Speaker 1: It is now clear that we've achieved the most incredible
Speaker 1: political there.
Speaker 2: Look what happened?
Speaker 3: Is this crazy? But it's a political victory that.
Speaker 1: Our country has never seen before, nothing like this.
Speaker 2: Look what happened. Indeed, Donald Trump is heading back to
Speaker 2: the White House after a stronger than expected showing in
Speaker 2: the presidential election, with the Senate and the House of
Speaker 2: Representatives destined to also be in Republican control.
Speaker 3: Which has major implications for Trump's policy agenda, including a
Speaker 3: host of tech created issues from AI regulation to cryptocurrencies,
Speaker 3: data and privacy law reform, and the tech arms race
Speaker 3: with China.
Speaker 2: This week, on the Business of Tech, powered by Two
Speaker 2: Degrees Business, we look at what a Trump administration means
Speaker 2: for tech, rold at influential tech billionaires and platforms played
Speaker 2: in the election campaign. I'm Peter Griffin and.
Speaker 3: I'm Ben Moore. Coming up on the show as our
Speaker 3: featured guest. Tony Shore, the New Zealand country manager for Snowflake,
Speaker 3: a company that isn't as well known as Microsoft, AWS
Speaker 3: or Google, but is working with those companies and a
Speaker 3: rapidly growing roster of Kiwi companies to help them store
Speaker 3: and manage their data, run data analytics and use AI.
Speaker 2: Yeah, Tony has some great advice for companies eyeing up data, analytics,
Speaker 2: machine learning, and AI, which is really about the importance
Speaker 2: of getting your data house in order before you delve
Speaker 2: into these things. So stick around for Ben's interview with Tony.
Speaker 2: But first we need to sift through the ashes of
Speaker 2: Wednesday night's election results. Ben, we weren't actually going to
Speaker 2: talk about the election on this episode because normally we
Speaker 2: record the podcast on a Tuesday afternoon, which was just
Speaker 2: before the election. Terrible timing, but yes.
Speaker 3: But I happen to you got quite sick on Tuesday night,
Speaker 3: so we pushed the publication of the Business of Tech
Speaker 3: out a day, which is why you're hearing this on a.
Speaker 2: Friday, Yeah, which sort of means we can reflect on
Speaker 2: what went down and look at what Trump may have
Speaker 2: in store on the tech front. And you know, I
Speaker 2: think before we get into that, we should really talk
Speaker 2: about the influence that tech billionaires and their platforms have
Speaker 2: had on this presidential election. I mean it's pretty clear
Speaker 2: in his victory speech from Florida, Trump very much talking
Speaker 2: about Elon Musk. You know, last time around, Elon Musk
Speaker 2: was relatively close to Trump. I remember that iconic meeting
Speaker 2: where he basically called in all the heads of the
Speaker 2: tech companies to Trump Tower and sort of had a
Speaker 2: chat with him. Peter Thiel was there, I think Tim
Speaker 2: Cook from Apple was there. Elon Musk was there, so
Speaker 2: it all seems sort of quite Trump appointed him to
Speaker 2: a couple of advisory councils and Musk ended up quitting them,
Speaker 2: so there was a bit of a falling out between
Speaker 2: him and Trump. He's really rekindled that relationship, and you
Speaker 2: know Elon Musk, I think he sees as being quite
Speaker 2: key to the success that he's had, so he'll need
Speaker 2: to repay Elon Musk. Just talk about Musk taking some
Speaker 2: sort of role the Department of Government Efficiency DOGE, so
Speaker 2: I think this will be pivotal. You've also got Musk
Speaker 2: clearly has his fingers in so many different pis, so
Speaker 2: many different companies, so lots of scope for conflicts of
Speaker 2: interest there. And of course with X and I think
Speaker 2: we saw this definitely the morphing of X in the
Speaker 2: last year or so during that campaign really into a
Speaker 2: conservative stronghold that's also been influential. So all of these
Speaker 2: things are intertwining. I think in Trump's favor.
Speaker 3: Yeah, it definitely has been a swell, a more public
Speaker 3: swell of support for Donald Trump from that kind of
Speaker 3: tech elite in the last over the selection. And I
Speaker 3: think it speaks a little bit to the way maybe
Speaker 3: that Trump does approach these these kinds of companies where
Speaker 3: he wants their favor, He wants them to kind of
Speaker 3: be on his side, and as in a return, he'll
Speaker 3: be on their side. And we've seen him, We saw
Speaker 3: him use these kind of anti monopoly laws to kind
Speaker 3: of put pressure on the ones that maybe weren't as
Speaker 3: vocal of support for him in the past. Not to
Speaker 3: say that there's a direct link there, but it's hard
Speaker 3: to not see some kind of correlation at least. So
Speaker 3: Elon Musk, with his ambitions for Tesla and for SpaceX
Speaker 3: and for a lot of the defense contracts, must be
Speaker 3: really glad to be in Trump's good graces now, especially
Speaker 3: because Trump has talked quite a lot about increasing the
Speaker 3: amount of private companies contracting to defense and working and
Speaker 3: spending a lot more money on defense, which will mean
Speaker 3: more money in the pockets of these tech companies working
Speaker 3: on defense technologies.
Speaker 2: Yeah, it's got to be good for SpaceX, I mean Tesla.
Speaker 2: Trump has been very anti electric vehicle, so will he
Speaker 2: now pivot to suddenly being influenced by Elon Musk along
Speaker 2: the lines of, well, no, EV's actually makes sense. I'm
Speaker 2: keen to support them. Whether there'll be more subsidies for evs,
Speaker 2: and likely given Trump's interest in the liquid gold, you know,
Speaker 2: the energy industry, the fossil fuel industry, but definitely on SpaceX,
Speaker 2: very much in bed with the US defense, so there'll
Speaker 2: be synergies there. And as yea, his other business obviously
Speaker 2: x AI and Grock building, you know, the biggest AI
Speaker 2: centric supercomputer stuff like that. I can see Musk sort
Speaker 2: of putting proposals to Trump about what the government should
Speaker 2: be doing with AI, and I think on a lot
Speaker 2: of these issues, Trump really he's not a tech guy.
Speaker 2: He doesn't really get this sort of stuff, but he's
Speaker 2: very influenced by the people around him. So you've got
Speaker 2: Musk there as a trusted advisor. You've got jd Vance,
Speaker 2: who has a previous life in venture capital, worked with
Speaker 2: Silicon Valley to fund companies, so he's very much embedded
Speaker 2: with some of that tech elite. You've got Peter Thiel
Speaker 2: who's a big backer of Trump as well. New Zealand citizen,
Speaker 2: founder of Pallenteer and AI company that does a lot
Speaker 2: of work for US agencies, police force, and military. There's
Speaker 2: share prices surging at the moment on the back of
Speaker 2: their latest results, all driven by AI. We've got this
Speaker 2: sort of cluster of people in Trump's orbit who have
Speaker 2: strong ideas about where the tech world should go, and
Speaker 2: he's listening to them and he owes them. A lot
Speaker 2: of them put money into these super packs to get
Speaker 2: him into power. They didn't raise as much money as
Speaker 2: Kamala Harris did in the Democrats, so it was all
Speaker 2: a bit of a waste of time and money. But
Speaker 2: he now has a lot of bills falling due, and
Speaker 2: what is that going to mean for the flavor of
Speaker 2: his policies. That's going to be the big question.
