Navigating Data Management Challenges with Cindy Sullivan, Principal Consulting at ORTECHA | Ep 331

Summary

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Podcast Summary: Oil and Gas Upstream – Ep 331

Title: Navigating Data Management Challenges with Cindy Sullivan, Principal Consulting at ORTECHA
Host: Elena Melchert
Guest: Cindy Sullivan, Principal Consultant at ORTECHA
Release Date: March 11, 2026

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Episode Overview

This episode dives into the evolving world of data management in the upstream oil and gas industry. Host Elena Melchert speaks with Cindy Sullivan, a Principal Consultant at ORTECHA, about the journey from manual, paper-based data tracking to sophisticated, AI-driven data architectures. The discussion covers core data management challenges, the importance of metadata and common language, innovations in governance and compliance, and strategies for organizations to unleash the value of their data effectively in today’s landscape.

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Key Discussion Points & Insights

1. From Analog to Digital: Data’s Journey in Oil & Gas

• Both host and guest reflect on their experiences starting out in engineering roles without personal computers or digital tools.
• Early workflows relied on physical spreadsheets and manual calculations, drawing attention to how far technology and data science have since progressed.
  [03:20–05:28]

“Back when there were no personal computers…I got my first love of engineering, of data, of analytics, et cetera. But it was very different back then.”
— Cindy Sullivan [03:20]

2. The Core of Data Management: Trust, Meaning, and Interoperability

• Cindy emphasizes that modern data management is not just technical—it's about the meaning of data, its architecture, ownership, lineage, and trustworthiness.
• Cross-specialty understanding is vital; different disciplines (geology, engineering, production) need a shared vocabulary to collaborate effectively.
• A major challenge is ensuring that models and analyses across teams use data consistently and meaningfully.
  [07:24–08:27]

“Being able to talk across, share information across in a way where there’s a common understanding…and understand how various concepts are related—this is what data management is all about.”
— Cindy Sullivan [07:55]

3. The Importance and Complexity of Metadata

• Metadata—data about data—enables organizations to understand definitions, naming, provenance, and ownership, giving data its true meaning and context.
• When things go wrong, knowing data ownership is key to resolving issues and maintaining control.
  [12:17–13:16]

“Between the data itself and the metadata is the data about the data, which is the piece that gives it meaning…Ownership of data is a very important concept.”
— Cindy Sullivan [12:19]

4. Modern Tools: AI, Models, and Internal Language Systems

• Companies are creating internal AIs and large language models using their proprietary data for secure, tailored query and analysis.
• Advances in knowledge graphs and ontologies help prevent “AI hallucinations” by grounding analysis in structured logic.
  [17:19–18:02, 19:59–21:04]

“Various companies are creating their own large language models…you interface with it the same way you would a generic chatGPT…only it’s controlled in-house.”
— Cindy Sullivan [17:19]

“When you get [language models and knowledge graphs] working together, they perform…a loop that prevents you from getting these AI hallucinations.”
— Cindy Sullivan [20:10]

5. Data Governance and Compliance Automation

• ORTECHA uses “design observability” to automate and digitize governance documents and compliance tracking.
• This reduces manual tracking efforts and supports audit-readiness, as processes can generate outputs proving compliance in real time.
  [14:05–16:03]

“You can automate…digitize all your design documents…so that you don’t have to be checking, ‘Did we do it?’ You can have outputs that prove that you’ve been in compliance.”
— Cindy Sullivan [14:29]

6. Interdisciplinary Challenges: Bridging Silos and Building Common Language

• As specialization deepens, communication across disciplines is challenged by siloed vocabularies.
• Data management frameworks now emphasize “business data knowledge”—the creation and sharing of universal vocabularies and definitions.
• The Enterprise Data Management Association’s capability assessment model, with Cindy’s input, now features a new component focused on this topic.
  [21:04–24:24]

“We created a whole new component…called business data knowledge…so that people have access to that language model, they start to understand common business terms.”
— Cindy Sullivan [22:20]

7. Data Architecture: Organizing Meaning, Not Just Bytes

• Data architecture involves mapping the relationships and meanings among various data entities.
• Effective design bridges business needs, data definitions, and technology implementations to promote consistency and common understanding.
  [24:41–26:34]

