Latest / The SME Stream / How to put your data to work, plus tech under Trump

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How to put your data to work, plus tech under Trump · The SME Stream

0:04
Speaker 1: It is now clear that we've achieved the most incredible
0:09
Speaker 1: political there. Look what happened?
0:11
Speaker 2: Is this crazy?
0:18
Speaker 1: But it's a political victory that our country has never
0:23
Speaker 1: seen before, nothing like this.
0:25
Speaker 3: Look what happened. Indeed, Donald Trump is heading back to
0:29
Speaker 3: the White House after a stronger than expected showing in
0:32
Speaker 3: the presidential election, with the Senate and the House of
0:35
Speaker 3: Representatives destined to also be in Republican control.
0:40
Speaker 4: Which has major implications for Trump's policy agenda, including a
0:43
Speaker 4: host of tech created issues from AI regulation to cryptocurrencies,
0:48
Speaker 4: data and privacy law reform, and the tech arms race
0:52
Speaker 4: with China.
0:53
Speaker 3: This week, on the Business of Tech, powered by Two
0:55
Speaker 3: Degrees Business, we look at what a Trump administration means
0:58
Speaker 3: for tech, rold at influential tech billionaires and platforms played
1:03
Speaker 3: in the election campaign. I'm Peter Griffin and.
1:06
Speaker 4: I'm Ben Moore. Coming up on the show as our
1:08
Speaker 4: featured guest. Tony Shore, the New Zealand country manager for Snowflake,
1:13
Speaker 4: a company that isn't as well known as Microsoft, AWS
1:17
Speaker 4: or Google, but is working with those companies and a
1:20
Speaker 4: rapidly growing roster of Kiwi companies to help them store
1:23
Speaker 4: and manage their data, run data analytics and use AI.
1:28
Speaker 3: Yeah, Tony has some great advice for companies eyeing up data, analytics,
1:32
Speaker 3: machine learning, and AI, which is really about the importance
1:35
Speaker 3: of getting your data house in order before you delve
1:38
Speaker 3: into these things. So stick around for Ben's interview with Tony.
1:42
Speaker 3: But first we need to sift through the ashes of
1:45
Speaker 3: Wednesday night's election results. Ben, we weren't actually going to
1:49
Speaker 3: talk about the election on this episode because normally we
1:52
Speaker 3: record the podcast on a Tuesday afternoon, which was just
1:56
Speaker 3: before the election. Terrible timing, but yes.
1:59
Speaker 4: But I happen to you got quite sick on Tuesday night,
2:02
Speaker 4: so we pushed the publication of the Business of Tech
2:04
Speaker 4: out a day, which is why you're hearing this on a.
2:07
Speaker 3: Friday, Yeah, which sort of means we can reflect on
2:10
Speaker 3: what went down and look at what Trump may have
2:14
Speaker 3: in store on the tech front. And you know, I
2:17
Speaker 3: think before we get into that, we should really talk
2:19
Speaker 3: about the influence that tech billionaires and their platforms have
2:25
Speaker 3: had on this presidential election. I mean it's pretty clear
2:28
Speaker 3: in his victory speech from Florida, Trump very much talking
2:34
Speaker 3: about Elon Musk. You know, last time around, Elon Musk
2:38
Speaker 3: was relatively close to Trump. I remember that iconic meeting
2:43
Speaker 3: where he basically called in all the heads of the
2:46
Speaker 3: tech companies to Trump Tower and sort of had a
2:50
Speaker 3: chat with him. Peter Thiel was there, I think Tim
2:54
Speaker 3: Cook from Apple was there. Elon Musk was there, so
2:57
Speaker 3: it all seems sort of quite Trump appointed him to
3:02
Speaker 3: a couple of advisory councils and Musk ended up quitting them,
3:07
Speaker 3: so there was a bit of a falling out between
3:09
Speaker 3: him and Trump. He's really rekindled that relationship, and you
3:14
Speaker 3: know Elon Musk, I think he sees as being quite
3:18
Speaker 3: key to the success that he's had, so he'll need
3:20
Speaker 3: to repay Elon Musk. Just talk about Musk taking some
3:24
Speaker 3: sort of role the Department of Government Efficiency DOGE, so
3:33
Speaker 3: I think this will be pivotal. You've also got Musk
3:35
Speaker 3: clearly has his fingers in so many different pis, so
3:39
Speaker 3: many different companies, so lots of scope for conflicts of
3:44
Speaker 3: interest there. And of course with X and I think
3:47
Speaker 3: we saw this definitely the morphing of X in the
3:50
Speaker 3: last year or so during that campaign really into a
3:52
Speaker 3: conservative stronghold that's also been influential. So all of these
3:57
Speaker 3: things are intertwining. I think in Trump's favor.
4:01
Speaker 4: Yeah, it definitely has been a swell, a more public
4:05
Speaker 4: swell of support for Donald Trump from that kind of
4:09
Speaker 4: tech elite in the last over the selection. And I
4:13
Speaker 4: think it speaks a little bit to the way maybe
4:16
Speaker 4: that Trump does approach these these kinds of companies where
4:21
Speaker 4: he wants their favor, He wants them to kind of
4:24
Speaker 4: be on his side, and as in a return, he'll
4:26
Speaker 4: be on their side. And we've seen him, We saw
4:29
Speaker 4: him use these kind of anti monopoly laws to kind
4:33
Speaker 4: of put pressure on the ones that maybe weren't as
4:36
Speaker 4: vocal of support for him in the past. Not to
4:38
Speaker 4: say that there's a direct link there, but it's hard
4:40
Speaker 4: to not see some kind of correlation at least. So
4:44
Speaker 4: Elon Musk, with his ambitions for Tesla and for SpaceX
4:49
Speaker 4: and for a lot of the defense contracts, must be
4:53
Speaker 4: really glad to be in Trump's good graces now, especially
4:56
Speaker 4: because Trump has talked quite a lot about increasing the
5:00
Speaker 4: amount of private companies contracting to defense and working and
5:07
Speaker 4: spending a lot more money on defense, which will mean
5:09
Speaker 4: more money in the pockets of these tech companies working
5:12
Speaker 4: on defense technologies.
5:14
Speaker 3: Yeah, it's got to be good for SpaceX, I mean Tesla.
5:17
Speaker 3: Trump has been very anti electric vehicle, so will he
5:21
Speaker 3: now pivot to suddenly being influenced by Elon Musk along
5:26
Speaker 3: the lines of, well, no EV's actually make sense, I'm
5:29
Speaker 3: keen to support them, whether there'll be more subsidies for evs,
5:34
Speaker 3: and likely given Trump's interest in the liquid gold, you know,
5:38
Speaker 3: the energy industry, the fossil fuel industry, but definitely on SpaceX,
5:44
Speaker 3: very much in bed with the US defense, so there'll
5:48
Speaker 3: be synergies there. And as yea, his other business obviously
5:54
Speaker 3: x AI and Grock building, you know, the biggest AI
5:57
Speaker 3: centric supercomputer stuff like that. I can see Musk sort
6:03
Speaker 3: of putting proposals to Trump about what the government should
6:07
Speaker 3: be doing with AI, and I think on a lot
6:10
Speaker 3: of these issues, Trump really he's not a tech guy.
