25:19.440
<v Speaker 1>can make those decisions.

0:25:19.680 --> 0:25:24.760
<v Speaker 3>Cool. Now, obviously you can. It's great to talk up

0:25:24.880 --> 0:25:28.600
<v Speaker 3>to customers that are doing really awesome stuff, but New

0:25:28.680 --> 0:25:31.320
<v Speaker 3>Zealand's definitely not perfect nowhere is in terms of how

0:25:31.359 --> 0:25:33.560
<v Speaker 3>it's utilizing data. So what are some of the areas

0:25:33.560 --> 0:25:35.919
<v Speaker 3>that you're seeing New Zealand lagging behind? Maybe some New

0:25:36.000 --> 0:25:39.280
<v Speaker 3>Zealand companies where you think, you know some areas of

0:25:39.320 --> 0:25:43.320
<v Speaker 3>focus could be to improve the usage of data within

0:25:43.359 --> 0:25:43.919
<v Speaker 3>New Zealand.

0:25:44.400 --> 0:25:46.919
<v Speaker 1>I think that the pitfalls that we always see is

0:25:48.119 --> 0:25:53.280
<v Speaker 1>making sure that there's executive sponsorship and outcomes that the

0:25:53.400 --> 0:25:56.639
<v Speaker 1>organization is trying to drive towards. So it's really important

0:25:56.760 --> 0:26:02.359
<v Speaker 1>that it doesn't become a science experiment or a program

0:26:02.400 --> 0:26:06.840
<v Speaker 1>that's just for the IT users. What the successful organizations

0:26:06.880 --> 0:26:09.320
<v Speaker 1>do is they've got very strong alignment to a specific

0:26:09.400 --> 0:26:14.040
<v Speaker 1>business outcome, whether that's a finance program looking at receivables

0:26:14.119 --> 0:26:19.000
<v Speaker 1>or finance transformation, whether it's marketing looking at customer experience, NPS, churn,

0:26:19.080 --> 0:26:22.560
<v Speaker 1>cross sale, etc. Or operations to streamline the efficiency with

0:26:22.600 --> 0:26:25.439
<v Speaker 1>which the organization works in it has to have that

0:26:25.520 --> 0:26:28.919
<v Speaker 1>business outcome that everybody can anchor themselves and align to.

0:26:29.440 --> 0:26:31.760
<v Speaker 1>When you've got that, that goes a long way to

0:26:31.800 --> 0:26:34.880
<v Speaker 1>making sure this program's success. And then the usual governance

0:26:34.920 --> 0:26:38.320
<v Speaker 1>across the program and making sure that there is steps

0:26:38.359 --> 0:26:40.600
<v Speaker 1>along the way that people are measuring to make sure

0:26:40.640 --> 0:26:43.120
<v Speaker 1>that that outcome happens. When you start to get those

0:26:43.119 --> 0:26:45.960
<v Speaker 1>sorts of things, then everything else just falls into place.

0:26:46.600 --> 0:26:52.000
<v Speaker 3>Cool. Now, let's say I'm one of the New Zealand

0:26:52.000 --> 0:26:55.040
<v Speaker 3>companies that hasn't started to get deep into data yam.

0:26:55.040 --> 0:26:57.639
<v Speaker 3>You know, maybe a medium sized company who is starting

0:26:57.680 --> 0:27:01.760
<v Speaker 3>to think about the potential there. What are my first

0:27:01.880 --> 0:27:03.080
<v Speaker 3>kind of steps?

0:27:03.440 --> 0:27:07.080
<v Speaker 1>The first step is defining what dial within the business

0:27:07.119 --> 0:27:09.600
<v Speaker 1>you're trying to move and what is that outcome you're

0:27:09.600 --> 0:27:13.280
<v Speaker 1>trying to achieve. So say it's a marketing outcome around

0:27:13.320 --> 0:27:16.120
<v Speaker 1>cross seal. Make sure that those objectives and those metrics

0:27:16.160 --> 0:27:19.600
<v Speaker 1>are well understood and documented, and then start small and

0:27:19.640 --> 0:27:22.439
<v Speaker 1>try and deliver that program so that you deliver that

0:27:22.440 --> 0:27:25.600
<v Speaker 1>specific outcome, get the win, and then build upon that.

0:27:26.240 --> 0:27:28.439
<v Speaker 1>You need to paint the vision to the organization in

0:27:28.520 --> 0:27:31.639
<v Speaker 1>terms of what is the analytic capability going to deliver.

0:27:32.280 --> 0:27:34.920
<v Speaker 1>So you need to have a vision and where we're

0:27:34.920 --> 0:27:37.520
<v Speaker 1>going as an organization, but you also need to have

0:27:37.680 --> 0:27:40.040
<v Speaker 1>a specific outcome that you're driving towards that you can

0:27:40.040 --> 0:27:43.320
<v Speaker 1>build on that success. Then you need to drive where

0:27:43.320 --> 0:27:44.840
<v Speaker 1>do I get the data from and how do I

0:27:44.840 --> 0:27:47.679
<v Speaker 1>get high quality information to solve that business problem and

0:27:47.720 --> 0:27:50.399
<v Speaker 1>answer the questions that you're looking to define or answer sorry,

0:27:51.119 --> 0:27:54.720
<v Speaker 1>And then it's getting the technical teams aligned to find

0:27:54.760 --> 0:27:58.160
<v Speaker 1>that data, source that data cleanse that's high quality decision

0:27:58.160 --> 0:28:00.520
<v Speaker 1>making because you want to make sure sure that the

0:28:00.520 --> 0:28:04.520
<v Speaker 1>information that's being used is of quality so that the

0:28:04.520 --> 0:28:06.480
<v Speaker 1>decisions out the back of it are influenced.

0:28:06.560 --> 0:28:08.919
<v Speaker 3>What does that mean cleansing data? Like, what does that

0:28:08.960 --> 0:28:11.960
<v Speaker 3>actually in real terms mean? Because if I'm a company

0:28:12.000 --> 0:28:14.160
<v Speaker 3>that's been around for twenty years, I've got a bunch

0:28:14.200 --> 0:28:17.240
<v Speaker 3>of spreadsheets and PDFs and all this kind of stuff

0:28:17.560 --> 0:28:20.520
<v Speaker 3>and it's ordered, it's in folders. We know where everything is.

0:28:21.119 --> 0:28:22.960
<v Speaker 3>But is that clean? Is that clean enough.

