The Disruptor Podcast
"The Disruptor Series," your blueprint for groundbreaking innovation, started as a periodic segment of the Apex Podcast. This is not your standard conversation around Design Thinking or Product Market Fit—this is the series that dares to go beyond conventional wisdom, confronting the status quo and exposing the raw power of disruptive thinking.
Our journey begins with intensely provocative dialogues that set the stage for the unexpected. With a focus on Experience Disruptors, Product Market Fit, and a host of other captivating topics, we bring you face-to-face with the ideas that are flipping the script on traditional buying and selling experiences.
But we don't stop at ideas; we dive into their real-world applications. "The Disruptor" brings you an unfiltered look into the lives and minds of those who are either being disrupted, creating disruption, or strategically navigating through disruption.
Our guests range from industry veterans to daring newcomers, all willing to share their experiences in shifting the paradigms that define their stakeholders' experiences.
If you're tired of business as usual and eager to question the preconceived notions that hold back innovation, "The Disruptor Series" is your ticket to a transformative journey. Tune in, disrupt yourself, and become an agent of change in an ever-evolving landscape.
...
The Disruptor Podcast
Disruption in Data: Why Digital Migrations Fail and How to Succeed
Welcome to another edition of The Disruptor Podcast!
In this episode, your host, John Kundtz, interviews Caitlyn Truong, CEO and Co-founder of Zengines.
This is part one of our two-part series.
Today's show explores how Zengines disrupts organizations' efforts to automate the end-to-end data conversion process.
Caitlyn shares information from her background in electrical and computer engineering and consulting. She often saw the problems with data conversion and migration in large organizations, especially in financial services.
This led her to co-found Zengines to help ensure data stays useful during modernization.
Points from the Discussion:
Understanding Data Migration: It includes all steps to get a new system running, from getting data to testing.
Common Pitfalls: Not accounting for all steps, lack of a plan, not using tools, and not underestimating the work effort.
Advice for Smoother Migration: Understand all steps, plan clearly, use tools (especially AI), empower business analysts, and work in steps.
Zengines' Disruptive Way:
- Zengines gives business analysts AI tools to automate mapping and changing data.
- This "shift left" approach reduces the need for large teams, making data conversion faster and more efficient by letting the business directly influence data changes.
- This approach moves the work earlier in the process, making data conversion less expensive, more productive, and quicker by letting the business directly affect how data is changed.
To learn more about Caitlyn and Zengines, visit their website (Zengines.AI) and connect with Caitlyn on LinkedIn.
Stay tuned for Part 2 of this conversation, where Caitlyn and I shift gears and explore the challenges many enterprises face with Mainframe Modernization.
You won't want to miss it.
Comments or Questions? Send us a text
***
Engage, Share, and Connect!
Spread the Word: Valuable insights are best when shared. Share this episode with peers who may benefit from it if you find it insightful.
Your Feedback Matters: How did this episode resonate with you? Share your thoughts, insights, or questions. Your engagement enriches our community.
Stay Updated: Don’t miss out on further insights.
Subscribe: You can listen to our podcast, read our blog posts on Medium, Substack, and LinkedIn, and watch our YouTube channel.
Collaborate with The Disruptor and connect with John Kundtz.
Got a disruptive story to share? We’re scouting for remarkable podcast guests. Nominate a Disruptor.
Thank you for being an integral part of our journey.
Together, let’s redefine the status quo!
Tips are welcomed and appreciated, too!
Disruption and Data Transforming, migration and Modernizing Mainframes. Hi everyone, I'm your host, john Kuntz, and welcome to another edition of the Disruptor Podcast. For those that are new to our show, we started this series back in December of 2022 as a periodic segment of the Apex Podcast. Our vision was to go beyond the conventional wisdom by confronting the status quo and exposing the raw power of disruptive thinking. Today, we talk with Caitlin Truong, ceo of Zen Engines, as we explore how her company disrupts organizations, automates the end-to-end data conversions. We will discuss valuable advice on the pitfalls, the mistakes that many executives make when attempting digital transformation. Welcome to the show, caitlin.
