Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

[00:00:00] Dr Genevieve Hayes: Hello and welcome to Value Driven Data Science, brought to you by Genevieve Hayes Consulting. I'm Dr. Genevieve Hayes, and today I'm joined by Matt O'Mara to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to address this issue. Matt is the Managing Director of Information and Insights Company Analysis Paralysis and is the Founder and Director of i3, which helps organizations use an information lens to realize significant value, increase productivity, and achieve business outcomes.
[00:00:38] He's also an international speaker, facilitator, and strategist. And is the first and only New Zealander to attain Records and Information Management Practitioners Alliance Global Certified Fellow status. Matt, welcome to the show.
[00:00:54] Matt O'Mara: Thank you.
[00:00:55] Dr Genevieve Hayes: When it comes to awareness and understanding, what we know and don't know can be split into four categories.
[00:01:02] Know and knowns, so things we know we know, unknown knowns, things we understand but are unaware of. Know and unknowns. Things we know we don't know and unknown unknowns, the things we don't know we don't know. And to quote former U. S. Secretary of Defense, Donald Rumsfeld, who brought attention to the concept of unknown unknowns, if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.
[00:01:36] When Rumsfeld made his famous unknown unknowns speech, he was referring to military intelligence, But the concept of unknown unknowns is just as relevant to data and data science. There are things we know we collect data about, things we collect data about but are unaware of, things we know we don't collect data about, and things we don't realize we don't collect data about.
[00:02:01] Those data dark spots, also known as information or data gaps. Can be a real issue when it comes to data driven decision making. Fortunately, data scientists are uniquely placed to lead the way in addressing this issue. And in today's episode, we're going to explore ways in which data scientists can do just that.
[00:02:22] Now, Matt, the issue of information gaps is one that's close to your heart. But how did you become aware of and interested in this issue to begin with?
[00:02:33] Matt O'Mara: So I guess for almost my entire life, I have been intrigued by information and the power of information if utilized to solve problems, create opportunities, and reveal new insights. Very early on in my career, I worked six years in the public emergency department. Department. And in that context, information is life and death in terms of when somebody comes in, we need to know if they've got any prior medical conditions or if they've got any adverse medical reactions to certain drugs.
[00:03:04] So that made a real impression on me. And in terms of information and the power of information, it can transform lives. And when used for good, it can assist humanity in tackling some of the most. pressing problems we have. One's so great that they threaten our very existence. And in turn, information can cause great harm if misused.
[00:03:25] So think of you know, examples like Cambridge Analytica scandal. And I think the biggest counter to this is awareness. And so I guess my interest is the culmination of many years of looking at problems and opportunities through what I call this Information lens and what I mean by that is looking at a problem or a situation and trying to understand what role information plays in it.
[00:03:48] And a key part of successfully using our information lens is to employ wider thinking. And I guess having worked for and consulted to numerous organizations, it is more common than not. That information, and I use that in its widest sense, data, records, et cetera, is at the heart of both the problem and the solution.
[00:04:11] And yet senior leaders and staff often do not use an information lens, or they Have an awareness of the vital role information plays in their organizations. And of course in terms of AI AI relies on huge amounts of data and information. It's the foundation of AI, so it's becoming very pertinent.
[00:04:30] So basically many years of pondering the role of information and how it can be utilized to achieve amazing results. And also to share a cautionary tale of understanding how information can be misused and the implications of this. And what I started to see is that every time I read the news, I start to notice how many stories there were that actually at the core of them was about information issues.
[00:04:55] And I guess in turn opportunities. Uh,
[00:04:58] Dr Genevieve Hayes: Give us an example of one of these information gaps that you've noticed while reading your newspaper, say in the last week or so?
[00:05:06] Matt O'Mara: so unfortunately, there's lots of examples and I'll start, off with an article I saw in August, and it was about forever chemicals across Sydney's drinking water catchment. So, Sydney Water in August had confirmed for the first time that these cancer linked forever chemicals, and they're called forever chemicals because they do not break.