6:14
Speaker 3: He doesn't really get this sort of stuff, but he's
6:16
Speaker 3: very influenced by the people around him. So you've got
6:20
Speaker 3: Musk there as a trusted advisor. You've got jd Vance,
6:25
Speaker 3: who has a previous life in venture capital, worked with
6:29
Speaker 3: Silicon Valley to fund companies, so he's very much embedded
6:35
Speaker 3: with some of that tech elite. You've got Peter Thiel
6:39
Speaker 3: who's a big backer of Trump as well. New Zealand citizen,
6:43
Speaker 3: founder of Pallenteer and AI company that does a lot
6:46
Speaker 3: of work for US agencies, police force, and military. There's
6:54
Speaker 3: share prices surging at the moment on the back of
6:56
Speaker 3: their latest results, all driven by AI. We've got this
7:01
Speaker 3: sort of cluster of people in Trump's orbit who have
7:06
Speaker 3: strong ideas about where the tech world should go, and
7:11
Speaker 3: he's listening to them and he owes them. A lot
7:13
Speaker 3: of them put money into these super packs to get
7:16
Speaker 3: him into power. They didn't raise as much money as
7:19
Speaker 3: Kamala Harris did in the Democrats, so that was all
7:22
Speaker 3: a bit of a waste of time and money. But
7:25
Speaker 3: he now has a lot of bills falling due and
7:29
Speaker 3: what is that going to mean for the flavor of
7:31
Speaker 3: his policies. That's going to be the big question.
7:34
Speaker 4: Yeah, I think a lot of it is going to
7:35
Speaker 4: central around deregulation. Personally, I think that's going to be
7:38
Speaker 4: a big flag that Trump will be waiving is getting
7:43
Speaker 4: out of the way of these particularly those Silicon Valley companies.
7:49
Speaker 4: I think the likes of Google and Microsoft may see
7:54
Speaker 4: some continuation of those anti trust kind of approaches. But
7:59
Speaker 4: when it comes to the those on the cutting edge
8:02
Speaker 4: with AI and the ones that are kind of more
8:04
Speaker 4: and Donald Trump's in a circle, will start to see
8:08
Speaker 4: that a lot of deregulation there seems to be pretty
8:12
Speaker 4: explicit in what he's been saying. And the same goes
8:15
Speaker 4: for cryptocurrency as well.
8:16
Speaker 3: Well. It's interesting on crypto. You know, Trump has done
8:19
Speaker 3: a bit of a U turn. He was quite sort
8:21
Speaker 3: of hawkish against crypto a few years ago, and again
8:26
Speaker 3: I think this is people getting to him, and probably Musk,
8:30
Speaker 3: who you know is a big fan of dogecoin and
8:32
Speaker 3: is a crypto advocate as well, basically saying to him, no,
8:36
Speaker 3: you need to support this. He's done a U turn.
8:39
Speaker 3: He wants minimal crypto regulation. He'll probably limit the rather
8:45
Speaker 3: hawkish moves by the SEC in the US to regulate
8:49
Speaker 3: cryptocurrencies and those digital asset markets. So that's all well
8:55
Speaker 3: and good, but is that going to lead to more
8:57
Speaker 3: of the sort of FTX style implosions that we've seen.
9:01
Speaker 3: He won't want that either. On deregulation, sure, last time
9:06
Speaker 3: he cut tax and red tape. Businesses love that. It
9:09
Speaker 3: means they can spend more money on R and D
9:11
Speaker 3: and return more money to shareholders. So any company, particularly
9:15
Speaker 3: those big tech companies that make a lot of profit,
9:19
Speaker 3: they'll love that. But it was Trump after all, that
9:22
Speaker 3: kicked off a lot of that antitrust stuff a few
9:26
Speaker 3: years ago, So yeah, will he continue that and see
9:30
Speaker 3: the breakup of Google and others. But I agree with you.
9:34
Speaker 3: I think you know, he clearly doesn't like a monopoly.
9:36
Speaker 3: He's a free market guy, that's his philosophy on this.
9:40
Speaker 3: But he does want to see all of the red
9:45
Speaker 3: tape and the restrictions removed from really innovative companies, and
9:50
Speaker 3: at the moment they're the AI one. So I don't
9:52
Speaker 3: see him carrying forward some of those executive orders around
9:56
Speaker 3: AI that Biden put in place. I think we he'll
9:59
Speaker 3: dial that back sognificantly.
10:01
Speaker 4: Yeah. I think the other area where we're going to
10:02
Speaker 4: see a big retraction in the US at least is
10:05
Speaker 4: green tech. So if they were, you know, we've had
10:08
Speaker 4: a lot of eggs in the green tech basket here
10:09
Speaker 4: in New Zealand with our startups, and that may indicate
10:13
Speaker 4: that the US is no longer a viable entry point
10:17
Speaker 4: for these companies to really scale. So maybe refocusing more
10:22
Speaker 4: on the EU. If the US was kind of a
10:25
Speaker 4: big part of your strategy.
10:27
Speaker 3: Yeah, and you know there have been as part of
10:31
Speaker 3: the Big Reconstruction Act that Biden passed after COVID, there
10:39
Speaker 3: was green tech funding in there. So whether that will continue.
10:43
Speaker 3: One area that will continue which Trump and the Democrats
10:47
Speaker 3: are on the same page on is the semiconductor industry
10:51
Speaker 3: the Chips Act. So Trump is very much of the
10:54
Speaker 3: view that we need more local production in the US
10:57
Speaker 3: of semiconductors, the really high end important stuff that runs
11:01
Speaker 3: ai to reduce reliance in the global supply chain on Taiwan,
11:06
Speaker 3: which is very vulnerable to attack from China. So he'll
11:10
Speaker 3: carry on that sort of stuff, things like five G.
11:13
Speaker 3: You know, he's expressed his dismay that a lot of
11:16
Speaker 3: that technology is provided by European companies. So again Biden
11:21
Speaker 3: was on the same page. And I think for Trump
11:24
Speaker 3: what it really all is about is taking on China
11:28
Speaker 3: and that continuing sort of pressure, whether it's through the
11:33
Speaker 3: form of taris big tariffs on stuff coming from China
11:36
Speaker 3: into the US sixty percent tariffs potentially, which is quite staggering,
11:41
Speaker 3: but really that polarization of technology between the Western world
11:46
Speaker 3: and the Chinese world, and we've seen China in the
11:49
Speaker 3: intervening few years since Trump was out of office, building
11:53
Speaker 3: its own operating systems, trying to generate higher capacity semiconductors
11:59
Speaker 3: to go into phones and AI devices. Trump will basically
12:04
Speaker 3: accelerate that further by putting more export controls on the
12:09
Speaker 3: exports of high technology to China, tariffs and local productions.
12:14
Speaker 3: So I think we'll just see an acceleration of that.
12:17
Speaker 4: And that's also going to roll over to New Zealand
12:20
Speaker 4: a little bit in terms of trade agreements. He's talked
12:22
Speaker 4: about getting rid of the Indo Pacific Partnership trade Agreement,
12:25
Speaker 4: which would impact New Zealand. So how that will interact
12:30
Speaker 4: with New Zealand's tech exports to the US would not
12:33
Speaker 4: one hundred percent clear at the moment, but you know
12:37
Speaker 4: there is potentially some impact there.
12:39
Speaker 3: Yeah. And the other sort of local angle I guess
12:42
Speaker 3: is orcus. You know, this agreement this packed between Australia,
12:47
Speaker 3: the US and the UK really about submarines, but you've
12:51
Speaker 3: got orcust pillar two, which is about other advanced technologies
12:54
Speaker 3: like AI, like quantum computing, stealth technologies, you know, high
13:01
Speaker 3: end military stuff. And I've been quite supportive of the
13:04
Speaker 3: idea of New Zealand being involved in Pillar two. Not
13:08
Speaker 3: necessarily around nuclear submarines or anything like that, but Pillar two,
13:12
Speaker 3: these advanced technologies, we should have a hand with our
13:15
Speaker 3: allies and developing those. And I think, you know, Orcus
13:19
Speaker 3: has run into some trouble. I mean, this submarine deal
13:22
Speaker 3: is so vastly expensive. Whether it will actually come to
13:26
Speaker 3: fruition is anyone's guess. I think the Australians are starting
13:31
Speaker 3: to realize what they've signed up for is massive. But
13:36
Speaker 3: the other you know, you've got South Korean others Japan
13:39
Speaker 3: are saying, hey, we want it on Pillar two because
13:43
Speaker 3: they're starting to see some of the stuff that the Americans,
13:46
Speaker 3: the Brits and the Aussies are working on and saying,
13:49
Speaker 3: you know, we want to see that the table in
13:50
Speaker 3: developing that stuff because there is a greater threat from China,
13:55
Speaker 3: so let's work on this together. Trump will just carry
13:59
Speaker 3: on thinking. I think around Orcus, he sees that as
14:02
Speaker 3: a way to shore up support military support among allies
14:07
Speaker 3: in the Pacific. Whether that will encourage New Zealand to
14:12
Speaker 3: join or maybe will there be more pressure with a
14:15
Speaker 3: new you right leaning ambassador in this country, will there
14:21
Speaker 3: be more pressure for New Zealand to actually put its
14:23
Speaker 3: cards on the table and join Orcus. I think that's
14:26
Speaker 3: a possibility.