Speaker 2:Thank you so much, John. Thank you for having me Two topics I love to talk about.
Speaker 1:Great. In prep for this show, we had a lot of interesting crosses of our professional experiences. Speaking of that, why don't you tell us a little bit about your background, your education, your experiences, how you came to start your company? And you can start anywhere you want.
Speaker 2:Thanks so much, John. So my background is in engineering. I am an electrical and computer engineer and spent a couple stints building circuits for mobile devices way back when then went to the dark side of consulting, where I spent a fair amount most of my career in the consulting industry helping large organizations with technology transformation type initiatives. Most of that was in financial services, John, and that was where I crossed paths with one of the other Zengens co-founders. We talked about data conversion, data migration.
Speaker 2:We ran into it all of the time and mostly it was because we saw that organizations are always changing, they're always modernizing, they're always looking to stay relevant and keeping their technology current. Your systems hold data. You want to preserve and make that data continuously usable, and so there's always this hurdle of going through a conversion or a migration to make sure you can execute on your business strategy. So that was the pain point that we saw. It was a challenge we saw organizations face all organizations, all sizes, all industry, and we said there's got to be a better way. So that was how this all came to be. I'm excited because it's a solvable problem.
Speaker 1:I agree. I had similar experiences. A good bit of my career I was running part of IBM's business where we did data center migrations and consolidations. The Achilles heel of those projects was our ability to migrate the data and applications. One little glitch would take the whole project down. Let's dive into data migration. What are some of the most common mistakes or pitfalls organizations tend to make when trying to take a more traditional approach to their digital transformation and or their data migration projects?
Speaker 2:Well, first, I always like to make sure that I clarify. I should not redefine data migration, john, because I find that, based on one's experience, you might believe or understand data migration to only be a part of it. When I think of data migration, I think it is all of the things associated with helping get a new system or a new data store to be live. In other words, it is that upfront ingestion of what might be the sources of data that you're going to move. It is analyzing it so that you understand what you will have to deal with when you're going through a migration. It is mapping the data so that you know this is what it looks like, this is where it's going to. It's changing or transforming that data because you probably will need it to fit a different way or look a different way. Then it's the physical portion of getting the data out, applying changes, putting it into a new data store and testing it. I say all of that's part of data migration because ultimately, we're talking about making that data still be a valuable asset to you, and it's important to make sure that's clear. My experience has been that, depending on where someone might have participated in that full journey, you might only think of data migration as just the mapping portion or just the ETL portion. It's important to think holistically because you need to solve for all of those things, all of those complexities, all of those potential pitfalls that come in across all steps. So first it was important to level set that, from my view, all of those things are important.
Speaker 2:Pitfall number one is not accounting for all of it. Sometimes, when folks aren't experienced, they might only think that data migration is just that portion of oh right, before go live, we need to move that data. But really you needed to have prepared all of those previous steps. So pitfall number one is not truly understanding that all of that is in the picture. And then pitfall number two is not having a really good plan as to how will you tackle this. Because in having all of those steps as part of the effort, who will do what? Who knows what? Is it your responsibility? Is it the software vendor or the servicer that's accountable for making sure the data shows up in the new target locations?
Speaker 2:Having a good plan, making sure you can execute well and bring in the right experts, is pitfall number two. And pitfall number three is not truly leveraging and capitalizing on all of the tools that are available. I think a lot of folks probably because they didn't think of all of the steps that are included and then they didn't have a good plan because, again, not everyone lives data conversions every day. But without having that the first two steps in place, then you don't know what tools can be applied and there are just tools that can really help, as opposed to thinking you have to go through it the old school way of humans and spreadsheet. So I would just start with those three to start, and then there's things that I think later we could talk about, john, which is patterns that we know are true around data migrations and how you can help people get through it successfully.