0:28:23.560 --> 0:28:26.320
<v Speaker 1>It depends a lot of the times those spreadsheets have

0:28:26.359 --> 0:28:29.239
<v Speaker 1>been built up by a couple of specific people. They

0:28:29.320 --> 0:28:32.200
<v Speaker 1>might be suitable for that use case or that specific

0:28:32.960 --> 0:28:35.640
<v Speaker 1>piece of information you're looking to deliver. An example, when

0:28:35.680 --> 0:28:38.200
<v Speaker 1>I was a pricing analyst, we used to have huge

0:28:38.200 --> 0:28:41.720
<v Speaker 1>amounts of spreadsheets everywhere that have interconnected links, and we

0:28:41.840 --> 0:28:44.520
<v Speaker 1>put out some pricing models. Then you'd come back about

0:28:44.520 --> 0:28:46.800
<v Speaker 1>a month later and change something because you'd found a

0:28:46.840 --> 0:28:49.320
<v Speaker 1>mistake in the spreadsheet in the formulas, and that would

0:28:49.400 --> 0:28:52.960
<v Speaker 1>change the entire pricing model. And you'd be sitting there going,

0:28:53.080 --> 0:28:56.000
<v Speaker 1>oh my goodness, now I've just completely stuffed this up.

0:28:56.480 --> 0:28:58.160
<v Speaker 1>You change something else to get it back, and then

0:28:58.160 --> 0:29:01.440
<v Speaker 1>the numbers would all change back again. Spreadsheets, whilst they're

0:29:01.480 --> 0:29:04.600
<v Speaker 1>across every single organization, are kind of the bane of

0:29:05.240 --> 0:29:09.800
<v Speaker 1>any enterprise organization's life because there is no real auditability.

0:29:09.840 --> 0:29:12.720
<v Speaker 1>So when I talk about high quality data, it's getting

0:29:12.720 --> 0:29:15.800
<v Speaker 1>that data from those source systems, making sure that it's

0:29:15.960 --> 0:29:20.200
<v Speaker 1>usable and in a format that's understandable, but it's consolidated

0:29:20.240 --> 0:29:22.920
<v Speaker 1>across multiple touch points so that you've got a consistent

0:29:23.000 --> 0:29:26.120
<v Speaker 1>view of customer, and then involving the business teams to

0:29:26.160 --> 0:29:29.240
<v Speaker 1>define what is the rules and logic so that everybody

0:29:29.320 --> 0:29:31.320
<v Speaker 1>knows what the definition of a customer is, what is

0:29:31.320 --> 0:29:34.200
<v Speaker 1>a definition of revenue or profit or what happens to be,

0:29:34.640 --> 0:29:37.160
<v Speaker 1>and then everyone's working off that consistent set of information.

0:29:37.680 --> 0:29:41.440
<v Speaker 1>We've all been in meetings where people are arguing about

0:29:41.480 --> 0:29:44.240
<v Speaker 1>the data rather than what they do with that information.

0:29:44.880 --> 0:29:46.680
<v Speaker 1>So what we want to try and do is consolidate

0:29:46.720 --> 0:29:49.640
<v Speaker 1>the information so that it's a single view across the business.

0:29:50.160 --> 0:29:52.040
<v Speaker 1>And then people are thinking about what are the decisions

0:29:52.040 --> 0:29:53.760
<v Speaker 1>I make, not hey, is that the right one? Am

0:29:53.760 --> 0:29:57.760
<v Speaker 1>I questioning the actual data validity rather than what I

0:29:57.760 --> 0:29:58.320
<v Speaker 1>can do with it?

0:29:59.520 --> 0:30:01.240
<v Speaker 3>What's the kind what's the kind of talent that you

0:30:01.280 --> 0:30:03.840
<v Speaker 3>would need to do that? Do you need to hire

0:30:03.920 --> 0:30:07.360
<v Speaker 3>an house data scientist? Is it okay to just kind

0:30:07.360 --> 0:30:10.240
<v Speaker 3>of get a consultant into kind of do some data

0:30:10.280 --> 0:30:11.680
<v Speaker 3>stuff for you to get you ready.

0:30:11.960 --> 0:30:14.320
<v Speaker 1>I think consultants have a place and they've got a

0:30:14.360 --> 0:30:18.320
<v Speaker 1>lot of experience that can bring to bear on organizations.

0:30:18.680 --> 0:30:22.360
<v Speaker 1>But I think the organizations themselves have a responsibility and

0:30:22.400 --> 0:30:26.280
<v Speaker 1>they have to have a capability internally. This can't be

0:30:26.400 --> 0:30:28.600
<v Speaker 1>done to an organization. You have to do it with

0:30:28.720 --> 0:30:32.560
<v Speaker 1>the organization and the people within the enterprise or the company.

0:30:32.960 --> 0:30:35.760
<v Speaker 1>They know what the business is trying to achieve, they

0:30:35.840 --> 0:30:38.520
<v Speaker 1>know where to get the data from. So you need

0:30:38.560 --> 0:30:41.440
<v Speaker 1>to have a set of skills within the organization, and

0:30:41.480 --> 0:30:44.040
<v Speaker 1>that skills from a technical capability to work out where

0:30:44.080 --> 0:30:45.560
<v Speaker 1>does the data come from and how do I get

0:30:45.560 --> 0:30:48.200
<v Speaker 1>it and then also how do I put that into

0:30:48.360 --> 0:30:50.720
<v Speaker 1>the hands of the users so they've got confidence that

0:30:50.760 --> 0:30:53.600
<v Speaker 1>they can start to drive analysis from it. But the

0:30:53.640 --> 0:30:56.640
<v Speaker 1>internal capability is critical. One of the things we're trying

0:30:56.680 --> 0:30:59.760
<v Speaker 1>to do at Snowflake is build a really big community.

0:31:00.200 --> 0:31:02.880
<v Speaker 1>So the event we just ran with a huge number

0:31:02.880 --> 0:31:06.000
<v Speaker 1>of people. We run user groups, we run meetups, we

0:31:06.080 --> 0:31:10.040
<v Speaker 1>run product specialist workshops. When we bring some of our

0:31:10.080 --> 0:31:13.320
<v Speaker 1>teams offshore into New Zealand, and it's really important to

0:31:13.400 --> 0:31:16.960
<v Speaker 1>build that community and network so we can share what's

0:31:17.000 --> 0:31:20.240
<v Speaker 1>working and to your point before, what's not working, so

0:31:20.280 --> 0:31:23.320
<v Speaker 1>that we can avoid those pitfalls where possible and start

0:31:23.400 --> 0:31:26.360
<v Speaker 1>to accelerate how do we deliver that outcome. But I

0:31:26.360 --> 0:31:29.840
<v Speaker 1>think internally the capability needs to be there. You know,

0:31:30.040 --> 0:31:32.680
<v Speaker 1>we need to train our teams, we need to cross

0:31:32.720 --> 0:31:35.720
<v Speaker 1>pollinate from existing teams, so you might have somebody who's

0:31:36.200 --> 0:31:39.720
<v Speaker 1>working internally within an organization, they've got a huge amount

0:31:39.760 --> 0:31:43.520
<v Speaker 1>of tribal knowledge within that organization. But then how do

0:31:43.560 --> 0:31:47.120
<v Speaker 1>we cross pollinate their skill sets with whatever it is

0:31:47.120 --> 0:31:50.080
<v Speaker 1>they need with it's technical or analysts. So they've got

0:31:50.080 --> 0:31:53.360
<v Speaker 1>the data literacy to drive that outcome. But absolutely internal

0:31:53.440 --> 0:31:53.960
<v Speaker 1>was critical.