[00:05:25] breakdown in the environment had been detected across the city's drinking water catchments. The scientific name is polyfluoroalkali substances, I'm glad I can pronounce that. And Sydney Water conducted tests just two weeks after the Herald revealed evidence of past contamination of Sydney's tap water.
[00:05:43] Now the kicker is there was a lack of widespread routine monitoring for the chemicals. So an information deficit, if you like, or an information gap. And as a result, Australia's guidelines are under review particularly in the U. S. There's been a dramatic policy shift deeming the chemicals were probable carcinogens and found there was no safe level of exposure.
[00:06:07] So there is pressure in Australia for an official inquiry into these chemicals. And new research also shows for the first time high levels in wildlife such as platypuses and in areas of Sydney's drinking water catchment where there are no known hot spots, suggesting that the chemicals have spread.
[00:06:28] Now, in terms of other kind of examples and thinking more widely, there's a number of trends that are coming to the surface. So. For example, over 55s are becoming the biggest homeless group in Australia. One third of students in Australia are failing to meet basic literacy skills. Recent research in the UK has shown under 25s likely to be more lonely than elderly.
[00:06:56] And just one more global increases in chronic diseases, and we're unsure of the causes, and if we think of things like changing demographics in Japan because people aren't having babies, this is having all All sorts of adverse implications. And so my point here is, did we spot these things coming?
[00:07:17] And if not, it's a serious information gap. Because if we can spot these trends early, we can respond to them more effectively before the problems Get too big, and it's too late. And it's not just about information gaps. So, sometimes we may have collected data, and we haven't used it, or information. So, back in 2011, there was a major shipping disaster, it was called the Rena.
[00:07:46] And essentially, the ship had come from China, it stopped off in Perth, and then it came to New Zealand, where it hit a reef. The cleanup was 235 million, one of New Zealand's biggest maritime disasters. 350 tons of oil spilled 169 containers were lost, 32 of them had hazardous substances 20, 000 seabirds were affected.
[00:08:12] Um, We had things like filter feeding whales were at risk from sticky oil clinging to their bailing plates. Now, my point here is Maritime New Zealand had reports from China. And then when the ship stopped in Perth, it got more reports to say this ship has a number of issues with a number of deficits.
[00:08:33] And if that information had been acted on. It probably wouldn't have been allowed to enter our waters, and this disaster could have been averted. So how many times do we collect information and there's this term dark data, which is data which is acquired but not used in any manner to derive insights or decision making.
[00:08:54] So a key point around this is if we're not managing our information well, there's costs associated with it. Because, yeah, why are we collecting information that we don't use? And so, in terms of these information gaps, there's many drivers from transparency, health and safety, productivity cost increases privacy, information assurance.
[00:09:16] And at the worst case, It can result in a fatality and just to give a couple of examples to back that up. We had one council in New Zealand which had a rotten tree. They had a report in their system to say the core of this tree is rotten, it should be removed. The information wasn't acted on in a storm, the tree blew down and actually killed a woman.
[00:09:39] And then years ago in Australia, you had the dream world tragedy where four people were killed on the Thunder River Rapids ride. Now, the upshot of that was that there were two buttons which could have stopped the ride. But neither of those two buttons were labelled as emergency stops so that's just a tragedy waiting to happen.
[00:10:05] And the ride had been extensively modified over 30 years, but there was little documentation showing where and how it had been altered. And so that lack of information became actually critical in terms of contributing to this terrible tragedy. And another sad aspect of it is the upgrades that had taken place did not include a relatively cheap water level sensor that according to the experts report following the incident could have prevented the tragedy by automatically stopping the entire ride when the water levels dropped.
[00:10:43] So there's some really, really strong drivers and. I don't think this is just an organizational thing. I think it's a societal thing. If we think about citizens engagement, I'm not sure if Australia is the same as New Zealand or it's worldwide, but we've had a lack of engagement with local government.
[00:11:02] Less and less people are voting in local government elections, even though local government does a lot in your communities. It does the roading, it does planning a lot of civic. stuff. And so citizens ability to engage meaningfully in local government can either be limited or enabled by the amount of information available to them about what councils in the areas are doing, their plans, their proposals, their decisions.