14:29
Speaker 4: It's hard to see exactly where we're going in terms
14:32
Speaker 4: of the ramifications. You know, with the potential for a
14:36
Speaker 4: Harris government, it was a lot more of the same,
14:38
Speaker 4: but a Trump government because his rhetoric can be quite inconsistent.
14:43
Speaker 4: You know, there is some stuff that we can guess about,
14:46
Speaker 4: but at the end of the day, it's really going
14:48
Speaker 4: to be just reacting as things happen. So it's going
14:53
Speaker 4: to be really important to actually pay attention, I think,
14:55
Speaker 4: to what is actually happening rather than what is being said.
15:00
Speaker 3: Trump presidency, and if his last stint as president has
15:06
Speaker 3: anything to go by, it'll be those key personalities around
15:10
Speaker 3: him because he really is a bit of an empty
15:12
Speaker 3: vessel in terms of his thinking on some of these issues,
15:15
Speaker 3: particularly around technology. Now, just listening to him explain on
15:19
Speaker 3: election night, you know, the starship returning to Earth, you know,
15:26
Speaker 3: when he was praising you on Muscus, just clear he
15:28
Speaker 3: doesn't really understand this stuff at all, which is fine,
15:31
Speaker 3: but it's the people around him, and the worry I
15:35
Speaker 3: think in the US at the moment is you know,
15:37
Speaker 3: this paranoia about the deep state in the US, this
15:42
Speaker 3: shadowy sort of left leaning cabal that runs America that
15:47
Speaker 3: he's been trying to root out. Is he just going
15:50
Speaker 3: to replace people at the SEC, his top tech advisors,
15:55
Speaker 3: people responsible for climate change policy. Is he just going
15:58
Speaker 3: to replace them with political appointees who don't really care
16:02
Speaker 3: about the evidence or the science or what technical advisors
16:06
Speaker 3: suggest is the right thing to do. And he's just
16:09
Speaker 3: going to take the advice off a small group of
16:12
Speaker 3: very wealthy, right leaning tech elites who he owes big
16:17
Speaker 3: time because they helped get him into office, you know,
16:19
Speaker 3: judging by past performance, that's what he tends to do.
16:22
Speaker 3: He surrounds himself with people who are loyal, but people
16:27
Speaker 3: he also relied on to get into office first time round.
16:30
Speaker 3: I think we'll see a lot more of that unfortunately.
16:32
Speaker 4: Yeah, it really is about an exchange of wealth and
16:37
Speaker 4: favors and keys and power and deals. It really is
16:41
Speaker 4: all about the deals.
16:42
Speaker 3: It's transactional with Trump, and people have advised, you know,
16:46
Speaker 3: if he does get in, you've got to treat it
16:48
Speaker 3: as a transaction with whether you're negotiating what to do
16:52
Speaker 3: with Ukraine or a trade deal with a country, it's transactional.
16:58
Speaker 3: You need to be in that mindset dealing with this guy.
17:02
Speaker 3: So maybe that's the approach that maybe we should take
17:04
Speaker 3: as well. Absolutely, So clearly it's going to be an
17:14
Speaker 3: interesting year. Head We'll keep you posted and give our
17:18
Speaker 3: analysis on everything related to tech as the Trump administration
17:22
Speaker 3: settles in. But Ben, whenever we interview companies around some
17:26
Speaker 3: of these issues like AI, predictive analytics, and all the
17:29
Speaker 3: cool things businesses can technically do these days with the
17:33
Speaker 3: data generated by the businesses, we get the same surprising response.
17:38
Speaker 4: Yeah, the conversation typically grinds to a halt, and we
17:40
Speaker 4: are told that a lot of our businesses just don't
17:43
Speaker 4: have their data in the right places, in the right
17:45
Speaker 4: format to do any of that.
17:47
Speaker 3: So talking about it's a bit of a waste of
17:49
Speaker 3: time if the basics really aren't done well.
17:52
Speaker 4: Which is why we're hearing a lot more from companies
17:54
Speaker 4: like Snowflake and data Bricks, companies that have emerged in
17:57
Speaker 4: recent years to help organizations manage that data.
18:01
Speaker 3: They're basically data warehousing and analytics platforms that try to
18:05
Speaker 3: get all your data in one place, process it in
18:08
Speaker 3: a uniform and secure way, and interact with the various
18:12
Speaker 3: applications you're using to run your business. I was actually
18:16
Speaker 3: staying at a hotel in Alkant recently and found myself
18:18
Speaker 3: walking into the middle of a Snowflake conference. It was
18:22
Speaker 3: actually quite a big affair.
18:24
Speaker 4: Well, data is a big affair now, it's big business
18:27
Speaker 4: and Snowflake has around two hundred customers in New Zealand
18:30
Speaker 4: to date. It did around one hundred million dollars in
18:33
Speaker 4: revenue last year just across Australia and New Zealand, according
18:37
Speaker 4: to its financial accounts filed with the company's office.
18:41
Speaker 3: And spending on data, warehousing and platforms is really growing fast.
18:45
Speaker 3: So Ben, this is a timely interview with Tony Shaw,
18:48
Speaker 3: who's been around a tech industry for a long time
18:50
Speaker 3: stintce at NCR, dell, IBM, MuleSoft as well. Let's listen
18:56
Speaker 3: to your interview with Tony Shaw and come back for
18:58
Speaker 3: some thoughts on the back end.
19:03
Speaker 4: Thank you so much, Tony for joining us on the
19:05
Speaker 4: Business of Tech podcast. It's really great to have you here.
19:08
Speaker 4: Why don't we start with just a little bit of
19:10
Speaker 4: background about who you are and what you do.
19:12
Speaker 2: Oh fantastic, hen thank you so much for having us
19:14
Speaker 2: on board. Today. My name is Tony Shaw. I'm the
19:17
Speaker 2: country manager for Snowflake in New Zealand. I've been with
19:20
Speaker 2: the company just coming up to six years now, so
19:22
Speaker 2: quite a long time to be with one organization. But
19:26
Speaker 2: I've always been in tech and for a long time
19:30
Speaker 2: in analytics. I originally started my career working for NCR
19:34
Speaker 2: as a financial analyst and pricing and planning and using
19:37
Speaker 2: data and realizing how important it can be to make
19:40
Speaker 2: financial decisions. And then from there I moved into more
19:45
Speaker 2: of the sales and business development side of things, both
19:47
Speaker 2: in New Zealand and had a long time in London,
19:51
Speaker 2: and then predominantly in the data and analytics side of things.
19:54
Speaker 4: Do you want to share maybe the perception of data
19:58
Speaker 4: maybe pre your Snowflake time, and then how it's changed
20:01
Speaker 4: since then?