0:31:54.160 --> 0:31:57.360
<v Speaker 3>Yeah, I mean, I guess the message that I'm getting

0:31:57.360 --> 0:32:00.920
<v Speaker 3>really is that you can't do this. Lais a fair.

0:32:01.360 --> 0:32:03.640
<v Speaker 3>You can't just be like, let's dabble in some data.

0:32:03.760 --> 0:32:06.600
<v Speaker 3>You really have to sit down and create a cohesive,

0:32:06.720 --> 0:32:11.360
<v Speaker 3>strong plan and roadmap and objectives and spend the time

0:32:11.400 --> 0:32:13.840
<v Speaker 3>to actually build that out. And if you're seeing gaps

0:32:14.000 --> 0:32:16.840
<v Speaker 3>in your organization, then you actually need to maybe fill

0:32:16.880 --> 0:32:19.960
<v Speaker 3>those gaps, whether that's with training or with bringing on

0:32:20.000 --> 0:32:23.200
<v Speaker 3>new staff. Does that kind of sound about right?

0:32:23.280 --> 0:32:27.400
<v Speaker 1>Yeah, one hundred percent agree. And it's building that strategy

0:32:27.760 --> 0:32:30.440
<v Speaker 1>into the business outcome of the business strategy, so that

0:32:30.840 --> 0:32:35.520
<v Speaker 1>the data strategy is part of your business strategy because

0:32:35.600 --> 0:32:38.360
<v Speaker 1>they shouldn't be separate. One can inform the other and

0:32:38.360 --> 0:32:41.000
<v Speaker 1>the other can form each other. A lot of times

0:32:41.080 --> 0:32:45.120
<v Speaker 1>the data teams have a really enterprise view of the

0:32:45.120 --> 0:32:49.240
<v Speaker 1>business because they're looking at data from multiple different areas,

0:32:49.560 --> 0:32:53.200
<v Speaker 1>so you're not siloed within say HR, or siloed within finance,

0:32:53.320 --> 0:32:57.760
<v Speaker 1>or siloed within marketing. The data teams get a strong

0:32:57.920 --> 0:33:02.920
<v Speaker 1>visibility across the organization. For example, we were talking in

0:33:02.960 --> 0:33:07.400
<v Speaker 1>New Zealand presented recently at our conference. Again they talked

0:33:07.400 --> 0:33:10.000
<v Speaker 1>about the concept of majors and miners, So you've got

0:33:10.040 --> 0:33:14.840
<v Speaker 1>a data team which has got majors in data and analytics,

0:33:15.080 --> 0:33:18.080
<v Speaker 1>but working with the lines of business who have a

0:33:18.120 --> 0:33:21.920
<v Speaker 1>minor and data, but a major is in their skill set,

0:33:21.920 --> 0:33:24.200
<v Speaker 1>whether it's HR, where it's cargo, whether it's financed, whatever

0:33:24.200 --> 0:33:28.200
<v Speaker 1>happens to be. And that dovetail together of the data

0:33:28.480 --> 0:33:32.400
<v Speaker 1>literacy and the data capability with the knowledge of that

0:33:32.560 --> 0:33:35.480
<v Speaker 1>specific line of business and what's important to that line

0:33:36.200 --> 0:33:39.080
<v Speaker 1>is really important because then you're marrying both of the

0:33:39.120 --> 0:33:42.680
<v Speaker 1>outcome and the capability together, which drives a lot of

0:33:42.720 --> 0:33:47.000
<v Speaker 1>value for the organization. Having that strategy which is aligned

0:33:47.080 --> 0:33:50.680
<v Speaker 1>into the business strategy is really important. And obviously with

0:33:50.840 --> 0:33:55.440
<v Speaker 1>the introduction of things like AI, AI is built upon

0:33:56.400 --> 0:33:59.600
<v Speaker 1>how you use data, whether it's internal data, external data

0:34:00.080 --> 0:34:02.720
<v Speaker 1>to drive that decision making. So you know, you can't

0:34:02.720 --> 0:34:05.400
<v Speaker 1>really have an AI strategy without a data strategy, and

0:34:05.440 --> 0:34:07.640
<v Speaker 1>all of this should be blended into what is the

0:34:07.680 --> 0:34:10.000
<v Speaker 1>objectives of the organization and what are they driving for?

0:34:10.840 --> 0:34:12.840
<v Speaker 3>Great, you just did my job then, and you pivoted

0:34:12.840 --> 0:34:14.560
<v Speaker 3>to exactly where I wanted to go next. We just

0:34:14.560 --> 0:34:16.839
<v Speaker 3>talk about AI because we have to, right because it's

0:34:16.840 --> 0:34:21.560
<v Speaker 3>twenty twenty four and the last three years of AI

0:34:21.800 --> 0:34:25.480
<v Speaker 3>has been just a different story completely to where it

0:34:25.600 --> 0:34:29.440
<v Speaker 3>was previously. From your experience as somebody who lives and

0:34:29.480 --> 0:34:33.080
<v Speaker 3>breathes data, what has that experience been like to watch

0:34:33.200 --> 0:34:38.080
<v Speaker 3>data go from this kind of dry but necessary thing

0:34:38.440 --> 0:34:40.640
<v Speaker 3>to the thing that is powering the future.

0:34:41.080 --> 0:34:43.440
<v Speaker 1>It's absolutely amazing, isn't it. You know, you know, in

0:34:43.480 --> 0:34:48.440
<v Speaker 1>your personal life you use things like GPT and the

0:34:48.480 --> 0:34:51.920
<v Speaker 1>output that it can deliver is just extraordinary. And where

0:34:51.920 --> 0:34:56.520
<v Speaker 1>it's going, I think is fantastic. It's outstanding. But you've

0:34:56.560 --> 0:35:00.840
<v Speaker 1>got to get the foundations right because otherwise you're building

0:35:00.880 --> 0:35:04.200
<v Speaker 1>on quicksand and you're analyzing inefficient data and you'll very

0:35:04.280 --> 0:35:07.200
<v Speaker 1>quickly lose confidence from those users. And there's also some

0:35:07.239 --> 0:35:10.319
<v Speaker 1>of those traditional techniques which are still incredibly valuable to

0:35:10.320 --> 0:35:14.839
<v Speaker 1>the organization. So just making sure that we understand the vision,

0:35:14.840 --> 0:35:16.839
<v Speaker 1>and we go after that, and we go after that

0:35:17.800 --> 0:35:22.200
<v Speaker 1>with speed, but at the same time we don't take

0:35:22.239 --> 0:35:24.640
<v Speaker 1>the focus off some of those other areas which we

0:35:24.680 --> 0:35:26.680
<v Speaker 1>can deliver very very quick value to the business.