[00:11:30] And if we think about the wider aspect of information management we're thinking about the information people need to do their jobs information required to manage operations and achieve strategic outcomes. And, of course, in our community, the information our community, our stakeholders and other organizations require.
[00:11:51] Dr Genevieve Hayes: So it seems to me that the problem's twofold. There's the issue of there being information that's It just doesn't exist, which is your unknown unknowns, but there's also the issue of there being information that does exist, but people don't understand what it means or don't understand that it actually exists.
[00:12:11] Matt O'Mara: Yeah, absolutely. And to comment on that further, I think it's not black and white. So, for example, it can take the form of simply looking in the wrong place for answers. If we think of antimicrobial, Resistance. So that's resistance to antibiotics. That is a major global health threat. Basically rendering antibiotics and other treatments ineffective, which leads to harder to treat infections, increased medical costs, and sadly higher mortality rates.
[00:12:42] So the spread of resistant pathogens compromises the ability to control infections and that in turn jeopardizes public health and safety. Now, the interesting aspect of that is, while there's been lots of studies, those studies have focused on high income nations and human antibiotic overuse, yet they've missed developing countries and there's been relatively little focus on antibiotic overuse in animals things like primary care providers and in low and middle income families.
[00:13:18] And so the major blind spot is the intense focus on curbing antibiotic abuse in humans, when actually antibiotic abuse in livestock animals is substantially higher than in humans. Humans. And indeed the rise of the resistance is closely linked with the growth of this multi-billion dollar meat industry.
[00:13:36] So that's a really good example of simply looking in the wrong place for answers. And, in New Zealand recently, health officials have urged Kiwis to be aware of bacteria in potting mix and compost.
[00:13:49] So, as it comes into spring and summer, people buy the potting mix and they don't realize that you've got to open it in somewhere that's well aired. And it can be as simple as having a really clear label on it to warn people because , when they looked at the issue, There was a lack of labeling on these bags to warn people, so it can be very simple in some cases.
[00:14:12] But on , the flip side of it, it can, be about probity issues. So in New Zealand in 2023, the relationship between Kiwi doctors and Big Pharma was exposed for the very first time. So for the first time, there was a study to reveal their financial relationship with individual Kiwi health workers, but the problem was it was a voluntary scheme run by Medicines New Zealand, and around 456, 000 was recorded as being paid out.
[00:14:45] But as the data offered was voluntary, the real amount and scale of that relationship between doctors and Big Pharma Is unknown, and this is probably a worldwide thing, and it's pretty scary because when you go see your doctor or your specialist, you want to know they're going to prescribe you things for the right reasons, not because there's some incentive to go on a holiday or attend something or whatever it is, and I'm not saying that's the case, but where there's information gaps, there is an absolute lack of transparency, which can lead to all sorts of other, issues.
[00:15:21] And another area is spiraling costs. So in several industries, spiraling costs have been a significant challenge. If we think of construction, healthcare, education, energy, and often it's not entirely possible to pinpoint all of the cost drivers behind these increases. I always say, follow the money, but if we could fill those gaps and understand what the cost drivers are, then we could hopefully address some of those spiralling cost issues.
[00:15:53] Dr Genevieve Hayes: One thing that struck me while you've been giving these examples is a lot of this comes down to causality. I mean, it's. Not understanding causal relationships that exist and where a causal relationship does exist, so A leads to B, not gathering information about factor A, and then suddenly, because that causal relationship exists, you see the consequence of it in the factor B goes up.
[00:16:25] For example, in the case that you gave before Literacy levels declining. There must be something that's causing that. But obviously that causal relationship wasn't understood, and whatever is causing the literacy levels to decline wasn't being monitored. And then all of a sudden we're seeing lower literacy levels.
[00:16:50] showing up in the information that we are gathering.
[00:16:53] Matt O'Mara: Yeah, look, I think you're absolutely right. I think that's that phrase about, you know, connecting up the dots. And if you're using an information lens, you're looking at it widely and thinking, what can affect these? And I think as we've learned, there are different types of information gaps or put it another way, information gaps can present themselves in different ways.