Speaker 3: Yeah, I think a lot of it is going to
Speaker 3: central around deregulation. Personally, I think that's going to be
Speaker 3: a big flag that Trump will be waiving is getting
Speaker 3: out of the way of these particularly those Silicon Valley companies.
Speaker 3: I think the likes of Google and Microsoft may see
Speaker 3: some continuation of those anti trust kind of approaches. But
Speaker 3: when it comes to the those on the cutting edge
Speaker 3: with AI and the ones that are kind of more
Speaker 3: and Donald Trump's in a circle, will start to see
Speaker 3: that a lot of deregulation there seems to be pretty
Speaker 3: explicit in what he's been saying. And the same goes
Speaker 3: for cryptocurrency as well.
Speaker 2: Well. It's interesting on crypto. You know, Trump has done
Speaker 2: a bit of a U turn. He was quite sort
Speaker 2: of hawkish against crypto a few years ago, and again
Speaker 2: I think this is people getting to him, and probably Musk,
Speaker 2: who you know is a big fan of dogecoin and
Speaker 2: is a crypto advocate as well, basically saying to him, no,
Speaker 2: you need to support this. He's done a U turn.
Speaker 2: He wants minimal crypto regulation. He'll probably limit the rather
Speaker 2: hawkish moves by the SEC in the US to regulate
Speaker 2: cryptocurrencies and those digital asset markets. So that's all well
Speaker 2: and good, but is that going to lead to more
Speaker 2: of the sort of FTX style implosions that we've seen.
Speaker 2: He won't want that either. On deregulation, sure, last time
Speaker 2: he cut tax and red tape. Businesses love that. It
Speaker 2: means they can spend more money on R and D
Speaker 2: and return more money to shareholders. So any company, particularly
Speaker 2: those big tech companies that make a lot of profit,
Speaker 2: they'll love that. But it was Trump after all, that
Speaker 2: kicked off a lot of that antitrust stuff a few
Speaker 2: years ago, So yeah, will he continue that and see
Speaker 2: the breakup of Google and others. But I agree with you.
Speaker 2: I think you know, he clearly doesn't like a monopoly.
Speaker 2: He's a free market guy, that's his philosophy on this.
Speaker 2: But he does want to see all of the red
Speaker 2: tape and the restrictions removed from really innovative companies, and
Speaker 2: at the moment they're the AI one. So I don't
Speaker 2: see him carrying forward some of those executive orders around
Speaker 2: AI that Biden put in place. I think we he'll
Speaker 2: dial that back sognificantly.
Speaker 3: Yeah. I think the other area where we're going to
Speaker 3: see a big retraction in the US at least is
Speaker 3: green tech. So if there were you know, we've had
Speaker 3: a lot of eggs in the green tech basket here
Speaker 3: in New Zealand with our startups, and that may indicate
Speaker 3: that the US is no longer a viable entry point
Speaker 3: for these companies to really scale. So maybe refocusing more
Speaker 3: on the EU. If the US was kind of a
Speaker 3: big part of your strategy.
Speaker 2: Yeah, and you know there have been as part of
Speaker 2: the Big Reconstruction Act that Biden passed after COVID, there
Speaker 2: was green tech funding in there. So whether that will continue.
Speaker 2: One area that will continue which Trump and the Democrats
Speaker 2: are on the same page on as the semiconductor industry,
Speaker 2: the Chips Act. So Trump is very much of the
Speaker 2: view that we need more local production in the US
Speaker 2: of semiconductors, the really high end important stuff that runs
Speaker 2: ai to reduce reliance in the global supply chain on Taiwan,
Speaker 2: which is very vulnerable to attack from China. So he'll
Speaker 2: carry on that sort of stuff, things like five G.
Speaker 2: You know, he's expressed his dismay that a lot of
Speaker 2: that technology is provided by European companies. So again Biden
Speaker 2: was on the same page. And I think for Trump
Speaker 2: what it really all is about is taking on China
Speaker 2: and that continuing sort of pressure, whether it's through the
Speaker 2: form of taris or big tariffs on stuff coming from
Speaker 2: China into the US sixty percent tariffs potentially, which is
Speaker 2: quite staggering, but really that polarization of technology between the
Speaker 2: Western world and the Chinese world, and We've seen China
Speaker 2: in the intervening few years since Trump was out of office,
Speaker 2: building its own operating systems, trying to generate higher capacity
Speaker 2: semiconductors to go into phones and AI devices. Trump will
Speaker 2: basically accelerate that further by putting more export controls on
Speaker 2: the exports of high technology to China, tariffs and local productions.
Speaker 2: So I think we'll just see an acceleration of that.
Speaker 3: And that's also going to roll over to New Zealand
Speaker 3: a little bit in terms of trade agreements. He's talked
Speaker 3: about getting rid of the Indo Pacific Partnership trade Agreement,
Speaker 3: which would impact New Zealand. So how that will interact
Speaker 3: with New Zealand's tech exports to the US would not
Speaker 3: one hundred percent clear at the moment, but you know
Speaker 3: there is potentially some impact there.
Speaker 2: Yeah. And the other sort of local angle I guess
Speaker 2: is orcus. You know, this agreement this packed between Australia,
Speaker 2: the US and the UK really about submarines, but you've
Speaker 2: got orcust Pillar two, which is about other advanced technologies
Speaker 2: like AI, like quantum computing, stealth technologies, you know, high
Speaker 2: end military stuff. And I've been quite supportive of the
Speaker 2: idea of New Zealand being involved in Pillar two. Not
Speaker 2: necessarily around nuclear submarines or anything like that, but Pillar two,
Speaker 2: these advanced technologies, we should have a hand with our
Speaker 2: allies and developing those. And I think, you know, Orcust
Speaker 2: has run into some trouble. I mean, this submarine deal
Speaker 2: is so vastly expensive. Whether it will actually come to
Speaker 2: fruition is anyone's guess. I think the Australians are starting
Speaker 2: to realize what they've signed up for is massive. But
Speaker 2: the other you know, you've got South Korean others Japan
Speaker 2: are saying, hey, we want it on Pillar two because
Speaker 2: they're starting to see some of the stuff that the Americans,
Speaker 2: the Brits and the Aussies are working on and saying,
Speaker 2: you know, we want to see that the table in
Speaker 2: developing that stuff because there is a greater threat from China,
Speaker 2: so let's work on this together. Trump will just carry
Speaker 2: on thinking, I think around orcus he sees that as
Speaker 2: a way to shore up support military support among allies
Speaker 2: in the Pacific. Whether that will encourage New Zealand to
Speaker 2: join or maybe will there be more pressure with a
Speaker 2: new you right leaning ambassador in this country, Will there
Speaker 2: be more pressure for New Zealand to actually put its
Speaker 2: cards on the table and join Orcus. I think that's
Speaker 2: a possibility.
