Podcast Transcript
Therese Raft
Welcome to Navigating the New Normal – Grant Thornton’s podcast exploring trends in business and the marketplace.
Today I'm joined by Ian Renwood, our head of Tech, Media and Telecommunications. It's quite timely that we're having a chat now about data analytics with two of the world's largest data companies, Facebook and Google, in Australian news. So welcome Ian!
Ian Renwood
Thanks Therese, nice to be talking to you.
Therese Raft
Now, Ian, consumers use these two huge platforms every day to socialise and to search. But both are inherently data businesses. For those of us who aren't in the know on the jargon, what does that actually mean?
Ian Renwood
It means that they derive an overwhelming majority of their revenue from the collection and the analysing and the leveraging of the data on the various individuals and organisations that use, interrogate, go through their websites and other ways they engage like Gmail etcetera. So, for example, the highlight one is really Google, which has been the example of this and has been for well – well over a decade or more now. And in 2019 they generated $162 billion US in revenue, and almost 85% of that came from analytics around the advertising, Adwords and a whole bunch of other revenue streams off the back of the advertising. And the reason it was such a strong performer for them was their suite of analytics tools they use internally to be able to interrogate, as I said, position the right advertising at the right time for the right person is a significant enabler for them and a competitive advantage. And that's why 85 – I think 85.6% – of their revenue came out of data analytics related insights.
Therese Raft
So what you're kind of describing there is…sounds a bit like uber personalisation. Is that inherently what it comes down to?
Ian Renwood
Yeah, that's absolutely correct. You know, where they have managed to achieve greater success is so much of their advertising they position to us is contextualised. So at the right time of the right day, you get advertising that's relevant to what you might be searching or looking at, and they were one of the first, if not the first firm to be able to generate such targeted advertising. So you would have noticed that if you go online and you're searching something around, let's just say the COVID-19 vaccine the next time you might do a Google search, you might have a whole bunch of, you know, medical centres and health clinics pop up that are targeted towards you and things like that. A lot of people wold have seen they've gone in, perhaps Google the price of a new car, looking at buying it. The next, you know, few times going on various sites you will be seeing a whole bunch of car advertising and other related mechanical products and services. So it's no mistake that they were so well targeted. And that's one of the reasons why advertisers tend to flock to them. And they're advertising tends to be a lot better targeted than even others such as Facebook. Although Facebook have a very successful platform globally, Google seem to have the edge or to have the edge when it comes to the application of the data and analytics and those algorithms that drive the targeting and the insights for their advertisers.
Therese Raft
So I find it really interesting. We have these really huge data powerhouses. So how did that actually happen? Does size, at least in this case, matter?
Ian Renwood
Yes and no. Size is what enabled organisations such as Google and other technology players to become… to develop more insights into organisations and into individuals that interact with the organisation. But further than that, the need to do that has driven a lot of innovation around algorithms and data analytics in Google. Many of those data analytics services and capabilities are now much more broadly available. And if you go back 10-15 years, you look at things like LinkedIn – so LinkedIn was a great example – there was no there was no database available at the time that was required to support all those millions and millions – at that stage – of contacts. So they developed their own database technology. Which has now led to no SQL database, you know, and amongst LinkedIn and a couple of other players in that market led toward the innovation around no SQL databases.
A similar things happened with Google and some of those data platforms and a whole bunch of algorithms that have now being commercialised and a lot more broadly available. That would have come about if it wasn't for the need to innovate and interrogate their own data. So that means that a lot of mid-sized and even much smaller companies these days have access to data sets, and not a lot of money (comparatively speaking). You don't need to be a large bank or a large airline with petabytes of data and, you know, large teradata farms costing tens, if not more millions of dollars to be able to interrogate the data that your customers bring to you and your organisation and get better insights into them, how they interact with you so you can better serve them and even look at ways to acquire them. So there's been very much a flow down of that technology.
It's really available to all businesses now, even up to and including, you know, the corner store. And most of us realise that when we're going by a coffee or a cup of tea there are a whole bunch of apps that you can use to, you know, go five times and get one free, that sort of thing – a lot of that enables them to collect data on you and your usage so they can better predict times that people come to their shop, the kind of things they're ordering. There's a whole raft of benefits they get out of that without even necessarily needing to know Therese or Ian is the end customer.