Speaker 1:I wholeheartedly agree with those pitfalls. Based on my experience, I've seen a number of projects underestimated the planning and the big picture. They just figured they just take a bunch of servers and the data on there and just sort of lift them, shift them into a new data center. Those were career-breaking projects for some of the executives I worked with, the ones that avoided the pitfalls you just explained, and the projects were super successful. Most of those guys and gals got promoted. Many of the CIOs that took it for granted and sort of try to do it on their own. As you mentioned, most people don't do this every day. They've never done it before. They underestimate the level of effort. It could be a career-ending move. I've seen it on both counts. So, based on that, what guidance would you give our listeners to avoid these pitfalls, to ensure that there is a smooth data migration process?
Speaker 2:First, make sure there is a clear understanding of all steps we promote. What is the data migration 101. I'm very clear in making sure that I think there is a picture that summarizes this well and is very digestible. Really understand all activities that go into a data conversion Call it many things data conversion. Sometimes it's a data migration. Is it part of the system's implementation? But make sure that there is an understanding of all of those steps and then having that plan so that you say who is going to be accountable for making the completing the mapping steps, who will be responsible for writing transformation rules, etc. And once you can have that in place, then you can say let's talk about accelerating. If this is how we're going to dole out responsibilities, let's talk about now not doing it manually and how do we take advantage of tools that are out there. And, more importantly, I think that one of the best things to consider here is that this is all about pattern recognition and it's really key that teams start leveraging AI.
Speaker 2:I know that everyone has maybe differing opinions and it's becoming more mainstream, john. Everyone's looking to try to throw AI at this. I think it's the right thing. This is about pattern recognition and the great part when I started sharing with you that I believe data migrations can be successful is because it's data and it's static and it's about pattern recognition. So, instead of leveraging humans who have experience and there's a risk to that experience being in a human who might walk away from that conversion project, who might not be interested in staying the full duration of that conversion project we use tools and software. So in that way, I think it's really important that folks start understanding that there are really good tools out in the marketplace that are powered by AI that can help you with that. Obviously, zengens is one of them.
Speaker 1:Excellent. Appreciate that. I remember back when we were doing data center consolidation work. There were tools, but they weren't very good, so much of this was manual. We had spreadsheets and these projects could take five or six months to physically consolidate a data center. But understanding the data, understanding how the applications use the data, which applications needed to get which data where, when, was a major part, and it was all done manually. If somebody left the project or missed one thing, we would have to redo the whole project plan again. So how does your approach with your company benefit your clients in terms of core data migration?
Speaker 2:One thing that we really looked at and said we have to do differently is why were there so many people? Why was it always an army of people to do the work? It's all those things we said before. Right, it was a bit of sometimes you didn't allocate it correctly and then you started just adding more and more people because you were finding experts. But it was that allocation.
Speaker 2:The second pain point that I talked about, john, around the not understanding who will do what. I feel strongly. This belongs to the business. Data belongs to the business. The business knows the data right and what I mean by the data belongs to the businesses. They are the ones to ultimately finalize that this is acceptable. This is what I want the data to look like by the time it shows up in the cloud, in my new system or in Snowflake and Databricks as part of the data product. They have enough familiarity with the data to decide what looks right or wrong or to set the requirements.
Speaker 2:One of the best things to do is to empower the analyst, empower the business team to do the data conversion, because today we found is that it was a bit of business comes in here to do, to share. Oh, this is where you'll find the data. They go away because then the technical team comes in to do the okay, we're going to set up and connect to the database, and then business comes back when someone starts to profile the data and says, well, does this? Is this right or wrong? Should this have null values? I'm looking for a pattern because this is a fixed list of items, but I'm seeing these random things. Is that right or wrong? Again, business has to be the one to define if that's right or wrong. Business is the one that starts to do some of that mapping right, because they say well, I know that this is here, but I don't know what that means over there. All I'm looking to emphasize here is one of the things that Zenges focuses on is to create a product that's for the business the BA, the business analyst and empower that person to finish the data conversion for across all of those steps that I talked about, because ultimately, they're the final decision makers. So take away the need to bring in a lot of people, because you brought in people because they knew bits and pieces of the process. So, in other words, make the tool or the product intelligent enough to be able to support those other areas, accelerate the work for the BA, because you don't want the BA to be doing mundane, rote, manual, rules-based types of activities.