“It’s the relationship of the meaning of the data, it’s organizing the data…The business architecture is about building out your processes and how you create value…and the data architect helps communicate that to the technology team.”
— Cindy Sullivan [24:41]

8. Prioritizing What Matters: Avoiding ‘Boiling the Ocean’

• Organizations should prioritize critical data aligned with business strategy, rather than attempting to comprehensively manage all data indiscriminately.
• Focus is on use-case-driven, business-valued approaches to data management.
  [28:45–29:24]

“You have to prioritize. You’ve got to decide what’s most important to you because it is absolutely a business consideration and you don’t want to boil the ocean.”
— Cindy Sullivan [28:49]

9. About ORTECHA and Their Global Work

• ORTECHA is a boutique data consultancy headquartered in London, with a growing presence in North America.
• They offer services from DCAM assessments to building data dictionaries, ontologies, and knowledge graphs, aiming to enable clients to maximize the business value of their data, especially through AI.
  [31:15–32:37]

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Notable Quotes & Memorable Moments

• The Human Touch in Data Evolution:
  “You get so many insights just visually observing the core and handling it, perhaps that's very important.”
  — Elena Melchert [06:24]

• On Overcoming Intimidation:
  “I feel like I do know a little more than I thought I did. There's this fear, there's this fear about this new topic…”
  — Elena Melchert [24:24]

• On Building a Culture of Data Confidence:
  “You don't have to boil the ocean just because you can. And you can really dial in…you're unlocking another piece of my brain.”
  — Elena Melchert [30:07]

• On the Future Direction:
  “Right now we're also really focused on helping build out those…knowledge graphs, all of that stuff to help companies be able to really get the most out of their AI.”
  — Cindy Sullivan [32:19]

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Useful Timestamps for Key Topics

• Origin stories & early data management in oil & gas: [00:29–05:28]
• Importance of vocabulary and cross-team communication: [07:24–08:27]
• Metadata and data stewardship: [12:17–13:16]
• AI and internal language models: [17:19–18:09]
• Governance, compliance, and automation: [14:05–16:03]
• Bridging disciplines and creating common business language: [21:04–24:24]
• Understanding data architecture: [24:41–26:34]
• Prioritizing business-critical data: [28:45–29:24]
• ORTECHA’s mission and services: [31:15–32:37]

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Takeaways for Oil & Gas Data Professionals

• Trusted, well-architected data (with robust metadata) is the linchpin for effective modeling, analytics, and AI in upstream operations.
• Fostering a common data language across business disciplines is imperative to unlock value and break down communication silos.
• Automation and digitization of governance processes reduce compliance costs and improve audit readiness.
• Prioritization and a business-focused approach prevent analysis paralysis and ensure data initiatives drive value.
• New roles and frameworks (e.g., business data knowledge) are emerging to address today’s data complexity.

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Host wrap-up: Elena credits Cindy and ORTECHA with demystifying the world of data management, expressing eagerness to continue these conversations and deepen her—and listeners’—understanding in future episodes.

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Transcript

[A]

Oil and gas production is the union of natural systems with advanced science and complex engineering. Smart people across the globe create this remarkable place we call Upstream. And each day brings a new challenge. This is the Oil and Gas Upstream podcast where we look at how these systems come together and learn from the people who make it happen.

[B]

Welcome to Oil and Gas Upstream. I'm Elena Melkert, your host. Some of you know me as the former director for Oil and gas Upstream research at the US Department of Energy. I retired from the doe, founded Energia Consulting, and became a podcast host. I'd like to welcome my new sponsor, IFS Upstream. Teams are being asked to cut costs, boost production, and digitize operations all at once. IFS delivers industrial AI that connects the entire oil and gas value chain on one platform. Land accounting, production assets and operations, helping companies reduce downtime, stay ahead of what's happening across their business, and optimize their assets faster. Learn [e-mail] and now I'd like to introduce today's guest, Cindy Sullivan. Cindy is a principal consultant at Orteca, a boutique data consultancy. And I'm so excited to talk with Cindy. I take data for granted. I gather data. I have challenges with managing data. The whole issue of data is something that for me is in the background, but we would not have AI without it. So Cindy is going to help me connect some of the dots that begin with my time. My first entree into the oil sector many moons ago. Some of you know that I started in the oil business, boots in the field and it was a change of major for a while. I'm a soil scientist. I got married, moved to Bakersfield, got into the oil business, and from the ground up, Getty Oil Co. Sent me to petroleum engineering master's program at the University of Southern California. And I worked for Getty Oil Company for four years. And then I went to work for the Department of Energy as a production engineer and then reservoir, all while finishing up the program, the petroleum engineering program. And of course the. This was a time before people had personal computers and that was, I guess we had a terminal tied to a large mainframe and we had to take turns on it. And in order to solve the large engineering calculations, we had to use spreadsheets. And so you do some portion of the calculation and put the answer in column, on the first column, right, first row, first column or whatever. And sometimes our spreadsheets were like 20 columns wide. It was just really, really huge, all on paper. And if you wanted to do it again, you'd go to the next row, we're talking about rows and columns, physical on paper and a pencil and lots of eraser time. I'm really excited about this conversation I'm about to have with Cindy because we've come such a long way and talked about that in terms of the role and value of models in oil and gas. Cindy, I'm delighted that you're here today. Thank you for joining us.