Speaker 3: It's hard to see exactly where we're going in terms
Speaker 3: of the ramifications. You know, with the potential for a
Speaker 3: Harris government, it was a lot more of the same,
Speaker 3: but a Trump government because his rhetoric can be quite inconsistent.
Speaker 3: You know, there is some stuff that we can guess about,
Speaker 3: but at the end of the day, it's really going
Speaker 3: to be just reacting as things happen. So it's going
Speaker 3: to be really important to actually pay attention, I think,
Speaker 3: to what is actually happening rather than what is being
Speaker 3: said through Trump presidency, And if.
Speaker 2: His last stint as president has anything to go by,
Speaker 2: it'll be those key personalities around him because he really
Speaker 2: is a bit of an empty vessel in terms of
Speaker 2: his thinking on some of these issues, particularly around technology. Now,
Speaker 2: just listening to him explain on election night, you know,
Speaker 2: the starship returning to Earth, you know, when he was
Speaker 2: praising you on Muscus, just clear he doesn't really understand
Speaker 2: this stuff at all, which is fine, but it's the
Speaker 2: people around him and the worry I think in the
Speaker 2: US at the moment is you know, this paranoia about
Speaker 2: the deep state in the US, this shadowy sort of
Speaker 2: left leaning cabal that runs America that he's been trying
Speaker 2: to root out. Is he just going to replace people
Speaker 2: at the SEC, his top tech advisors, people responsible for
Speaker 2: climate change policy. Is he just going to replace them
Speaker 2: with political appointees who don't really care about the evidence
Speaker 2: or the science or what technical advisors suggest is the
Speaker 2: right thing to do. And he's just going to take
Speaker 2: the advice off a small group of very wealthy, right
Speaker 2: leaning tech elites who he owes big time because they
Speaker 2: helped get him into office, you know, judging by past performance,
Speaker 2: that's what he tends to do. He surrounds himself with
Speaker 2: people who are loyal, but people he also relied on
Speaker 2: to get into office first time round. I think we'll
Speaker 2: see a lot more of that unfortunately.
Speaker 3: Yeah, it really is about an exchange of wealth and
Speaker 3: favors and keys and power and deals. It really is
Speaker 3: all about the deals.
Speaker 2: It's transactional with Trump, and people have advised you know,
Speaker 2: if he does get in, you've got to treat it
Speaker 2: as a transaction with whether you're negotiating what to do
Speaker 2: with Ukraine or a trade deal with a country. It's transactional.
Speaker 2: You need to be in that mindset dealing with this guy.
Speaker 2: So maybe that's the approach that maybe we should take
Speaker 2: as well. Absolutely, so clearly it's going to be an
Speaker 2: interesting year. Head We'll keep you posted and give our
Speaker 2: analysis on everything related to tech as the Trump administration
Speaker 2: settles in. But Ben, whenever we interview companies around some
Speaker 2: of these issues like AI, predictive analytics, and all the
Speaker 2: cool things businesses can technically do these days with the
Speaker 2: data generated by the businesses, we get the same surprising response.
Speaker 3: Yeah, the conversation typically grinds to a halt, and we
Speaker 3: are told that a lot of our businesses just don't
Speaker 3: have their data in the right places, in the right
Speaker 3: formats to do any of that.
Speaker 2: So talking about it's a bit of a waste of
Speaker 2: time if the basics really aren't done well.
Speaker 3: Which is why we're hearing a lot more from companies
Speaker 3: like Snowflake and data Bricks, companies that have emerged in
Speaker 3: recent years to help organizations manage that data.
Speaker 2: They're basically data warehousing and analytics platforms that try to
Speaker 2: get all your data in one place, process it in
Speaker 2: a uniform and secure way and interact with the various
Speaker 2: applications you're using to run your business. I was actually
Speaker 2: staying at a hotel in Awkant recently and found myself
Speaker 2: walking into the middle of a Snowflake conference. It was
Speaker 2: actually quite a big affair.
Speaker 3: Well, data is a big affair now, it's big business
Speaker 3: and Snowflake has around two hundred customers in New Zealand
Speaker 3: to date. It did around one hundred million dollars in
Speaker 3: revenue last year just across Australia and New Zealand, according
Speaker 3: to its financial accounts filed with the company's office.
Speaker 2: And spending on data, warehousing and platforms is really growing fast.
Speaker 2: So Ben, this is a timely interview with Tony Shaw,
Speaker 2: who's been around a tech industry for a long time
Speaker 2: since at NCR, dell, IBM, MuleSoft as well. Let's listen
Speaker 2: to your interview with Tony Shaw and come back for
Speaker 2: some thoughts on the back end.
Speaker 3: Thank you so much, Tony for joining us on the
Speaker 3: Business of Tech podcast. It's really great to have you here.
Speaker 3: Why don't we start with just a little bit of
Speaker 3: background about who you are and what you do.
Speaker 1: Oh fantastic, Heyn, Thank you so much for having us
Speaker 1: on board today. My name is Tony Shaw. I'm the
Speaker 1: country manager for Snowflake in New Zealand. I've been with
Speaker 1: the company just coming up to six years now, so
Speaker 1: quite a long time to be with one organization. But
Speaker 1: I've always been in tech and for a long time
Speaker 1: in analytics. I originally started my career working for NCR
Speaker 1: as a financial analyst and pricing and planning and using
Speaker 1: data and realizing how important it can be to make
Speaker 1: financial decisions. And then from there I moved into more
Speaker 1: of the sales and business development side of things, both
Speaker 1: in New Zealand and I had a long time in London,
Speaker 1: and then predominantly in the data and analytics side of things.
Speaker 3: Do you want to share maybe the perception of data
Speaker 3: maybe pre your Snowflake time, and then how it's changed
Speaker 3: since then?
