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Speaker 1: It is now clear that we've achieved the most incredible

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Speaker 1: political there.

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Speaker 2: Look what happened?

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Speaker 3: Is this crazy? But it's a political victory that.

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Speaker 1: Our country has never seen before, nothing like this.

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Speaker 2: Look what happened. Indeed, Donald Trump is heading back to

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Speaker 2: the White House after a stronger than expected showing in

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Speaker 2: the presidential election, with the Senate and the House of

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Speaker 2: Representatives destined to also be in Republican control.

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Speaker 3: Which has major implications for Trump's policy agenda, including a

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Speaker 3: host of tech created issues from AI regulation to cryptocurrencies,

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Speaker 3: data and privacy law reform, and the tech arms race

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Speaker 3: with China.

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Speaker 2: This week, on the Business of Tech, powered by Two

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Speaker 2: Degrees Business, we look at what a Trump administration means

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Speaker 2: for tech, rold at influential tech billionaires and platforms played

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Speaker 2: in the election campaign. I'm Peter Griffin and.

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Speaker 3: I'm Ben Moore. Coming up on the show as our

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Speaker 3: featured guest. Tony Shore, the New Zealand country manager for Snowflake,

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Speaker 3: a company that isn't as well known as Microsoft, AWS

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Speaker 3: or Google, but is working with those companies and a

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Speaker 3: rapidly growing roster of Kiwi companies to help them store

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Speaker 3: and manage their data, run data analytics and use AI.

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Speaker 2: Yeah, Tony has some great advice for companies eyeing up data, analytics,

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Speaker 2: machine learning, and AI, which is really about the importance

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Speaker 2: of getting your data house in order before you delve

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

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Speaker 2: But first we need to sift through the ashes of

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

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Speaker 2: talk about the election on this episode because normally we

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Speaker 2: record the podcast on a Tuesday afternoon, which was just

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Speaker 2: before the election. Terrible timing, but yes.

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Speaker 3: But I happen to you got quite sick on Tuesday night,

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Speaker 3: so we pushed the publication of the Business of Tech

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

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Speaker 2: Friday, Yeah, which sort of means we can reflect on

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Speaker 2: what went down and look at what Trump may have

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Speaker 2: in store on the tech front. And you know, I

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Speaker 2: think before we get into that, we should really talk

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Speaker 2: about the influence that tech billionaires and their platforms have

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Speaker 2: had on this presidential election. I mean it's pretty clear

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Speaker 2: in his victory speech from Florida, Trump very much talking

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Speaker 2: about Elon Musk. You know, last time around, Elon Musk

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Speaker 2: was relatively close to Trump. I remember that iconic meeting

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Speaker 2: where he basically called in all the heads of the

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Speaker 2: tech companies to Trump Tower and sort of had a

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Speaker 2: chat with him. Peter Thiel was there, I think Tim

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Speaker 2: Cook from Apple was there. Elon Musk was there, so

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Speaker 2: it all seems sort of quite Trump appointed him to

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Speaker 2: a couple of advisory councils and Musk ended up quitting them,

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Speaker 2: so there was a bit of a falling out between

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Speaker 2: him and Trump. He's really rekindled that relationship, and you

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Speaker 2: know Elon Musk, I think he sees as being quite

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Speaker 2: key to the success that he's had, so he'll need

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Speaker 2: to repay Elon Musk. Just talk about Musk taking some

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Speaker 2: sort of role the Department of Government Efficiency DOGE, so

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Speaker 2: I think this will be pivotal. You've also got Musk

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Speaker 2: clearly has his fingers in so many different pis, so

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Speaker 2: many different companies, so lots of scope for conflicts of

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Speaker 2: interest there. And of course with X and I think

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Speaker 2: we saw this definitely the morphing of X in the

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Speaker 2: last year or so during that campaign really into a

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Speaker 2: conservative stronghold that's also been influential. So all of these

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Speaker 2: things are intertwining. I think in Trump's favor.

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Speaker 3: Yeah, it definitely has been a swell, a more public

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Speaker 3: swell of support for Donald Trump from that kind of

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Speaker 3: tech elite in the last over the selection. And I

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Speaker 3: think it speaks a little bit to the way maybe

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Speaker 3: that Trump does approach these these kinds of companies where

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Speaker 3: he wants their favor, He wants them to kind of

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Speaker 3: be on his side, and as in a return, he'll

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

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Speaker 3: him use these kind of anti monopoly laws to kind

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Speaker 3: of put pressure on the ones that maybe weren't as

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Speaker 3: vocal of support for him in the past. Not to

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Speaker 3: say that there's a direct link there, but it's hard

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Speaker 3: to not see some kind of correlation at least. So

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Speaker 3: Elon Musk, with his ambitions for Tesla and for SpaceX

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Speaker 3: and for a lot of the defense contracts, must be

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Speaker 3: really glad to be in Trump's good graces now, especially

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Speaker 3: because Trump has talked quite a lot about increasing the

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Speaker 3: amount of private companies contracting to defense and working and

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Speaker 3: spending a lot more money on defense, which will mean

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Speaker 3: more money in the pockets of these tech companies working

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Speaker 3: on defense technologies.

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

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Speaker 2: Trump has been very anti electric vehicle, so will he

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Speaker 2: now pivot to suddenly being influenced by Elon Musk along

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

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

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

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Speaker 2: the energy industry, the fossil fuel industry, but definitely on SpaceX,

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Speaker 2: very much in bed with the US defense, so there'll

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Speaker 2: be synergies there. And as yea, his other business obviously

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Speaker 2: x AI and Grock building, you know, the biggest AI

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Speaker 2: centric supercomputer stuff like that. I can see Musk sort

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Speaker 2: of putting proposals to Trump about what the government should

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Speaker 2: be doing with AI, and I think on a lot

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

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

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Speaker 2: very influenced by the people around him. So you've got

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

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Speaker 2: who has a previous life in venture capital, worked with

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Speaker 2: Silicon Valley to fund companies, so he's very much embedded

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Speaker 2: with some of that tech elite. You've got Peter Thiel

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

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Speaker 2: founder of Pallenteer and AI company that does a lot

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

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Speaker 2: share prices surging at the moment on the back of

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

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Speaker 2: sort of cluster of people in Trump's orbit who have

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Speaker 2: strong ideas about where the tech world should go, and

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Speaker 2: he's listening to them and he owes them. A lot

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Speaker 2: of them put money into these super packs to get

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Speaker 2: him into power. They didn't raise as much money as

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Speaker 2: Kamala Harris did in the Democrats, so it was all

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Speaker 2: a bit of a waste of time and money. But

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Speaker 2: he now has a lot of bills falling due, and

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Speaker 2: what is that going to mean for the flavor of

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Speaker 2: his policies. That's going to be the big question.

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Speaker 3: Yeah, I think a lot of it is going to

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

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Speaker 3: a big flag that Trump will be waiving is getting

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Speaker 3: out of the way of these particularly those Silicon Valley companies.

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Speaker 3: I think the likes of Google and Microsoft may see

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Speaker 3: some continuation of those anti trust kind of approaches. But

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Speaker 3: when it comes to the those on the cutting edge

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Speaker 3: with AI and the ones that are kind of more

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Speaker 3: and Donald Trump's in a circle, will start to see

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Speaker 3: that a lot of deregulation there seems to be pretty

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Speaker 3: explicit in what he's been saying. And the same goes

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Speaker 3: for cryptocurrency as well.

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

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Speaker 2: a bit of a U turn. He was quite sort

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Speaker 2: of hawkish against crypto a few years ago, and again

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Speaker 2: I think this is people getting to him, and probably Musk,

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Speaker 2: who you know is a big fan of dogecoin and

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Speaker 2: is a crypto advocate as well, basically saying to him, no,

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Speaker 2: you need to support this. He's done a U turn.

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Speaker 2: He wants minimal crypto regulation. He'll probably limit the rather

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Speaker 2: hawkish moves by the SEC in the US to regulate

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Speaker 2: cryptocurrencies and those digital asset markets. So that's all well

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Speaker 2: and good, but is that going to lead to more

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Speaker 2: of the sort of FTX style implosions that we've seen.

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

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Speaker 2: he cut tax and red tape. Businesses love that. It

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Speaker 2: means they can spend more money on R and D

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Speaker 2: and return more money to shareholders. So any company, particularly

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Speaker 2: those big tech companies that make a lot of profit,

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Speaker 2: they'll love that. But it was Trump after all, that

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Speaker 2: kicked off a lot of that antitrust stuff a few

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Speaker 2: years ago, So yeah, will he continue that and see

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Speaker 2: the breakup of Google and others. But I agree with you.

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

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

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Speaker 2: But he does want to see all of the red

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Speaker 2: tape and the restrictions removed from really innovative companies, and

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Speaker 2: at the moment they're the AI one. So I don't

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Speaker 2: see him carrying forward some of those executive orders around

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Speaker 2: AI that Biden put in place. I think we he'll

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Speaker 2: dial that back sognificantly.

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Speaker 3: Yeah. I think the other area where we're going to

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Speaker 3: see a big retraction in the US at least is

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

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Speaker 3: a lot of eggs in the green tech basket here

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Speaker 3: in New Zealand with our startups, and that may indicate

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Speaker 3: that the US is no longer a viable entry point

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Speaker 3: for these companies to really scale. So maybe refocusing more

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Speaker 3: on the EU. If the US was kind of a

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Speaker 3: big part of your strategy.

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Speaker 2: Yeah, and you know there have been as part of

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Speaker 2: the Big Reconstruction Act that Biden passed after COVID, there

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Speaker 2: was green tech funding in there. So whether that will continue.

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Speaker 2: One area that will continue which Trump and the Democrats

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Speaker 2: are on the same page on as the semiconductor industry,

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Speaker 2: the Chips Act. So Trump is very much of the

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Speaker 2: view that we need more local production in the US

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Speaker 2: of semiconductors, the really high end important stuff that runs

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Speaker 2: ai to reduce reliance in the global supply chain on Taiwan,

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Speaker 2: which is very vulnerable to attack from China. So he'll

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Speaker 2: carry on that sort of stuff, things like five G.