0:35:27.520 --> 0:35:31.360
<v Speaker 3>Somebody said to me the other day that actually, with

0:35:31.719 --> 0:35:35.920
<v Speaker 3>new AI models, data cleanliness is actually not as important

0:35:35.960 --> 0:35:37.839
<v Speaker 3>as it used to be, because if you look at

0:35:37.840 --> 0:35:40.880
<v Speaker 3>something like chat GPT, you know, the training is not

0:35:41.520 --> 0:35:45.640
<v Speaker 3>necessarily there's so much of it they couldn't possibly go

0:35:45.719 --> 0:35:47.640
<v Speaker 3>through and clean it all. Do you think that's true

0:35:48.040 --> 0:35:51.440
<v Speaker 3>that if you were wanting to create kind of a

0:35:51.480 --> 0:35:56.920
<v Speaker 3>GENAI model that's used you can use to analyze certain levels,

0:35:56.920 --> 0:35:59.880
<v Speaker 3>certain kinds of data within the organization, that there is

0:36:01.280 --> 0:36:04.279
<v Speaker 3>less need for data cleanliness than they used to be.

0:36:05.440 --> 0:36:08.799
<v Speaker 1>I'd say, where you're looking at the entire Internet for

0:36:08.960 --> 0:36:13.200
<v Speaker 1>consumers like you just describe, maybe when organizations are looking

0:36:13.200 --> 0:36:16.880
<v Speaker 1>to use information which is internal to their organization, that

0:36:17.120 --> 0:36:20.239
<v Speaker 1>has to be very high quality. It has to be robust,

0:36:20.480 --> 0:36:23.040
<v Speaker 1>it has to be trusted, and it has to be

0:36:23.160 --> 0:36:27.320
<v Speaker 1>using the information and knowledge from that organization to prevent

0:36:27.400 --> 0:36:32.719
<v Speaker 1>things like hallucinations and bad decisions because the data is incorrect, inaccurate,

0:36:33.120 --> 0:36:35.200
<v Speaker 1>not full enough. There's not a quorum of data to

0:36:35.200 --> 0:36:38.960
<v Speaker 1>make an informed decision. I think the data quality aspects

0:36:39.040 --> 0:36:42.840
<v Speaker 1>are even more important for an organization using some of

0:36:42.840 --> 0:36:48.000
<v Speaker 1>these advanced capabilities like genai. The Genai capability helps a

0:36:48.120 --> 0:36:51.120
<v Speaker 1>lot in terms of being able to put some of

0:36:51.160 --> 0:36:55.799
<v Speaker 1>that tagging, say, or definitions around what some of that

0:36:55.880 --> 0:36:59.359
<v Speaker 1>data means. It helps speed up the efficiency to make

0:36:59.480 --> 0:37:05.440
<v Speaker 1>the data more reliable and higher quality and understood. But

0:37:05.520 --> 0:37:09.520
<v Speaker 1>without putting the thought into having that high quality data,

0:37:09.880 --> 0:37:12.239
<v Speaker 1>it's going to fall flat. In my opinion, I think

0:37:12.239 --> 0:37:17.160
<v Speaker 1>we need to absolutely focus on the availability, the security

0:37:17.160 --> 0:37:25.640
<v Speaker 1>and governance, the privacy, the quality and trusted data and

0:37:25.680 --> 0:37:28.480
<v Speaker 1>then apply these techniques on top of it. And one

0:37:28.480 --> 0:37:31.440
<v Speaker 1>of the things where Big believers on is bring the

0:37:31.440 --> 0:37:34.760
<v Speaker 1>processing and the workload to the data rather than pushing

0:37:34.760 --> 0:37:37.520
<v Speaker 1>all the data out to different systems. And the reason

0:37:37.680 --> 0:37:40.400
<v Speaker 1>for that is because you've got that single view of

0:37:40.440 --> 0:37:42.960
<v Speaker 1>the business, you've got one place to make sure that

0:37:43.000 --> 0:37:45.560
<v Speaker 1>the data is of that high quality we're just describing,

0:37:45.600 --> 0:37:48.120
<v Speaker 1>and you've got the privacy and governance so that only

0:37:48.120 --> 0:37:51.040
<v Speaker 1>the right people are allowed to see it. Because what

0:37:51.080 --> 0:37:53.600
<v Speaker 1>we're doing is we're opening up the access to a

0:37:53.680 --> 0:37:58.640
<v Speaker 1>huge wide range of different consumers of the data, So

0:37:58.680 --> 0:38:00.440
<v Speaker 1>we've got to make sure that it's protect and we've

0:38:00.480 --> 0:38:01.960
<v Speaker 1>got to make sure that it's of high quality.

0:38:02.080 --> 0:38:05.400
<v Speaker 3>Yeah, you're not on the difference between creating something from

0:38:05.400 --> 0:38:07.920
<v Speaker 3>a mass market and creating something that is to improve

0:38:08.120 --> 0:38:11.080
<v Speaker 3>organizational performance, and those are two very different goals completely.

0:38:12.360 --> 0:38:16.000
<v Speaker 3>The other thing that I've been considering about generative AI

0:38:16.120 --> 0:38:20.080
<v Speaker 3>lately is there's this kind of to and fro about

0:38:20.600 --> 0:38:25.520
<v Speaker 3>how much we let the GENAI actually do if that

0:38:25.600 --> 0:38:28.720
<v Speaker 3>kind of makes sense, where it can be quite creative

0:38:28.760 --> 0:38:33.399
<v Speaker 3>and thoughtful and very have high contextual understanding, but that

0:38:33.520 --> 0:38:38.640
<v Speaker 3>may potentially, you know, open up the hallucinations or we're

0:38:38.680 --> 0:38:40.480
<v Speaker 3>not quite sure where that's going to go. Or we

0:38:40.520 --> 0:38:42.239
<v Speaker 3>can be very tight and strict and be like, it

0:38:42.239 --> 0:38:46.560
<v Speaker 3>can only return these information from these sources in these modes,

0:38:47.080 --> 0:38:49.799
<v Speaker 3>and trying to find the balance of that can be

0:38:50.440 --> 0:38:54.520
<v Speaker 3>tricky at a kind of data level when you're figuring

0:38:54.600 --> 0:38:58.040
<v Speaker 3>out what to include and what not to include, how

0:38:58.040 --> 0:38:59.880
<v Speaker 3>do you start making some of those decisions.