[00:17:13] And so, If we're training our information lens, we might realize we have to kind of take this all into account. Another example along what you've just described is obfuscation, you know, deliberately making something unclear or difficult to understand. And politicians are often quite good at this, if I'm brave enough to say that.
[00:17:34] But often it involves using more complex language and descriptions to convey information. So an example in New Zealand, in 2020, we had a professor who was a world renowned expert in Crohn's disease and ulcerative colitis. He accused Pharmac of gross obfuscation in an open letter. He sent to the Prime Minister and PHARMAC is our government agency who supplies and manages the funding of medicines in New Zealand.
[00:18:02] Part of the core of this professor's argument was PHARMAC's lack of knowledge on the direct health costs of Crohn's and colitis patients who were failing conventional treatments. But the kicker was, it turned out that Pharmac did not actually have the data on current treatment costs.
[00:18:22] So this doctor found out after talking directly with them that it did not actually have the data on the most important current treatments. And incredibly, they asked him and his colleagues if they could provide the data to them. And this doctor pointed out that the drugs they urgently required were mainstream drugs funded through the Western world, 37 countries, but not in New Zealand.
[00:18:46] And without the drugs for New Zealand patients, there were multiple hospitalizations, usually through the emergency department, sadly, irreversible surgeries to remove sections of their bowel, and often culminating in permanent stoma bags. And this was a range of people, including children and young adults who were living in severe pain, disfiguring surgery, and social isolation.
[00:19:09] And what they did say is that they would save around 234 million in all of these surgeries and other treatments if they funded the drugs. So, in some respects, we trust our agencies and we trust that this analysis has been done, but we do need to verify and, this was a very, very powerful example of information gaps and the lack of analysis.
[00:19:38] Dr Genevieve Hayes: It's intelligence versus knowledge. The people who were working at Pharmac. I'm sure they are very intelligent people with medical degrees or similar training. But they didn't have the knowledge. And yet a trap that a lot of intelligent people fall into is that they think if they don't know something that might suggest to people that they lack intelligence.
[00:20:01] And so they're less likely to admit that they don't have knowledge, when intelligence and knowledge are two completely different things.
[00:20:09] Matt O'Mara: Yeah. And I think, there's a salient lesson in there to co design these things because if they had engaged early on with the specialists and they understood some of those information gaps, you know, for example, you can go to emergency department with a Crohn's or colitis condition and that doesn't get recorded.
[00:20:30] So there were significant gaps contributing. This as well. And so I think that's very, very relevant.
[00:20:37] Dr Genevieve Hayes: One example you mentioned to me when we first spoke about information gaps, which I thought was very relevant to data scientists, was with regard to organizational productivity. So, many organizations fail to measure and report on the outcomes of internal projects and initiatives. So there's an information gap that exists around the productivity of those initiatives.
[00:21:02] And that often results in them being less productive than they could be. And this really struck a chord with me because this is a mistake I see data scientists making all the time. Data scientists develop a solution to a problem, be it a model or report or whatever, they deploy it into production.
[00:21:24] And then they move on to the next project and forget about tracking the outcomes of that previous solution. And this is bad for the organization because if the solution isn't performing as well as expected, no one's going to notice or fix it. And it's bad for the data scientists in that if the solution is doing well, then no one's going to know, so they can't reap the benefits of their hard work.
[00:21:51] How have you seen this play out in the organizations that you've worked with?
[00:21:56] Matt O'Mara: So often you'll get a new chief executive coming into an organization and it's quite a common thing for them to make a lot of changes and sometimes quite quickly. That might be doing a restructure. It might be you Stopping and starting various initiatives or projects and often the claim with those changes is that they're going to save often millions of dollars over time.
[00:22:21] And in New Zealand at the moment, we've got a big discussion in government about the cost of back office versus, core services. But often that can be an ideology rather than a where's the firm evidence that we've got an understanding of all our costs. At the moment and visibility of these, so Australia and New Zealand as we know have had low productivity For many years now compared to, other countries and so in terms of information gaps in our topic today, I think it's about having very clear line of sight between the projects that are initiated, whether it's the chief executive or what have you and the outcomes delivered.