[A]

Thank you so much, Elana. I'm thrilled to be here with you today. My beginnings of my work career were not unlike yours. So back when there were no personal computers and I started out working as an intern at Exxon's chemical plant in Linden, New Jersey, being just an intern, chemical engineer, and that's where I got my first love of engineering, of data, of analytics, et cetera. So I. But it was very different back then, and things have changed over time from huge databases to certainly not having to deal with pen and paper anymore. So thank goodness. But moved from there. Had a lot of experience working in various manufacturing operations. So also very boots on the ground experience my first half of my career. But then I migrated into the financial world via a friend who said to me, gee, Cindy, come and check out what's happening here at a huge bank there. It's just like working for any other factory, only they're processing money and checks. And I thought, oh, look at that. Let's see if I can apply a lot of my process skills, process engineering and quality engineering to a transactional organization, which is what I did, and developed some expertise in Six Sigma, became a master black belt, and through applying those skills in reducing variability and helping other people with projects is how I got involved in data management. So that's been. The last 15 years of my career has been very focused on data management. And that really is all about, not just exclusively from a technical perspective, but the whole industry for managing data is more about managing the meaning of data. How it's architected, how it's defined, who owns it, how it moves its lineage. Where should you go to get trusted data? How do you keep it in control? All of those types of things.

[B]

Oh, my gosh, we're way over my head now.

[A]

Oh, no. Oh, no. We'll bring it back down. That's. That's what I've been doing most recently. So loving it.

[B]

Oh, man. Okay, we have a lot to talk about. Let me express for some of the young people what it was like before we were able to do all of this wonderful stuff. Geologists would draw maps, base maps, and then using the Various logs. They would draw subsections of the geology. It was called a structure map, and it had the various layers of rock, and they would have to hand draw what their understanding was. And then we'd put it up on the wall. And sometimes there'd always be color. Geologists love color. And we put on the wall, we'd like talk about the reservoir. What do you think it does and looks like, and how's it shaped and connected or whatever. And as engineers, we wanted that to be tied to something like to the actual rock, to the core that we could look at and touch, really feel like we understood it visually. And to this day, I really feel that you can do all the analyses you want to, but you get so many insights. Just visually observing the core and handling it, perhaps that's very important. But then we started moving into modeling, where we use a computer to create a mathematical representation of the geology and its various layers. And then we got more and more sophisticated in the sense of little tools that would digitize the maps and digitize the logs and kind of put all that stuff in the computer and all that is data. And then when we would try to run models, run analyses, we couldn't have very large grids because there just wasn't the memory or the processing speed to do it. And you wouldn't want to wait a month for your results. And it's just those kinds of things. Cindy, you're nodding your head. So give us the data side of the world of what I very roughly, and not very carefully expressed in terms of the experience from my side of it as a user, I think a

[A]

big piece of that is understanding where that data is coming from. Right. How did you get that information in the first place? Was it trustworthy in the first place? Did it come from a reliable source? That's my understanding of some of the biggest challenges there. And then how do you make sure that across all the different aspects of upstream, that when you're talking cross specialities, that you have the similar vocabulary to even build models that might cross. This is a big part of it, that. So as you expand out the interconnectivity of the different areas that are of interest to you in the oil world, being able to talk across, share information across in a way where there's common understanding, and you call things the same, refer to them the same way, and understand how various concepts are related, this is what data management is all about.