0:39:00.520 --> 0:39:04.520
<v Speaker 1>You're absolutely right, it is a tricky decision or tricky

0:39:05.960 --> 0:39:09.880
<v Speaker 1>consideration to think around. Where we think around it is

0:39:11.000 --> 0:39:14.720
<v Speaker 1>having access to the right amount of information, but putting

0:39:14.760 --> 0:39:17.640
<v Speaker 1>those governance and controls on there so that you've got

0:39:17.880 --> 0:39:20.880
<v Speaker 1>things like role based access so that only I'm allowed

0:39:20.880 --> 0:39:23.680
<v Speaker 1>to see the information that's purten in to my specific

0:39:23.760 --> 0:39:27.359
<v Speaker 1>role and I can't see anything else outside of that.

0:39:27.760 --> 0:39:30.360
<v Speaker 1>And that's why it's really important to get that governance

0:39:30.480 --> 0:39:34.960
<v Speaker 1>and that privacy foundations set and defined upfront so that

0:39:35.000 --> 0:39:38.000
<v Speaker 1>it's not being made up and make sure that you've

0:39:38.000 --> 0:39:40.880
<v Speaker 1>got the right level of data to support the decision

0:39:40.920 --> 0:39:43.200
<v Speaker 1>that you're trying to solve. And my view would be

0:39:43.360 --> 0:39:46.560
<v Speaker 1>start small, start to prove out some value, and then

0:39:46.640 --> 0:39:50.560
<v Speaker 1>expand as you've got that confidence within the business. But

0:39:50.640 --> 0:39:53.680
<v Speaker 1>it's moving incredibly fast, right you know. You think even

0:39:53.680 --> 0:39:56.200
<v Speaker 1>a couple of years ago, you know, Chat GPT was

0:39:56.320 --> 0:39:58.640
<v Speaker 1>just coming of age and people had only just staid

0:39:58.640 --> 0:40:01.880
<v Speaker 1>to hear about it, and now AI is embedded into

0:40:02.680 --> 0:40:05.959
<v Speaker 1>just about every single platform and process. What we're looking

0:40:06.000 --> 0:40:09.440
<v Speaker 1>to do is understand how we can use each of

0:40:09.480 --> 0:40:13.360
<v Speaker 1>those different silos of informations and applications bring that together

0:40:13.440 --> 0:40:15.880
<v Speaker 1>so you've still got that holistic view at the data level,

0:40:16.239 --> 0:40:18.759
<v Speaker 1>not just at the application level. So you want to

0:40:18.800 --> 0:40:21.160
<v Speaker 1>be able to bring that data together from multiple places

0:40:21.560 --> 0:40:23.920
<v Speaker 1>and then apply AI across it, depending on what it

0:40:24.000 --> 0:40:26.400
<v Speaker 1>is you're trying to do, but you know it's moving

0:40:26.520 --> 0:40:28.880
<v Speaker 1>so quickly it's really exciting. To be parely honest.

0:40:29.719 --> 0:40:33.520
<v Speaker 3>What is exciting about it for you? Because you know,

0:40:33.640 --> 0:40:38.280
<v Speaker 3>for office workers there's that kind of productivity gain stuff

0:40:38.320 --> 0:40:42.160
<v Speaker 3>that's being talked about. For consumers there's like access to

0:40:42.239 --> 0:40:46.759
<v Speaker 3>information that they may not have or ability to proof

0:40:46.880 --> 0:40:50.359
<v Speaker 3>read and do these kinds of everyday tasks. But as

0:40:50.400 --> 0:40:52.960
<v Speaker 3>somebody who is like super deep in the world of data,

0:40:53.480 --> 0:40:56.320
<v Speaker 3>what is actually super exciting for you about the generative

0:40:56.360 --> 0:40:56.960
<v Speaker 3>AI stuff?

0:40:57.239 --> 0:41:00.440
<v Speaker 1>The productivity part that'll be part of it, But I

0:41:00.480 --> 0:41:05.560
<v Speaker 1>don't think that organizations are looking at just the productivity.

0:41:05.640 --> 0:41:09.440
<v Speaker 1>Sure there's efficiency, but I think it's the upside that

0:41:09.480 --> 0:41:12.440
<v Speaker 1>people can drive from it. Is the better network planning

0:41:12.480 --> 0:41:15.920
<v Speaker 1>in TALCOS is the better supply chain management. Because you're

0:41:15.920 --> 0:41:19.400
<v Speaker 1>pulling information from third party suppliers as well as the

0:41:19.400 --> 0:41:23.440
<v Speaker 1>internal information. You can run and advance large language model

0:41:23.440 --> 0:41:26.080
<v Speaker 1>across that to work out what is the route processing

0:41:26.160 --> 0:41:29.520
<v Speaker 1>or where do you deliver things quicker? That outcome that's

0:41:29.560 --> 0:41:32.759
<v Speaker 1>going to move the dial with those organizations to drive

0:41:32.800 --> 0:41:38.080
<v Speaker 1>revenue or make them more profitable. That's really exciting and

0:41:38.120 --> 0:41:40.040
<v Speaker 1>obviously the productivity gains will come as well.

0:41:41.160 --> 0:41:44.160
<v Speaker 3>Are we already seeing some of those gains in certain

0:41:44.200 --> 0:41:46.680
<v Speaker 3>areas using the new air models? Like can do you

0:41:46.719 --> 0:41:48.440
<v Speaker 3>have examples of that? Yeah?

0:41:48.520 --> 0:41:52.600
<v Speaker 1>Absolutely? Mine to ten was just talking. They've spoke to

0:41:52.600 --> 0:41:55.440
<v Speaker 1>our conference again last year. They've had a very small team,

0:41:56.160 --> 0:41:57.560
<v Speaker 1>so they've managed to consult it a lot of their

0:41:57.600 --> 0:42:01.400
<v Speaker 1>information one year on what done as they've applied some

0:42:01.480 --> 0:42:05.280
<v Speaker 1>of these large language models to look at water supply

0:42:05.360 --> 0:42:09.040
<v Speaker 1>chain and how can they deliver better outcomes across the

0:42:09.080 --> 0:42:12.839
<v Speaker 1>retail organization. So they're starting to embed some of these

0:42:13.120 --> 0:42:17.759
<v Speaker 1>capabilities into their processes. We're seeing the talcos doing the

0:42:17.800 --> 0:42:21.279
<v Speaker 1>same things. A lot of them have had proof of

0:42:21.280 --> 0:42:24.640
<v Speaker 1>concepts that they're now starting to put into production. So

0:42:24.760 --> 0:42:26.359
<v Speaker 1>I think that there's going to be a lot of

0:42:26.400 --> 0:42:30.600
<v Speaker 1>the pilot and prototype pieces of we're really accelerating now,

0:42:30.640 --> 0:42:33.319
<v Speaker 1>and to be honest, some of the organizations they see

0:42:33.320 --> 0:42:36.439
<v Speaker 1>that as a competitive differentiator, so they are actually keeping

0:42:36.480 --> 0:42:39.279
<v Speaker 1>some of them relatively close to their chests because the

0:42:39.360 --> 0:42:44.239
<v Speaker 1>faster they can move, they're looking to leap frog the competitors.