Speaker 1: Yeah, no problem. I think data's always been important. Organizations
Speaker 1: have always had the aspiration to be using data better
Speaker 1: to make better and informed decisions. But what's happened in
Speaker 1: the last five to ten years is the accessibility of
Speaker 1: the information has become so much easier. The cost to
Speaker 1: get that data and analyze it has dropped significantly, and
Speaker 1: that's opened up massive opportunities because it allows organizations to
Speaker 1: bring all of their data from all of their disparate
Speaker 1: systems into one environment where that structured data unstructured data,
Speaker 1: and then can imply analytics to that. Historically, it used
Speaker 1: to be a lot of backwards looking, a lot of
Speaker 1: reporting what did happen? And now where we're seeing is
Speaker 1: a lot more predictive analytics, opening up the information to
Speaker 1: a lot more of the business users and allowing that
Speaker 1: decision making to be a lot more in the front
Speaker 1: line rather than just the back office. So we're seeing
Speaker 1: that dissemination of information across multiple channels, multiple users, and
Speaker 1: the ease of use. So it's no longer just a
Speaker 1: back office function as lines of business making decisions every
Speaker 1: day which are moving the dial within those organizations.
Speaker 3: Right. And you know, traditionally when we think of data,
Speaker 3: we think big data, right especially these days, and we
Speaker 3: think big companies. But that's increasingly changing as well. I
Speaker 3: would imagine, like you say, as the accessibility, the affordability
Speaker 3: of data and data analytics tools are starting to shift
Speaker 3: a little bit, are you starting to see smaller companies,
Speaker 3: you know, not necessarily your one person companies, but maybe
Speaker 3: your medium size businesses gaining a better understanding of how
Speaker 3: to utilize their data.
Speaker 1: Yeah, it's been remarkable. Since we started the business in
Speaker 1: New Zealand in twenty nineteen, we were looking at what
Speaker 1: is the segments and what is the segmentation and customers
Speaker 1: that we're going to look to try and require. We
Speaker 1: had two stomers in New Zealand when we started the
Speaker 1: business here and now we've got north of two hundred,
Speaker 1: and it was really interesting. We started to think around
Speaker 1: that segmentation and we thought it might be some mid
Speaker 1: tier customers and then you know, maybe we can work
Speaker 1: our way up or down across the different spectrum of
Speaker 1: size and scale and complexity. But what happened was we've
Speaker 1: got organizations of all size and scale very early. And
Speaker 1: I think one of the things with Snowflake, and one
Speaker 1: of the reasons why we had such fantastic adoption, was
Speaker 1: it's the ability to scale down to New Zealand size companies,
Speaker 1: not just being able to scale up. So there's the
Speaker 1: global organizations, you know, there's the capital ones and the
Speaker 1: sinespres etc. But within New Zealand because the platform scales
Speaker 1: down and you only pay for what you use on
Speaker 1: a true consumption basis. We've been able to scale to
Speaker 1: organizations that are getting enterprise enterprise grade capability, but they're
Speaker 1: only paying for what they use based on the size
Speaker 1: of the organization or how much they actually need to
Speaker 1: use of the platform.
Speaker 3: To what extent a New Zealand companies really using all
Speaker 3: of the capabilities of Snowflake. Are we up there in
Speaker 3: the most advanced users or are we kind of just
Speaker 3: using the very basics because we're smaller.
Speaker 1: And yeah, absolutely. We just had a conference last week.
Speaker 1: It was unbelievable. We had over a thousand people there,
Speaker 1: which makes it the largest data and analytics event in
Speaker 1: New Zealand, and we had some fabulous customers. So we'd
Speaker 1: organizations like in New Zealand, Tavado, Aura in zed Health,
Speaker 1: in zet, super one, end, z MITA ten, Spark, shares
Speaker 1: e'se the kind of list goes on and it was
Speaker 1: a really great opportunity for organizations to share what they're
Speaker 1: doing and how they deliver value from the platform, and
Speaker 1: also to build that community so organizations can network with
Speaker 1: their peers and learn from each other. But in terms
Speaker 1: of taking on the global stage, shares Y's is one
Speaker 1: of our fantastic customers. They actually recently won the APJA
Speaker 1: Data Driver Award for powered by So what that means
Speaker 1: is they're powering their application using Snowflake to help drive
Speaker 1: the adoption and understand their customer behaviors in order to
Speaker 1: deliver a better service. And they've just had phenomenal growth.
Speaker 1: So you know, they've got seven hundred thousand customers. So
Speaker 1: we're taking on the world. Shares is being successful across
Speaker 1: here and across in Australia, and we've got a number
Speaker 1: of tech startups that we're working with who are winning
Speaker 1: awards and delivering really fantastic results for their business on
Speaker 1: a global scale.
Speaker 3: Fantastic. Yeah, so it sounds like you've got some real
Speaker 3: power users. Then that's what you're.
Speaker 1: Saying, unbelievable. It's both the business users. So we had
Speaker 1: the co CEOs presenting around how that's driving value. Data
Speaker 1: analysts we have technical capabilities. It's the ability to work
Speaker 1: with all of the different personas across an organization, not
Speaker 1: just the technical people though they love the platform, so
Speaker 1: the architects, the engineers, the really deep data people, but
Speaker 1: then also the people who are consuming it, so technically
Speaker 1: literate business analysts people just writing natural language questions in English,
Speaker 1: executive writing. Sorry, just analyzing what's happened and what's going
Speaker 1: to happen and their business. That's across the board, those
Speaker 1: different personas that all use data in a slightly different nuance,
Speaker 1: but they want consistency of information, they want high quality,
Speaker 1: they want real time, they want accurate information so they
Speaker 1: can make those decisions.
Speaker 3: Cool. Now, obviously you can. It's great to talk up
Speaker 3: to customers that are doing really awesome stuff, but New
Speaker 3: Zealand's definitely not perfect nowhere is in terms of how
Speaker 3: it's utilizing data. So what are some of the areas
Speaker 3: that you're seeing New Zealand lagging behind? Maybe some New
Speaker 3: Zealand companies where you think, you know some areas of
Speaker 3: focus could be to improve the usage of data within
Speaker 3: New Zealand.
Speaker 1: I think that the pitfalls that we always see is
Speaker 1: making sure that there's executive sponsorship and outcomes that the
Speaker 1: organization is trying to drive towards. So it's really important
Speaker 1: that it doesn't become a science experiment or a program
Speaker 1: that's just for the IT users. What the successful organizations
Speaker 1: do is they've got very strong alignment to a specific
Speaker 1: business outcome, whether that's a finance program looking at receivables
Speaker 1: or finance transformation, whether it's marketing looking at customer experience, NPS, churn,
Speaker 1: cross sale, etc. Or operations to streamline the efficiency with
Speaker 1: which the organization works in it has to have that
Speaker 1: business outcome that everybody can anchor themselves and align to.
Speaker 1: When you've got that, that goes a long way to
Speaker 1: making sure this program's success. And then the usual governance
Speaker 1: across the program and making sure that there is steps
Speaker 1: along the way that people are measuring to make sure
Speaker 1: that that outcome happens. When you start to get those
Speaker 1: sorts of things, then everything else just falls into place.
Speaker 3: Cool. Now, let's say I'm one of the New Zealand
Speaker 3: companies that hasn't started to get deep into data yam.