[B]

Oh, my gosh. Yes, that's right. So reservoir engineer, geologist, reservoir engineer and drilling engineer would get together and have these conversations and the team have these conversations about these. But then we never talked to the production engineers. We just made assumptions for the production engineer. Especially when you take drawings and you actually take a bit and put it into the ground, start drilling a well and you get what you get and you go where you go as best as you can, control the process. And then when you put the well on production, then you get the production that you want, but you don't really know why until you can go back and start doing some more analyses. Fast forward to where we are now. We have data that's collected by equipment that is immediately digital and you get tons of it, as you say. And the quality of the data is something that is examined by the people who are expert in that kind of data and putting it into a. I want to say program or I'm not even sure what the right word is, where from there you can take and build a model and have the data accessible and not lose the data and not mess up the data while you're using it and those kinds of things. I'm going to stop talking now because that's the end of it for me. But you are the data guru, so maybe you can pretend that we're talking about Elena's oil and gas company. And I bought a small field that's got 100 wells or whatever information, and maybe it's just now looking at going into hydraulic fracturing or whatever, different kinds of rock. So how can we use modeling, how we tackle that and mostly the data? Because if I bought this company from somebody else, they must have, and it was very old, it would have different vintages of style or management practices or something like that.

[A]

Sure. I think just taking this up a level, just about basic design principles, I'd want to know and understand what exactly your company does, how you produce value and what processes you go through to produce value, and then what information feeds that and what information is produced. Start to really nail down what's most key and important to have well controlled for you so that you can get the feeds to your models that you need. That's number one thing, understanding where all of that data is, what's most important to you, and how well that data is understood in terms of how it's defined, how it's used, who owns it, who's responsible for it, all of that kind of thing.

[B]

That's a really important element because there is data that we usually use and then there are data that we don't use because we don't know what it can tell us yet. We don't know yet. And we know that every time that we have an intersection with the rock, that there's data, there's something's coming out. And just because we don't speak that language, that geologic language in the sense of the way the rock speaks, so we don't always understand it. But over time, we've gotten more and more comfortable with knowing that every time we intersect the reservoir, it's telling us something and we should be able to gather. So there's two elements. One is, as you're saying, what do you need, what do you want and where do you want to go? And then the next question is, what's possible? If I had looked at some other data from drilling cuttings or something like that.

[A]

Yes, yes. Applications of AI in these models are brilliant at pattern recognition, which is one of the huge things that I would imagine would be important in your industry.

[B]

Yeah, yeah, absolutely. Okay. So I was thinking about there's so many things, as you say, manage the data in terms of storage or curating the data. That's one big, important piece. Is that fair to say?

[A]

That is fair to say. And let me make the distinction clearly. So between the data itself and the metadata is the data about the data, which is the piece that gives it meaning. So metadata comes in the form of definitions of the data. How is it named, how does it fit into a larger model of concepts of data, how is it architected, that kind of thing? Ownership of data is a very important concept. If something goes wrong or there are quality issues with the information, who are you going to call to address that? Who is in the position to actually influence that, bring resources to fix the problem, that kind of thing. Those are very important concepts, and it helps. The whole architecting of the data is really around understanding that meaning of the data and how and where it's located.

[B]

Okay. Then you asked, what kinds of questions do you want to answer? What kinds of questions do you want to explore answers for? And do you have the information you need in order to answer that question, except there's too much of it and you can't do it on your brain? Something like that.

[A]

That's right. At a high level, that's exactly what it's about.

[B]

Oh, my gosh. Yes.

[A]

I know so much.

[B]

How does someone. I just realized there is a whole new major called data science, so I guess there is. That's what we will learn from that piece of it with respect to. At the highest level. So in oil and gas, we have standards for Pipes, standards for procedures. We just have kind of standards, safety standards, least of which or whatever are there people in charge of master data scientists or how does that all work

[A]

when it comes to your standards and policies and things that you need to manage too? This is another great use of data management at orteca. We specifically talk about. At Orteca we leverage a concept called design observability where you can take and automate all of those and digitize basically all of your governance documents. So you've got the intent of what you want to be able to do, all created within a nice model that you can actually build controls again and automate the execution of those controls so that you don't have to be checking. This is our intent on a piece of paper that we have on a shelf somewhere. Did we do it? When you're trying to audit and make sure that you're in compliance with all of your rules and standards, you can have outputs of your processes that prove that you've been in, that you've been in compliance with your, with your rules and standards. So it's a cool way to start to now even automate, digitize all of your design documents in your organization to actually track and keep track of those controls and that they're working.