0:42:45.040 --> 0:42:48.200
<v Speaker 3>If twenty twenty one twenty two was kind of the

0:42:48.239 --> 0:42:51.240
<v Speaker 3>emergence and the testing and the seeing what could go wrong?

0:42:51.760 --> 0:42:53.839
<v Speaker 3>You know, twenty twenty three and twenty twenty four has

0:42:53.880 --> 0:42:56.839
<v Speaker 3>been about getting those prototypes and starting to see what

0:42:56.920 --> 0:43:00.279
<v Speaker 3>can happen. Is twenty twenty five to twenty six is

0:43:00.280 --> 0:43:02.680
<v Speaker 3>that going to be the acceleration time? Where are we

0:43:02.719 --> 0:43:06.400
<v Speaker 3>at in terms of starting to really see mass adoption

0:43:06.480 --> 0:43:09.600
<v Speaker 3>of this tech at an enterprise level, at a fundamentally

0:43:09.640 --> 0:43:10.640
<v Speaker 3>restructuring level.

0:43:10.719 --> 0:43:12.439
<v Speaker 1>Yeah, I think over the next twelve to eighty months

0:43:12.480 --> 0:43:15.239
<v Speaker 1>you will see a massive acceleration of that. I think

0:43:15.400 --> 0:43:19.759
<v Speaker 1>those organizations that have done those that foundational work are

0:43:19.760 --> 0:43:22.640
<v Speaker 1>in a much better position to be able to accelerate faster.

0:43:22.840 --> 0:43:28.000
<v Speaker 1>So those organizations that have got trusted, high quality, consolidated information,

0:43:28.719 --> 0:43:31.000
<v Speaker 1>then they're looking at what do they do to exploit it.

0:43:31.040 --> 0:43:33.239
<v Speaker 1>They've got that quorum of data, and now how do

0:43:33.280 --> 0:43:36.840
<v Speaker 1>we use it and exploit it quickly? There's still organizations

0:43:36.880 --> 0:43:39.719
<v Speaker 1>which have yet to do that foundational work, and that

0:43:39.760 --> 0:43:43.080
<v Speaker 1>foundational work is critical before you can start to exploit

0:43:43.120 --> 0:43:45.399
<v Speaker 1>it in a really meaningful way. So I think there's

0:43:45.440 --> 0:43:48.400
<v Speaker 1>going to be those that are ahead of the curve

0:43:48.440 --> 0:43:50.120
<v Speaker 1>and have been ahead of the curve for the last

0:43:50.440 --> 0:43:53.080
<v Speaker 1>few years are going to be able to accelerate quicker

0:43:54.160 --> 0:43:56.759
<v Speaker 1>than those who haven't done that homework and done the

0:43:56.800 --> 0:44:01.680
<v Speaker 1>foundational stuff. And it's no different to whether it's GENAI

0:44:01.960 --> 0:44:05.319
<v Speaker 1>or large language models. Those organizations that have got that

0:44:05.440 --> 0:44:10.160
<v Speaker 1>high quality data, they've spent the time to ensure that

0:44:10.200 --> 0:44:13.640
<v Speaker 1>the lines of businesses have data literacy and data skills

0:44:14.040 --> 0:44:16.160
<v Speaker 1>and know what they can do with the information to

0:44:16.480 --> 0:44:19.960
<v Speaker 1>change the processes will be in a better position. So

0:44:20.520 --> 0:44:23.120
<v Speaker 1>adding on top of that things like genai, it will

0:44:23.160 --> 0:44:27.200
<v Speaker 1>allow those organizations to go faster. But it's just accelerated

0:44:27.239 --> 0:44:29.719
<v Speaker 1>how quickly organizations can start to exploit it. I don't

0:44:29.719 --> 0:44:31.880
<v Speaker 1>think it changes the fundamental that you've got to get

0:44:31.880 --> 0:44:35.840
<v Speaker 1>the basics right and do that well before you can accelerate.

0:44:37.000 --> 0:44:39.680
<v Speaker 3>What would you say are the biggest risks that we

0:44:39.719 --> 0:44:42.920
<v Speaker 3>need to be thinking about as we enter this accelerative phase.

0:44:43.719 --> 0:44:48.320
<v Speaker 1>I think the privacy and just because we've got the data,

0:44:48.600 --> 0:44:51.759
<v Speaker 1>does that give us the right to use that data mentality?

0:44:51.880 --> 0:44:54.680
<v Speaker 1>And I think we've got to be really considerate that

0:44:54.880 --> 0:44:57.799
<v Speaker 1>most of these organizations is not their data, it's their

0:44:57.840 --> 0:45:01.680
<v Speaker 1>customers data, So we need to really consider what is

0:45:01.719 --> 0:45:04.080
<v Speaker 1>it that we're going to do with that data and

0:45:04.440 --> 0:45:08.080
<v Speaker 1>make sure that it's doing the right things for their

0:45:08.080 --> 0:45:10.600
<v Speaker 1>customers as well as the internal organization. So we've got

0:45:10.600 --> 0:45:15.320
<v Speaker 1>to think around the privacy, the use of it, the

0:45:15.360 --> 0:45:20.680
<v Speaker 1>AI governance and governance of the customer's use of it,

0:45:20.760 --> 0:45:22.800
<v Speaker 1>and the permissions and things like that. So I think

0:45:22.960 --> 0:45:26.600
<v Speaker 1>the accessibility is great, but just because we've got it

0:45:26.600 --> 0:45:29.759
<v Speaker 1>doesn't necessarily mean we should use it in a certain way.

0:45:30.440 --> 0:45:32.920
<v Speaker 1>There's going to be a lot of focus around the

0:45:33.719 --> 0:45:37.719
<v Speaker 1>obviously security, privacy, and then also how do we just

0:45:37.800 --> 0:45:40.120
<v Speaker 1>continue to evolve on that as well?

0:45:40.880 --> 0:45:41.839
<v Speaker 3>What do you mean by that.

0:45:42.760 --> 0:45:46.360
<v Speaker 1>In terms of as the technology moves so much faster,

0:45:47.239 --> 0:45:49.279
<v Speaker 1>how do we keep up with that? And how do

0:45:49.360 --> 0:45:52.200
<v Speaker 1>we think about the new use cases? How do we

0:45:52.239 --> 0:45:55.360
<v Speaker 1>think around what is that business driver again taking it

0:45:55.360 --> 0:45:59.160
<v Speaker 1>away from just a technology problem, what is the business

0:45:59.160 --> 0:46:02.280
<v Speaker 1>trying to achieve ross that line of business, finance, marketing,

0:46:02.320 --> 0:46:06.200
<v Speaker 1>et cetera. And how do we align to that outcome right?

0:46:06.280 --> 0:46:10.200
<v Speaker 1>Otherwise we can spend a huge amount of money with

0:46:10.320 --> 0:46:12.760
<v Speaker 1>science experiments that don't actually do much for the business.