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Speaker 2: You know, he's expressed his dismay that a lot of

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Speaker 2: that technology is provided by European companies. So again Biden

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Speaker 2: was on the same page. And I think for Trump

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Speaker 2: what it really all is about is taking on China

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Speaker 2: and that continuing sort of pressure, whether it's through the

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Speaker 2: form of taris or big tariffs on stuff coming from

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Speaker 2: China into the US sixty percent tariffs potentially, which is

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Speaker 2: quite staggering, but really that polarization of technology between the

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Speaker 2: Western world and the Chinese world, and We've seen China

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Speaker 2: in the intervening few years since Trump was out of office,

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Speaker 2: building its own operating systems, trying to generate higher capacity

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Speaker 2: semiconductors to go into phones and AI devices. Trump will

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Speaker 2: basically accelerate that further by putting more export controls on

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Speaker 2: the exports of high technology to China, tariffs and local productions.

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Speaker 2: So I think we'll just see an acceleration of that.

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Speaker 3: And that's also going to roll over to New Zealand

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Speaker 3: a little bit in terms of trade agreements. He's talked

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Speaker 3: about getting rid of the Indo Pacific Partnership trade Agreement,

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Speaker 3: which would impact New Zealand. So how that will interact

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Speaker 3: with New Zealand's tech exports to the US would not

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Speaker 3: one hundred percent clear at the moment, but you know

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Speaker 3: there is potentially some impact there.

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Speaker 2: Yeah. And the other sort of local angle I guess

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Speaker 2: is orcus. You know, this agreement this packed between Australia,

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Speaker 2: the US and the UK really about submarines, but you've

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Speaker 2: got orcust Pillar two, which is about other advanced technologies

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Speaker 2: like AI, like quantum computing, stealth technologies, you know, high

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Speaker 2: end military stuff. And I've been quite supportive of the

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Speaker 2: idea of New Zealand being involved in Pillar two. Not

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Speaker 2: necessarily around nuclear submarines or anything like that, but Pillar two,

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Speaker 2: these advanced technologies, we should have a hand with our

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Speaker 2: allies and developing those. And I think, you know, Orcust

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Speaker 2: has run into some trouble. I mean, this submarine deal

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Speaker 2: is so vastly expensive. Whether it will actually come to

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Speaker 2: fruition is anyone's guess. I think the Australians are starting

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Speaker 2: to realize what they've signed up for is massive. But

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Speaker 2: the other you know, you've got South Korean others Japan

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Speaker 2: are saying, hey, we want it on Pillar two because

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Speaker 2: they're starting to see some of the stuff that the Americans,

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Speaker 2: the Brits and the Aussies are working on and saying,

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Speaker 2: you know, we want to see that the table in

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Speaker 2: developing that stuff because there is a greater threat from China,

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Speaker 2: so let's work on this together. Trump will just carry

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Speaker 2: on thinking, I think around orcus he sees that as

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Speaker 2: a way to shore up support military support among allies

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Speaker 2: in the Pacific. Whether that will encourage New Zealand to

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Speaker 2: join or maybe will there be more pressure with a

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Speaker 2: new you right leaning ambassador in this country, Will there

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Speaker 2: be more pressure for New Zealand to actually put its

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Speaker 2: cards on the table and join Orcus. I think that's

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Speaker 2: a possibility.

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Speaker 3: It's hard to see exactly where we're going in terms

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Speaker 3: of the ramifications. You know, with the potential for a

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Speaker 3: Harris government, it was a lot more of the same,

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Speaker 3: but a Trump government because his rhetoric can be quite inconsistent.

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Speaker 3: You know, there is some stuff that we can guess about,

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Speaker 3: but at the end of the day, it's really going

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Speaker 3: to be just reacting as things happen. So it's going

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Speaker 3: to be really important to actually pay attention, I think,

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Speaker 3: to what is actually happening rather than what is being

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Speaker 3: said through Trump presidency, And if.

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Speaker 2: His last stint as president has anything to go by,

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Speaker 2: it'll be those key personalities around him because he really

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Speaker 2: is a bit of an empty vessel in terms of

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Speaker 2: his thinking on some of these issues, particularly around technology. Now,

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Speaker 2: just listening to him explain on election night, you know,

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Speaker 2: the starship returning to Earth, you know, when he was

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Speaker 2: praising you on Muscus, just clear he doesn't really understand

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Speaker 2: this stuff at all, which is fine, but it's the

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Speaker 2: people around him and the worry I think in the

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Speaker 2: US at the moment is you know, this paranoia about

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Speaker 2: the deep state in the US, this shadowy sort of

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Speaker 2: left leaning cabal that runs America that he's been trying

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Speaker 2: to root out. Is he just going to replace people

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Speaker 2: at the SEC, his top tech advisors, people responsible for

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Speaker 2: climate change policy. Is he just going to replace them

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Speaker 2: with political appointees who don't really care about the evidence

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Speaker 2: or the science or what technical advisors suggest is the

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Speaker 2: right thing to do. And he's just going to take

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Speaker 2: the advice off a small group of very wealthy, right

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Speaker 2: leaning tech elites who he owes big time because they

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Speaker 2: helped get him into office, you know, judging by past performance,

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Speaker 2: that's what he tends to do. He surrounds himself with

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Speaker 2: people who are loyal, but people he also relied on

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Speaker 2: to get into office first time round. I think we'll

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Speaker 2: see a lot more of that unfortunately.

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Speaker 3: Yeah, it really is about an exchange of wealth and

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Speaker 3: favors and keys and power and deals. It really is

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Speaker 3: all about the deals.

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Speaker 2: It's transactional with Trump, and people have advised you know,

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Speaker 2: if he does get in, you've got to treat it

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Speaker 2: as a transaction with whether you're negotiating what to do

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Speaker 2: with Ukraine or a trade deal with a country. It's transactional.

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Speaker 2: You need to be in that mindset dealing with this guy.

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Speaker 2: So maybe that's the approach that maybe we should take

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Speaker 2: as well. Absolutely, so clearly it's going to be an

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Speaker 2: interesting year. Head We'll keep you posted and give our

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Speaker 2: analysis on everything related to tech as the Trump administration

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Speaker 2: settles in. But Ben, whenever we interview companies around some

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Speaker 2: of these issues like AI, predictive analytics, and all the

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Speaker 2: cool things businesses can technically do these days with the

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Speaker 2: data generated by the businesses, we get the same surprising response.

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Speaker 3: Yeah, the conversation typically grinds to a halt, and we

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Speaker 3: are told that a lot of our businesses just don't

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Speaker 3: have their data in the right places, in the right

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Speaker 3: formats to do any of that.

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Speaker 2: So talking about it's a bit of a waste of

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Speaker 2: time if the basics really aren't done well.

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Speaker 3: Which is why we're hearing a lot more from companies

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Speaker 3: like Snowflake and data Bricks, companies that have emerged in

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Speaker 3: recent years to help organizations manage that data.

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Speaker 2: They're basically data warehousing and analytics platforms that try to

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Speaker 2: get all your data in one place, process it in

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Speaker 2: a uniform and secure way and interact with the various

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Speaker 2: applications you're using to run your business. I was actually

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Speaker 2: staying at a hotel in Awkant recently and found myself

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Speaker 2: walking into the middle of a Snowflake conference. It was

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Speaker 2: actually quite a big affair.

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Speaker 3: Well, data is a big affair now, it's big business

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Speaker 3: and Snowflake has around two hundred customers in New Zealand

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Speaker 3: to date. It did around one hundred million dollars in

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Speaker 3: revenue last year just across Australia and New Zealand, according

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Speaker 3: to its financial accounts filed with the company's office.

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Speaker 2: And spending on data, warehousing and platforms is really growing fast.

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Speaker 2: So Ben, this is a timely interview with Tony Shaw,

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Speaker 2: who's been around a tech industry for a long time

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Speaker 2: since at NCR, dell, IBM, MuleSoft as well. Let's listen

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Speaker 2: to your interview with Tony Shaw and come back for

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Speaker 2: some thoughts on the back end.

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Speaker 3: Thank you so much, Tony for joining us on the

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Speaker 3: Business of Tech podcast. It's really great to have you here.

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Speaker 3: Why don't we start with just a little bit of

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Speaker 3: background about who you are and what you do.

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Speaker 1: Oh fantastic, Heyn, Thank you so much for having us

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Speaker 1: on board today. My name is Tony Shaw. I'm the

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Speaker 1: country manager for Snowflake in New Zealand. I've been with

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Speaker 1: the company just coming up to six years now, so

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Speaker 1: quite a long time to be with one organization. But

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Speaker 1: I've always been in tech and for a long time

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Speaker 1: in analytics. I originally started my career working for NCR

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Speaker 1: as a financial analyst and pricing and planning and using

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Speaker 1: data and realizing how important it can be to make

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Speaker 1: financial decisions. And then from there I moved into more

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Speaker 1: of the sales and business development side of things, both

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Speaker 1: in New Zealand and I had a long time in London,

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Speaker 1: and then predominantly in the data and analytics side of things.

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Speaker 3: Do you want to share maybe the perception of data

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Speaker 3: maybe pre your Snowflake time, and then how it's changed

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Speaker 3: since then?