[B]

I have no idea. I bet all my listeners are laughing at me because they get it, but I didn't get it. I didn't realize that you could actually look across your whole organization and put everything into, quote, I want to say a master computer or something like that. I don't know the lingo because I'm not there. But you could put everything in one place and cross check. Are you doing what you're supposed to be doing? Are you getting the best return on your investment on all fronts? Because a human can't really. You need teams and teams of people meeting constantly in order to get that kind of information and exchange and insights so that you can make your next decisions. Am I starting to get it?

[A]

Yeah, you are starting to get it and you're starting to get how powerful this is. And this is something fairly unique, orteca, to tell you the truth, that we've been really excited about and working to implement in various organizations. So we've started with data design, data management, design documents, and there is no reason why that can't be expanded more broadly across all of your compliance type work. And there are intersections between data management and risk management, for example, and the amount of money and time and effort that companies spend to manually track these things to get Prepared for regulatory reviews and all of that. It's amazing how you can design this in a way to be much less person intensive and automated so that you can keep track of this stuff and to ensure that the intent that you're designing for in your organization is actually being met.

[B]

How do you ask yourself or your data a particular question that you may need to know right away and you could go send somebody to go look it up. That's going to take a long time. Is there some sort of interface that you can like, just ask the computer like I do on my, you know, on my phone? Yes.

[A]

So various companies are creating their own large language models and interfaces, their own internal AIs that run off of their own unique data that they've got in their own company. And you interface with it the exact same way you would a generic 1chatgpt or anything like that. Only it's controlled with in house. Right. It's not. You're not giving your private information out to the world. Right. It's not shared in a huge environment. It's well controlled internally.

[B]

Oh, my gosh. So you can ask yourself all kinds of questions about your company.

[A]

Amazing. Yes.

[B]

Next year's strategy or whatever.

[A]

Yes.

[B]

Oh my gosh. So no wonder data centers are so big.

[A]

That's really. I know. As everyone gets on board with doing that. Yeah. That's why it's exploding.

[B]

Yeah. Oh my gosh.

[A]

I was just wondering, what has your experience in your industry been for? What is most challenging? What are the biggest blockers? Does it have to do with access or data quality or change management? Where are the biggest struggles there?

[B]

Actually, I worked in research for a long time at the Department of Energy before retiring. So a couple things now, maybe since then, since 2021, these things have been solved. But what has to do with visualizing the subsurface? We can't see it, but we have lots and lots of information about it. We know what the rock is like, we know how it's put together. We've taken measurements. So we can deduce that we know what fluids are in there. We know where the fluids are, what kind of fluids they are and what their potential interaction might be. We have all this information about the subsurface, but we really can't see it. And what I would love to do is be like a little oil or gas molecule down in the subsurface and cruising around all the fractures or through the pores or whatever. So this whole visualization thing, which I imagine would take lots and lots of. I want to say, data, but processing time and power and all that kind of stuff, that's a challenge. And then another one has to do. As I was saying earlier, there are fluids and rock, maybe drilling cuttings and the like that come out of the subsurface when we drill or anytime we intersect. And that we don't use that information and we don't know what it can tell us, and we don't know what we don't know. That that kind of. Let's see, what's another one is asking the right question in the right way to get an answer that makes sense and that harmonizes with what we already know. So there aren't any kind of leaps in logic, if you will.

[A]

Yeah. And I think the key there is having that internal language model for your AI to reference. So that. And. But on top of that, actually something really interesting. There's a guy I follow, his name is Tony Seal on LinkedIn. And he talks the intersection of two kinds of intelligence. There's that large language model that has amazing, extraordinary pattern recognition, but it also operates in a purely statistical world. But knowledge graphs. In the building of knowledge graphs and building out those ontologies, they're grounded in some precise, structured and grounded logic about how your concepts in your organization work. And they operate in more of a symbolic world. So it's like when you get them working together, they perform a type of a loop that prevents you from getting. These AI hallucinations can ensure that you're really getting the most value, most reliable value out of your data.