Speaker 3: You know, maybe a medium sized company who is starting
Speaker 3: to think about the potential there. What are my first
Speaker 3: kind of steps?
Speaker 1: The first step is defining what dial within the business
Speaker 1: you're trying to move and what is that outcome you're
Speaker 1: trying to achieve. So say it's a marketing outcome around
Speaker 1: cross seal. Make sure that those objectives and those metrics
Speaker 1: are well understood and documented, and then start small and
Speaker 1: try and deliver that program so that you deliver that
Speaker 1: specific outcome, get the win, and then build upon that.
Speaker 1: You need to paint the vision to the organization in
Speaker 1: terms of what is the analytic capability going to deliver.
Speaker 1: So you need to have a vision and where we're
Speaker 1: going as an organization, but you also need to have
Speaker 1: a specific outcome that you're driving towards that you can
Speaker 1: build on that success. Then you need to drive where
Speaker 1: do I get the data from and how do I
Speaker 1: get high quality information to solve that business problem and
Speaker 1: answer the questions that you're looking to define or answer sorry,
Speaker 1: And then it's getting the technical teams aligned to find
Speaker 1: that data, source that data cleanse that's high quality decision
Speaker 1: making because you want to make sure sure that the
Speaker 1: information that's being used is of quality so that the
Speaker 1: decisions out the back of it are influenced.
Speaker 3: What does that mean cleansing data? Like, what does that
Speaker 3: actually in real terms mean? Because if I'm a company
Speaker 3: that's been around for twenty years, I've got a bunch
Speaker 3: of spreadsheets and PDFs and all this kind of stuff
Speaker 3: and it's ordered, it's in folders. We know where everything is.
Speaker 3: But is that clean? Is that clean enough.
Speaker 1: It depends a lot of the times those spreadsheets have
Speaker 1: been built up by a couple of specific people. They
Speaker 1: might be suitable for that use case or that specific
Speaker 1: piece of information you're looking to deliver. An example, when
Speaker 1: I was a pricing analyst, we used to have huge
Speaker 1: amounts of spreadsheets everywhere that have interconnected links, and we
Speaker 1: put out some pricing models. Then you'd come back about
Speaker 1: a month later and change something because you'd found a
Speaker 1: mistake in the spreadsheet in the formulas, and that would
Speaker 1: change the entire pricing model. And you'd be sitting there going,
Speaker 1: oh my goodness, now I've just completely stuffed this up.
Speaker 1: You change something else to get it back, and then
Speaker 1: the numbers would all change back again. Spreadsheets, whilst they're
Speaker 1: across every single organization, are kind of the bane of
Speaker 1: any enterprise organization's life because there is no real auditability.
Speaker 1: So when I talk about high quality data, it's getting
Speaker 1: that data from those source systems, making sure that it's
Speaker 1: usable and in a format that's understandable, but it's consolidated
Speaker 1: across multiple touch points so that you've got a consistent
Speaker 1: view of customer, and then involving the business teams to
Speaker 1: define what is the rules and logic so that everybody
Speaker 1: knows what the definition of a customer is, what is
Speaker 1: a definition of revenue or profit or what happens to be,
Speaker 1: and then everyone's working off that consistent set of information.
Speaker 1: We've all been in meetings where people are arguing about
Speaker 1: the data rather than what they do with that information.
Speaker 1: So what we want to try and do is consolidate
Speaker 1: the information so that it's a single view across the business.
Speaker 1: And then people are thinking about what are the decisions
Speaker 1: I make, not hey, is that the right one? Am
Speaker 1: I questioning the actual data validity rather than what I
Speaker 1: can do with it?
Speaker 3: What's the kind what's the kind of talent that you
Speaker 3: would need to do that? Do you need to hire
Speaker 3: an house data scientist? Is it okay to just kind
Speaker 3: of get a consultant into kind of do some data
Speaker 3: stuff for you to get you ready.
Speaker 1: I think consultants have a place and they've got a
Speaker 1: lot of experience that can bring to bear on organizations.
Speaker 1: But I think the organizations themselves have a responsibility and
Speaker 1: they have to have a capability internally. This can't be
Speaker 1: done to an organization. You have to do it with
Speaker 1: the organization and the people within the enterprise or the company.
Speaker 1: They know what the business is trying to achieve, they
Speaker 1: know where to get the data from. So you need
Speaker 1: to have a set of skills within the organization, and
Speaker 1: that skills from a technical capability to work out where
Speaker 1: does the data come from and how do I get
Speaker 1: it and then also how do I put that into
Speaker 1: the hands of the users so they've got confidence that
Speaker 1: they can start to drive analysis from it. But the
Speaker 1: internal capability is critical. One of the things we're trying
Speaker 1: to do at Snowflake is build a really big community.
Speaker 1: So the event we just ran with a huge number
Speaker 1: of people. We run user groups, we run meetups, we
Speaker 1: run product specialist workshops. When we bring some of our
Speaker 1: teams offshore into New Zealand, and it's really important to
Speaker 1: build that community and network so we can share what's
Speaker 1: working and to your point before, what's not working, so
Speaker 1: that we can avoid those pitfalls where possible and start
Speaker 1: to accelerate how do we deliver that outcome. But I
Speaker 1: think internally the capability needs to be there. You know,
Speaker 1: we need to train our teams, we need to cross
Speaker 1: pollinate from existing teams, so you might have somebody who's
Speaker 1: working internally within an organization, they've got a huge amount
Speaker 1: of tribal knowledge within that organization. But then how do
Speaker 1: we cross pollinate their skill sets with whatever it is
Speaker 1: they need with it's technical or analysts. So they've got
Speaker 1: the data literacy to drive that outcome. But absolutely internal
Speaker 1: was critical.
Speaker 3: Yeah, I mean, I guess the message that I'm getting
Speaker 3: really is that you can't do this. Lais a fair.
Speaker 3: You can't just be like, let's dabble in some data.
Speaker 3: You really have to sit down and create a cohesive,
Speaker 3: strong plan and roadmap and objectives and spend the time
Speaker 3: to actually build that out. And if you're seeing gaps
Speaker 3: in your organization, then you actually need to maybe fill
Speaker 3: those gaps, whether that's with training or with bringing on
Speaker 3: new staff. Does that kind of sound about right?