Therese Raft
When you're talking to clients, or maybe some of the non-typical suspects, is this something they realise or are they surprised that there's all this information at their fingertips?
Ian Renwood
They're almost always invariably surprised by it. There was a large company I visited 18 months ago, and I won't mention them, but they were running a pretty significant manufacturing plant. And there’s all this wonderful German made engineering machines. And as an engineer by training. I got quite excitable. All this beautiful clean engineering machines pushing out their product, product imprints per second. And I said, “Wow, you've got a lot of data you're gathering here”. I can see the data log going crazy. All the data was gathering on the plant. And I said, “Where does it go? What do you do with it?” And he said, “I don’t know, mate. I don’t know.” So they're actually gathering all this wonderful data that could be used for understanding quality control, mean time between break and failure of their plant and equipment, and a whole raft of other things that they just weren’t leveraging. But you still had that you know, the shift leader, still manually logging some of the key information they needed to capture how many widgets an hour and wastage and all that. Which is great, but 90% of that information was available online via their machines, and a whole raft of other data that they just didn't even know existed. So every organisation is collecting data right now, even if they don't know it, on their customers, their suppliers, their interactions with their accountants, their banking details that most of them are not aware of. And if they are, they don't know how to use it.
Therese Raft
This is a brilliant segue because you and I have spoken before about how terrible Australia's track record is when it comes to commercialising tech. Does the same go with how we commercialise our data?
Ian Renwood
It does. If you go back a few years – say eight year – when the whole startup world really started to get up a head of steam in Australia and you had a number of startup innovation hubs being established in Melbourne, Sydney and elsewhere, a number of those organisations going into there were innovative data analytics type companies. One example that is Data Republic, which has become quite a great Australian success story. There are others that weren't necessarily in those hubs, but there's Data 61 which was spun out of CSIRO. So, like in most areas from medical technology, to manufacturing, to agriculture, Australia still is and always will be, seen to be at the forefront of innovation, but we're not very good at taking it to next level, the next stage and commercialising. What that’s meant is, in the last half a dozen years or so, those firms that have had some great analytics idea or some algorithm, they’ve tended to package it up in something that's consumable. So instead of them going out and selling Algorithm X to a whole bunch of banks or, you know, travel industry organisations, they’ve actually thought of a pain point in an industry and they've leveraged their algorithm to actually solve that pain point – that problem. So the regtech market, which is quite hot, and the agtech market as well, are two examples there where you’ve got a lot of really smart, you know, data analytics solutions. But they're being wrapped up in a solution that they can roll out via a cloud environment that enables that AI to be leveraged to provide – to solve a pain point – in a specific industry. So they've got a lot more applied in leveraging the algorithm than they were eight years ago. Whereas eight years ago, they were more esoteric in theoretical. They've learnt the lesson and they become applied around “How do I solve a problem”? “How does our unique thinking or our unique algorithm solve a customer care problem in this industry or solve a medical or health care problem or broader regulatory problem?” when it comes to the regtech organisations.
Therese Raft
What I found really interesting about your background is you worked for IBM in the past, and in addition to your consulting role here at Grant Thornton, you also mentor a number of startups. So you've really seen beneath the hood off big tech, mid tech, startup tech. Are there differences in how things are progressing or what they're focusing on, the speed of change or other common themes between all those different sizes and the different levels of startups?
Ian Renwood
They are largely common themes. If you take a big bank in Australia, or their equivalent overseas, a lot of the data analytics are going into two key areas. One is customer insights: so how they engage upon the relationship with the customer in a more effective, efficient way – which can add additional products for customer, but it can also increase their efficiencies and take costs out of that relationship. And the other key area is protecting the bank, protecting the organisation. So there's a huge amount of money that's spent on compliance around regulatory needs from APRA, the RBA, other regulators about how they get insights, how they look at that capital adequacy, how they look at anti-money laundering, how they know their customer effectively to minimise the instances of money laundering, that sort of thing. So it's still quite narrow that application of data analytics even with the big four banks. They’ve got large data science departments and some very, very capable chief data officers and that, but they really haven't gone beyond a lot of those well known use cases that I've just outlined. So, being leveraged for innovation at the grass roots isn’t as strong as a lot of those organisations would like. Which is one of the reason the big four banks have been so passionate about getting involved in some of those startups because they're trying to find ways to leverage interesting solutions that, based upon data and technology, can improve the bank and transform it.