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Speaker 1: Yeah, no problem. I think data's always been important. Organizations

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Speaker 1: have always had the aspiration to be using data better

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Speaker 1: to make better and informed decisions. But what's happened in

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Speaker 1: the last five to ten years is the accessibility of

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Speaker 1: the information has become so much easier. The cost to

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Speaker 1: get that data and analyze it has dropped significantly, and

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Speaker 1: that's opened up massive opportunities because it allows organizations to

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Speaker 1: bring all of their data from all of their disparate

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Speaker 1: systems into one environment where that structured data unstructured data,

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Speaker 1: and then can imply analytics to that. Historically, it used

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Speaker 1: to be a lot of backwards looking, a lot of

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Speaker 1: reporting what did happen? And now where we're seeing is

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Speaker 1: a lot more predictive analytics, opening up the information to

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Speaker 1: a lot more of the business users and allowing that

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Speaker 1: decision making to be a lot more in the front

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Speaker 1: line rather than just the back office. So we're seeing

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Speaker 1: that dissemination of information across multiple channels, multiple users, and

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Speaker 1: the ease of use. So it's no longer just a

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Speaker 1: back office function as lines of business making decisions every

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Speaker 1: day which are moving the dial within those organizations.

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Speaker 3: Right. And you know, traditionally when we think of data,

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Speaker 3: we think big data, right especially these days, and we

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Speaker 3: think big companies. But that's increasingly changing as well. I

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Speaker 3: would imagine, like you say, as the accessibility, the affordability

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Speaker 3: of data and data analytics tools are starting to shift

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Speaker 3: a little bit, are you starting to see smaller companies,

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Speaker 3: you know, not necessarily your one person companies, but maybe

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Speaker 3: your medium size businesses gaining a better understanding of how

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Speaker 3: to utilize their data.

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Speaker 1: Yeah, it's been remarkable. Since we started the business in

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Speaker 1: New Zealand in twenty nineteen, we were looking at what

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Speaker 1: is the segments and what is the segmentation and customers

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Speaker 1: that we're going to look to try and require. We

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Speaker 1: had two stomers in New Zealand when we started the

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Speaker 1: business here and now we've got north of two hundred,

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Speaker 1: and it was really interesting. We started to think around

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Speaker 1: that segmentation and we thought it might be some mid

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Speaker 1: tier customers and then you know, maybe we can work

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Speaker 1: our way up or down across the different spectrum of

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Speaker 1: size and scale and complexity. But what happened was we've

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Speaker 1: got organizations of all size and scale very early. And

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Speaker 1: I think one of the things with Snowflake, and one

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Speaker 1: of the reasons why we had such fantastic adoption, was

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Speaker 1: it's the ability to scale down to New Zealand size companies,

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Speaker 1: not just being able to scale up. So there's the

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Speaker 1: global organizations, you know, there's the capital ones and the

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Speaker 1: sinespres etc. But within New Zealand because the platform scales

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Speaker 1: down and you only pay for what you use on

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Speaker 1: a true consumption basis. We've been able to scale to

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Speaker 1: organizations that are getting enterprise enterprise grade capability, but they're

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Speaker 1: only paying for what they use based on the size

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Speaker 1: of the organization or how much they actually need to

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Speaker 1: use of the platform.

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Speaker 3: To what extent a New Zealand companies really using all

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Speaker 3: of the capabilities of Snowflake. Are we up there in

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Speaker 3: the most advanced users or are we kind of just

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Speaker 3: using the very basics because we're smaller.

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Speaker 1: And yeah, absolutely. We just had a conference last week.

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Speaker 1: It was unbelievable. We had over a thousand people there,

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Speaker 1: which makes it the largest data and analytics event in

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Speaker 1: New Zealand, and we had some fabulous customers. So we'd

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Speaker 1: organizations like in New Zealand, Tavado, Aura in zed Health,

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Speaker 1: in zet, super one, end, z MITA ten, Spark, shares

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Speaker 1: e'se the kind of list goes on and it was

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Speaker 1: a really great opportunity for organizations to share what they're

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Speaker 1: doing and how they deliver value from the platform, and

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Speaker 1: also to build that community so organizations can network with

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Speaker 1: their peers and learn from each other. But in terms

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Speaker 1: of taking on the global stage, shares Y's is one

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Speaker 1: of our fantastic customers. They actually recently won the APJA

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Speaker 1: Data Driver Award for powered by So what that means

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Speaker 1: is they're powering their application using Snowflake to help drive

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Speaker 1: the adoption and understand their customer behaviors in order to

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Speaker 1: deliver a better service. And they've just had phenomenal growth.

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Speaker 1: So you know, they've got seven hundred thousand customers. So

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Speaker 1: we're taking on the world. Shares is being successful across

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Speaker 1: here and across in Australia, and we've got a number

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Speaker 1: of tech startups that we're working with who are winning

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Speaker 1: awards and delivering really fantastic results for their business on

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Speaker 1: a global scale.

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Speaker 3: Fantastic. Yeah, so it sounds like you've got some real

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Speaker 3: power users. Then that's what you're.

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Speaker 1: Saying, unbelievable. It's both the business users. So we had

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Speaker 1: the co CEOs presenting around how that's driving value. Data

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Speaker 1: analysts we have technical capabilities. It's the ability to work

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Speaker 1: with all of the different personas across an organization, not

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Speaker 1: just the technical people though they love the platform, so

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Speaker 1: the architects, the engineers, the really deep data people, but

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Speaker 1: then also the people who are consuming it, so technically

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Speaker 1: literate business analysts people just writing natural language questions in English,

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Speaker 1: executive writing. Sorry, just analyzing what's happened and what's going

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Speaker 1: to happen and their business. That's across the board, those

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Speaker 1: different personas that all use data in a slightly different nuance,

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Speaker 1: but they want consistency of information, they want high quality,

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Speaker 1: they want real time, they want accurate information so they

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Speaker 1: can make those decisions.

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Speaker 3: Cool. Now, obviously you can. It's great to talk up

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Speaker 3: to customers that are doing really awesome stuff, but New

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Speaker 3: Zealand's definitely not perfect nowhere is in terms of how

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Speaker 3: it's utilizing data. So what are some of the areas

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Speaker 3: that you're seeing New Zealand lagging behind? Maybe some New

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Speaker 3: Zealand companies where you think, you know some areas of

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Speaker 3: focus could be to improve the usage of data within

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Speaker 3: New Zealand.

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Speaker 1: I think that the pitfalls that we always see is

436
00:25:48,119 --> 00:25:53,280
Speaker 1: making sure that there's executive sponsorship and outcomes that the

437
00:25:53,400 --> 00:25:56,639
Speaker 1: organization is trying to drive towards. So it's really important

438
00:25:56,760 --> 00:26:02,359
Speaker 1: that it doesn't become a science experiment or a program

439
00:26:02,400 --> 00:26:06,840
Speaker 1: that's just for the IT users. What the successful organizations

440
00:26:06,880 --> 00:26:09,320
Speaker 1: do is they've got very strong alignment to a specific

441
00:26:09,400 --> 00:26:14,040
Speaker 1: business outcome, whether that's a finance program looking at receivables

442
00:26:14,119 --> 00:26:19,000
Speaker 1: or finance transformation, whether it's marketing looking at customer experience, NPS, churn,

443
00:26:19,080 --> 00:26:22,560
Speaker 1: cross sale, etc. Or operations to streamline the efficiency with

444
00:26:22,600 --> 00:26:25,439
Speaker 1: which the organization works in it has to have that

445
00:26:25,520 --> 00:26:28,919
Speaker 1: business outcome that everybody can anchor themselves and align to.

446
00:26:29,440 --> 00:26:31,760
Speaker 1: When you've got that, that goes a long way to

447
00:26:31,800 --> 00:26:34,880
Speaker 1: making sure this program's success. And then the usual governance

448
00:26:34,920 --> 00:26:38,320
Speaker 1: across the program and making sure that there is steps

449
00:26:38,359 --> 00:26:40,600
Speaker 1: along the way that people are measuring to make sure

450
00:26:40,640 --> 00:26:43,120
Speaker 1: that that outcome happens. When you start to get those

451
00:26:43,119 --> 00:26:45,960
Speaker 1: sorts of things, then everything else just falls into place.

452
00:26:46,600 --> 00:26:52,000
Speaker 3: Cool. Now, let's say I'm one of the New Zealand

453
00:26:52,000 --> 00:26:55,040
Speaker 3: companies that hasn't started to get deep into data yam.

454
00:26:55,040 --> 00:26:57,639
Speaker 3: You know, maybe a medium sized company who is starting

455
00:26:57,680 --> 00:27:01,760
Speaker 3: to think about the potential there. What are my first

456
00:27:01,880 --> 00:27:03,080
Speaker 3: kind of steps?

457
00:27:03,440 --> 00:27:07,080
Speaker 1: The first step is defining what dial within the business

458
00:27:07,119 --> 00:27:09,600
Speaker 1: you're trying to move and what is that outcome you're

459
00:27:09,600 --> 00:27:13,280
Speaker 1: trying to achieve. So say it's a marketing outcome around

460
00:27:13,320 --> 00:27:16,120
Speaker 1: cross seal. Make sure that those objectives and those metrics

461
00:27:16,160 --> 00:27:19,600
Speaker 1: are well understood and documented, and then start small and

462
00:27:19,640 --> 00:27:22,439
Speaker 1: try and deliver that program so that you deliver that

463
00:27:22,440 --> 00:27:25,600
Speaker 1: specific outcome, get the win, and then build upon that.

464
00:27:26,240 --> 00:27:28,439
Speaker 1: You need to paint the vision to the organization in

465
00:27:28,520 --> 00:27:31,639
Speaker 1: terms of what is the analytic capability going to deliver.

466
00:27:32,280 --> 00:27:34,920
Speaker 1: So you need to have a vision and where we're

467
00:27:34,920 --> 00:27:37,520
Speaker 1: going as an organization, but you also need to have

468
00:27:37,680 --> 00:27:40,040
Speaker 1: a specific outcome that you're driving towards that you can

469
00:27:40,040 --> 00:27:43,320
Speaker 1: build on that success. Then you need to drive where

470
00:27:43,320 --> 00:27:44,840
Speaker 1: do I get the data from and how do I

471
00:27:44,840 --> 00:27:47,679
Speaker 1: get high quality information to solve that business problem and

472
00:27:47,720 --> 00:27:50,399
Speaker 1: answer the questions that you're looking to define or answer sorry,

473
00:27:51,119 --> 00:27:54,720
Speaker 1: And then it's getting the technical teams aligned to find

474
00:27:54,760 --> 00:27:58,160
Speaker 1: that data, source that data cleanse that's high quality decision

475
00:27:58,160 --> 00:28:00,520
Speaker 1: making because you want to make sure sure that the

476
00:28:00,520 --> 00:28:04,520
Speaker 1: information that's being used is of quality so that the

477
00:28:04,520 --> 00:28:06,480
Speaker 1: decisions out the back of it are influenced.