20:02
Speaker 2: Yeah, no problem. I think data's always been important. Organizations
20:06
Speaker 2: have always had the aspiration to be using data better
20:09
Speaker 2: to make better and informed decisions. But what's happened in
20:12
Speaker 2: the last five to ten years is the accessibility of
20:17
Speaker 2: the information has become so much easier. The cost to
20:20
Speaker 2: get that data and analyze it has dropped significantly, and
20:24
Speaker 2: that's opened up massive opportunities because it allows organizations to
20:28
Speaker 2: bring all of their data from all of their disparate
20:31
Speaker 2: systems into one environment where that structured data unstructured data,
20:35
Speaker 2: and then can imply analytics to that. Historically, it used
20:39
Speaker 2: to be a lot of backwards looking, a lot of
20:41
Speaker 2: reporting what did happen? And now where we're seeing is
20:44
Speaker 2: a lot more predictive analytics, opening up the information to
20:49
Speaker 2: a lot more of the business users and allowing that
20:52
Speaker 2: decision making to be a lot more in the front
20:54
Speaker 2: line rather than just the back office. So we're seeing
20:58
Speaker 2: that dissemination of information across multiple channels, multiple users, and
21:03
Speaker 2: the ease of use. So it's no longer just a
21:06
Speaker 2: back office function as lines of business making decisions every
21:09
Speaker 2: day which are moving the dial within those organizations.
21:13
Speaker 4: Right. And you know, traditionally when we think of data,
21:17
Speaker 4: we think big data, right, especially these days, and we
21:20
Speaker 4: think big companies. But that's increasingly changing as well. I
21:25
Speaker 4: would imagine, like you say, as the accessibility, the affordability
21:28
Speaker 4: of data and data analytics tools are starting to shift
21:32
Speaker 4: a little bit. Are you starting to see smaller companies,
21:37
Speaker 4: you know, not necessarily your one person companies, but maybe
21:40
Speaker 4: your medium size businesses gaining a better understanding of how
21:45
Speaker 4: to utilize their data.
21:47
Speaker 2: Yeah, it's been remarkable. Since we started the business in
21:49
Speaker 2: New Zealand in twenty nineteen, we were looking at what
21:54
Speaker 2: is the segments and what is the segmentation and customers
21:56
Speaker 2: that we're going to look to try and require. We
21:59
Speaker 2: had two stomers in New Zealand when we started the
22:01
Speaker 2: business here and now we've got north of two hundred,
22:05
Speaker 2: and it was really interesting. We started to think around
22:08
Speaker 2: that segmentation and we thought it might be some mid
22:10
Speaker 2: tier customers and then you know, maybe we can work
22:13
Speaker 2: our way up or down across the different spectrum of
22:15
Speaker 2: size and scale and complexity. But what happened was we've
22:19
Speaker 2: got organizations of all size and scale very early. And
22:23
Speaker 2: I think one of the things with Snowflake, and one
22:25
Speaker 2: of the reasons why we had such fantastic adoption, was
22:28
Speaker 2: it's the ability to scale down to New Zealand size companies,
22:32
Speaker 2: not just being able to scale up. So there's the
22:35
Speaker 2: global organizations, you know, there's the capital ones and the
22:37
Speaker 2: sinespres etc. But within New Zealand, because the platform scales
22:41
Speaker 2: down and you only pay for what you use on
22:44
Speaker 2: a true consumption basis, we've been able to scale to
22:47
Speaker 2: organizations that are getting enterprise enterprise grade capability, but they're
22:51
Speaker 2: only paying for what they use based on the size
22:54
Speaker 2: of the organization or how much they actually need to
22:56
Speaker 2: use of the platform.
22:57
Speaker 4: To what extent a New Zealand companies really using all
23:01
Speaker 4: of the capabilities of Snowflake. Are we up there in
23:04
Speaker 4: the most advanced users or are we kind of just
23:08
Speaker 4: using the very basics because we're smaller.
23:10
Speaker 2: And yeah, absolutely. We just had a conference last week.
23:14
Speaker 2: It was unbelievable. We had over a thousand people there,
23:17
Speaker 2: which makes it the largest data and analytics event in
23:19
Speaker 2: New Zealand, and we had some fabulous customers. So we'd
23:22
Speaker 2: organizations like in New Zealand, Tavado, Aura, in zed Health,
23:27
Speaker 2: in zet, super one, end, z MITA ten, Spark, shares E's,
23:31
Speaker 2: the kind of list goes on and it was a
23:33
Speaker 2: really great opportunity for organizations to share what they're doing
23:37
Speaker 2: and how they deliver value from the platform, and also
23:40
Speaker 2: to build that community so organizations can network with their
23:43
Speaker 2: peers and learn from each other. But in terms of
23:46
Speaker 2: taking on the global stage, shares Y's is one of
23:49
Speaker 2: our fantastic customers. They actually recently won the APJA Data
23:53
Speaker 2: Driver Award for powered by So what that means is
23:56
Speaker 2: they're powering their application using Snowflake to help drive the
24:00
Speaker 2: adoption and understand their customer behaviors in order to deliver
24:04
Speaker 2: a better service. And they've just had phenomenal growth. So
24:07
Speaker 2: you know, they've got seven hundred thousand customers. So we're
24:09
Speaker 2: taking on the world. Shares is being successful across here
24:12
Speaker 2: and across in Australia, and we've got a number of
24:16
Speaker 2: tech startups that we're working with who are winning awards
24:19
Speaker 2: and delivering really fantastic results for their business on a
24:23
Speaker 2: global scale.
24:25
Speaker 4: Fantastic. Yeah, so it sounds like you've got some real
24:26
Speaker 4: power users. Then that's what you're.
24:28
Speaker 2: Saying, unbelievable. It's both the business users. So we had
24:33
Speaker 2: the co CEOs presenting around how that's driving value. Data
24:37
Speaker 2: analysts we have technical capabilities. It's the ability to work
24:40
Speaker 2: with all of the different personas across an organization, not
24:44
Speaker 2: just the technical people though they love the platform, so
24:47
Speaker 2: the architects, the engineers, the really deep data people, but
24:51
Speaker 2: then also the people who are consuming it, so technically
24:54
Speaker 2: literate business analysts, people just writing natural language questions in English.
24:59
Speaker 2: Executive writing sorry, just analyzing what's happened and what's going
25:03
Speaker 2: to happen and their business. That's across the board, those
25:07
Speaker 2: different personas that all use data in a slightly different nuance,
25:11
Speaker 2: but they want consistency of information, they want high quality,
25:14
Speaker 2: they want real time, they want accurate information so they
25:18
Speaker 2: can make those decisions. Cool.
25:21
Speaker 4: Now, obviously you can. It's great to talk up to
25:25
Speaker 4: customers that are doing really awesome stuff, but New Zealand's
25:29
Speaker 4: definitely not perfect nowhere is in terms of how it's
25:31
Speaker 4: utilizing data. So what are some of the areas that
25:33
Speaker 4: you're seeing New Zealand lagging behind? Maybe some New Zealand
25:36
Speaker 4: companies where you think, you know some areas of focus
25:39
Speaker 4: could be to improve the usage of data within New Zealand.
25:44
Speaker 2: I think that the pitfalls that we always see is
25:48
Speaker 2: making sure that there's executive sponsorship and outcomes that the
25:53
Speaker 2: organization is trying to drive towards. So it's really important
25:56
Speaker 2: that it doesn't become a science experiment or a program
26:02
Speaker 2: that's just for the IT users. What the successful organizations
26:06
Speaker 2: do is they've got very strong alignment to a specific
26:09
Speaker 2: business outcome, whether that's a finance program looking at receivables
26:14
Speaker 2: or finance transformation, whether it's marketing, looking at customer experience, NPS, churn,
26:19
Speaker 2: cross seal, etc. Or operations to streamline the efficiency with
26:22
Speaker 2: which the organization works in it has to have that
26:25
Speaker 2: business outcome that everybody can anchor themselves and align to.
26:29
Speaker 2: When you've got that, that goes a long way to
26:31
Speaker 2: making sure this program's success, and then the usual governance
26:34
Speaker 2: across the program and making sure that there is steps
26:38
Speaker 2: along the way that people are measuring to make sure
26:40
Speaker 2: that that outcome happens. When you start to get those
26:43
Speaker 2: sorts of things, then everything else just falls into place. Cool.