Speaker 1: Yeah, one hundred percent agree. And it's building that strategy
Speaker 1: into the business outcome of the business strategy, so that
Speaker 1: the data strategy is part of your business strategy because
Speaker 1: they shouldn't be separate. One can inform the other and
Speaker 1: the other can form each other. A lot of times
Speaker 1: the data teams have a really enterprise view of the
Speaker 1: business because they're looking at data from multiple different areas,
Speaker 1: so you're not siloed within say HR, or siloed within finance,
Speaker 1: or siloed within marketing. The data teams get a strong
Speaker 1: visibility across the organization. For example, we were talking in
Speaker 1: New Zealand presented recently at our conference. Again they talked
Speaker 1: about the concept of majors and miners, So you've got
Speaker 1: a data team which has got majors in data and analytics,
Speaker 1: but working with the lines of business who have a
Speaker 1: minor and data, but a major is in their skill set,
Speaker 1: whether it's HR, where it's cargo, whether it's financed, whatever
Speaker 1: happens to be. And that dovetail together of the data
Speaker 1: literacy and the data capability with the knowledge of that
Speaker 1: specific line of business and what's important to that line
Speaker 1: is really important because then you're marrying both of the
Speaker 1: outcome and the capability together, which drives a lot of
Speaker 1: value for the organization. Having that strategy which is aligned
Speaker 1: into the business strategy is really important. And obviously with
Speaker 1: the introduction of things like AI, AI is built upon
Speaker 1: how you use data, whether it's internal data, external data
Speaker 1: to drive that decision making. So you know, you can't
Speaker 1: really have an AI strategy without a data strategy, and
Speaker 1: all of this should be blended into what is the
Speaker 1: objectives of the organization and what are they driving for?
Speaker 3: Great, you just did my job then, and you pivoted
Speaker 3: to exactly where I wanted to go next. We just
Speaker 3: talk about AI because we have to, right because it's
Speaker 3: twenty twenty four and the last three years of AI
Speaker 3: has been just a different story completely to where it
Speaker 3: was previously. From your experience as somebody who lives and
Speaker 3: breathes data, what has that experience been like to watch
Speaker 3: data go from this kind of dry but necessary thing
Speaker 3: to the thing that is powering the future.
Speaker 1: It's absolutely amazing, isn't it. You know, you know, in
Speaker 1: your personal life you use things like GPT and the
Speaker 1: output that it can deliver is just extraordinary. And where
Speaker 1: it's going, I think is fantastic. It's outstanding. But you've
Speaker 1: got to get the foundations right because otherwise you're building
Speaker 1: on quicksand and you're analyzing inefficient data and you'll very
Speaker 1: quickly lose confidence from those users. And there's also some
Speaker 1: of those traditional techniques which are still incredibly valuable to
Speaker 1: the organization. So just making sure that we understand the vision,
Speaker 1: and we go after that, and we go after that
Speaker 1: with speed, but at the same time we don't take
Speaker 1: the focus off some of those other areas which we
Speaker 1: can deliver very very quick value to the business.
Speaker 3: Somebody said to me the other day that actually, with
Speaker 3: new AI models, data cleanliness is actually not as important
Speaker 3: as it used to be, because if you look at
Speaker 3: something like chat GPT, you know, the training is not
Speaker 3: necessarily there's so much of it they couldn't possibly go
Speaker 3: through and clean it all. Do you think that's true
Speaker 3: that if you were wanting to create kind of a
Speaker 3: GENAI model that's used you can use to analyze certain levels,
Speaker 3: certain kinds of data within the organization, that there is
Speaker 3: less need for data cleanliness than they used to be.
Speaker 1: I'd say, where you're looking at the entire Internet for
Speaker 1: consumers like you just describe, maybe when organizations are looking
Speaker 1: to use information which is internal to their organization, that
Speaker 1: has to be very high quality. It has to be robust,
Speaker 1: it has to be trusted, and it has to be
Speaker 1: using the information and knowledge from that organization to prevent
Speaker 1: things like hallucinations and bad decisions because the data is incorrect, inaccurate,
Speaker 1: not full enough. There's not a quorum of data to
Speaker 1: make an informed decision. I think the data quality aspects
Speaker 1: are even more important for an organization using some of
Speaker 1: these advanced capabilities like genai. The Genai capability helps a
Speaker 1: lot in terms of being able to put some of
Speaker 1: that tagging, say, or definitions around what some of that
Speaker 1: data means. It helps speed up the efficiency to make
Speaker 1: the data more reliable and higher quality and understood. But
Speaker 1: without putting the thought into having that high quality data,
Speaker 1: it's going to fall flat. In my opinion, I think
Speaker 1: we need to absolutely focus on the availability, the security
Speaker 1: and governance, the privacy, the quality and trusted data and
Speaker 1: then apply these techniques on top of it. And one
Speaker 1: of the things where Big believers on is bring the
Speaker 1: processing and the workload to the data rather than pushing
Speaker 1: all the data out to different systems. And the reason
Speaker 1: for that is because you've got that single view of
Speaker 1: the business, you've got one place to make sure that
Speaker 1: the data is of that high quality we're just describing,
Speaker 1: and you've got the privacy and governance so that only
Speaker 1: the right people are allowed to see it. Because what
Speaker 1: we're doing is we're opening up the access to a
Speaker 1: huge wide range of different consumers of the data, So
Speaker 1: we've got to make sure that it's protect and we've
Speaker 1: got to make sure that it's of high quality.
Speaker 3: Yeah, you're not on the difference between creating something from
Speaker 3: a mass market and creating something that is to improve
Speaker 3: organizational performance, and those are two very different goals completely.
Speaker 3: The other thing that I've been considering about generative AI
Speaker 3: lately is there's this kind of to and fro about
Speaker 3: how much we let the GENAI actually do if that
Speaker 3: kind of makes sense, where it can be quite creative
Speaker 3: and thoughtful and very have high contextual understanding, but that
Speaker 3: may potentially, you know, open up the hallucinations or we're
Speaker 3: not quite sure where that's going to go. Or we
Speaker 3: can be very tight and strict and be like, it
Speaker 3: can only return these information from these sources in these modes,
Speaker 3: and trying to find the balance of that can be
Speaker 3: tricky at a kind of data level when you're figuring
Speaker 3: out what to include and what not to include, how
Speaker 3: do you start making some of those decisions.
Speaker 1: You're absolutely right, it is a tricky decision or tricky
Speaker 1: consideration to think around. Where we think around it is
Speaker 1: having access to the right amount of information, but putting
Speaker 1: those governance and controls on there so that you've got
Speaker 1: things like role based access so that only I'm allowed
Speaker 1: to see the information that's purten in to my specific
Speaker 1: role and I can't see anything else outside of that.
Speaker 1: And that's why it's really important to get that governance
Speaker 1: and that privacy foundations set and defined upfront so that
Speaker 1: it's not being made up and make sure that you've
Speaker 1: got the right level of data to support the decision
Speaker 1: that you're trying to solve. And my view would be
Speaker 1: start small, start to prove out some value, and then
Speaker 1: expand as you've got that confidence within the business. But
Speaker 1: it's moving incredibly fast, right you know. You think even
Speaker 1: a couple of years ago, you know, Chat GPT was
Speaker 1: just coming of age and people had only just staid
Speaker 1: to hear about it, and now AI is embedded into
Speaker 1: just about every single platform and process. What we're looking
Speaker 1: to do is understand how we can use each of
Speaker 1: those different silos of informations and applications bring that together
Speaker 1: so you've still got that holistic view at the data level,
Speaker 1: not just at the application level. So you want to
Speaker 1: be able to bring that data together from multiple places
Speaker 1: and then apply AI across it, depending on what it
Speaker 1: is you're trying to do, but you know it's moving
Speaker 1: so quickly it's really exciting. To be parely honest.