478
00:28:06,560 --> 00:28:08,919
Speaker 3: What does that mean cleansing data? Like, what does that

479
00:28:08,960 --> 00:28:11,960
Speaker 3: actually in real terms mean? Because if I'm a company

480
00:28:12,000 --> 00:28:14,160
Speaker 3: that's been around for twenty years, I've got a bunch

481
00:28:14,200 --> 00:28:17,240
Speaker 3: of spreadsheets and PDFs and all this kind of stuff

482
00:28:17,560 --> 00:28:20,520
Speaker 3: and it's ordered, it's in folders. We know where everything is.

483
00:28:21,119 --> 00:28:22,960
Speaker 3: But is that clean? Is that clean enough.

484
00:28:23,560 --> 00:28:26,320
Speaker 1: It depends a lot of the times those spreadsheets have

485
00:28:26,359 --> 00:28:29,239
Speaker 1: been built up by a couple of specific people. They

486
00:28:29,320 --> 00:28:32,200
Speaker 1: might be suitable for that use case or that specific

487
00:28:32,960 --> 00:28:35,640
Speaker 1: piece of information you're looking to deliver. An example, when

488
00:28:35,680 --> 00:28:38,200
Speaker 1: I was a pricing analyst, we used to have huge

489
00:28:38,200 --> 00:28:41,720
Speaker 1: amounts of spreadsheets everywhere that have interconnected links, and we

490
00:28:41,840 --> 00:28:44,520
Speaker 1: put out some pricing models. Then you'd come back about

491
00:28:44,520 --> 00:28:46,800
Speaker 1: a month later and change something because you'd found a

492
00:28:46,840 --> 00:28:49,320
Speaker 1: mistake in the spreadsheet in the formulas, and that would

493
00:28:49,400 --> 00:28:52,960
Speaker 1: change the entire pricing model. And you'd be sitting there going,

494
00:28:53,080 --> 00:28:56,000
Speaker 1: oh my goodness, now I've just completely stuffed this up.

495
00:28:56,480 --> 00:28:58,160
Speaker 1: You change something else to get it back, and then

496
00:28:58,160 --> 00:29:01,440
Speaker 1: the numbers would all change back again. Spreadsheets, whilst they're

497
00:29:01,480 --> 00:29:04,600
Speaker 1: across every single organization, are kind of the bane of

498
00:29:05,240 --> 00:29:09,800
Speaker 1: any enterprise organization's life because there is no real auditability.

499
00:29:09,840 --> 00:29:12,720
Speaker 1: So when I talk about high quality data, it's getting

500
00:29:12,720 --> 00:29:15,800
Speaker 1: that data from those source systems, making sure that it's

501
00:29:15,960 --> 00:29:20,200
Speaker 1: usable and in a format that's understandable, but it's consolidated

502
00:29:20,240 --> 00:29:22,920
Speaker 1: across multiple touch points so that you've got a consistent

503
00:29:23,000 --> 00:29:26,120
Speaker 1: view of customer, and then involving the business teams to

504
00:29:26,160 --> 00:29:29,240
Speaker 1: define what is the rules and logic so that everybody

505
00:29:29,320 --> 00:29:31,320
Speaker 1: knows what the definition of a customer is, what is

506
00:29:31,320 --> 00:29:34,200
Speaker 1: a definition of revenue or profit or what happens to be,

507
00:29:34,640 --> 00:29:37,160
Speaker 1: and then everyone's working off that consistent set of information.

508
00:29:37,680 --> 00:29:41,440
Speaker 1: We've all been in meetings where people are arguing about

509
00:29:41,480 --> 00:29:44,240
Speaker 1: the data rather than what they do with that information.

510
00:29:44,880 --> 00:29:46,680
Speaker 1: So what we want to try and do is consolidate

511
00:29:46,720 --> 00:29:49,640
Speaker 1: the information so that it's a single view across the business.

512
00:29:50,160 --> 00:29:52,040
Speaker 1: And then people are thinking about what are the decisions

513
00:29:52,040 --> 00:29:53,760
Speaker 1: I make, not hey, is that the right one? Am

514
00:29:53,760 --> 00:29:57,760
Speaker 1: I questioning the actual data validity rather than what I

515
00:29:57,760 --> 00:29:58,320
Speaker 1: can do with it?

516
00:29:59,520 --> 00:30:01,240
Speaker 3: What's the kind what's the kind of talent that you

517
00:30:01,280 --> 00:30:03,840
Speaker 3: would need to do that? Do you need to hire

518
00:30:03,920 --> 00:30:07,360
Speaker 3: an house data scientist? Is it okay to just kind

519
00:30:07,360 --> 00:30:10,240
Speaker 3: of get a consultant into kind of do some data

520
00:30:10,280 --> 00:30:11,680
Speaker 3: stuff for you to get you ready.

521
00:30:11,960 --> 00:30:14,320
Speaker 1: I think consultants have a place and they've got a

522
00:30:14,360 --> 00:30:18,320
Speaker 1: lot of experience that can bring to bear on organizations.

523
00:30:18,680 --> 00:30:22,360
Speaker 1: But I think the organizations themselves have a responsibility and

524
00:30:22,400 --> 00:30:26,280
Speaker 1: they have to have a capability internally. This can't be

525
00:30:26,400 --> 00:30:28,600
Speaker 1: done to an organization. You have to do it with

526
00:30:28,720 --> 00:30:32,560
Speaker 1: the organization and the people within the enterprise or the company.

527
00:30:32,960 --> 00:30:35,760
Speaker 1: They know what the business is trying to achieve, they

528
00:30:35,840 --> 00:30:38,520
Speaker 1: know where to get the data from. So you need

529
00:30:38,560 --> 00:30:41,440
Speaker 1: to have a set of skills within the organization, and

530
00:30:41,480 --> 00:30:44,040
Speaker 1: that skills from a technical capability to work out where

531
00:30:44,080 --> 00:30:45,560
Speaker 1: does the data come from and how do I get

532
00:30:45,560 --> 00:30:48,200
Speaker 1: it and then also how do I put that into

533
00:30:48,360 --> 00:30:50,720
Speaker 1: the hands of the users so they've got confidence that

534
00:30:50,760 --> 00:30:53,600
Speaker 1: they can start to drive analysis from it. But the

535
00:30:53,640 --> 00:30:56,640
Speaker 1: internal capability is critical. One of the things we're trying

536
00:30:56,680 --> 00:30:59,760
Speaker 1: to do at Snowflake is build a really big community.

537
00:31:00,200 --> 00:31:02,880
Speaker 1: So the event we just ran with a huge number

538
00:31:02,880 --> 00:31:06,000
Speaker 1: of people. We run user groups, we run meetups, we

539
00:31:06,080 --> 00:31:10,040
Speaker 1: run product specialist workshops. When we bring some of our

540
00:31:10,080 --> 00:31:13,320
Speaker 1: teams offshore into New Zealand, and it's really important to

541
00:31:13,400 --> 00:31:16,960
Speaker 1: build that community and network so we can share what's

542
00:31:17,000 --> 00:31:20,240
Speaker 1: working and to your point before, what's not working, so

543
00:31:20,280 --> 00:31:23,320
Speaker 1: that we can avoid those pitfalls where possible and start

544
00:31:23,400 --> 00:31:26,360
Speaker 1: to accelerate how do we deliver that outcome. But I

545
00:31:26,360 --> 00:31:29,840
Speaker 1: think internally the capability needs to be there. You know,

546
00:31:30,040 --> 00:31:32,680
Speaker 1: we need to train our teams, we need to cross

547
00:31:32,720 --> 00:31:35,720
Speaker 1: pollinate from existing teams, so you might have somebody who's

548
00:31:36,200 --> 00:31:39,720
Speaker 1: working internally within an organization, they've got a huge amount

549
00:31:39,760 --> 00:31:43,520
Speaker 1: of tribal knowledge within that organization. But then how do

550
00:31:43,560 --> 00:31:47,120
Speaker 1: we cross pollinate their skill sets with whatever it is

551
00:31:47,120 --> 00:31:50,080
Speaker 1: they need with it's technical or analysts. So they've got

552
00:31:50,080 --> 00:31:53,360
Speaker 1: the data literacy to drive that outcome. But absolutely internal

553
00:31:53,440 --> 00:31:53,960
Speaker 1: was critical.

554
00:31:54,160 --> 00:31:57,360
Speaker 3: Yeah, I mean, I guess the message that I'm getting

555
00:31:57,360 --> 00:32:00,920
Speaker 3: really is that you can't do this. Lais a fair.

556
00:32:01,360 --> 00:32:03,640
Speaker 3: You can't just be like, let's dabble in some data.

557
00:32:03,760 --> 00:32:06,600
Speaker 3: You really have to sit down and create a cohesive,

558
00:32:06,720 --> 00:32:11,360
Speaker 3: strong plan and roadmap and objectives and spend the time

559
00:32:11,400 --> 00:32:13,840
Speaker 3: to actually build that out. And if you're seeing gaps

560
00:32:14,000 --> 00:32:16,840
Speaker 3: in your organization, then you actually need to maybe fill

561
00:32:16,880 --> 00:32:19,960
Speaker 3: those gaps, whether that's with training or with bringing on

562
00:32:20,000 --> 00:32:23,200
Speaker 3: new staff. Does that kind of sound about right?