Speaker 2:Ie, this is where AI we put pattern recognition into something like mapping and we say you don't have to do guesswork, let AI do the guesswork and we give you some sense of level of confidence associated with that prediction. You, as the business decision maker, can validate whether or not you like that mapping. And then we know that business drives transformation changes. They say I want you to join these two fields, I want you to concatenate this or split this. So let's give business a tool to allow them to get that data change applied but not have to do the technical steps of writing the syntax.
Speaker 2:So again, this is where Zengens offers an LLM. We allow the user to write plain English on what that data transformation rule should be and they see what syntax is auto-generated and then they approve or disapprove that. One of the biggest things was always thinking that you had to bring the representative of the different areas to the table, but instead it really should be to cater to the decision maker, which is the business, and give them good tools to make sure that they can get through that process really fast, right. Take away the technical challenges. Take away the fact that you don't always know what's on the other side, so let AI predict what's best on the other side.
Speaker 1:This is great. This is the classic what we would call a shift left approach. At the end of the day, as you said, the business units anything that has to do with technology. The only reason it exists is so the business can do something, make money, get more customers, sell more stuff. The more you can move the actual doing towards where the responsibility lies, ie the businesses a shift left approach. It's less expensive, more productive and faster, so all three benefits. A classic shift left approach where the businesses who own the data, the business response, the P&L, so to speak, let them be more productive. In the old days, we couldn't do that because they didn't have the technical capability. That's why we had to bring in armies of people and it's why it took so long. So what I'm hearing and what is super exciting about what your company is doing is now you're enabling, using English language, large language models, ai approaches to help these businesses do what they need to do without engaging a ton of other people.
Speaker 2:Yes, I think that was part of our secret sauce really understanding the problem and making sure that we solve the problem for the right person. John, and one example that came to mind this was one of our customers was that the business analysts. As I said, they ultimately made the final decision as to this is what I want it to look like by the time it shows up in the production system. And that teammate was working with the DBA, who would write the SQL query, to extract it and show it to the business before it got approved. Well, the business user would send an email or have a conversation and say okay, these are the fields I want and this is what I want you to do to those fields. And that was written in plain English over an email sent off to the DBA. The DBA picked it up, saw something, interpreted it and, in this case, as probably half the time, interpreted it somewhat incorrectly, did the query put in the transformation rule, sent it back to the business the business doesn't see the query, just the output and said that's not what I want. Can you do this now that iteration goes back and forth and back and forth because, as I said, data is ultimately the truth teller. Business is the decision maker, but then you just always had that friction because they couldn't get to the data directly or they didn't know how to write the right query in order to get the data to look the way they wanted to.
Speaker 2:But this second piece that I would say is that Data conversions are always unpredictable. With AI that's pattern recognition Unfortunately, we can't go back and make it be predictable. I can't go back in time 20, 30 years ago and change what was inserted into the database way back when, but what we can do is at least have some tool that analyzes it really fast and allows you to look at it and gives you some sense of this is what I think it looks like, and the business user can iterate, and iteration is key. So that was the other thing that I think is really important is that you just take a shot at it, you run it through, you take a look at what you've mapped, you take a look at what you've converted and then you say, does that look right?
Speaker 2:And start with a small data sample set, because then you can build rules on top of rules, as opposed to believing that you've got everything you need and now you can accommodate for all of the variations of transformation rules that you might need to put in there. So just start with a first data set, get it through very fast and, like I said, we're talking about generating a load file in minutes. Look at that load file and then say do you like that? If it looks good, all right, let's continue to add more upgrades in and continue to see if your rule applies and if you need to change it, then put more rules on top of that. But I'm just saying it's the fact that you just don't know what is in the data. So give you a tool that allows you to get through iterations really fast and allows you to play around with getting it perfect.