26:48
Speaker 4: Now, let's say I'm one of the New Zealand companies
26:52
Speaker 4: that hasn't started to get deep into data YAT and
26:55
Speaker 4: you know, maybe a medium sized company who is starting
26:57
Speaker 4: to think about the potential there. What are my first
27:01
Speaker 4: kind of steps?
27:03
Speaker 2: The first step is defining what dial within the business
27:07
Speaker 2: you're trying to move and what is that outcome you're
27:09
Speaker 2: trying to achieve. So say it's a marketing outcome around
27:13
Speaker 2: cross seal. Make sure that those objectives and those metrics
27:16
Speaker 2: are well understood and documented, and then start small and
27:19
Speaker 2: try and deliver that program so that you deliver that
27:22
Speaker 2: specific outcome, get the win, and then build upon that.
27:26
Speaker 2: You need to paint the vision to the organization in
27:28
Speaker 2: terms of what is the analytic capability going to deliver.
27:32
Speaker 2: So you need to have a vision and where we're
27:34
Speaker 2: going as an organization, but you also need to have
27:37
Speaker 2: a specific outcome that you're driving towards that you can
27:40
Speaker 2: build on that success. Then you need to drive where
27:43
Speaker 2: do I get the data from and how do I
27:44
Speaker 2: get high quality information to solve that business problem and
27:47
Speaker 2: answer the questions that you're looking to define or answer sorry,
27:51
Speaker 2: And then it's getting the technical teams aligned to find
27:54
Speaker 2: that data, source that data cleanse It's high quality decision
27:58
Speaker 2: making because you want to make sure sure that the
28:00
Speaker 2: information that's being used is of quality so that the
28:04
Speaker 2: decisions out the back of it are influenced.
28:06
Speaker 4: What does that mean cleansing data? Like, what does that
28:08
Speaker 4: actually in real terms mean? Because if I'm a company
28:12
Speaker 4: that's been around for twenty years, I've got a bunch
28:14
Speaker 4: of spreadsheets and PDFs and all this kind of stuff
28:17
Speaker 4: and it's ordered, it's in folders. We know where everything is.
28:21
Speaker 4: But is that clean? Is that clean enough?
28:23
Speaker 2: It depends a lot of the times those spreadsheets have
28:26
Speaker 2: been built up by a couple of specific people. They
28:29
Speaker 2: might be suitable for that use case or that specific
28:32
Speaker 2: piece of information you're looking to deliver. An example, when
28:35
Speaker 2: I was a pricing analyst, we used to have huge
28:38
Speaker 2: amounts of spreadsheets everywhere that have interconnected links, and we
28:41
Speaker 2: put out some pricing models. Then you'd come back about
28:44
Speaker 2: a month later and change something because you'd found a
28:46
Speaker 2: mistake in the spreadsheet in the formulas, and that would
28:49
Speaker 2: change the entire pricing model. And you'd be sitting there going,
28:53
Speaker 2: oh my goodness, now I've just completely stuffed this up.
28:56
Speaker 2: You change something else to get it back, and then
28:58
Speaker 2: the numbers would all change back again. Spreadsheets, whilst they're
29:01
Speaker 2: across every single organization, are kind of the bane of
29:05
Speaker 2: an enterprise organization's life because there is no real auditability.
29:09
Speaker 2: So when I talk about high quality data, it's getting
29:12
Speaker 2: that data from those source systems, making sure that it's
29:15
Speaker 2: usable and in a format that's understandable, but it's consolidated
29:20
Speaker 2: across multiple touch points so that you've got a consistent
29:23
Speaker 2: view of customer, and then involving the business teams to
29:26
Speaker 2: define what is the rules and logic so that everybody
29:29
Speaker 2: knows what the definition of a customer is, what is
29:31
Speaker 2: a definition of revenue or profit or what happens to be,
29:34
Speaker 2: and then everyone's working off that consistent set of information.
29:37
Speaker 2: We've all been in meetings where people are arguing about
29:41
Speaker 2: the data rather than what they do with that information.
29:44
Speaker 2: So what we want to try and do is consolidate
29:46
Speaker 2: the information so that it's a single view across the business.
29:50
Speaker 2: And then people are thinking about what are the decisions
29:52
Speaker 2: I make, not hey, is that the right one? Am
29:53
Speaker 2: I questioning the actual data validity rather than what I
29:57
Speaker 2: can do with it?
29:59
Speaker 4: What's the kind what's the kind of talent that you
30:01
Speaker 4: would need to do that? Do you need to hire
30:03
Speaker 4: a house data scientist? Is it okay to just kind
30:07
Speaker 4: of get a consultant into kind of do some data
30:10
Speaker 4: stuff for you to get you ready.
30:11
Speaker 2: I think consultants have a place and they've got a
30:14
Speaker 2: lot of experience that can bring to bear on organizations.
30:18
Speaker 2: But I think the organizations themselves have a responsibility and
30:22
Speaker 2: they have to have a capability internally. This can't be
30:26
Speaker 2: done to an organization. You have to do it with
30:28
Speaker 2: the organization and the people within the enterprise or the company.
30:32
Speaker 2: They know what the business is trying to achieve, they
30:35
Speaker 2: know where to get the data from. So you need
30:38
Speaker 2: to have a set of skills within the organization, and
30:41
Speaker 2: that skills from a technical capability to work out where
30:44
Speaker 2: does the data come from and how do I get
30:45
Speaker 2: it and then also how do I put that into
30:48
Speaker 2: the hands of the users so they've got confidence that
30:50
Speaker 2: they can start to drive analysis from it. But the
30:53
Speaker 2: internal capability is critical. One of the things we're trying
30:56
Speaker 2: to do at Snowflake is build a really big community.
31:00
Speaker 2: So the event we just ran with a huge number
31:02
Speaker 2: of people. We run user groups, we run meetups, we
31:06
Speaker 2: run product specialist workshops when we bring some of our
31:10
Speaker 2: teams offshore into New Zealand, and it's really important to
31:13
Speaker 2: build that community and network so we can share what's
31:17
Speaker 2: working and to your point before, what's not working, so
31:20
Speaker 2: that we can avoid those pitfalls where possible and start
31:23
Speaker 2: to accelerate how do we deliver that outcome. But I
31:26
Speaker 2: think internally the capability needs to be there. You know,
31:30
Speaker 2: we need to train our teams, we need to cross
31:32
Speaker 2: pollinate from existing teams, so you might have somebody who's
31:36
Speaker 2: working internally within an organization. They've got a huge amount
31:39
Speaker 2: of tribal knowledge within that organization, But then how do
31:43
Speaker 2: we cross pollinate their skill sets with whatever it is
31:47
Speaker 2: they need with it's technical or analysts. So they've got
31:50
Speaker 2: the data literacy to drive that outcome, but absolutely internal
31:53
Speaker 2: was critical.
31:54
Speaker 4: Yeah, I mean, I guess the message that I'm getting
31:57
Speaker 4: really is that you can't do this lais a fair.
32:01
Speaker 4: You can't just be like, let's dabble in some data.
32:03
Speaker 4: You really have to sit down and create a cohesive,
32:06
Speaker 4: strong plan and roadmap and objectives and spend the time
32:11
Speaker 4: to actually build that out. And if you're seeing gaps
32:14
Speaker 4: in your organization, then you actually need to maybe fill
32:16
Speaker 4: those gaps, whether that's with training or with bringing on
32:20
Speaker 4: new staff. Does that kind of sound about right?
32:23
Speaker 2: Yeah, one hundred percent agree. And it's building that strategy
32:27
Speaker 2: into the business outcome of the business strategy, so that
32:30
Speaker 2: the data strategy is part of your business strategy because
32:35
Speaker 2: they shouldn't be separate. One can inform the other and
32:38
Speaker 2: the other can form each other. A lot of times
32:41
Speaker 2: the data teams have a really enterprise view of the
32:45
Speaker 2: business because they're looking at data from multiple different areas.
