Why This Is The Last Podcast On Data That You Should Listen To

Speaker 0 00:00:10 The goal of data transformers podcast is to celebrate digital transformation by bridging the gap between business outcomes and rapidly advancing technologies. And we aim to bridge this gap by focusing on data. I am Peggy PSI, top 50 women in tech influencer co author of the AI book and data governance experts, and entrepreneurial blogger on AI. This is really our second episode where we are really launching our podcast journey. I thought we would highlight some of the key trends, challenges, and opportunities that we see. So I know you and I have, um, really a good balance in terms of our expertise and also a good overlap in terms of what we see happening in data management, data governance and technology. So maybe I'll start with first start with you or mash what, first of all, what are the three key trends that you really see happening in today's organizations? Speaker 1 00:01:22 Right. So, so Peggy, um, depending on the area, depending on who you talk to, right, different people have, uh, opinions about what are the trends, the one trend that, that seems to come across, whether it's from a Gardner, whether it's from Delloyd, whether it's from McKinsey, whatever research that you do, right. Is, uh, and also looking at the investment that's going in on the venture capital model, the artificial intelligence and machine learning, right? So, and people even categorize that as that intelligence transformation, right? So you put an intelligence in front of everything intelligent, this intelligent that, but essentially the trend that you cannot escape right now is artificial intelligence and machine learning right. In how companies are transforming, uh, how they're spending money. So that's number one, right? So as we go into subsequent episodes, we've been talking to more people on that front. The second trend that I see is the analytics piece of it, right? Speaker 1 00:02:27 So where, how businesses are using analytics, data analytics to drive businesses, or at least they want to do it, whether they are there or not. It's a different aspect of it, the, uh, data analytics aspect of it, the second major trend that I see out there. And then the last major trend that I'm seeing, I don't, I would like to really know more about you is this whole, uh, data literacy, right? So the need for organizations to be more data aware, data literate, uh, I know, uh, investing, spending time and changing the culture within an organization to be much more data. So that is a trend that I am seeing much more recently. And, and maybe because of the AML, maybe because of the analytics, but this data aware, uh, trend that, uh, that I see. So Peggy turning the tables on you. So what are the trends you're seeing? Speaker 0 00:03:30 Yeah, so having a, uh, background in data management, data governance, and, uh, what I do a lot now is actually talk about privacy and, um, being, working in financial services, it's a very regulated industry. So certainly I've been involved in the past with many different types of regulations and privacy, really being the latest one, um, impacting all industries, all companies of all sizes. We suddenly have to be aware of the data that they're collecting about their customers, about their employees. Um, so just being, uh, more aware about the data, what they collect and what purpose that they collected from, and it's just taken on a whole new magnitude. And I think a lot of the conversations that you hear in the news and the media, um, it's, it's all about privacy regulations, whether you live in a us, um, in Europe with GDPR and Asia or Latin America, every continent has its own data, privacy slash protection law that, uh, any global organization or even any local organization needs to really put in the forefront in terms of the projects or technology budgets. Speaker 0 00:04:49 So it's a huge, huge topic. And I think second of all, um, personally, um, think is a, is a key trend in a really a basis, right? For, for a lot of the reasons why organizations fail to be in compliant is the fact that they simply lack a good data governance program. And I think that is the foundation for the data analytics for really being able to do AI machine learning. So it really ties into two of the trends that you see, um, out there. And I completely agree. Those are very, uh, actually buzzwords right now that actually catch the attention of a lot of executives. But if you peel back the onion layers of, um, why AI machine learning sometimes fail and why analytics don't actually give the right predictive results, it's all goes back to the data. So I think as part of these conversations we're having, I always like bring it back to the basic building blocks and that, and that is, um, data. Speaker 0 00:05:54 And, you know, another key trend is really technology, right? Uh, as you mentioned, AI machine learning, it's all about leveraging and utilizing new technology. But what I've seen in the past is technology, new technology, you know, is so focused and only given to a technic small technology group or to a small innovation team. How do we