[B]

That's another spot right there that is a challenge, and that is that we still are a little stovepipe in our areas of expertise. And at the interface of two different people, two different areas of expertise, two different subjects, that interface is always difficult to navigate. You don't know if it's translating the way that you intend it by the word choice that you're using. And this is part of the challenge. As we become more specialized, because we have to, because we can measure more, we can know more, we have a deeper understanding so you can ask better questions. But then when we come up with our point of view of this whole universe of information that I have just in my area, and then you try to interface with someone else in their area, sometimes there's not language, there's not vocabulary that means the same to them as it means to you. And you have to use a lot of words, and by the time you do that, you've lost them, that kind of thing.

[A]

It's so funny. This is hitting On a very common problem that's experienced, experienced across all companies is having that common language in place. As a matter of fact, one of the things that Orteca really does leverage. We're very closely associated with and working with the Enterprise Data Management association and they have developed this data management capability assessment model. And in the latest version that I had the pleasure to help them work on, as a matter of fact, we created a whole new component, a whole new area of focus called business data knowledge that attacks that very thing and really emphasizes how important it is to have that common language not only created, but very easily accessible and thoughtfully shared throughout the organization so that people have access to that language model, they start to understand common business terms. There's an education component to help people access data and to be trained in how to leverage it. So when you were talking as well about you don't know what you don't know, once you start opening up access to this information, people can come up with all different types of ways to use it, new and different ways. This data management framework that's created by the Enterprise Data Management association is a really useful tool to develop your data management capabilities in a very holistic and balanced way from some very foundational concepts around your strategies for data, how you manage that, how you support data operations in your organization, as well as how you execute through those, through things like data architecture. We've been talking about hugely important, more important than ever in this age of AI, this data business, data knowledge stuff, the data quality management, how you manage the quality of your data, how you govern all that and then how you integrate that core capability throughout your organization there, how you interface with risk or other groups in the business itself and technology. So I'll end and bringing it back round to applications back in analytics, which is what we're all using that data for.

[B]

That's right, that's right. I'm feeling more comfortable as we're having this conversation because I feel like I do know a little more than I thought I did. There's this fear, there's this fear about this new topic, but architecture, I'm not quite getting what data architecture means. Is it the relationship of one kind of data to another?

[A]

It's the relationship of the meaning of the data, it's organizing the data. In the training that they do for DKAMP and we talk about data architecture, they'd give a nice little story about creating a library or somebody is owning, just walks into a system of libraries and he needs to know and understand where all the information resides. So information about the people who work at the library, information about the collections of books that they have, information about circulation. All of these information groups exist in their own domains of data, and they're designed and they might reside that information in different types of applications from a technology perspective, but he needs to understand how the concept of an employee relates to his footprint of where his libraries are, relates to where the books are located in the library. So all of this, the relationships there are, the ontology that you create between those different concepts we have so that we all understand what a book is, what an author is. That's in the very simplest of terms. So understanding what the business needs. So the, the biz, there's a business design aspect. The business architecture is all around building out your processes and how you create value in your business. The data architect comes to help ensure that the meaning, you know, what data you need is communicated back to the technology team. So who is actually constructing and architecting how they're building the detailed models. So conceptual models are the realm of data management.

[B]

Okay, okay. At a certain point, at the beginning, you have to have some sort of inventory of the kind of day that you have and maybe even remind yourself why you have it.

[A]

That's right.

[B]

What you thought you were going to do with it. And have you been able to do that? And can I do that better if I'm doing it? Or maybe I'm not there anymore and I can just ignore that part and really focus on what I. Sometimes you do things or you have things that don't serve anymore, but you just can't get rid of them.

[A]

Yeah, there you go. There you go. Might as well. For example, I worked at a very large bank and I had a team. I was a domain manager. So for, let's say, for example, retail lending, we had over a thousand applications worth of data. Right. That rolled up into this one domain. So it was important to know and understand who owned the data. Which of these data elements were most important? We typically back at that point were mostly driven by wanting to be compliant with regulatory issues. Right. So in all of our regulatory reporting, all the data that went into that needed to be handled in such a way so that we had great controls over it because it's very important for it to be highly accurate. So it's just managing in that way that what are those terms? How are they important? Where do they come from? What, there's multiple sources. Where do I get it from to be authoritative? Because another thing that plagues the financial industry is various financial reports. Where one group will generate one, another one will and they won't match. And so there'll be a lot of arguing and fighting over who's correct or not. Right. But you can start to, when you start to understand where and how data flows and what the timings are and all of that, you can start to create and establish what the authoritative source is so that everything fits all the time.