[00:23:06] The return on investment the dis benefits, because no solution is going to be a hundred percent meet every requirement and so I think that's absolutely critical. And I don't think quite often we have that line of sight. And it might be you have a board made up of various members who have certain skills But, don't necessarily think with this information lens and I think things like understanding technical debt, understanding, if we're going to implement a new system, what are the real costs and the lifetime costs, because quite often you don't see tools like a strategic asset management plan that, you know, has every system your organization uses and the costs of upgrading or replacing over a long time period.
[00:23:57] So, I think to me, it's that line of sight. And having that and you raise a very, very good point because. It costs money to build these models in the first place and in these systems and if there's no measurement in place, then it's actually going to be an added cost that could have been used for something else.
[00:24:19] So prioritization comes into it as well. Yeah,
[00:24:22] Dr Genevieve Hayes: The other thing I think might be a cause of this I'm thinking of the Australian TV show Utopia at the moment.
[00:24:29] Matt O'Mara: yes, love it,
[00:24:30] Dr Genevieve Hayes: Oh yeah. And, in that, you have the constant revolving door of ministers coming in, and each of them wants something new to announce to the electorate, so that they can say, look at us, we're doing stuff.
[00:24:45] And I suspect that you end up with something similar to that with executives and boards in big companies. They want to be able to keep saying, look what we're doing, which is all about creating new projects. It's not impressive to say, and look how well we've monitored projects that were initiated by the people before us.
[00:25:09] Matt O'Mara: Yeah, and I guess that comes back to my point about obfuscation. So, so yeah, very relevant.
[00:25:15] Dr Genevieve Hayes: So, if you had told me that this was a problem back 20 years ago, this wouldn't have come as a big surprise to me, but it seems like every time you pick up the business section of the paper, there's some article about some big company making some huge investment in AI technology or doing a data upgrade.
[00:25:39] If they're spending all this money on AI and data upgrades, why is this still a problem?
[00:25:46] Matt O'Mara: So before I answer that, there was one other topic I wanted to add to the mix, which was an understanding of critical information sources. In terms of information, there's numerous critical information sources we should be aware of, and we're often not. So, these are often related to the topic of existential threats, because understanding what a critical information in various situations is essential, for example, to respond to a civil emergency situation.
[00:26:16] And an example of that is the supply chain information. For example, the ongoing conflict in Sudan. And that conflict threatens the global supply of gum Arabic. And that's a key ingredient used in fizzy drinks like Coca Cola and Pepsi. And with up to 70 percent of the world's gum Arabic supply coming from Sudan the turmoil and violence has disrupted trade routes, causing a halt in exports, and raising concerns about stockpiles running out.
[00:26:46] And, sorry, one last example of a critical information source is aircraft meteorological data relay system. And that provides data obtained from aircraft to enhance weather forecasting and monitoring. And it's actually quite essential in making the accuracy and reliability of weather forecasts and supports various applications and weather services particularly around the severe weather detection and prediction.
[00:27:13] So part of the question you've just mentioned is do we understand what's critical? And for me, I think the core of it is we haven't developed our information lens. We need to think beyond our traditional construct of information. For example, we're seeing new information forms, things like 3D printing that's been around for a while, but now we're having bioprinting, which is.
[00:27:42] Actually, the printing of organs such as bladders and tracheas and the ability to print more complex organs, such as livers and kidneys could come in the next decade. So that is a different form of information and in this case, it's a digital 3D model of the object to be printed. Digital twins is another example.
[00:28:04] So imagine you have a model house that not only looks like your actual house. But also behaves like it. So the model house is called a digital twin. It's a virtual replica of the real building capturing all the details and dynamics in the digital world. And it's created by collecting data from sensors, blueprints and other sources.