32:49
Speaker 2: So you're not siloed within say HR, or siloed within finance,
32:53
Speaker 2: or siloed within marketing. The data teams get a strong
32:57
Speaker 2: visibility across the organization. For example, we were talking in
33:02
Speaker 2: New Zealand presented recently at our conference. Again they talked
33:07
Speaker 2: about the concept of majors and miners. So you've got
33:10
Speaker 2: a data team which has got majors in data and analytics,
33:15
Speaker 2: but working with the lines of business who have a
33:18
Speaker 2: minor and data, but a major is in their skill set,
33:21
Speaker 2: whether it's HR, where it's cargo, whether it's financed, whatever
33:24
Speaker 2: happens to be. And that dovetail together of the data
33:28
Speaker 2: literacy and the data capability with the knowledge of that
33:32
Speaker 2: specific line of business and what's important to that line
33:36
Speaker 2: is really important because then you're marrying both of the
33:39
Speaker 2: outcome and the capability together, which drives a lot of
33:42
Speaker 2: value for the organization. Having that strategy which is aligned
33:47
Speaker 2: into the business strategy is really important. And obviously with
33:50
Speaker 2: the introduction of things like AI, AI is built upon
33:56
Speaker 2: how you use data, whether it's internal data, external data
34:00
Speaker 2: to drive that decision making. So you know, you can't
34:02
Speaker 2: really have an AI strategy without a data strategy and
34:05
Speaker 2: all of this should be blended into what is the
34:07
Speaker 2: objectives of the organization and what are they driving for?
34:10
Speaker 4: Great, you just did my job then, and you pivoted
34:12
Speaker 4: to exactly where I wanted to go next. We just
34:14
Speaker 4: talk about AI because we have to, right because it's
34:16
Speaker 4: twenty twenty four and the last three years of AI
34:21
Speaker 4: has been just a different story completely to where it
34:25
Speaker 4: was previously. From your experience as somebody who lives and
34:29
Speaker 4: breathes data, what has that experience been like to watch
34:33
Speaker 4: data go from this kind of dry but necessary thing
34:38
Speaker 4: to the thing that is powering the future.
34:41
Speaker 2: It's absolutely amazing, isn't it. You know, you know, in
34:43
Speaker 2: your personal life you use things like GPT and the
34:48
Speaker 2: output that it can deliver is just extraordinary. And where
34:51
Speaker 2: it's going, I think is fantastic. It's outstanding. But you've
34:56
Speaker 2: got to get the foundations right because otherwise you're building
35:00
Speaker 2: on quicksand and you're analyzing inefficient data and you'll very
35:04
Speaker 2: quickly lose confidence from those users. And there's also some
35:07
Speaker 2: of those traditional techniques which are still incredibly valuable to
35:10
Speaker 2: the organization. So just making sure that we understand the
35:14
Speaker 2: vision and we go after that, and we go after
35:16
Speaker 2: that with speed, but at the same time we don't
35:22
Speaker 2: take the focus off some of those other areas which
35:24
Speaker 2: we can deliver very very quick value to the business.
35:27
Speaker 4: Somebody said to me the other day that actually, with
35:31
Speaker 4: new AI models, data cleanliness is actually not as important
35:35
Speaker 4: as it used to be, because if you look at
35:37
Speaker 4: something like chat GPT, you know, the training is not
35:41
Speaker 4: necessarily there's so much of it they couldn't possibly go
35:45
Speaker 4: through and clean it all. Do you think that's true
35:48
Speaker 4: that if you were wanting to create kind of a
35:51
Speaker 4: GENAI model that's used you can use to analyze certain levels,
35:56
Speaker 4: certain kinds of data within the organization, that there is
36:01
Speaker 4: less need for data cleanliness than they used to be.
36:05
Speaker 2: I'd say, where you're looking at the entire internet for
36:08
Speaker 2: consumers like you just describe, maybe when organizations are looking
36:13
Speaker 2: to use information which is internal to their organization, that
36:17
Speaker 2: has to be very high quality. It has to be robust,
36:20
Speaker 2: it has to be trusted, and it has to be
36:23
Speaker 2: using the information and knowledge from that organization to prevent
36:27
Speaker 2: things like hallucinations and bad decisions. Because the data is incorrect, inaccurate,
36:33
Speaker 2: not full enough, there's not a quorum of data to
36:35
Speaker 2: make an informed decision. I think the data quality aspects
36:39
Speaker 2: are even more important for an organization using some of
36:42
Speaker 2: these advanced capabilities like Genai. The Genai capability helps a
36:48
Speaker 2: lot in terms of being able to put some of
36:51
Speaker 2: that tagging, say, or definitions around what some of that
36:55
Speaker 2: data means. So it helps speed up the efficiency to
36:59
Speaker 2: make the data more reliable and higher quality and understood.
37:05
Speaker 2: But without putting the thought into having that high quality data,
37:09
Speaker 2: it's going to fall flat. In my opinion, I think
37:12
Speaker 2: we need to absolutely focus on the availability, the security
37:17
Speaker 2: and governance, the privacy, the quality and trusted data and
37:25
Speaker 2: then apply these techniques on top of it. And one
37:28
Speaker 2: of the things where Big believers on is bring the
37:31
Speaker 2: processing and the workload to the data rather than pushing
37:34
Speaker 2: all the data out to different systems. And the reason
37:37
Speaker 2: for that is because you've got that single view of
37:40
Speaker 2: the business, you've got one place to make sure that
37:43
Speaker 2: the data is of that high quality we're just describing,
37:45
Speaker 2: and you've got the privacy and governance so that only
37:48
Speaker 2: the right people are allowed to see it. Because what
37:51
Speaker 2: we're doing is we're opening up the access to a
37:53
Speaker 2: huge wide range of different consumers of the data. So
37:58
Speaker 2: we've got to make sure that it's protect and we've
38:00
Speaker 2: got to make sure that it's of high quality.
38:02
Speaker 4: Yeah, you're not on the difference between creating something from
38:05
Speaker 4: a mass market and creating something that is to improve
38:08
Speaker 4: organizational performance, and those are two very different goals completely.
38:12
Speaker 4: The other thing that I've been considering about generative AI
38:16
Speaker 4: lately is there's this kind of to and fro about
38:20
Speaker 4: how much we let the GENAI actually do if that
38:25
Speaker 4: kind of makes sense, where it can be quite creative
38:28
Speaker 4: and thoughtful and very have high contextual understanding, but that
38:33
Speaker 4: may potentially, you know, open up the hallucinations or we're
38:38
Speaker 4: not quite sure where that's going to go. Or we
38:40
Speaker 4: can be very tight and strict and be like, it
38:42
Speaker 4: can only return these information from these sources in these modes,
38:47
Speaker 4: and trying to find the balance of that can be
38:50
Speaker 4: tricky at a kind of data level when you're figuring
38:54
Speaker 4: out what to include and what not to include. How
38:58
Speaker 4: do you start making some of those decisions.
39:00
Speaker 2: You're absolutely right, it is a tricky decision or tricky
39:05
Speaker 2: consideration to think around. Where we think around it is
39:11
Speaker 2: having access to the right amount of information, but putting
39:14
Speaker 2: those governance and controls on there so that you've got
39:17
Speaker 2: things like role based access so that only I'm allowed
39:20
Speaker 2: to see the information that's burden into my specific role
39:25
Speaker 2: and I can't see anything else outside of that. And
39:27
Speaker 2: that's why it's really important to get that governance and
39:30
Speaker 2: that privacy foundations set and defined upfront so that it's
39:35
Speaker 2: not being made up, and make sure that you've got
39:38
Speaker 2: the right level of data to support the decision that
39:41
Speaker 2: you're trying to solve. And my view would be start small,
39:44
Speaker 2: start to prove out some value, and then expand as
39:47
Speaker 2: you've got that confidence within the business. But it's moving
39:51
Speaker 2: incredibly fast, right you know. You think even a couple
39:54
Speaker 2: of years ago, you know, chat GPT was just coming
39:56
Speaker 2: of age and people had only just said to hear
39:58
Speaker 2: about it, and now AI is embedded into just about
40:03
Speaker 2: every single platform and process. What we're looking to do
40:06
Speaker 2: is understand how we can use each of those different
40:10
Speaker 2: silos of informations and applications bring that together so you've
40:13
Speaker 2: still got that holistic view at the data level, not
40:16
Speaker 2: just at the application level. So you want to be
40:18
Speaker 2: able to bring that data together from multiple places and
40:21
Speaker 2: then apply AI across it, depending on what is you're
40:24
Speaker 2: trying to do, but you know it's moving so quickly.