bring that to the masses within an organization that needs to visualize that needs to read data or analyze it? We need to give them those are the people that need to be hands-on musing and learning new technology. And again, the reason why we work so well together and meshes speaks to the whole data, culture of data literacy, um, that I also see in many organizations because we're not giving them the right training or the right skill set. We're not upskilling them enough for them to be able to, again, bridge the gap between, um, the right business objectives and implementing their business objectives with the right technology. Speaker 1 00:07:05 Right. So actually it's a big, let me ask you, I mean, you, you, you, you focused a lot on financial industry. I mean, you have a very, very deep knowledge in that industry. Now you're working for a product and service company. So when you go into an organization, because now you're trying to market and write some, and what is the first, um, I don't know, a challenge and objection that they throw at you. It's like, uh, yeah. So let me ask you stop there. So what is the objection that you hear, um, to market your product or service within the data service, Speaker 0 00:07:43 Depending on the, the type of organization it is and their, their appetite reason why, why they are coming to us? I think one common, um, comment that I always hear is that, um, Hey, we're doing this process manually. It's worked well for us. For the past two years, we are compliant with GDPR and CCPA. Uh, we don't get enough data, subject access requests. We're doing it manually. Our technology teams are able to understand and discover all our data. We don't need AI machine learning. I see. So, you know, one comment I like to make as, as a trend for AI machine learning is yes, it's a buzz word. Yes, it's almost overused. And in many publications and, um, commonly used in social media, I don't think many people understand the extent really how AI machine learning has really changed. I was certainly involved in, in the last 10 to 10 years. Speaker 0 00:08:50 I remember myself in, in data management. Um, never had these tools that we have today really fine then automatically classify and understand data and, um, gives the business insights that, um, machine learning can do on such a large scale. So I think the, the, the mindset of people to embrace technology, to understand that there is a cost to, uh, learning, um, deploying technology. It's, it's an investment that many organizations need to realize that they have to do versus a compliance or a regulator coming in and coming into the, into their doors and saying that this must be done. You must automate your processes. So it's, it's a challenge. And depending on how open the organization is to, to new change, Speaker 1 00:09:51 Actually, you brought up a very good point and let me dig into it. Right? So this is essentially the essence of our podcast, right? So what you said is a regulator who is interested, or the company who is interested in complying with the regulation. They don't care what technology they use, right? The company was telling you, Hey, I'm fine with my, you know, manual method or whatever process I'm compliant, right. I'm under regulator says, you know, Hey, are you, are you compliant? That's all they cared about. But the other side you pointed out when you're going to organization, the organization will tell you, Hey, you know, I hear about this AML. I kind of feel the pressure. Maybe I have to invest in AML because I see this buzzword, I hear this buzzword all over the place, but I'm, I'm okay. Happy with what I'm doing, but I may, I don't need it, but it looks like I'm being pressured. Is that, is that the dilemma that you're hearing here? Speaker 0 00:10:53 I think that's certainly that that's a common struggle that, um, organizations are facing with. And you think about, uh, the fact that regulators and internal audit personnel, they're getting more data savvy every day. Uh, they're intelligent. Uh, they certainly read the news and they know that there are, um, certainly data quality, errors, um, prone in any type of manual process. Um, there's always a risk involved in, in terms of, um, not knowing what you don't know right there, which is the biggest problem. I think many organizations face, like you don't know what data you have, um, the, a black hole for many organizations. So the, the risk and the compliance teams there, they're all really on top of the business units to, to make sure that they are, um, using and leveraging, uh, the newest and latest technologies obviously, uh, weighing all the costs and the benefits, obviously, but it's, it's coming from the fact that everyone is, you know, getting more into this mainstream in terms of new technologies, uh, technologies that can help automate and, um, relieve and just ensure compliance. Right? So, um, that, that's really the, the key thing here is, and I don't think any organization can get away with, uh, simply a complete a hundred percent manual process. He says, it's not, it's not scalable. Um, and organizations are just putting themselves at risk if that's