[B]

Oh my goodness. So if you're talking about number data, that number is in Google or Google Blogs. Really big data sets.

[A]

Yes. This is why you have to prioritize. You've got to decide what's most important to you because it is absolutely a business consideration and you don't want to boil the ocean. Right. Typically when we're working with our clients, it's all about understanding. It's all use, case driven, it's all business. What is top of the mind for the business to achieve. It ties back to the business's strategy, typically. What kind of transformations are they going to through and what kind of very important data is going to be needed to drive that. It's all about making sure we're focused on that fuel, so to speak.

[B]

Absolutely. Oh my gosh. This is. I thought I was going to come away having a whole lot more confidence. I have enough confidence now that I can have another conversation and we're definitely going to have to have you come back and have another conversation.

[A]

Yeah. And I would just also offer that I've been doing this as a practitioner for a long time. I also am lucky enough to be working with a number of different experts who are very much based on the technical side of this equation and I'm sure who would be happy to come back and speak at any time, so.

[B]

Oh, absolutely, that would be great. That would be great. Just to continue to put a bow around it. You don't have to boil the ocean just because you can.

[A]

That's right.

[B]

And you can really dial in because there are questions that we have hesitated to ask because we know that we could never get to the answer. So that's a kind of a muscle that needs to be strengthened again because now maybe we can. And then we have the notion of expediency and you say, okay, I don't know the answer. It's going to take a long time to get the answer. But the answer is going to range from here to here. And we don't have to do that anymore because we can get a little tighter range or we can get enough information that the assumption we're making about the decision that we're going to make, but is not. Maybe we can determine that it's not so sensitive to that parameter or whatever. So you're unlocking another piece of my brain. So that's good.

[A]

There you go.

[B]

Data part that I was afraid to really think about because I'm not really comfortable in digital space. Yes. Everybody knows how old I am, so I'm admitting that part. This is fascinating. This is fascinating. Did I ask you about Orteca? I said it was a boutique consult data consultancy, but I don't know that I got to ask you much about it. Do you want to share?

[A]

Sure. Let me tell you a little bit about it. So it's been around for a number of years. The main location is actually in London. That's where a majority of our experts sit. But we also have a decent sized team here in the US as well. In Boston, Nashville. I reside in Atlanta and we have others in Charlotte and we just recently expanded into Toronto as well, so. So we've got pretty good coverage. While we're a smaller company and recently merged with another company. So we've. We're a couple hundred strong now and work with companies all over the globe. Yeah, I think on most continents. So it's been a fun trip. Like I said, we do often focus on delivering some of the DCAM related services, doing DCAM assessments to understand where people are at in there data journeys. And that is often followed up by helping them with their strategies, building out operating models, helping them with their metadata management, implementation of data dictionaries or data warehouses for example. And right now we're also really focused on helping build out those doing the data architecture on ontology, builds knowledge graphs, all of that stuff to help companies be able to really get the most out of their AI.

[B]

Oh my gosh. Yes. I think we're definitely going to have to go to London for the next recording.

[A]

Yeah, that would be fun.

[B]

That would be fun, right?

[A]

Yes.

[B]

Oh gosh, Cindy, this has been so fascinating. Thank you so much. I know that some of the people who do this every day or listeners aren't going to want to hear how far behind I am, but I do appreciate all your time and patience in teaching me what this is all about and we definitely will have another conversation after. I digress just what you've shared with me so far. See if I can come up with a use case and you can tell us how to structure our data management to be able to answer some of these questions. And is there anything else you want to share? We are out of time, but I did want to give you a chance to say anything else you wanted to share.

[A]

Thank you. I think you allowed me to cover all the major points. You've been such a gracious host, so thank you.

[B]

Thank you. Cindy Sullivan, principal consultant at Orteca. Thank you so much for joining us.

[A]

Thanks again, Helen.

[B]

Elena Melkert, your host. Thank you. More next time.

[A]

Thanks for listening to oggn, the world's largest and most listened to podcast network for the oil and energy industry. If you like this show, leave us a review and then go to oggn.com to learn about all our other shows. And don't forget to sign up for our weekly newsletter. This show has been a production of the Oil and Gas Global Network.