40:27
Speaker 2: It's really exciting. To be pavily honest.
40:29
Speaker 4: What is exciting about it for you? Because you know,
40:33
Speaker 4: for office workers there's that kind of productivity gain stuff
40:38
Speaker 4: that's being talked about. For consumers there's like access to
40:42
Speaker 4: information that they may not have or ability to proof
40:46
Speaker 4: read and do these kinds of everyday tasks. But as
40:50
Speaker 4: somebody who is like super deep in the world of data,
40:53
Speaker 4: what is actually super exciting for you about the generative
40:56
Speaker 4: AI stuff?
40:57
Speaker 2: The productivity part that'll be part of it, But I
41:00
Speaker 2: don't think that organizations are looking at just the productivity. Sure,
41:05
Speaker 2: there's efficiency, but I think it's the upside that people
41:09
Speaker 2: can drive from it. Is the better network planning in
41:12
Speaker 2: TALCOS is the better supply chain management because you're pulling
41:16
Speaker 2: information from third party suppliers as well as the internal information.
41:20
Speaker 2: You can run and advance large language model across that
41:24
Speaker 2: to work out what is the route processing or where
41:26
Speaker 2: do you deliver things quicker? That outcome that's going to
41:29
Speaker 2: move the dial with those organizations to drive revenue or
41:34
Speaker 2: make them more profitable. That's really exciting, and obviously the
41:38
Speaker 2: productivity gains will come as well.
41:41
Speaker 4: Are we already seeing some of those gains in certain
41:44
Speaker 4: areas using the new air models? Like can do you
41:46
Speaker 4: have examples of that?
41:48
Speaker 2: Yeah? Absolutely, Mine to ten was just talking. They've spoke
41:52
Speaker 2: to our conference again last year. They've had a very
41:54
Speaker 2: small team, so I've managed to consult it a lot
41:57
Speaker 2: of their information one year on what done as they've
42:00
Speaker 2: applied some of these large language models to look at
42:04
Speaker 2: water supply chain and how can they deliver better outcomes
42:08
Speaker 2: across the retail organization. So they're starting to embed some
42:12
Speaker 2: of these capabilities into their processes. We're seeing the talcos
42:16
Speaker 2: doing the same things. A lot of them have had
42:20
Speaker 2: proof of concepts that they're now starting to put into production.
42:24
Speaker 2: So I think that there's going to be a lot
42:26
Speaker 2: of the pilot and prototype pieces of we're really accelerating now,
42:30
Speaker 2: And to be honest, some of the organizations they see
42:33
Speaker 2: that as a competitive differentiator, so they are actually keeping
42:36
Speaker 2: some of them relatively close to their chests because the
42:39
Speaker 2: faster they can move, they're looking to leap frog the competitors.
42:45
Speaker 4: If twenty twenty one twenty two was kind of the
42:48
Speaker 4: emergence and the testing and the seeing what could go wrong.
42:51
Speaker 4: You know, twenty twenty three and twenty twenty four has
42:53
Speaker 4: been about getting those prototypes and starting to see what
42:56
Speaker 4: can happen. Is twenty twenty five to twenty six is
43:00
Speaker 4: that going to be the acceleration time? Where are we
43:02
Speaker 4: at in terms of starting to really see mass adoption
43:06
Speaker 4: of this tech at an enterprise level, at a fundamentally
43:09
Speaker 4: restructuring level.
43:10
Speaker 2: Yeah, I think over the next twelve to eighty months
43:12
Speaker 2: you will see a massive acceleration of that. I think
43:15
Speaker 2: those organizations that have done those that foundational work are
43:19
Speaker 2: in a much better position to be able to accelerate faster.
43:22
Speaker 2: So those organizations that have got trusted, high quality, consolidated information,
43:28
Speaker 2: then they're looking at what do they do to exploit it.
43:31
Speaker 2: They've got that quorum of data, and now how do
43:33
Speaker 2: we use it and exploit it quickly? There's still organizations
43:36
Speaker 2: which have yet to do that foundational work, and that
43:39
Speaker 2: foundational work is critical before you can start to exploit
43:43
Speaker 2: it in a really meaningful way. So I think there's
43:45
Speaker 2: going to be those that are ahead of the curve
43:48
Speaker 2: and have been ahead of the curve for the last
43:50
Speaker 2: few years, are going to be able to accelerate quicker
43:54
Speaker 2: than those who haven't done that homework and done the
43:56
Speaker 2: foundational stuff. And it's no different to whether it's GENAI
44:01
Speaker 2: or large language models. Those organizations that have got that
44:05
Speaker 2: high quality data, they've spent the time to ensure that
44:10
Speaker 2: the lines of businesses have data literacy and data skills
44:14
Speaker 2: and know what they can do with the information to
44:16
Speaker 2: change the processes will be in a better position. So
44:20
Speaker 2: adding on top of that things like genai, it will
44:23
Speaker 2: allow those organizations to go faster. But it's just accelerated
44:27
Speaker 2: how quickly organizations can start to exploit it. I don't
44:29
Speaker 2: think it changes the fundamental that you've got to get
44:31
Speaker 2: the basics right and do that well before you can accelerate.
44:37
Speaker 4: What would you say are the biggest risks that we
44:39
Speaker 4: need to be thinking about as we enter this accelerative phase.
44:43
Speaker 2: I think the privacy and just because we've got the data,
44:48
Speaker 2: does that give us the right to use that data mentality?
44:51
Speaker 2: And I think we've got to be really considerate that
44:54
Speaker 2: most of these organizations is not their data, it's their
44:57
Speaker 2: customers data, So we need to really consider what is
45:01
Speaker 2: it that we're going to do with that data and
45:04
Speaker 2: make sure that it's doing the right things for their
45:08
Speaker 2: customers as well as the internal organization. So we've got
45:10
Speaker 2: to think around the privacy, the use of it, the
45:15
Speaker 2: AI governance and governance of the customer's use of it,
45:20
Speaker 2: and the permissions and things like that. So I think
45:22
Speaker 2: the accessibility is great, but just because we've got it
45:26
Speaker 2: doesn't necessarily mean we should use it in a certain way.
45:30
Speaker 2: There's going to be a lot of focus around the
45:33
Speaker 2: obviously security, privacy, and then also how do we just
45:37
Speaker 2: continue to evolve on that as well?
45:40
Speaker 4: What do you mean by that.
45:42
Speaker 2: In terms of as the technology moves so much faster,
45:47
Speaker 2: how do we keep up with that? And how do
45:49
Speaker 2: we think about the new use cases? How do we
45:52
Speaker 2: think around what is that business driver? Again taking it
45:55
Speaker 2: away from just a technology problem, what is the business
45:59
Speaker 2: trying to achieve ross that line of business, finance, marketing,
46:02
Speaker 2: et cetera. And how do we align to that outcome right?
46:06
Speaker 2: Otherwise we can spend a huge amount of money with
46:10
Speaker 2: science experiments that don't actually do much for the business.
46:12
Speaker 2: Another thing that will be important will be looking at
46:15
Speaker 2: the cost considerations, making sure that the whatever we're doing
46:21
Speaker 2: is aligned to the outcome so that it's cost and
46:24
Speaker 2: value tightly coupled. Otherwise you know, they're not cheap things
46:29
Speaker 2: to run, So we need to make sure we've got
46:31
Speaker 2: the guardrails across it. So cost management is going to
46:34
Speaker 2: be efficient, going to be important, That the governance and
46:37
Speaker 2: privacy is going to be important, and that all ties
46:40
Speaker 2: back to what is what are we're using it for
46:42
Speaker 2: and what is the business value we're trying to drive
46:43
Speaker 2: out the back of it.