[00:28:25] So understanding those different forms of information and how can we manage them robustly. But I think it also comes down to a poor information management culture and. A lack of understanding of things like data quality and process issues. I came across, and I won't mention the agency, but in New Zealand, there was a government agency that had a 40 million building, and the building hadn't been insured for two years, as someone hadn't updated the asset data for two years.
[00:28:55] When the building was completed. Now imagine if they had a fire or flood or what have you. Imagine the front page of the newspaper. So it can come down to simple processes. We also have fragmented processes. So we have duplication, we have redundancy, we have paper based spreadsheets, or we manage everything through Excel.
[00:29:20] And I've seen that a lot even now. One other thing I want to cover up, which is a real key part of the issue, is what I call the discipline disconnect. So data scientists know their stuff, but do they understand Human resources. Do we understand IT? And typically, because of people like Frederick Taylor, who came up with the scientific management, which was about specialization, that's trickled through to our modern day organizations.
[00:29:53] They're all siloed. So we have finance, we have IT, we have HR. But do they understand each other? And it's normally people like a business analyst who sees the end to end processes that understand the whole system. So systems thinking is really, really important in this space. But also a question I often ask when I go into organizations is, do you have an enterprise data model?
[00:30:21] And sadly, the response I get back is, what's that? And even at a high level, an enterprise data model would be useful because. As you know, it would say what are our data topics or subjects, what's the data quality, who are the data custodians, etc. And leading into that, for some years the New Zealand government was very excited about open data, as I'm sure most data scientists were.
[00:30:48] So they went about promoting, have open data, come on, agencies, put out your open data. But my question when they came to ask me, I was working as a chief information officer at the time for one of our bigger agencies, was, have you developed a high level enterprise data model before you go about Promoting open data because we want to know which agencies hold what, where it might be duplicated, what the opportunities were.
[00:31:16] So it was about face it was all about the open data, but no systematic way of going about, , because there was an opportunity to develop this government wide enterprise data model. And then that would have given us a whole lot of useful information.
[00:31:33] Dr Genevieve Hayes: As you've been talking, you've been sharing with us elements of the solution to this information gap problem, but so that people can piece it all together, if you were going into an organisation where you had identified that there was an information gap problem, and it sounds like most organisations do have such a problem, what would be the steps that that organisation should take, or the first few steps that that organisation should take in order to solve that problem.
[00:32:03] Matt O'Mara: so I'm a big fan of having an information management strategy, and it may be called something different. It could be a data strategy. It could be part of a technology road map, that kind of thing. And the caveat I say to my clients is anybody can write a strategy, it doesn't mean it's a good one. So it's key to understand what the goals the organization wants to achieve.
[00:32:32] And some people say, oh, you know, I don't like strategies. We don't need a strategy. We're not going to read it. We're not going to do anything with it. But my argument there is. Without it, you're like Alice in Wonderland. You can take any path you like. It really doesn't matter because you don't have a plan.
[00:32:48] Because we don't need to make a strategy an esoteric thing. It is simply a plan to obtain your goals. But it can be much, much more than that. So a good information management strategy understands the current state issues. The requirements, the themes, the gaps the organizational drivers and the key role information plays in an organization, both.
[00:33:14] Operational and strategic and a lot of boards don't necessarily go down to the coalface and go, do we really understand these issues that are happening in the organization that we need to think about one session I had with an executive and their leadership team, the leadership team found out they thought different things about certain processes and how they occurred.
[00:33:36] And so it was a great session because they confirmed their thinking was different and they needed to actually find out what. Was actually happening at the Coalface and another caveat is be wary of the Emperor's New Clothes Syndrome. And what I mean by that is back to my comment about anyone can write a strategy.
[00:33:55] You really do need to understand the current state, the organizational context. What are the required information management capabilities? And the return on investment. And it can be as simple as people aren't trained to use a system properly. Because often you'll get a new chief executive come in and they'll say that system's rubbish, it needs replacing.
[00:34:15] But actually, no, let's look at it. And are people properly trained? Does it have validation rules? Is there a data entry standard? You know, some basic things like And above all, Test your assumptions. So if you have made assumptions and these might be around what future state requirements you need. Test them, check across different stakeholders.