where they want to stay at. Speaker 1 00:12:37 Correct. And then I'll give you my own experience and example, right. And again, um, and just in my mind, I, um, you know, I just want to let the listeners and viewers know we do not, we are not here to drink the Kool-Aid right. We are not going to say, Oh, you got invest in AML. It's, you know, uh, that is the next best thing. Or you gotta do analytics, right. So we are not here to say that is what it is. Right. So we want to give the raw information where we see there is a fit where there is no fit. And then we want, we want to be able to transparently honestly, you know, talk about those things. Right. So from that angle, so having gotten that out of the way. So my own example of what I have seen is there's this large technology company, Peggy, where they felt the pressure, a need to invest in latest technologies, right. Speaker 1 00:13:31 AML and all that stuff. But they did not want to invest in it just for the sake of investing. Right? So, and then people don't have money flying into just, just set aside a people set aside to do it. I said, they need to dabble in that to solve a problem that they have. And that is the only way they can start investing. So in this case, what happened was there is a lot of big data involved. There's a jobs that are, they have to execute. And then they're talking about millions of jobs and the infrastructure need to run. These jobs is increasing on a year to year basis, significantly so that they are being forced to invest more on the computing infrastructure to execute these jobs. And then they said, okay, I will take a piece of it. Right. So a small portion of one division or whatever, I will invest in AML to see, uh, you know, if it can help me manage my infrastructure and then manage my future purchases infrastructure in a more intelligent way. Right. So that, so they've started what is called a lighthouse project, right? It's one area to solve a business problem, but at the same time, invest in AI technologies to solve this problem. That's actually Speaker 0 00:14:52 A very pragmatic, uh, approach because, um, on one hand you don't want to be constantly changing, um, chasing new technology. On the other hand, you know, you don't want to, uh, introduce zero technology. So somewhere in the middle, I think there's, you know, finding a tangible business problem to solve and looking at all the options and then selecting, you know, in this case, something that does involve AI machine learning and seeing how it works, right. See how this business problem could be solved. And I think it's very, very smart, um, you know, the best use of the budget and the time. And then if it actually works, you know, something that can be replicated with, you know, perhaps other departments or finding another similar solution, um, a problem that can use the same solution. So, Speaker 1 00:15:48 Right, right. So, and then actually that's another, I want to get other tech, which is, it seemed to solve another problem, Peggy, which is people working in the company felt that they are being left behind. I know not working on this latest technologies because everybody's talking about them, right. I don't want to be a cobalt programmer forever for my life. Right. So I want to work on the ML thing. So it gives them an opportunity to kick the tires and then feel that, Hey, I'm upgrading my skills. I mean, are you seeing this a need, uh, for organizations to invest in tech technologies? So their employees feel that they're also involved? Speaker 0 00:16:31 Yeah. I I've seen that in some of the organizations, uh, I worked with, uh, usually, um, new technologies come through and, uh, innovation center or a center of excellence that does, uh, uh, review and cultivate and make sure the right technology is selected. And then, you know, training the right technology teams, um, support teams to use the technology. And I've seen many people being able to transition their careers or grow their careers or expand their skillset. Um, and I really think that's the best, that's the best way to go. And certainly there are, um, you know, the people that you work that work for you in the organization, they have the, the business knowledge and you want to retain those employees. And the best way to do that really is to continually keep them engaged with learning a new technology or being part of a new project. Speaker 0 00:17:33 Everyone wants to be, um, part of the latest and greatest or involved in some way, right? Not everyone is a coder or programmer. I know I'm certainly not, but, uh, these types of large scale digital transformation projects require lots of different skill sets. Um, so there are opportunities for people of all levels, right? Even junior people, you know, can participate in some small way to, uh, to senior executives all want to, you know, kind of dip their hands and be involved in, um, you know, the latest and greatest project, but there's hopefully organizations, um, have that mindset though, to bring in and involve more, um, of their employees. So if they do, that's great. That's fantastic. Speaker 1 00:18:24 I think the, I think the