563
00:32:23,280 --> 00:32:27,400
Speaker 1: Yeah, one hundred percent agree. And it's building that strategy

564
00:32:27,760 --> 00:32:30,440
Speaker 1: into the business outcome of the business strategy, so that

565
00:32:30,840 --> 00:32:35,520
Speaker 1: the data strategy is part of your business strategy because

566
00:32:35,600 --> 00:32:38,360
Speaker 1: they shouldn't be separate. One can inform the other and

567
00:32:38,360 --> 00:32:41,000
Speaker 1: the other can form each other. A lot of times

568
00:32:41,080 --> 00:32:45,120
Speaker 1: the data teams have a really enterprise view of the

569
00:32:45,120 --> 00:32:49,240
Speaker 1: business because they're looking at data from multiple different areas,

570
00:32:49,560 --> 00:32:53,200
Speaker 1: so you're not siloed within say HR, or siloed within finance,

571
00:32:53,320 --> 00:32:57,760
Speaker 1: or siloed within marketing. The data teams get a strong

572
00:32:57,920 --> 00:33:02,920
Speaker 1: visibility across the organization. For example, we were talking in

573
00:33:02,960 --> 00:33:07,400
Speaker 1: New Zealand presented recently at our conference. Again they talked

574
00:33:07,400 --> 00:33:10,000
Speaker 1: about the concept of majors and miners, So you've got

575
00:33:10,040 --> 00:33:14,840
Speaker 1: a data team which has got majors in data and analytics,

576
00:33:15,080 --> 00:33:18,080
Speaker 1: but working with the lines of business who have a

577
00:33:18,120 --> 00:33:21,920
Speaker 1: minor and data, but a major is in their skill set,

578
00:33:21,920 --> 00:33:24,200
Speaker 1: whether it's HR, where it's cargo, whether it's financed, whatever

579
00:33:24,200 --> 00:33:28,200
Speaker 1: happens to be. And that dovetail together of the data

580
00:33:28,480 --> 00:33:32,400
Speaker 1: literacy and the data capability with the knowledge of that

581
00:33:32,560 --> 00:33:35,480
Speaker 1: specific line of business and what's important to that line

582
00:33:36,200 --> 00:33:39,080
Speaker 1: is really important because then you're marrying both of the

583
00:33:39,120 --> 00:33:42,680
Speaker 1: outcome and the capability together, which drives a lot of

584
00:33:42,720 --> 00:33:47,000
Speaker 1: value for the organization. Having that strategy which is aligned

585
00:33:47,080 --> 00:33:50,680
Speaker 1: into the business strategy is really important. And obviously with

586
00:33:50,840 --> 00:33:55,440
Speaker 1: the introduction of things like AI, AI is built upon

587
00:33:56,400 --> 00:33:59,600
Speaker 1: how you use data, whether it's internal data, external data

588
00:34:00,080 --> 00:34:02,720
Speaker 1: to drive that decision making. So you know, you can't

589
00:34:02,720 --> 00:34:05,400
Speaker 1: really have an AI strategy without a data strategy, and

590
00:34:05,440 --> 00:34:07,640
Speaker 1: all of this should be blended into what is the

591
00:34:07,680 --> 00:34:10,000
Speaker 1: objectives of the organization and what are they driving for?

592
00:34:10,840 --> 00:34:12,840
Speaker 3: Great, you just did my job then, and you pivoted

593
00:34:12,840 --> 00:34:14,560
Speaker 3: to exactly where I wanted to go next. We just

594
00:34:14,560 --> 00:34:16,839
Speaker 3: talk about AI because we have to, right because it's

595
00:34:16,840 --> 00:34:21,560
Speaker 3: twenty twenty four and the last three years of AI

596
00:34:21,800 --> 00:34:25,480
Speaker 3: has been just a different story completely to where it

597
00:34:25,600 --> 00:34:29,440
Speaker 3: was previously. From your experience as somebody who lives and

598
00:34:29,480 --> 00:34:33,080
Speaker 3: breathes data, what has that experience been like to watch

599
00:34:33,200 --> 00:34:38,080
Speaker 3: data go from this kind of dry but necessary thing

600
00:34:38,440 --> 00:34:40,640
Speaker 3: to the thing that is powering the future.

601
00:34:41,080 --> 00:34:43,440
Speaker 1: It's absolutely amazing, isn't it. You know, you know, in

602
00:34:43,480 --> 00:34:48,440
Speaker 1: your personal life you use things like GPT and the

603
00:34:48,480 --> 00:34:51,920
Speaker 1: output that it can deliver is just extraordinary. And where

604
00:34:51,920 --> 00:34:56,520
Speaker 1: it's going, I think is fantastic. It's outstanding. But you've

605
00:34:56,560 --> 00:35:00,840
Speaker 1: got to get the foundations right because otherwise you're building

606
00:35:00,880 --> 00:35:04,200
Speaker 1: on quicksand and you're analyzing inefficient data and you'll very

607
00:35:04,280 --> 00:35:07,200
Speaker 1: quickly lose confidence from those users. And there's also some

608
00:35:07,239 --> 00:35:10,319
Speaker 1: of those traditional techniques which are still incredibly valuable to

609
00:35:10,320 --> 00:35:14,839
Speaker 1: the organization. So just making sure that we understand the vision,

610
00:35:14,840 --> 00:35:16,839
Speaker 1: and we go after that, and we go after that

611
00:35:17,800 --> 00:35:22,200
Speaker 1: with speed, but at the same time we don't take

612
00:35:22,239 --> 00:35:24,640
Speaker 1: the focus off some of those other areas which we

613
00:35:24,680 --> 00:35:26,680
Speaker 1: can deliver very very quick value to the business.

614
00:35:27,520 --> 00:35:31,360
Speaker 3: Somebody said to me the other day that actually, with

615
00:35:31,719 --> 00:35:35,920
Speaker 3: new AI models, data cleanliness is actually not as important

616
00:35:35,960 --> 00:35:37,839
Speaker 3: as it used to be, because if you look at

617
00:35:37,840 --> 00:35:40,880
Speaker 3: something like chat GPT, you know, the training is not

618
00:35:41,520 --> 00:35:45,640
Speaker 3: necessarily there's so much of it they couldn't possibly go

619
00:35:45,719 --> 00:35:47,640
Speaker 3: through and clean it all. Do you think that's true

620
00:35:48,040 --> 00:35:51,440
Speaker 3: that if you were wanting to create kind of a

621
00:35:51,480 --> 00:35:56,920
Speaker 3: GENAI model that's used you can use to analyze certain levels,

622
00:35:56,920 --> 00:35:59,880
Speaker 3: certain kinds of data within the organization, that there is

623
00:36:01,280 --> 00:36:04,279
Speaker 3: less need for data cleanliness than they used to be.

624
00:36:05,440 --> 00:36:08,799
Speaker 1: I'd say, where you're looking at the entire Internet for

625
00:36:08,960 --> 00:36:13,200
Speaker 1: consumers like you just describe, maybe when organizations are looking

626
00:36:13,200 --> 00:36:16,880
Speaker 1: to use information which is internal to their organization, that

627
00:36:17,120 --> 00:36:20,239
Speaker 1: has to be very high quality. It has to be robust,

628
00:36:20,480 --> 00:36:23,040
Speaker 1: it has to be trusted, and it has to be

629
00:36:23,160 --> 00:36:27,320
Speaker 1: using the information and knowledge from that organization to prevent

630
00:36:27,400 --> 00:36:32,719
Speaker 1: things like hallucinations and bad decisions because the data is incorrect, inaccurate,

631
00:36:33,120 --> 00:36:35,200
Speaker 1: not full enough. There's not a quorum of data to

632
00:36:35,200 --> 00:36:38,960
Speaker 1: make an informed decision. I think the data quality aspects

633
00:36:39,040 --> 00:36:42,840
Speaker 1: are even more important for an organization using some of

634
00:36:42,840 --> 00:36:48,000
Speaker 1: these advanced capabilities like genai. The Genai capability helps a

635
00:36:48,120 --> 00:36:51,120
Speaker 1: lot in terms of being able to put some of

636
00:36:51,160 --> 00:36:55,799
Speaker 1: that tagging, say, or definitions around what some of that

637
00:36:55,880 --> 00:36:59,359
Speaker 1: data means. It helps speed up the efficiency to make

638
00:36:59,480 --> 00:37:05,440
Speaker 1: the data more reliable and higher quality and understood. But

639
00:37:05,520 --> 00:37:09,520
Speaker 1: without putting the thought into having that high quality data,

640
00:37:09,880 --> 00:37:12,239
Speaker 1: it's going to fall flat. In my opinion, I think

641
00:37:12,239 --> 00:37:17,160
Speaker 1: we need to absolutely focus on the availability, the security

642
00:37:17,160 --> 00:37:25,640
Speaker 1: and governance, the privacy, the quality and trusted data and

643
00:37:25,680 --> 00:37:28,480
Speaker 1: then apply these techniques on top of it. And one

644
00:37:28,480 --> 00:37:31,440
Speaker 1: of the things where Big believers on is bring the

645
00:37:31,440 --> 00:37:34,760
Speaker 1: processing and the workload to the data rather than pushing

646
00:37:34,760 --> 00:37:37,520
Speaker 1: all the data out to different systems. And the reason

647
00:37:37,680 --> 00:37:40,400
Speaker 1: for that is because you've got that single view of

648
00:37:40,440 --> 00:37:42,960
Speaker 1: the business, you've got one place to make sure that

649
00:37:43,000 --> 00:37:45,560
Speaker 1: the data is of that high quality we're just describing,

650
00:37:45,600 --> 00:37:48,120
Speaker 1: and you've got the privacy and governance so that only

651
00:37:48,120 --> 00:37:51,040
Speaker 1: the right people are allowed to see it. Because what

652
00:37:51,080 --> 00:37:53,600
Speaker 1: we're doing is we're opening up the access to a

653
00:37:53,680 --> 00:37:58,640
Speaker 1: huge wide range of different consumers of the data, So

654
00:37:58,680 --> 00:38:00,440
Speaker 1: we've got to make sure that it's protect and we've

655
00:38:00,480 --> 00:38:01,960
Speaker 1: got to make sure that it's of high quality.