Speaker 3: What is exciting about it for you? Because you know,
Speaker 3: for office workers there's that kind of productivity gain stuff
Speaker 3: that's being talked about. For consumers there's like access to
Speaker 3: information that they may not have or ability to proof
Speaker 3: read and do these kinds of everyday tasks. But as
Speaker 3: somebody who is like super deep in the world of data,
Speaker 3: what is actually super exciting for you about the generative
Speaker 3: AI stuff?
Speaker 1: The productivity part that'll be part of it, But I
Speaker 1: don't think that organizations are looking at just the productivity.
Speaker 1: Sure there's efficiency, but I think it's the upside that
Speaker 1: people can drive from it. Is the better network planning
Speaker 1: in TALCOS is the better supply chain management. Because you're
Speaker 1: pulling information from third party suppliers as well as the
Speaker 1: internal information. You can run and advance large language model
Speaker 1: across that to work out what is the route processing
Speaker 1: or where do you deliver things quicker? That outcome that's
Speaker 1: going to move the dial with those organizations to drive
Speaker 1: revenue or make them more profitable. That's really exciting and
Speaker 1: obviously the productivity gains will come as well.
Speaker 3: Are we already seeing some of those gains in certain
Speaker 3: areas using the new air models? Like can do you
Speaker 3: have examples of that? Yeah?
Speaker 1: Absolutely? Mine to ten was just talking. They've spoke to
Speaker 1: our conference again last year. They've had a very small team,
Speaker 1: so they've managed to consult it a lot of their
Speaker 1: information one year on what done as they've applied some
Speaker 1: of these large language models to look at water supply
Speaker 1: chain and how can they deliver better outcomes across the
Speaker 1: retail organization. So they're starting to embed some of these
Speaker 1: capabilities into their processes. We're seeing the talcos doing the
Speaker 1: same things. A lot of them have had proof of
Speaker 1: concepts that they're now starting to put into production. So
Speaker 1: I think that there's going to be a lot of
Speaker 1: the pilot and prototype pieces of we're really accelerating now,
Speaker 1: and to be honest, some of the organizations they see
Speaker 1: that as a competitive differentiator, so they are actually keeping
Speaker 1: some of them relatively close to their chests because the
Speaker 1: faster they can move, they're looking to leap frog the competitors.
Speaker 3: If twenty twenty one twenty two was kind of the
Speaker 3: emergence and the testing and the seeing what could go wrong?
Speaker 3: You know, twenty twenty three and twenty twenty four has
Speaker 3: been about getting those prototypes and starting to see what
Speaker 3: can happen. Is twenty twenty five to twenty six is
Speaker 3: that going to be the acceleration time? Where are we
Speaker 3: at in terms of starting to really see mass adoption
Speaker 3: of this tech at an enterprise level, at a fundamentally
Speaker 3: restructuring level.
Speaker 1: Yeah, I think over the next twelve to eighty months
Speaker 1: you will see a massive acceleration of that. I think
Speaker 1: those organizations that have done those that foundational work are
Speaker 1: in a much better position to be able to accelerate faster.
Speaker 1: So those organizations that have got trusted, high quality, consolidated information,
Speaker 1: then they're looking at what do they do to exploit it.
Speaker 1: They've got that quorum of data, and now how do
Speaker 1: we use it and exploit it quickly? There's still organizations
Speaker 1: which have yet to do that foundational work, and that
Speaker 1: foundational work is critical before you can start to exploit
Speaker 1: it in a really meaningful way. So I think there's
Speaker 1: going to be those that are ahead of the curve
Speaker 1: and have been ahead of the curve for the last
Speaker 1: few years are going to be able to accelerate quicker
Speaker 1: than those who haven't done that homework and done the
Speaker 1: foundational stuff. And it's no different to whether it's GENAI
Speaker 1: or large language models. Those organizations that have got that
Speaker 1: high quality data, they've spent the time to ensure that
Speaker 1: the lines of businesses have data literacy and data skills
Speaker 1: and know what they can do with the information to
Speaker 1: change the processes will be in a better position. So
Speaker 1: adding on top of that things like genai, it will
Speaker 1: allow those organizations to go faster. But it's just accelerated
Speaker 1: how quickly organizations can start to exploit it. I don't
Speaker 1: think it changes the fundamental that you've got to get
Speaker 1: the basics right and do that well before you can accelerate.
Speaker 3: What would you say are the biggest risks that we
Speaker 3: need to be thinking about as we enter this accelerative phase.
Speaker 1: I think the privacy and just because we've got the data,
Speaker 1: does that give us the right to use that data mentality?
Speaker 1: And I think we've got to be really considerate that
Speaker 1: most of these organizations is not their data, it's their
Speaker 1: customers data, So we need to really consider what is
Speaker 1: it that we're going to do with that data and
Speaker 1: make sure that it's doing the right things for their
Speaker 1: customers as well as the internal organization. So we've got
Speaker 1: to think around the privacy, the use of it, the
Speaker 1: AI governance and governance of the customer's use of it,
Speaker 1: and the permissions and things like that. So I think
Speaker 1: the accessibility is great, but just because we've got it
Speaker 1: doesn't necessarily mean we should use it in a certain way.
Speaker 1: There's going to be a lot of focus around the
Speaker 1: obviously security, privacy, and then also how do we just
Speaker 1: continue to evolve on that as well?
Speaker 3: What do you mean by that.
Speaker 1: In terms of as the technology moves so much faster,
Speaker 1: how do we keep up with that? And how do
Speaker 1: we think about the new use cases? How do we
Speaker 1: think around what is that business driver again taking it
Speaker 1: away from just a technology problem, what is the business
Speaker 1: trying to achieve ross that line of business, finance, marketing,
Speaker 1: et cetera. And how do we align to that outcome right?
Speaker 1: Otherwise we can spend a huge amount of money with
Speaker 1: science experiments that don't actually do much for the business.
Speaker 1: Another thing that will be important will be looking at
Speaker 1: the cost considerations, making sure that the whatever we're doing
Speaker 1: is aligned to the outcome so that it's cost and
Speaker 1: value tightly coupled. Otherwise, you know, they're not cheap things
Speaker 1: to run, so we need to make sure we've got
Speaker 1: the guardrails across it. So cost management is going to
Speaker 1: be efficient, going to be important, that the governance and
Speaker 1: privacy is going to be important, and that all ties
Speaker 1: back to what is what are we're using it for
Speaker 1: and what is the business value we're trying to drive
Speaker 1: out the back of it.