organizations have to think about, uh, you know, their employees are upgrading their skills and then the need to upgrade. Right. So I think somehow addressed that thing. And the other thing that I want to mention that I want to get your take on this is, um, this whole cloud computing thing, we have not talked much about it. And, and the reason probably we didn't talk about it, it's here to stay. It's not a, it's not the new trend, right. It's already there. Right. But, uh, the, the previous project that I was talking to you about, uh, when we were looking at it, the existing infrastructure did not address their needs. Right. So they needed to upgrade the infrastructure to, uh, you know, take advantage of AML, take advantage of the latest tools as you mentioned, but the lag time for them to go procure and then even the budget, right. So it was not sufficient. And then not in time. And then they were forced to write for the right reasons to go and explore cloud computing, right. It's easily available, readily accessible, and you don't have to invest, you know, for life, right. So you invest only for the time that you need, it kind of stops. So on this front, how cloud computing is helping or not helping these latest trends, we talked about, what's your take on this Peggy. Speaker 0 00:19:43 So it's a very interesting, um, coming from financial services, um, or the organizations I worked for actually never fully, uh, deployed, uh, cloud computing. Interesting. Yeah, I think it varies. And now that I'm in a role where I can certainly speak to and I choose to other types of industries outside of such a regulated financial services industry, I can see the differences. So certainly, um, and the reason why I think is, is the fact that, um, traditionally, still privacy and compliance are very risk averse. So very clear all about the data that's going to be put on a cloud and the security involved. And I think that companies are still, you know, slowly dipping into dipping their toes into cloud computing, maybe putting their non essential data into a cloud, testing it out, you know, as he said earlier, doing a proof of concept, maybe a specific segment of the data can be put into the cloud and they can then figure out, you know, all the benefits though to, and the cost savings. Speaker 0 00:21:02 Um, and just the whole deployment process, you know, is, is much more, more rapid than traditional approaches. So, um, you know, from what I've seen in the past financial services is back a little bit, but certainly other industries like, uh, retail, I've seen a little bit more, um, talk about, um, putting, putting data in the cloud. Um, but interestingly enough, for those of you that have been in, in data data for as long as I have, if not longer, the problem with, um, data lakes, you know, it's the same concept of aggregating the data warehouses, right? Where the concept is that gravy, uh, organizations when physically Arabic, um, compiling and putting data, moving data to a single place to make for easier reporting or for, um, easier access, single point of access. But, you know, you have the problems of multiple copies of data, um, you know, ownership of a shared data Lake or warehouse. Um, you're muddying up, um, the actual quality of the data because no one actually takes ownership or governance. So the Lake becomes really a swamp. And I know that there are many vendors out there that talk about, um, cleaning up the data. That's my only concern with cloud computing is making sure that there's, again, um, data governance processes and people's structure in place. Otherwise the cloud is going to be easily data Lake 2.0 Speaker 1 00:22:43 Exactly. I actually bringing upside in, in this future episodes, we will dig deep into these aspects, uh, that you're just bringing up. So Peggy, in the final section of our podcast, uh, I was thinking that we will talk about the opportunities where, um, you know, the lighthouses that I saw, where some people are seeing in specific industries maybe, or maybe specific functions within organizations, um, you know, there is, uh, more uptake and more, more opportunities. So coming from your vantage point, I mean, I'll talk about it a little bit later. Um, but I just wanted to get your take first coming. If when you walk into organizations, what are the low hanging fruits that you're seeing either industries or functions within specific companies? Speaker 0 00:23:35 Yeah, I mean, um, I've actually been very fortunate. Most of the companies I know have already identified they're the low hanging fruits, certainly, um, hiring the right people to run an execute on a data strategy program. Um, having, you know, the framework, the executive committees or a chief data officer, or a chief analytics officer, um, being able to also recognize where their data quality issues are, um, and being, and having some sort of ad hoc, perhaps if not fully consistent process and way of fixing, fixing the data. Um, identifying opportunities is, is also really the first step and that early re-imagining of data analytics is commonly quoted now as an opportunity, uh, for, for people to, to really, uh, leverage the