656
00:38:02,080 --> 00:38:05,400
Speaker 3: Yeah, you're not on the difference between creating something from

657
00:38:05,400 --> 00:38:07,920
Speaker 3: a mass market and creating something that is to improve

658
00:38:08,120 --> 00:38:11,080
Speaker 3: organizational performance, and those are two very different goals completely.

659
00:38:12,360 --> 00:38:16,000
Speaker 3: The other thing that I've been considering about generative AI

660
00:38:16,120 --> 00:38:20,080
Speaker 3: lately is there's this kind of to and fro about

661
00:38:20,600 --> 00:38:25,520
Speaker 3: how much we let the GENAI actually do if that

662
00:38:25,600 --> 00:38:28,720
Speaker 3: kind of makes sense, where it can be quite creative

663
00:38:28,760 --> 00:38:33,399
Speaker 3: and thoughtful and very have high contextual understanding, but that

664
00:38:33,520 --> 00:38:38,640
Speaker 3: may potentially, you know, open up the hallucinations or we're

665
00:38:38,680 --> 00:38:40,480
Speaker 3: not quite sure where that's going to go. Or we

666
00:38:40,520 --> 00:38:42,239
Speaker 3: can be very tight and strict and be like, it

667
00:38:42,239 --> 00:38:46,560
Speaker 3: can only return these information from these sources in these modes,

668
00:38:47,080 --> 00:38:49,799
Speaker 3: and trying to find the balance of that can be

669
00:38:50,440 --> 00:38:54,520
Speaker 3: tricky at a kind of data level when you're figuring

670
00:38:54,600 --> 00:38:58,040
Speaker 3: out what to include and what not to include, how

671
00:38:58,040 --> 00:38:59,880
Speaker 3: do you start making some of those decisions.

672
00:39:00,520 --> 00:39:04,520
Speaker 1: You're absolutely right, it is a tricky decision or tricky

673
00:39:05,960 --> 00:39:09,880
Speaker 1: consideration to think around. Where we think around it is

674
00:39:11,000 --> 00:39:14,720
Speaker 1: having access to the right amount of information, but putting

675
00:39:14,760 --> 00:39:17,640
Speaker 1: those governance and controls on there so that you've got

676
00:39:17,880 --> 00:39:20,880
Speaker 1: things like role based access so that only I'm allowed

677
00:39:20,880 --> 00:39:23,680
Speaker 1: to see the information that's purten in to my specific

678
00:39:23,760 --> 00:39:27,359
Speaker 1: role and I can't see anything else outside of that.

679
00:39:27,760 --> 00:39:30,360
Speaker 1: And that's why it's really important to get that governance

680
00:39:30,480 --> 00:39:34,960
Speaker 1: and that privacy foundations set and defined upfront so that

681
00:39:35,000 --> 00:39:38,000
Speaker 1: it's not being made up and make sure that you've

682
00:39:38,000 --> 00:39:40,880
Speaker 1: got the right level of data to support the decision

683
00:39:40,920 --> 00:39:43,200
Speaker 1: that you're trying to solve. And my view would be

684
00:39:43,360 --> 00:39:46,560
Speaker 1: start small, start to prove out some value, and then

685
00:39:46,640 --> 00:39:50,560
Speaker 1: expand as you've got that confidence within the business. But

686
00:39:50,640 --> 00:39:53,680
Speaker 1: it's moving incredibly fast, right you know. You think even

687
00:39:53,680 --> 00:39:56,200
Speaker 1: a couple of years ago, you know, Chat GPT was

688
00:39:56,320 --> 00:39:58,640
Speaker 1: just coming of age and people had only just staid

689
00:39:58,640 --> 00:40:01,880
Speaker 1: to hear about it, and now AI is embedded into

690
00:40:02,680 --> 00:40:05,959
Speaker 1: just about every single platform and process. What we're looking

691
00:40:06,000 --> 00:40:09,440
Speaker 1: to do is understand how we can use each of

692
00:40:09,480 --> 00:40:13,360
Speaker 1: those different silos of informations and applications bring that together

693
00:40:13,440 --> 00:40:15,880
Speaker 1: so you've still got that holistic view at the data level,

694
00:40:16,239 --> 00:40:18,759
Speaker 1: not just at the application level. So you want to

695
00:40:18,800 --> 00:40:21,160
Speaker 1: be able to bring that data together from multiple places

696
00:40:21,560 --> 00:40:23,920
Speaker 1: and then apply AI across it, depending on what it

697
00:40:24,000 --> 00:40:26,400
Speaker 1: is you're trying to do, but you know it's moving

698
00:40:26,520 --> 00:40:28,880
Speaker 1: so quickly it's really exciting. To be parely honest.

699
00:40:29,719 --> 00:40:33,520
Speaker 3: What is exciting about it for you? Because you know,

700
00:40:33,640 --> 00:40:38,280
Speaker 3: for office workers there's that kind of productivity gain stuff

701
00:40:38,320 --> 00:40:42,160
Speaker 3: that's being talked about. For consumers there's like access to

702
00:40:42,239 --> 00:40:46,759
Speaker 3: information that they may not have or ability to proof

703
00:40:46,880 --> 00:40:50,359
Speaker 3: read and do these kinds of everyday tasks. But as

704
00:40:50,400 --> 00:40:52,960
Speaker 3: somebody who is like super deep in the world of data,

705
00:40:53,480 --> 00:40:56,320
Speaker 3: what is actually super exciting for you about the generative

706
00:40:56,360 --> 00:40:56,960
Speaker 3: AI stuff?

707
00:40:57,239 --> 00:41:00,440
Speaker 1: The productivity part that'll be part of it, But I

708
00:41:00,480 --> 00:41:05,560
Speaker 1: don't think that organizations are looking at just the productivity.

709
00:41:05,640 --> 00:41:09,440
Speaker 1: Sure there's efficiency, but I think it's the upside that

710
00:41:09,480 --> 00:41:12,440
Speaker 1: people can drive from it. Is the better network planning

711
00:41:12,480 --> 00:41:15,920
Speaker 1: in TALCOS is the better supply chain management. Because you're

712
00:41:15,920 --> 00:41:19,400
Speaker 1: pulling information from third party suppliers as well as the

713
00:41:19,400 --> 00:41:23,440
Speaker 1: internal information. You can run and advance large language model

714
00:41:23,440 --> 00:41:26,080
Speaker 1: across that to work out what is the route processing

715
00:41:26,160 --> 00:41:29,520
Speaker 1: or where do you deliver things quicker? That outcome that's

716
00:41:29,560 --> 00:41:32,759
Speaker 1: going to move the dial with those organizations to drive

717
00:41:32,800 --> 00:41:38,080
Speaker 1: revenue or make them more profitable. That's really exciting and

718
00:41:38,120 --> 00:41:40,040
Speaker 1: obviously the productivity gains will come as well.

719
00:41:41,160 --> 00:41:44,160
Speaker 3: Are we already seeing some of those gains in certain

720
00:41:44,200 --> 00:41:46,680
Speaker 3: areas using the new air models? Like can do you

721
00:41:46,719 --> 00:41:48,440
Speaker 3: have examples of that? Yeah?

722
00:41:48,520 --> 00:41:52,600
Speaker 1: Absolutely? Mine to ten was just talking. They've spoke to

723
00:41:52,600 --> 00:41:55,440
Speaker 1: our conference again last year. They've had a very small team,

724
00:41:56,160 --> 00:41:57,560
Speaker 1: so they've managed to consult it a lot of their

725
00:41:57,600 --> 00:42:01,400
Speaker 1: information one year on what done as they've applied some

726
00:42:01,480 --> 00:42:05,280
Speaker 1: of these large language models to look at water supply

727
00:42:05,360 --> 00:42:09,040
Speaker 1: chain and how can they deliver better outcomes across the

728
00:42:09,080 --> 00:42:12,839
Speaker 1: retail organization. So they're starting to embed some of these

729
00:42:13,120 --> 00:42:17,759
Speaker 1: capabilities into their processes. We're seeing the talcos doing the

730
00:42:17,800 --> 00:42:21,279
Speaker 1: same things. A lot of them have had proof of

731
00:42:21,280 --> 00:42:24,640
Speaker 1: concepts that they're now starting to put into production. So

732
00:42:24,760 --> 00:42:26,359
Speaker 1: I think that there's going to be a lot of

733
00:42:26,400 --> 00:42:30,600
Speaker 1: the pilot and prototype pieces of we're really accelerating now,

734
00:42:30,640 --> 00:42:33,319
Speaker 1: and to be honest, some of the organizations they see

735
00:42:33,320 --> 00:42:36,439
Speaker 1: that as a competitive differentiator, so they are actually keeping

736
00:42:36,480 --> 00:42:39,279
Speaker 1: some of them relatively close to their chests because the

737
00:42:39,360 --> 00:42:44,239
Speaker 1: faster they can move, they're looking to leap frog the competitors.

738
00:42:45,040 --> 00:42:48,200
Speaker 3: If twenty twenty one twenty two was kind of the

739
00:42:48,239 --> 00:42:51,240
Speaker 3: emergence and the testing and the seeing what could go wrong?

740
00:42:51,760 --> 00:42:53,839
Speaker 3: You know, twenty twenty three and twenty twenty four has

741
00:42:53,880 --> 00:42:56,839
Speaker 3: been about getting those prototypes and starting to see what

742
00:42:56,920 --> 00:43:00,279
Speaker 3: can happen. Is twenty twenty five to twenty six is

743
00:43:00,280 --> 00:43:02,680
Speaker 3: that going to be the acceleration time? Where are we

744
00:43:02,719 --> 00:43:06,400
Speaker 3: at in terms of starting to really see mass adoption

745
00:43:06,480 --> 00:43:09,600
Speaker 3: of this tech at an enterprise level, at a fundamentally

746
00:43:09,640 --> 00:43:10,640
Speaker 3: restructuring level.