[00:34:39] And of course, there's a raft of traditional tools as I've mentioned, data dictionaries, enterprise data models, but there's also clever things like rich picture analysis. And if you haven't come across rich picture analysis, it's simply a pictorial representation of a problem domain. And I've got some examples on my I3 website.
[00:35:01] Thanks. But it's very, very powerful because an A3 picture speaks a thousand words, and you can show it to an executive team and say, Hey, I think this is the problem. And they can look at that picture, that pictorial representation and understand it far more easily and quicker than, than if I give them a big document to read.
[00:35:21] And it can start discussions. It can show the concerns of important stakeholders that can show where there's conflict. You can use it for all sorts of purposes.
[00:35:32] Dr Genevieve Hayes: So it's some sort of network diagram type thing, rather than a picture as in something you'd see in an art gallery, I take it.
[00:35:40] Matt O'Mara: It can be a picture so you can have a picture of the different stakeholders you can have a boundary for the internal versus the external. And so, as I say, there's a good example on my website because I think it's a very powerful technique. The challenge is distilling a complex problem domain down into the key features and you don't have to be an artist to do one.
[00:36:04] I've used. Clip art and word and things like that. And I think at the core of everything we've talked about today is one of my favorite quotes. So I must say it when we change the way we look at things, the things we look at change by Max Planck. And I want to explain that further with an example if I may.
[00:36:26] So the ANZ trucker meetup and what the ANZ bank did is they took some open data that had been released by our transport agency and they basically Came up with a set of two economic indicators, derived from using traffic volume data from around the country. And the traffic flows they found out were a real time and real world proxy for economic activity.
[00:36:56] So to boil it down, when certain roads were busy and they had certain trucks on them of a certain tonnage, six months later the economy was doing really well. And inversely, when these roads weren't so busy, six months later, the economy wasn't doing that well. And if you Google ANZ Truckermeter, you'll get the full story.
[00:37:19] So, that quote by Max, you know, when you change the way you look at things, the things you look at change. How many times is there this hidden information that we haven't considered? And as you were saying previously, if we can join the dots up, how powerful is that? So how many times, are there organizations who have this opportunity?
[00:37:42] So the key point here is what other non obvious linkages can we make with data slash information?
[00:37:52] Dr Genevieve Hayes: With that Max Planck quote, is that what you mean when you're talking about an information lens?
[00:37:57] Matt O'Mara: Absolutely. Absolutely. And so sometimes Even though I've been involved in this for many, many years, I will look at a problem and think, I know there's an information aspect to this, but I'm not sure what that is, and I have to look at it in different ways. And sometimes it'll come to me. So it's not always obvious.
[00:38:20] Dr Genevieve Hayes: And how do you develop that information lens, because you've said several times people need to develop their information lens, but how do you go about that?
[00:38:28] Matt O'Mara: I think it's constantly looking at a problem or an opportunity with an information viewpoint, trying to understand what is the role information plays here. And in some cases, it might be In all the stories, we've said the absence of information. It might be the fact we haven't connected up the dots.
[00:38:52] It might be using some of these tools I've mentioned, like an enterprise data model, like rich picture analysis, that kind of thing. And I think it's storytelling as well. So if I can give one more story in personalized medicine. So, researchers are looking now for early biomarkers of changes in the lungs and blood vessels to understand the effects of vaping.
[00:39:19] And personalized medicine has not only the significant potential to deliver more effective treatments, but But it also can improve outcomes for patients. And I've got a personal story on this. My son last year, who's almost 12, was diagnosed with Crohn's disease and he was put on two drugs. One drug is to treat the Crohn's disease.
[00:39:42] The other drug is to make sure he doesn't develop antibodies to the disease. From the first drug. Now, the problem we had as parents was that the second drug was in the States. They've stopped prescribing it because there's a high risk of developing lymphomas that are non treatable and can lead to death.
[00:40:00] So, obviously, naturally, as parents, we weren't very keen on this. On talking to the specialist, and this comes back right to your introduction about unknowing unknowance, we did not know there was a DNA test we could do to see whether my son was likely to develop antibodies to the strike. And we found this out talking to the specialist.