For example, one of the major challenges they've got is innovation within the bank and a good way to acquire innovation is to go and look in the marketplace and if somebody's been successful applying their algorithm or their AI on this industry or that industry, taking it into the bank and using those insights to solve a similar problem – that's exponentially larger too, you know, petabytes potentially of data, or definitely terabytes of data, and their tens or hundreds of thousands of customers, versus a few hundred that the startup but normally do, can really give them significant scale. Often to go and do that innovation internally runs into so many brick walls or compliance or other issues, or allocation of scarce resources about. We need to dice and slice our customer usage to find out our net promoter score and things like that, and they are understandably high priorities in how they day to day run the bank. But they're going out to the market and by uncovering innovative startups, that are using data and different ways they can leverage that and bring it into their bank and really scale it up, and as I said, get real exponential benefit for their own organisation.
Therese Raft
Now, technology has a very well earned reputation for moving at a rapid pace. Maybe more so for clients or companies who were thinking about putting it off: what is the risk of not jumping on the data analytics bandwagon right now?
Ian Renwood
There's a significant risk if you don't do it and leverage it, because if competitors not doing it now, they will be shortly. I think a lot of speed was expedited by the whole COVID-19 scenario, where organisations have been forced to undertake data analytics type programmes to really understand their customers and to find the best possible way they can to engage services in a very remote virtual environment. So I think there's been a lot of innovation or haste that's been driven in leveraging DNA off the back of COVID because of the physical constraints.
But there is still opportunity for people to get in there and understand what data they collect, how to leverage it, architect it, how they can access it in an efficient way. But I wouldn't leave it for too much longer because everybody's starting to move and exploit it all the way from the corner store up to the global bank like an HSBC or whoever. So the time to move is now and if you’re not already doing it, you should be doing it. But there still is time to…we need to crack on and move in that direction.
The other area I would mention is… So there’s a risk of not doing anything around being left behind by your competitors or other market entrants in your sector that are more agile, innovative, perhaps smaller now, but I guess data and analytics native around what they do. Everything they do is permeated by data analytics insight.
The other risk is, people are concerned about is the actual security of the information, privacy and data. There are enough ways in this day and age to actually leverage that data in an anonymised way that protects individual’s rights and their privacy. That gives you insight and enables your organisation to leverage the benefits outlined a minute ago. So there's no need any more to have that level of concern or fear. And in some cases it was downright fear about doing things with data because they were worried about some privacy breach. Sure, it happens from time to time. But that’s no reason why you shouldn't be doing it. There are enough tools and safeguards in place that you can actually get that sort of…leverage those benefits without putting your organisation at risk from breaching a regulation and being fined, or reputational risk because you can't be trusted with personal data.
Therese Raft
And I'm so glad you kind of touched on that, because privacy and consent is certainly an issue that we can't avoid talking about. And we, obviously we have GDPR from a couple of years ago. As more people move online, as there's more data online, how do you see this evolving?
Ian Renwood
Well we have the old GDPR and other privacy provisions progressively rolled out over the last, well best part of the last decade, but most organisations and governments are now seeing the access to data and providing access to even their data to third parties, business, both large and small, was being advantageous to the way services are delivered to their constituents. Even the federal and other privacy commissioners in Australia have been working on, in many cases implemented frameworks, that allow people to access federal government data, interrogate it, leverage it for insights and that, without breaching individual privacy concerns. And I think that's really positive. And I think a lot of organisations actually don't – are not aware of how proactive privacy commissioners have been to actually try and ensure that data, with the right framework around it, is fairly readily accessible and can be exchanged. Yeah, and I think that's a big opportunity that a lot of people have missed. And there are… there are literally rivers and rivers of data out there that are available for next to nothing that could be interrogated to give you insights to your business alongside the data you already collect. That in many cases is not – it is not being exploited.
Therese Raft
So quite clearly that flow of data isn't going to stop. In fact, it's just going to flow even faster in future. And looking forward, so we've obviously got Data Right that's being implemented in Australia. You've mentioned that there's plenty of open source or cloud based data to aggregate and make sense of that data. How do you see this playing out for consumers?