747
00:43:10,719 --> 00:43:12,439
Speaker 1: Yeah, I think over the next twelve to eighty months

748
00:43:12,480 --> 00:43:15,239
Speaker 1: you will see a massive acceleration of that. I think

749
00:43:15,400 --> 00:43:19,759
Speaker 1: those organizations that have done those that foundational work are

750
00:43:19,760 --> 00:43:22,640
Speaker 1: in a much better position to be able to accelerate faster.

751
00:43:22,840 --> 00:43:28,000
Speaker 1: So those organizations that have got trusted, high quality, consolidated information,

752
00:43:28,719 --> 00:43:31,000
Speaker 1: then they're looking at what do they do to exploit it.

753
00:43:31,040 --> 00:43:33,239
Speaker 1: They've got that quorum of data, and now how do

754
00:43:33,280 --> 00:43:36,840
Speaker 1: we use it and exploit it quickly? There's still organizations

755
00:43:36,880 --> 00:43:39,719
Speaker 1: which have yet to do that foundational work, and that

756
00:43:39,760 --> 00:43:43,080
Speaker 1: foundational work is critical before you can start to exploit

757
00:43:43,120 --> 00:43:45,399
Speaker 1: it in a really meaningful way. So I think there's

758
00:43:45,440 --> 00:43:48,400
Speaker 1: going to be those that are ahead of the curve

759
00:43:48,440 --> 00:43:50,120
Speaker 1: and have been ahead of the curve for the last

760
00:43:50,440 --> 00:43:53,080
Speaker 1: few years are going to be able to accelerate quicker

761
00:43:54,160 --> 00:43:56,759
Speaker 1: than those who haven't done that homework and done the

762
00:43:56,800 --> 00:44:01,680
Speaker 1: foundational stuff. And it's no different to whether it's GENAI

763
00:44:01,960 --> 00:44:05,319
Speaker 1: or large language models. Those organizations that have got that

764
00:44:05,440 --> 00:44:10,160
Speaker 1: high quality data, they've spent the time to ensure that

765
00:44:10,200 --> 00:44:13,640
Speaker 1: the lines of businesses have data literacy and data skills

766
00:44:14,040 --> 00:44:16,160
Speaker 1: and know what they can do with the information to

767
00:44:16,480 --> 00:44:19,960
Speaker 1: change the processes will be in a better position. So

768
00:44:20,520 --> 00:44:23,120
Speaker 1: adding on top of that things like genai, it will

769
00:44:23,160 --> 00:44:27,200
Speaker 1: allow those organizations to go faster. But it's just accelerated

770
00:44:27,239 --> 00:44:29,719
Speaker 1: how quickly organizations can start to exploit it. I don't

771
00:44:29,719 --> 00:44:31,880
Speaker 1: think it changes the fundamental that you've got to get

772
00:44:31,880 --> 00:44:35,840
Speaker 1: the basics right and do that well before you can accelerate.

773
00:44:37,000 --> 00:44:39,680
Speaker 3: What would you say are the biggest risks that we

774
00:44:39,719 --> 00:44:42,920
Speaker 3: need to be thinking about as we enter this accelerative phase.

775
00:44:43,719 --> 00:44:48,320
Speaker 1: I think the privacy and just because we've got the data,

776
00:44:48,600 --> 00:44:51,759
Speaker 1: does that give us the right to use that data mentality?

777
00:44:51,880 --> 00:44:54,680
Speaker 1: And I think we've got to be really considerate that

778
00:44:54,880 --> 00:44:57,799
Speaker 1: most of these organizations is not their data, it's their

779
00:44:57,840 --> 00:45:01,680
Speaker 1: customers data, So we need to really consider what is

780
00:45:01,719 --> 00:45:04,080
Speaker 1: it that we're going to do with that data and

781
00:45:04,440 --> 00:45:08,080
Speaker 1: make sure that it's doing the right things for their

782
00:45:08,080 --> 00:45:10,600
Speaker 1: customers as well as the internal organization. So we've got

783
00:45:10,600 --> 00:45:15,320
Speaker 1: to think around the privacy, the use of it, the

784
00:45:15,360 --> 00:45:20,680
Speaker 1: AI governance and governance of the customer's use of it,

785
00:45:20,760 --> 00:45:22,800
Speaker 1: and the permissions and things like that. So I think

786
00:45:22,960 --> 00:45:26,600
Speaker 1: the accessibility is great, but just because we've got it

787
00:45:26,600 --> 00:45:29,759
Speaker 1: doesn't necessarily mean we should use it in a certain way.

788
00:45:30,440 --> 00:45:32,920
Speaker 1: There's going to be a lot of focus around the

789
00:45:33,719 --> 00:45:37,719
Speaker 1: obviously security, privacy, and then also how do we just

790
00:45:37,800 --> 00:45:40,120
Speaker 1: continue to evolve on that as well?

791
00:45:40,880 --> 00:45:41,839
Speaker 3: What do you mean by that.

792
00:45:42,760 --> 00:45:46,360
Speaker 1: In terms of as the technology moves so much faster,

793
00:45:47,239 --> 00:45:49,279
Speaker 1: how do we keep up with that? And how do

794
00:45:49,360 --> 00:45:52,200
Speaker 1: we think about the new use cases? How do we

795
00:45:52,239 --> 00:45:55,360
Speaker 1: think around what is that business driver again taking it

796
00:45:55,360 --> 00:45:59,160
Speaker 1: away from just a technology problem, what is the business

797
00:45:59,160 --> 00:46:02,280
Speaker 1: trying to achieve ross that line of business, finance, marketing,

798
00:46:02,320 --> 00:46:06,200
Speaker 1: et cetera. And how do we align to that outcome right?

799
00:46:06,280 --> 00:46:10,200
Speaker 1: Otherwise we can spend a huge amount of money with

800
00:46:10,320 --> 00:46:12,760
Speaker 1: science experiments that don't actually do much for the business.

801
00:46:12,960 --> 00:46:15,200
Speaker 1: Another thing that will be important will be looking at

802
00:46:15,239 --> 00:46:21,000
Speaker 1: the cost considerations, making sure that the whatever we're doing

803
00:46:21,239 --> 00:46:24,520
Speaker 1: is aligned to the outcome so that it's cost and

804
00:46:24,960 --> 00:46:29,439
Speaker 1: value tightly coupled. Otherwise, you know, they're not cheap things

805
00:46:29,440 --> 00:46:31,480
Speaker 1: to run, so we need to make sure we've got

806
00:46:31,480 --> 00:46:34,279
Speaker 1: the guardrails across it. So cost management is going to

807
00:46:34,280 --> 00:46:37,880
Speaker 1: be efficient, going to be important, that the governance and

808
00:46:37,880 --> 00:46:40,600
Speaker 1: privacy is going to be important, and that all ties

809
00:46:40,640 --> 00:46:42,160
Speaker 1: back to what is what are we're using it for

810
00:46:42,239 --> 00:46:43,799
Speaker 1: and what is the business value we're trying to drive

811
00:46:43,840 --> 00:46:44,400
Speaker 1: out the back of it.

812
00:46:45,680 --> 00:46:49,480
Speaker 3: So get excited, but not too excited, and be thoughtful.

813
00:46:49,680 --> 00:46:50,719
Speaker 3: That's kind of there, I think.

814
00:46:50,800 --> 00:46:56,400
Speaker 1: Be excited, but be thoughtful. Don't don't limit what you

815
00:46:56,640 --> 00:46:59,520
Speaker 1: think you can do because you probably can. And it's

816
00:46:59,560 --> 00:47:02,359
Speaker 1: exciting time to go and test some of these hypotheses

817
00:47:02,480 --> 00:47:05,239
Speaker 1: and see how it works. So be excited, to be

818
00:47:05,280 --> 00:47:08,480
Speaker 1: really excited, it's going to be fantastic next couple of years.

819
00:47:08,800 --> 00:47:10,880
Speaker 1: But just be thoughtful about how you're using it and

820
00:47:10,920 --> 00:47:12,160
Speaker 1: thoughtful about your customers.

821
00:47:19,640 --> 00:47:21,960
Speaker 3: So, if ever there was a man who lives and

822
00:47:22,040 --> 00:47:25,920
Speaker 3: breathed data, I think it's Tony Shaw. He has clearly

823
00:47:25,960 --> 00:47:29,160
Speaker 3: been in the industry for a long time, and his

824
00:47:29,320 --> 00:47:32,520
Speaker 3: advice I think, while some of it isn't necessarily novel.

825
00:47:32,560 --> 00:47:35,200
Speaker 3: It's the stuff we've been hearing for a while about

826
00:47:35,239 --> 00:47:38,120
Speaker 3: getting data and order. I think that the way that

827
00:47:38,160 --> 00:47:42,360
Speaker 3: he has put it really was very clear and concise

828
00:47:42,480 --> 00:47:45,120
Speaker 3: and actionable as well, which is what I appreciated about

829
00:47:45,160 --> 00:47:45,520
Speaker 3: the chat.

830
00:47:46,840 --> 00:47:52,799
Speaker 2: Yeah, he really talked about this transition into data and analytics,

831
00:47:52,800 --> 00:47:57,560
Speaker 2: the importance of data in financial decision making, and for years,

832
00:47:57,640 --> 00:47:59,879
Speaker 2: you know, we've been talking to New Zealand businesses about

833
00:48:00,200 --> 00:48:03,759
Speaker 2: writing about it, and they're all up for it, and

834
00:48:03,800 --> 00:48:06,960
Speaker 2: some of them are really doing that, doing really smart

835
00:48:06,960 --> 00:48:09,719
Speaker 2: things with data, but we were a bit slower to

836
00:48:10,280 --> 00:48:14,439
Speaker 2: the move to the cloud and getting data in order

837
00:48:14,560 --> 00:48:18,440
Speaker 2: as part of that digital transformation. So a lot of

838
00:48:18,440 --> 00:48:21,239
Speaker 2: businesses talk about this stuff, but are they actually using it?