data that they've started to collect and curate. Um, and then take it a step further. Speaker 1 00:24:39 It's easily justifiable for the organizations. Is that right? Somewhat, Speaker 0 00:24:44 Um, yeah, there's a co it clearly aligns to a business problem or clearly aligns to, um, a bullet point probably in their business strategy for the year, I suppose, a big outcome. Um, so the more that people can explain how some of these business objectives can be tactically executed with data and technology, I think it helps tell a better story and give it a better, um, opportunity to be funded and to pass for, uh, for full fledged project. Speaker 1 00:25:21 That's correct. Okay. So that is good. And then from my perspective to answer that question is, uh, I think process automation, I think you alluded to that before, uh, it seems to be easily justifiable opportunity for people, um, to, to invest in right. Latest technology. So if you're automating and, uh, it's not that it replaces people, but at least, you know, it's, it's, it can prove their efficiencies. That's one thing that I'm seeing and then the success will once, uh, people, um, in the cost management is more than the revenue generation is somewhat easily, uh, justifiable from, uh, I've seen, right. So it's, uh, uh, that's, uh, one aspect of the team and Speaker 0 00:26:08 Yeah, exactly costs and efficiency, time savings. Those. Yeah. Speaker 1 00:26:12 Yeah. Yeah. So I, I think there are more, as opposed to trying to pitch a project, we will increase the revenues by this much it's somewhat difficult to prove. And then to justify from my experience, that's what I have seen. Speaker 0 00:26:27 Yeah. But, you know, I feel, I always feel very sad and negative about that because, um, it's easy to prove cost savings and head count. Um, you know, whereas it's hard to pinpoint and measure the benefits, right? How much revenue can be increased, how many more product lines, how many more customers can be saved. Um, so that's, that's a bigger, um, question I, I would love for organizations to focus on and, you know, perhaps one of the things that we can help out during this podcast, these feature podcast episodes as well. Speaker 1 00:27:06 Yeah, that's right. And then, um, so to wrap this up with a final thought, I mean, anything that you, uh, we have not addressed so far in trends or challenges or opportunities? Speaker 0 00:27:19 No, I, I mean, I talked about, you know, privacy regulations and really being a data governance, being the core foundation and technology as a driver. I mean, these are three of my, um, personal, um, things that I've been seeing, but you know, those of you in the audience loved for you to share with us, um, what you see are key trends, challenges, and opportunities. And hopefully we can, um, highlight those in our future episodes as well. Speaker 1 00:27:52 That's right. So, and then from my perspective, I think, uh, having a strategy data strategy know that in combination with the data governance is absolutely crucial. And, uh, whether you start the data strategy from a tops down from the highest levels down to below, or you start, uh, developing a strategy based on a lighthouse project in a particular department is up to the organizations how to do it. But you've got to think about a data strategy in terms of the overall picture. I think that's very, very important. And then the second aspect of, um, uh, that I mentioned earlier is a data culture, right? So, uh, the culture starts with the education and literacy, right? Increasing that and the awareness about data across all levels of organization, right. It's just not at the lowest levels. And having now a lot of people are talking about a chief data officer or chief monetization, chief data monetization officer at the highest level. But having that, uh, I think is absolutely crucial. And then we will talk more about it, but now the tools and products like a, for example, self service analytics here, right? Everybody possibly can do analytics. So, so we will talk more about it. That means it's much more pervasive. Um, that's why I Peggy your point of data governance is so absolutely absolutely important because now everybody is doing analytics, then which data are they using? Is it a clean data who owns the data? Right. It becomes much more powerful. Speaker 0 00:29:22 It's chaos. It can become chaotic. Yeah. So that's why I love to dive deeper into these trends. And hopefully we wet your appetite. You know, you've been audience, we wet your appetite to the things that we will be focusing and talking about. And some of the key people we think are big players in this space. Speaker 1 00:29:45 Excellent. So with that, uh, Peggy, uh, it's a wrap until next time. Bye everyone. Bye bye everybody. Thank you for listening to today's episode. If you liked what you heard today and would like to hear more, please subscribe to our podcast on your favorite player like iTunes and Spotify. And please do rate our podcast. Also, please go to our website, www.data transformers, podcast.com for more episodes, blogs, and information on our speakers. Thank you. Speaker 0 00:30:29 <inaudible>.