Ian Renwood
Yeah, I think that’s an excellent way to bring together the conversation we had because you know, there are two issues. There's data that's anonymised or publicly available data that has been anonymised by the government that protects individuals. And increasingly there are tools that enable organisations to anonymise the data so they're protecting themselves from any breaches. There's other data that has personal information about individuals and an increasing number of end user licence agreements allow consumers opt in or opt out on allowing an organisation to have a more personalised approach to how they provide services and products and deliver insights to that individual.
Still, people are quite nervous about that and understandably. And some of that's a demographic issue because there are a lot of people in their late teens, early twenties who have a lot less concern around giving up their identity if they're going to get quid pro quo – where they're going to get a return in terms of features and benefits of those services and products. But it has to be a quid pro quo. So if an organisation is looking at embarking upon a having more access to an individual's data and being even more targeted, and looking at them to sign some user agreement that permits them to do so, within certain constraints, then they have to be extremely careful and make sure they are actually giving up something – and they're giving a much better service, quality of product, or whatever for the end user opting in, enabling them to leverage their data.
But there's no doubt in my mind, increasingly people are, particularly in that Millennial and Gen X and Y generations mature and become more comfortable with giving to get from a data sharing perspective – it will be more prevalent, I think for services being even more customised to individuals right down to their household and people who live within their household, and you know what their personal preferences are. There’s still a long way to go on that front, but the journey has already begun if you look at some of the end user licencing agreement now.
Therese Raft
And, I think you and I have kind of also discussed, I have a six year old and she's already highly used to that level of personalisation from Kids YouTube. So now when she's watching the analogue TV, and she's got no control over watching what she wants when she wants and how she watches it and becomes quite frustrated. So I think there's this thinking about it not only as a transaction, but there's also going to be an expectation of that kind of give and take. I'll give you information if you give me that personalisation. As those consumers who might be six now, one day will be the consumers in like 15 years.
Ian Renwood
And that's our question. And actually, some of those six year old consumers are going to be consumers probably a lot quicker than they or their parents realise. As they get into their early to mid-teens. And I think there's a lot of analogue TV’s out there with thumb prints in the middle where young kids have been pushing them thinking it's an interactive iPad or it's on YouTube to be able to fast forward or change channel. That really is… Just as the banks had to play catch up because customers… bank for most of the last century really defined customer service. You understood customer service from going into an analogue, a bricks and mortar bank. At the advent of the early to mid-nineties, a lot more online businesses – they defined what customer service was all about, and the banks had to play catch up and in some cases, are still catching up. This next generation of consumers are going to have expectations around the customer service and how targeted, like your daughter, the videos that she consumes are to their own personal interests, tastes and career. So, there's a lot. There's still a lot of opportunity, a lot of development and evolution to go over the next decade.
Therese Raft
And do you think we're already thinking that far ahead? Or is the thinking around how you tap into data a little bit more short term?
Ian Renwood
I think some of the, you know, the digital native data analytics / native type organisations have got that sort of built into their DNA and they're always looking for ways to improve the customer experience. For others that have been around a lot longer and don't have that inculcated in their DNA, it's going to be a much longer journey, and they will always be questioning “Is this the right thing that we're doing?” Whereas some of those innovative startups, even those so-called startups, they're now generating US$160 billion revenue, like Google, it's part of their core DNA and they’ll always be challenging themselves about how they can use data insights better to provide a better customer service, to provide more services to their enterprise clients, as well as us as end users, but also how they interact with their own suppliers. So again, they will have a significant role over the next decade or more, I think, in managing and setting expectations about how we consume products and services based upon data insights. There's no doubt in my mind there's a long way to go in terms of that quality of service and other organisations picking up, taking up the mantle, leveraging data analytics, as well as some of those platforms do.
Therese Raft
So what I'm really kind of hearing is the future is coming. It's going to be data driven. But if you focus on it is being a mutually beneficial transaction there's a lot of really exciting opportunities. So thank you so much for your time. If anyone would like to reach out to you, say on LinkedIn, you're happy to continue having this conversation?
Ian Renwood
Absolutely. Yeah. Always, always keen to hear from people and answer their questions and share insights. Thanks for your time Therese. I've really enjoyed it as well and much appreciated.
Therese Raft
Awesome, thanks Ian.
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