Speaker 3: So get excited, but not too excited, and be thoughtful.
Speaker 3: That's kind of there, I think.
Speaker 1: Be excited, but be thoughtful. Don't don't limit what you
Speaker 1: think you can do because you probably can. And it's
Speaker 1: exciting time to go and test some of these hypotheses
Speaker 1: and see how it works. So be excited, to be
Speaker 1: really excited, it's going to be fantastic next couple of years.
Speaker 1: But just be thoughtful about how you're using it and
Speaker 1: thoughtful about your customers.
Speaker 3: So, if ever there was a man who lives and
Speaker 3: breathed data, I think it's Tony Shaw. He has clearly
Speaker 3: been in the industry for a long time, and his
Speaker 3: advice I think, while some of it isn't necessarily novel.
Speaker 3: It's the stuff we've been hearing for a while about
Speaker 3: getting data and order. I think that the way that
Speaker 3: he has put it really was very clear and concise
Speaker 3: and actionable as well, which is what I appreciated about
Speaker 3: the chat.
Speaker 2: Yeah, he really talked about this transition into data and analytics,
Speaker 2: the importance of data in financial decision making, and for years,
Speaker 2: you know, we've been talking to New Zealand businesses about
Speaker 2: writing about it, and they're all up for it, and
Speaker 2: some of them are really doing that, doing really smart
Speaker 2: things with data, but we were a bit slower to
Speaker 2: the move to the cloud and getting data in order
Speaker 2: as part of that digital transformation. So a lot of
Speaker 2: businesses talk about this stuff, but are they actually using it?
Speaker 2: And when I took to them sort of off the record,
Speaker 2: they say, well, actually, know where we've done pilots, we're
Speaker 2: doing limited use cases related to data analytics and the like,
Speaker 2: but we don't have the data in the right shape.
Speaker 2: We need to build a data warehouse or a data lake.
Speaker 2: We need to standardize our data and that literally for
Speaker 2: some of them is taking years. So we've seen that's
Speaker 2: why we've seen the rise of snowflake and data Bricks
Speaker 2: and others. The big tech platforms can only do so much.
Speaker 2: It's really up to you, and there's a layer between
Speaker 2: the customer and the big platform. We're all of your
Speaker 2: data potentially is going to be and these companies are
Speaker 2: playing a really valuable role there.
Speaker 3: Yeah, and the couple that we mentioned Data Bricks and Snowflake,
Speaker 3: and these are the ones that have really come out
Speaker 3: swinging and have shown that the value over and over again.
Speaker 3: And Snowflake listed on the NASDAK and has shown really
Speaker 3: great growth since doing that. So you know, its success
Speaker 3: is I think a good indicator of the value that
Speaker 3: it is offering to organizations globally. And you know Tony
Speaker 3: talking about the fact that it can scale up to
Speaker 3: these massive, massive international corporates, but it can also scale
Speaker 3: down to fit the needs of organizations and countries like
Speaker 3: New Zealand. And if we want to be the country
Speaker 3: that is using AI, that is using our data to
Speaker 3: improve our productivity, to enter the brave new digital world
Speaker 3: and kind of stay relevant on a global scale, then
Speaker 3: these kinds of products, these kinds of projects of what
Speaker 3: needs to be done on a bigger scale. And what
Speaker 3: Tony was saying about not doing science experiments anymore, right
Speaker 3: the time for kind of these doing science experiments over
Speaker 3: and over again. The small scale dabbling is kind of
Speaker 3: if you're still in that phase, you might need to
Speaker 3: put a bit of welly behind it and get on
Speaker 3: with it.
Speaker 2: Yeah, yeah, yeah, I mean, I think his advice is
Speaker 2: sort of what we've heard, which is start small, don't
Speaker 2: necessarily go big bang, because if you've designed it wrong,
Speaker 2: suddenly it becomes a very expensive failure. So target a
Speaker 2: part of the business where having great insights into your
Speaker 2: data is going to really help the business. Start that
Speaker 2: experiment a little bit, then grow a bigger But he's
Speaker 2: clearly predicting a major acceleration of AI adoption over the
Speaker 2: next twelve to eighteen months as organizations do that foundational work.
Speaker 2: Trying to get ahead of the curve is a competitive advantage.
Speaker 2: Hopefully that message is getting through in New Zealand. We've
Speaker 2: seen so much research over the last year or so
Speaker 2: to suggest that we're a little bit behind the curve.
Speaker 2: But if companies like Snowflake can help accelerate that, because
Speaker 2: as you say, it scales down to medium sized businesses
Speaker 2: quite well, that's basically where New Zealand plays and a
Speaker 2: lot of those companies have been playing around with co
Speaker 2: pilots and chatbots and AI related applications, so maybe some
Speaker 2: of them have done enough work to actually in twenty
Speaker 2: twenty five and beyond make really good use of AI.
Speaker 2: And again what we've heard from others is emphasizing the
Speaker 2: importance of aligning sort of AI and data initiatives with
Speaker 2: business outcomes and having internal sponsors, people in the executive
Speaker 2: off the business, people on the board who are real
Speaker 2: champions for this. There's no point doing something where the
Speaker 2: CEO and the executive team is sort of saying how
Speaker 2: much is this going to cost? If they're not convinced
Speaker 2: if the value of investing in these sorts of platforms
Speaker 2: to the business, you've got a problem. They've all got
Speaker 2: to be on board. So thanks very much to Tony
Speaker 2: Shaw from Snowflake for his thoughts on the data landscape
Speaker 2: and what's needed to spur AI adoption.
Speaker 3: We'll be touching on that and next week's episode two,
Speaker 3: and we have a panel of AI experts joining us
Speaker 3: to look at the year in AI, big developments in
Speaker 3: the technology, regulation and government's use of AI, and what
Speaker 3: may be in store in twenty twenty.
Speaker 2: Five Show notes for the Business of Tech are in
Speaker 2: the podcast section at Business Desk dot co dot nz,
Speaker 2: where you can stream this podcast in full every week.
Speaker 2: It's also available from iHeartRadio or your podcast platform of choice.
Speaker 3: Get in touch with your feedback and we'd love to
Speaker 3: hear your suggestions for upcoming tests too. You can email
Speaker 3: me Ben at business Desk dot Co dot and z,
Speaker 3: and you.
Speaker 2: Can find both of us on x and LinkedIn, where
Speaker 2: you can follow the Business of Tech page for all
Speaker 2: of our updates.
Speaker 3: That's it for this week. We'll be back talk AI
Speaker 3: and way through the election debris next Thursday.
Speaker 2: We'll catch you in
Speaker 1: Mm hmm