0:46:12.960 --> 0:46:15.200
<v Speaker 1>Another thing that will be important will be looking at

0:46:15.239 --> 0:46:21.000
<v Speaker 1>the cost considerations, making sure that the whatever we're doing

0:46:21.239 --> 0:46:24.520
<v Speaker 1>is aligned to the outcome so that it's cost and

0:46:24.960 --> 0:46:29.439
<v Speaker 1>value tightly coupled. Otherwise, you know, they're not cheap things

0:46:29.440 --> 0:46:31.480
<v Speaker 1>to run, so we need to make sure we've got

0:46:31.480 --> 0:46:34.279
<v Speaker 1>the guardrails across it. So cost management is going to

0:46:34.280 --> 0:46:37.880
<v Speaker 1>be efficient, going to be important, that the governance and

0:46:37.880 --> 0:46:40.600
<v Speaker 1>privacy is going to be important, and that all ties

0:46:40.640 --> 0:46:42.160
<v Speaker 1>back to what is what are we're using it for

0:46:42.239 --> 0:46:43.799
<v Speaker 1>and what is the business value we're trying to drive

0:46:43.840 --> 0:46:44.400
<v Speaker 1>out the back of it.

0:46:45.680 --> 0:46:49.480
<v Speaker 3>So get excited, but not too excited, and be thoughtful.

0:46:49.680 --> 0:46:50.719
<v Speaker 3>That's kind of there, I think.

0:46:50.800 --> 0:46:56.400
<v Speaker 1>Be excited, but be thoughtful. Don't don't limit what you

0:46:56.640 --> 0:46:59.520
<v Speaker 1>think you can do because you probably can. And it's

0:46:59.560 --> 0:47:02.359
<v Speaker 1>exciting time to go and test some of these hypotheses

0:47:02.480 --> 0:47:05.239
<v Speaker 1>and see how it works. So be excited, to be

0:47:05.280 --> 0:47:08.480
<v Speaker 1>really excited, it's going to be fantastic next couple of years.

0:47:08.800 --> 0:47:10.880
<v Speaker 1>But just be thoughtful about how you're using it and

0:47:10.920 --> 0:47:12.160
<v Speaker 1>thoughtful about your customers.

0:47:19.640 --> 0:47:21.960
<v Speaker 3>So, if ever there was a man who lives and

0:47:22.040 --> 0:47:25.920
<v Speaker 3>breathed data, I think it's Tony Shaw. He has clearly

0:47:25.960 --> 0:47:29.160
<v Speaker 3>been in the industry for a long time, and his

0:47:29.320 --> 0:47:32.520
<v Speaker 3>advice I think, while some of it isn't necessarily novel.

0:47:32.560 --> 0:47:35.200
<v Speaker 3>It's the stuff we've been hearing for a while about

0:47:35.239 --> 0:47:38.120
<v Speaker 3>getting data and order. I think that the way that

0:47:38.160 --> 0:47:42.360
<v Speaker 3>he has put it really was very clear and concise

0:47:42.480 --> 0:47:45.120
<v Speaker 3>and actionable as well, which is what I appreciated about

0:47:45.160 --> 0:47:45.520
<v Speaker 3>the chat.

0:47:46.840 --> 0:47:52.799
<v Speaker 2>Yeah, he really talked about this transition into data and analytics,

0:47:52.800 --> 0:47:57.560
<v Speaker 2>the importance of data in financial decision making, and for years,

0:47:57.640 --> 0:47:59.879
<v Speaker 2>you know, we've been talking to New Zealand businesses about

0:48:00.200 --> 0:48:03.759
<v Speaker 2>writing about it, and they're all up for it, and

0:48:03.800 --> 0:48:06.960
<v Speaker 2>some of them are really doing that, doing really smart

0:48:06.960 --> 0:48:09.719
<v Speaker 2>things with data, but we were a bit slower to

0:48:10.280 --> 0:48:14.439
<v Speaker 2>the move to the cloud and getting data in order

0:48:14.560 --> 0:48:18.440
<v Speaker 2>as part of that digital transformation. So a lot of

0:48:18.440 --> 0:48:21.239
<v Speaker 2>businesses talk about this stuff, but are they actually using it?

0:48:21.880 --> 0:48:23.920
<v Speaker 2>And when I took to them sort of off the record,

0:48:23.960 --> 0:48:27.399
<v Speaker 2>they say, well, actually, know where we've done pilots, we're

0:48:27.440 --> 0:48:32.360
<v Speaker 2>doing limited use cases related to data analytics and the like,

0:48:32.480 --> 0:48:34.960
<v Speaker 2>but we don't have the data in the right shape.

0:48:34.960 --> 0:48:37.520
<v Speaker 2>We need to build a data warehouse or a data lake.

0:48:38.120 --> 0:48:42.000
<v Speaker 2>We need to standardize our data and that literally for

0:48:42.040 --> 0:48:44.600
<v Speaker 2>some of them is taking years. So we've seen that's

0:48:44.600 --> 0:48:47.720
<v Speaker 2>why we've seen the rise of snowflake and data Bricks

0:48:47.960 --> 0:48:52.960
<v Speaker 2>and others. The big tech platforms can only do so much.

0:48:53.280 --> 0:48:55.680
<v Speaker 2>It's really up to you, and there's a layer between

0:48:56.160 --> 0:48:59.560
<v Speaker 2>the customer and the big platform. We're all of your

0:48:59.640 --> 0:49:02.880
<v Speaker 2>data potentially is going to be and these companies are

0:49:02.920 --> 0:49:04.440
<v Speaker 2>playing a really valuable role there.

0:49:05.120 --> 0:49:08.719
<v Speaker 3>Yeah, and the couple that we mentioned Data Bricks and Snowflake,

0:49:08.800 --> 0:49:11.439
<v Speaker 3>and these are the ones that have really come out

0:49:12.040 --> 0:49:15.760
<v Speaker 3>swinging and have shown that the value over and over again.