[00:40:25] And so we got the test. It didn't cost us anything. It was available through the public service. And I found out that luckily he was not likely to develop antibodies to this core drug. So he could stop the drug that may cause lymphoma. And after that, I also found out that he was the first person in New Zealand to have this test.
[00:40:49] So, again, an information gap in terms of does he really need this drug, why is it are there tests, to see that, and that personalized medicine. So, there are some huge, huge opportunities here. And if we think about it, of organizations that harness information to its maximum potential, they reap the awards.
[00:41:13] So if I can give a couple of examples, ones we're very familiar with. So if we think of Meta, i. e. Facebook, their market capitalization is 1% 3. 44 trillion, alphabet, 3. 336 trillion New Zealand dollars. And a little quiz for our listeners. How much do you think Amazon makes every hour? Take a stab.
[00:41:42] Dr Genevieve Hayes: I'm guessing it's bigger than the GDP of some small countries.
[00:41:48] Matt O'Mara: Are you, you know, would you like to take a guess?
[00:41:51] Dr Genevieve Hayes: Honestly, I, I can't even think in numbers that
[00:41:54] Matt O'Mara: No, it is 59 million every hour and rightly or wrongly Bezos's vision for Amazon was a company with data at its heart. So for any organization, for any individual for any government, If we harness information to its maximum potential and we train and develop our information lens, there is substantial benefits from solving difficult problems, realizing new insights, saving lives, improving productivity, transparency, and.
[00:42:33] Back to your fundamental question, how do we do this? It's constantly looking at the role of data and information and having that inquisitive mindset, you know, don't stop asking these questions.
[00:42:47] Dr Genevieve Hayes: What final advice would you give to data scientists looking to create business value from data?
[00:42:53] Matt O'Mara: I think it's about, Finding the truth data scientists can help find the truth if we look at some of these trends, if we look at, you know, we had this example some time ago in New Zealand when health prices were experiencing significant and rapid increases, and everybody was pointing the finger at different things, it's foreign buyers, it's this, it's that, nobody had the data, so I think data scientists Can assist in connecting the dots and, where there's gaps, how do we fill those information deficits, and also help with governance, because, we don't want things to be a black box.
[00:43:36] People won't trust that. And I think too, One of my biggest issues is information asymmetry, so we have, a lot of folk who don't have exposure to data literacy or the right information. So from a societal level, I think data scientists play a big part in that. An absolutely critical role in ensuring we address things like bias like, access to information and data.
[00:44:07] And I think, there's this big, Issue of we can become AI dumb because my concern around AI amongst other things is , we become reliant on it to the point where we don't have an information lens anymore. We can't think for ourselves very clearly. So I think data scientists have a huge responsibility to assist with all of this.
[00:44:35] Amidst things like. Existential threats, climate change, you know, it's not just climate change per se, it's going to increase certain types of diseases like malaria, dengue fever, Lyme disease and not only that. People's immunity is going to be affected nutrients in the soil are going to be affected.
[00:44:56] So how can we use a data led approach to address those existential threats? And things like greater need for future strategic data foresight. So they're predicting in Australia megadroughts lasting over two years. Decades. Particularly affecting, agricultural regions and as I said, hotter weather leading to weaker immunity.
[00:45:21] So I think data scientists are imminently placed to assist with all of these things.
[00:45:29] Dr Genevieve Hayes: So, for listeners who want to learn more about you or get in contact, what can they do?
[00:45:35] Matt O'Mara: I'm on LinkedIn and they can visit my website, which is i3. co. nz and that's i t h r e e. So yeah, happy to talk to anyone about anything we've covered today and it's been a really useful discussion.
[00:45:53] Dr Genevieve Hayes: Thanks for joining me today, Matt.
[00:45:55] Matt O'Mara: Thanks so much.
[00:45:57] Dr Genevieve Hayes: And for those in the audience, thank you for listening. I'm Dr. Genevieve Hayes, and this has been Value Driven Data Science, brought to you by Genevieve Hayes Consulting.