839
00:48:21,880 --> 00:48:23,920
Speaker 2: And when I took to them sort of off the record,

840
00:48:23,960 --> 00:48:27,399
Speaker 2: they say, well, actually, know where we've done pilots, we're

841
00:48:27,440 --> 00:48:32,360
Speaker 2: doing limited use cases related to data analytics and the like,

842
00:48:32,480 --> 00:48:34,960
Speaker 2: but we don't have the data in the right shape.

843
00:48:34,960 --> 00:48:37,520
Speaker 2: We need to build a data warehouse or a data lake.

844
00:48:38,120 --> 00:48:42,000
Speaker 2: We need to standardize our data and that literally for

845
00:48:42,040 --> 00:48:44,600
Speaker 2: some of them is taking years. So we've seen that's

846
00:48:44,600 --> 00:48:47,720
Speaker 2: why we've seen the rise of snowflake and data Bricks

847
00:48:47,960 --> 00:48:52,960
Speaker 2: and others. The big tech platforms can only do so much.

848
00:48:53,280 --> 00:48:55,680
Speaker 2: It's really up to you, and there's a layer between

849
00:48:56,160 --> 00:48:59,560
Speaker 2: the customer and the big platform. We're all of your

850
00:48:59,640 --> 00:49:02,880
Speaker 2: data potentially is going to be and these companies are

851
00:49:02,920 --> 00:49:04,440
Speaker 2: playing a really valuable role there.

852
00:49:05,120 --> 00:49:08,719
Speaker 3: Yeah, and the couple that we mentioned Data Bricks and Snowflake,

853
00:49:08,800 --> 00:49:11,439
Speaker 3: and these are the ones that have really come out

854
00:49:12,040 --> 00:49:15,760
Speaker 3: swinging and have shown that the value over and over again.

855
00:49:15,960 --> 00:49:19,319
Speaker 3: And Snowflake listed on the NASDAK and has shown really

856
00:49:19,360 --> 00:49:23,160
Speaker 3: great growth since doing that. So you know, its success

857
00:49:23,880 --> 00:49:26,319
Speaker 3: is I think a good indicator of the value that

858
00:49:26,360 --> 00:49:31,160
Speaker 3: it is offering to organizations globally. And you know Tony

859
00:49:31,320 --> 00:49:33,840
Speaker 3: talking about the fact that it can scale up to

860
00:49:33,880 --> 00:49:37,040
Speaker 3: these massive, massive international corporates, but it can also scale

861
00:49:37,080 --> 00:49:41,759
Speaker 3: down to fit the needs of organizations and countries like

862
00:49:41,840 --> 00:49:44,480
Speaker 3: New Zealand. And if we want to be the country

863
00:49:44,760 --> 00:49:48,840
Speaker 3: that is using AI, that is using our data to

864
00:49:49,040 --> 00:49:52,040
Speaker 3: improve our productivity, to enter the brave new digital world

865
00:49:52,480 --> 00:49:57,160
Speaker 3: and kind of stay relevant on a global scale, then

866
00:49:57,440 --> 00:50:01,880
Speaker 3: these kinds of products, these kinds of projects of what

867
00:50:02,040 --> 00:50:04,480
Speaker 3: needs to be done on a bigger scale. And what

868
00:50:04,600 --> 00:50:08,759
Speaker 3: Tony was saying about not doing science experiments anymore, right

869
00:50:08,840 --> 00:50:12,200
Speaker 3: the time for kind of these doing science experiments over

870
00:50:12,200 --> 00:50:15,000
Speaker 3: and over again. The small scale dabbling is kind of

871
00:50:15,480 --> 00:50:17,760
Speaker 3: if you're still in that phase, you might need to

872
00:50:18,120 --> 00:50:19,560
Speaker 3: put a bit of welly behind it and get on

873
00:50:19,600 --> 00:50:19,719
Speaker 3: with it.

874
00:50:20,719 --> 00:50:23,560
Speaker 2: Yeah, yeah, yeah, I mean, I think his advice is

875
00:50:23,640 --> 00:50:27,239
Speaker 2: sort of what we've heard, which is start small, don't

876
00:50:27,280 --> 00:50:31,000
Speaker 2: necessarily go big bang, because if you've designed it wrong,

877
00:50:31,040 --> 00:50:34,040
Speaker 2: suddenly it becomes a very expensive failure. So target a

878
00:50:34,080 --> 00:50:37,160
Speaker 2: part of the business where having great insights into your

879
00:50:37,239 --> 00:50:41,239
Speaker 2: data is going to really help the business. Start that

880
00:50:42,160 --> 00:50:44,520
Speaker 2: experiment a little bit, then grow a bigger But he's

881
00:50:44,560 --> 00:50:48,920
Speaker 2: clearly predicting a major acceleration of AI adoption over the

882
00:50:48,960 --> 00:50:54,120
Speaker 2: next twelve to eighteen months as organizations do that foundational work.

883
00:50:55,280 --> 00:50:59,960
Speaker 2: Trying to get ahead of the curve is a competitive advantage.

884
00:51:00,160 --> 00:51:03,520
Speaker 2: Hopefully that message is getting through in New Zealand. We've

885
00:51:03,560 --> 00:51:06,839
Speaker 2: seen so much research over the last year or so

886
00:51:06,920 --> 00:51:09,280
Speaker 2: to suggest that we're a little bit behind the curve.

887
00:51:10,719 --> 00:51:15,839
Speaker 2: But if companies like Snowflake can help accelerate that, because

888
00:51:15,880 --> 00:51:19,520
Speaker 2: as you say, it scales down to medium sized businesses

889
00:51:19,600 --> 00:51:24,160
Speaker 2: quite well, that's basically where New Zealand plays and a

890
00:51:24,160 --> 00:51:27,799
Speaker 2: lot of those companies have been playing around with co

891
00:51:27,960 --> 00:51:32,600
Speaker 2: pilots and chatbots and AI related applications, so maybe some

892
00:51:32,680 --> 00:51:35,680
Speaker 2: of them have done enough work to actually in twenty

893
00:51:35,719 --> 00:51:37,960
Speaker 2: twenty five and beyond make really good use of AI.

894
00:51:38,520 --> 00:51:42,520
Speaker 2: And again what we've heard from others is emphasizing the

895
00:51:42,520 --> 00:51:46,000
Speaker 2: importance of aligning sort of AI and data initiatives with

896
00:51:46,080 --> 00:51:51,640
Speaker 2: business outcomes and having internal sponsors, people in the executive

897
00:51:51,960 --> 00:51:55,279
Speaker 2: off the business, people on the board who are real

898
00:51:55,360 --> 00:51:58,440
Speaker 2: champions for this. There's no point doing something where the

899
00:51:58,480 --> 00:52:01,400
Speaker 2: CEO and the executive team is sort of saying how

900
00:52:01,480 --> 00:52:03,440
Speaker 2: much is this going to cost? If they're not convinced

901
00:52:03,440 --> 00:52:06,480
Speaker 2: if the value of investing in these sorts of platforms

902
00:52:06,560 --> 00:52:08,640
Speaker 2: to the business, you've got a problem. They've all got

903
00:52:08,680 --> 00:52:11,640
Speaker 2: to be on board. So thanks very much to Tony

904
00:52:11,640 --> 00:52:15,200
Speaker 2: Shaw from Snowflake for his thoughts on the data landscape

905
00:52:15,480 --> 00:52:17,720
Speaker 2: and what's needed to spur AI adoption.

906
00:52:18,400 --> 00:52:20,440
Speaker 3: We'll be touching on that and next week's episode two,

907
00:52:20,480 --> 00:52:22,920
Speaker 3: and we have a panel of AI experts joining us

908
00:52:22,920 --> 00:52:25,600
Speaker 3: to look at the year in AI, big developments in

909
00:52:25,640 --> 00:52:29,239
Speaker 3: the technology, regulation and government's use of AI, and what

910
00:52:29,360 --> 00:52:31,399
Speaker 3: may be in store in twenty twenty.

911
00:52:31,080 --> 00:52:33,600
Speaker 2: Five Show notes for the Business of Tech are in

912
00:52:33,640 --> 00:52:37,040
Speaker 2: the podcast section at Business Desk dot co dot nz,

913
00:52:37,200 --> 00:52:39,920
Speaker 2: where you can stream this podcast in full every week.

914
00:52:40,200 --> 00:52:44,480
Speaker 2: It's also available from iHeartRadio or your podcast platform of choice.

915
00:52:44,600 --> 00:52:46,400
Speaker 3: Get in touch with your feedback and we'd love to

916
00:52:46,440 --> 00:52:49,480
Speaker 3: hear your suggestions for upcoming tests too. You can email

917
00:52:49,560 --> 00:52:52,160
Speaker 3: me Ben at business Desk dot Co dot and z,

918
00:52:52,520 --> 00:52:52,719
Speaker 3: and you.

919
00:52:52,719 --> 00:52:55,120
Speaker 2: Can find both of us on x and LinkedIn, where

920
00:52:55,120 --> 00:52:57,680
Speaker 2: you can follow the Business of Tech page for all

921
00:52:57,719 --> 00:52:58,520
Speaker 2: of our updates.

922
00:52:58,640 --> 00:53:00,640
Speaker 3: That's it for this week. We'll be back talk AI

923
00:53:01,040 --> 00:53:03,680
Speaker 3: and way through the election debris next Thursday.

924
00:53:04,000 --> 00:53:04,680
Speaker 2: We'll catch you in

925
00:53:08,960 --> 00:53:09,399
Speaker 1: Mm hmm