Speaker 1:That's tremendous advice. We've covered really essential ground on modernizing and data migration. I heard 60% of all digital transformation initiatives fail. They don't understand what they're getting into or the level of effort. What you've discussed is a huge benefit for digital transformation. Wow, Thanks for sharing all that. Why don't we wrap this up? I appreciate your insights and experiences. My last question is is there anything I haven't asked you that you'd like to share before we wrap the show up?
Speaker 2:When we think of data migration, john, a major aha moment for me was we need to empower the business analysts because, again, I believe that it's the data. And we need to empower the business analysts because, again, I believe that it's the data and the decision belongs to the business. And I think all the years before one, it was that there was always a need to have multiple translation points. Right, oh, business only knows one side of the transformation the conversion. They knew their system but they didn't know the target state system, and then also, business wasn't as technically adept as some of the SMEs. So let's unpack that and solve for the business. So I just think that that was one aspect that I think is really important because, compared to a lot of the other products in the marketplace today, data mapping, data integration that's a need.
Speaker 2:In some cases that's an internal talk, the data pipes and that's a technology problem. I'm trying to solve the onboarding, the new implementation, the post-merger integration, where you want that data to truly be right. So that's one thing that I would say on the data conversion, data migration side. So this is a little bit of the product mindset I think organizations and teams can imagine and design and design and talk about it for a really long time and I say you'll learn and you will get so much more value just by doing. You might make mistakes because you didn't plan for all of it, but you're at least already started and not waiting to believe that you'll discover all of it through planning.
Speaker 1:Great advice, I agree. How do you eat the elephant? One?
Speaker 2:bite at a time.
Speaker 1:Yes. How can people learn more about you and your services? What are your socials? What's the best way? We'll include all of these in the show notes.
Speaker 2:Thank you, john. So Zengens' website is wwwai and that's Zengens with a z or a z in front of the word engines, plural and, by the way, our domain. We had AI from the very beginning, so way before a lot of these other companies started to change to a ai on the domain name and I'm on LinkedIn, so folks can always connect with me or send me a direct message and we have information as to how to reach out to our team, to get a demo and work with us, to go through a POC or a trial. In this world, everyone likes to take a look and see how it works and we would be delighted to have people give it a shot. I really believe, when it comes to data conversions, it doesn't take an army. I really believe everyone can do it themselves. As I said, it's to the business user. So if we start to change the mindset and say, hey, this can be self-service, that's what I'm hoping to move to, so that everyone can change faster.
Speaker 1:Excellent. This has been a great interview. You're so energetic, so knowledgeable. It's a pleasure to have you as a guest on the Disruptor Everybody listening. Please don't forget to connect with Caitlin on LinkedIn. Check out their website. And, caitlin, before I wrap up, I always give the guest the last word. Then we'll say goodbye.
Speaker 2:Thanks, john. Well, first I wanted to say thank you. We had so much fun and I think I capitalized on the talk time here, but your listeners should know just what an interesting and knowledgeable person you are and I had so much fun learning from you in our conversations. And then also, just to wrap, I say that it's all about gratitude. I appreciate so much the opportunity to spend time with you, that you've made time and space for me, and that this experience of building a company to deliver value to people. I believe data is solvable. Let's help people focus on other things and let's make the data be not something that they have to worry about.
Speaker 1:We're going to come back with part two of our podcast and when we return we're going to shift gears and we're going to explore the challenges that many enterprises face on mainframe modernization. Stick with us and we'll be back All righty everybody. So thanks, caitlin, for being on our show I'm John Kuntz and thanks for joining this edition of the Disruptor Podcast. Have a great day, take care.
Speaker 2:Thanks, John.