0:49:15.960 --> 0:49:19.319
<v Speaker 3>And Snowflake listed on the NASDAK and has shown really

0:49:19.360 --> 0:49:23.160
<v Speaker 3>great growth since doing that. So you know, its success

0:49:23.880 --> 0:49:26.319
<v Speaker 3>is I think a good indicator of the value that

0:49:26.360 --> 0:49:31.160
<v Speaker 3>it is offering to organizations globally. And you know Tony

0:49:31.320 --> 0:49:33.840
<v Speaker 3>talking about the fact that it can scale up to

0:49:33.880 --> 0:49:37.040
<v Speaker 3>these massive, massive international corporates, but it can also scale

0:49:37.080 --> 0:49:41.759
<v Speaker 3>down to fit the needs of organizations and countries like

0:49:41.840 --> 0:49:44.480
<v Speaker 3>New Zealand. And if we want to be the country

0:49:44.760 --> 0:49:48.840
<v Speaker 3>that is using AI, that is using our data to

0:49:49.040 --> 0:49:52.040
<v Speaker 3>improve our productivity, to enter the brave new digital world

0:49:52.480 --> 0:49:57.160
<v Speaker 3>and kind of stay relevant on a global scale, then

0:49:57.440 --> 0:50:01.880
<v Speaker 3>these kinds of products, these kinds of projects of what

0:50:02.040 --> 0:50:04.480
<v Speaker 3>needs to be done on a bigger scale. And what

0:50:04.600 --> 0:50:08.759
<v Speaker 3>Tony was saying about not doing science experiments anymore, right

0:50:08.840 --> 0:50:12.200
<v Speaker 3>the time for kind of these doing science experiments over

0:50:12.200 --> 0:50:15.000
<v Speaker 3>and over again. The small scale dabbling is kind of

0:50:15.480 --> 0:50:17.760
<v Speaker 3>if you're still in that phase, you might need to

0:50:18.120 --> 0:50:19.560
<v Speaker 3>put a bit of welly behind it and get on

0:50:19.600 --> 0:50:19.719
<v Speaker 3>with it.

0:50:20.719 --> 0:50:23.560
<v Speaker 2>Yeah, yeah, yeah, I mean, I think his advice is

0:50:23.640 --> 0:50:27.239
<v Speaker 2>sort of what we've heard, which is start small, don't

0:50:27.280 --> 0:50:31.000
<v Speaker 2>necessarily go big bang, because if you've designed it wrong,

0:50:31.040 --> 0:50:34.040
<v Speaker 2>suddenly it becomes a very expensive failure. So target a

0:50:34.080 --> 0:50:37.160
<v Speaker 2>part of the business where having great insights into your

0:50:37.239 --> 0:50:41.239
<v Speaker 2>data is going to really help the business. Start that

0:50:42.160 --> 0:50:44.520
<v Speaker 2>experiment a little bit, then grow a bigger But he's

0:50:44.560 --> 0:50:48.920
<v Speaker 2>clearly predicting a major acceleration of AI adoption over the

0:50:48.960 --> 0:50:54.120
<v Speaker 2>next twelve to eighteen months as organizations do that foundational work.

0:50:55.280 --> 0:50:59.960
<v Speaker 2>Trying to get ahead of the curve is a competitive advantage.

0:51:00.160 --> 0:51:03.520
<v Speaker 2>Hopefully that message is getting through in New Zealand. We've

0:51:03.560 --> 0:51:06.839
<v Speaker 2>seen so much research over the last year or so

0:51:06.920 --> 0:51:09.280
<v Speaker 2>to suggest that we're a little bit behind the curve.

0:51:10.719 --> 0:51:15.839
<v Speaker 2>But if companies like Snowflake can help accelerate that, because

0:51:15.880 --> 0:51:19.520
<v Speaker 2>as you say, it scales down to medium sized businesses

0:51:19.600 --> 0:51:24.160
<v Speaker 2>quite well, that's basically where New Zealand plays and a

0:51:24.160 --> 0:51:27.799
<v Speaker 2>lot of those companies have been playing around with co

0:51:27.960 --> 0:51:32.600
<v Speaker 2>pilots and chatbots and AI related applications, so maybe some

0:51:32.680 --> 0:51:35.680
<v Speaker 2>of them have done enough work to actually in twenty

0:51:35.719 --> 0:51:37.960
<v Speaker 2>twenty five and beyond make really good use of AI.

0:51:38.520 --> 0:51:42.520
<v Speaker 2>And again what we've heard from others is emphasizing the

0:51:42.520 --> 0:51:46.000
<v Speaker 2>importance of aligning sort of AI and data initiatives with

0:51:46.080 --> 0:51:51.640
<v Speaker 2>business outcomes and having internal sponsors, people in the executive

0:51:51.960 --> 0:51:55.279
<v Speaker 2>off the business, people on the board who are real

0:51:55.360 --> 0:51:58.440
<v Speaker 2>champions for this. There's no point doing something where the

0:51:58.480 --> 0:52:01.400
<v Speaker 2>CEO and the executive team is sort of saying how

0:52:01.480 --> 0:52:03.440
<v Speaker 2>much is this going to cost? If they're not convinced

0:52:03.440 --> 0:52:06.480
<v Speaker 2>if the value of investing in these sorts of platforms

0:52:06.560 --> 0:52:08.640
<v Speaker 2>to the business, you've got a problem. They've all got

0:52:08.680 --> 0:52:11.640
<v Speaker 2>to be on board. So thanks very much to Tony

0:52:11.640 --> 0:52:15.200
<v Speaker 2>Shaw from Snowflake for his thoughts on the data landscape

0:52:15.480 --> 0:52:17.720
<v Speaker 2>and what's needed to spur AI adoption.

0:52:18.400 --> 0:52:20.440
<v Speaker 3>We'll be touching on that and next week's episode two,

0:52:20.480 --> 0:52:22.920
<v Speaker 3>and we have a panel of AI experts joining us

0:52:22.920 --> 0:52:25.600
<v Speaker 3>to look at the year in AI, big developments in

0:52:25.640 --> 0:52:29.239
<v Speaker 3>the technology, regulation and government's use of AI, and what

0:52:29.360 --> 0:52:31.399
<v Speaker 3>may be in store in twenty twenty.

0:52:31.080 --> 0:52:33.600
<v Speaker 2>Five Show notes for the Business of Tech are in

0:52:33.640 --> 0:52:37.040
<v Speaker 2>the podcast section at Business Desk dot co dot nz,

0:52:37.200 --> 0:52:39.920
<v Speaker 2>where you can stream this podcast in full every week.

0:52:40.200 --> 0:52:44.480
<v Speaker 2>It's also available from iHeartRadio or your podcast platform of choice.

0:52:44.600 --> 0:52:46.400
<v Speaker 3>Get in touch with your feedback and we'd love to

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<v Speaker 3>hear your suggestions for upcoming tests too. You can email

0:52:49.560 --> 0:52:52.160
<v Speaker 3>me Ben at business Desk dot Co dot and z,

0:52:52.520 --> 0:52:52.719
<v Speaker 3>and you.

0:52:52.719 --> 0:52:55.120
<v Speaker 2>Can find both of us on x and LinkedIn, where

0:52:55.120 --> 0:52:57.680
<v Speaker 2>you can follow the Business of Tech page for all

0:52:57.719 --> 0:52:58.520
<v Speaker 2>of our updates.

0:52:58.640 --> 0:53:00.640
<v Speaker 3>That's it for this week. We'll be back talk AI

0:53:01.040 --> 0:53:03.680
<v Speaker 3>and way through the election debris next Thursday.

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<v Speaker 2>We'll catch you in

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<v Speaker 1>Mm hmm