46:45
Speaker 4: So get excited, but not too excited, and be thoughtful.
46:49
Speaker 4: That's kind of the I think.
46:50
Speaker 2: Be excited, but be thoughtful, don't don't limit what you
46:56
Speaker 2: think you can do because you probably can. And it's
46:59
Speaker 2: exciting time to go and test some of these hypotheses
47:02
Speaker 2: and see how it works. So be excited, to be
47:05
Speaker 2: really excited it's going to be fantastic next couple of years,
47:08
Speaker 2: but just be thoughtful about how you're using it and
47:10
Speaker 2: thoughtful about your customers.
47:19
Speaker 4: So if ever there was a man who lives and
47:22
Speaker 4: breathed data, I think it's Tony Shaw. He has clearly
47:25
Speaker 4: been in the industry for a long time. And his
47:29
Speaker 4: advice I think, while some of it isn't necessarily novel,
47:32
Speaker 4: it's the stuff we've been hearing for a while about
47:35
Speaker 4: getting data in order. I think that the way that
47:38
Speaker 4: he has put it really was very clear and concise
47:42
Speaker 4: and actionable as well, which is what I appreciated about
47:45
Speaker 4: the chat.
47:46
Speaker 3: Yeah, he really talked about this transition into data and analytics,
47:52
Speaker 3: the importance of data in financial decision making. And for years,
47:57
Speaker 3: you know, we've been talking to New Zealand businesses about
48:00
Speaker 3: writing about it and they're all up for it, and
48:03
Speaker 3: some of them are really doing that, doing really smart
48:06
Speaker 3: things with data. But we were a bit slower to
48:10
Speaker 3: the move to the cloud and getting data in order
48:14
Speaker 3: as part of that digital transformation. So a lot of
48:18
Speaker 3: businesses talk about this stuff, but are they actually using it?
48:21
Speaker 3: And when I talk to them sort of off the record,
48:23
Speaker 3: they say, well, actually, know where we've done pilots, we're
48:27
Speaker 3: doing limited use cases related to data analytics and the like,
48:32
Speaker 3: but we don't have the data in the right shape.
48:34
Speaker 3: We need to build a data warehouse or a data lake.
48:38
Speaker 3: We need to standardize our data, and that literally for
48:42
Speaker 3: some of them is taking years. So we've seen that's
48:44
Speaker 3: why we've seen the rise of snowflake and data bricks
48:47
Speaker 3: and others. The big tech platforms can only do so much.
48:53
Speaker 3: It's really up to you, and there's a layer between
48:56
Speaker 3: the customer and the big platform we're all of your
48:59
Speaker 3: data potentially is going to be and these companies are
49:02
Speaker 3: playing a really valuable role there.
49:05
Speaker 4: Yeah, and the couple that we mentioned Data Bricks and Snowflake,
49:08
Speaker 4: and these are the ones that have really come out
49:12
Speaker 4: swinging and have shown that the value over and over again.
49:15
Speaker 4: And Snowflake listed on the NASDAK and has shown really
49:19
Speaker 4: great growth since doing that. So you know, its success
49:23
Speaker 4: is I think a good indicator of the value that
49:26
Speaker 4: it is offering to organizations globally. And you know Tony
49:31
Speaker 4: talking about the fact that it can scale up to
49:33
Speaker 4: these massive, massive international corporates, but it can also scale
49:37
Speaker 4: down to fit the needs of organizations and countries like
49:41
Speaker 4: New Zealand. And if we want to be the country
49:44
Speaker 4: that is using AI, that is using our data to
49:49
Speaker 4: improve our productivity, to enter the brave new digital world
49:52
Speaker 4: and kind of stay relevant on a global scale, then
49:57
Speaker 4: these kinds of products, these kinds of projects of what
50:02
Speaker 4: needs to be done on a bigger scale. And what
50:04
Speaker 4: Tony was saying about not doing science experiments anymore, right
50:08
Speaker 4: the time for kind of these doing science experiments over
50:12
Speaker 4: and over again. The small scale dabbling is kind of
50:15
Speaker 4: if you're still in that phase, you might need to
50:18
Speaker 4: put a bit of welly behind it and get on
50:19
Speaker 4: with that.
50:20
Speaker 3: Yeah, yeah, yeah, I mean, I think his advice is
50:23
Speaker 3: sort of what we've heard, which is start small, don't
50:27
Speaker 3: necessarily go big bang, because if you've designed it wrong,
50:31
Speaker 3: suddenly it becomes a very expensive failure. So target a
50:34
Speaker 3: part of the business where having great insights into your
50:37
Speaker 3: data is going to really help the business. Start that
50:42
Speaker 3: experiment a little bit, then grow a bigger But he's
50:44
Speaker 3: clearly predicting a major acceleration of AI adoption over the
50:48
Speaker 3: next twelve to eighteen months as organizations do that foundational work.
50:55
Speaker 3: Trying to get ahead of the curve is a competitive advantage.
51:00
Speaker 3: Hopefully that message is getting through in New Zealand. We've
51:03
Speaker 3: seen so much research over the last year or so
51:06
Speaker 3: to suggest that we're a little bit behind the curve.
51:10
Speaker 3: But if companies like Snowflake can help accelerate that, because
51:15
Speaker 3: as you say, it scales down to medium sized businesses
51:19
Speaker 3: quite well, that's basically where New Zealand plays and a
51:24
Speaker 3: lot of those companies have been playing around with co
51:27
Speaker 3: pilots and chatbots and AI related applications, So maybe some
51:32
Speaker 3: of them have done enough work to actually in twenty
51:35
Speaker 3: twenty five and beyond make really good use of AI.
51:38
Speaker 3: And again what we've heard from others is emphasizing the
51:42
Speaker 3: importance of aligning sort of AI and data initiatives with
51:46
Speaker 3: business outcomes and having internal sponsors, people in the executive
51:51
Speaker 3: off the business, people on the board who are real
51:55
Speaker 3: champions for this. There's no point doing something where the
51:58
Speaker 3: CEO and the executive team is sort of saying how
52:01
Speaker 3: much is this going to cost? If they're not convinced
52:03
Speaker 3: if the value of investing in these sorts of platforms
52:06
Speaker 3: to the business, you've got a problem. They've all got
52:08
Speaker 3: to be on board. So thanks very much to Tony
52:11
Speaker 3: Shaw from Snowflake for his thoughts on the data landscape
52:15
Speaker 3: and what's needed to spur AI adoption.
52:18
Speaker 4: We'll be touching on that and next week's episode two,
52:20
Speaker 4: and we have a panel of AI experts joining us
52:22
Speaker 4: to look at the year in AI, big developments in
52:25
Speaker 4: the technology, regulation and government's use of AI, and what
52:29
Speaker 4: may be in store in twenty twenty five.
52:31
Speaker 3: Show notes for the Business of Tech are in the
52:33
Speaker 3: podcast section at Business Desk dot co dot nz, where
52:37
Speaker 3: you can stream this podcast in full every week. It's
52:40
Speaker 3: also available from iHeartRadio or your podcast platform of choice.
52:44
Speaker 4: Get in touch with your feedback and we'd love to
52:46
Speaker 4: hear your suggestions for upcoming tests too. You can email
52:49
Speaker 4: me Ben at business desk dot co dot and z,
52:52
Speaker 4: and you.
52:52
Speaker 3: Can find both of us on x and LinkedIn, where
52:55
Speaker 3: you can follow the Business of Tech page for all
52:57
Speaker 3: of our updates.
52:58
Speaker 4: That's it for this week. We'll be back talk AI
53:01
Speaker 4: and wade through the election debris next Thursday.
53:04
Speaker 3: We'll catch you in
53:09
Speaker 2: Mm hmm