← The Digital Executive
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- [00:00]
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Foreign.
- [00:09]
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Welcome to Coruscant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Wendy Lynch. Dr. Wendy lynch is a researcher, author, and longtime analytic translator who helps organizations turn data and AI into decisions that actually stick. For more than three decades, she has worked with Fortune 100 companies to bridge the gap between business leaders and technical teams, improving how questions are framed, results are communicated, and value is realized. Wendy is the author of multiple books on communication and analytics, including get to what Matters and Become an Analytic Translator. Through her writing, teaching, and advisory work, she trains leaders and data professionals to use translation, not just technology, to unlock the real potential of analytics and AI. Well, good afternoon, Wendy. Welcome to the show.
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Thank you for having me. Glad to be here.
- [01:15]
B
Absolutely, my friend. I appreciate it. I know you're in Steamboat Steamboat Springs, Colorado, north of Denver by about three and a half hours. I'm in Kansas City. So luckily we're only an hour apart today and I appreciate you making the time. So, Wendy, let's jump right into your first question. You describe yourself as an analytic translator. What does that role mean in practice? And why is translation, rather than more technology, the missing link in most data and AI initiatives?
- [01:46]
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Yes, analytic translators, as you can imagine, the obvious part of that is that they can explain really complicated types of analytics. But more important than that, analytic translators are focused on creating an environment where there is mutual appreciation between the teams and getting the questions right the first time. So it is as much about team building and defining the problem as it is about explaining results. Now, the reason why we need these sorts of people is that we have enough analysts and we have enough horsepower these days. But what happens is that between those two teams, the business team and the analytic team, we often have miscommunication. And I did a series of surveys and chief analytic officers said their teams are 50 to 80, 80% less effective than they should be because of miscommunication.
- [02:51]
B
Wow. That's. Gosh, you know, what I love about this is on this podcast I learned so much from people and I again, I get always amazed by the statistics that come about. But I like how you explained as an analytic translator, you can explain those complex analytical whether it's terminology or explaining, some sort of dashboard or report or visualization, but you can do that in layman's terms or business terms, as I like to say. But what I really took away was analytic, analytical People, people that work in analytics really can create that environment that is synergistic between teams. Again, asking the right questions, building the strong relationships between business and the analytic or technical teams. So I appreciate that, Wendy Brain, just.
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For your audience to think about. If you think about high school, there was a group of people who ran the robotics club and loved learning about computer coding and another group of people who were, let's say, on the football team or a cheerleader and running for class president. Those two groups of people did not sit together in the cafeteria. And now we're asking those two groups to work really well together as adults.
- [04:10]
B
That's awesome. And I love hearing the stories as well, so thank you. Wendy, in your book Become an Analytic Translator, you emphasize framing the right questions before running models. How can leaders improve the way they ask questions of data teams?
- [04:25]
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Yeah, I'm going to answer that question in sort of a different way. Business leaders who are not already really highly data savvy or AI savvy, I don't think that their job is to become pseudo analysts or AI experts. The CEOs that I work with are already scrambling just to plan and use future strategy. So it's not that I don't encourage them to learn as much as they possibly can. That. That's not what I'm saying. But if they don't already know, then it's how to have the relationship with their analysts or hire an analytic translator in such a way that they can define what information that they need. So it's less about how they ask questions, and it's more about their willingness to spend five minutes answering what the analytic translator needs from them. And what I mean by that is that too often a busy leader will say, go get me information X or I need a dashboard that shows Y. But then they march off to their next meeting. And if I don't have the context, the purpose, the expectations, the knowledge of what decision that leader is trying to make, then I'm guessing. And those guesses are rarely correct. So it's more about being willing to take a breath. And I'm not talking about hours of conversation. It seriously is probably five minutes to help the person who's going to answer the question have a good background understanding of why it's being asked.
- [06:12]
B
That's awesome. Thank you and I appreciate that. And Wendy, I've worked on both sides early in my career. Obviously I worked with a lot of databases and reporting as a developer. But growing further up in the ladder in my career, I would agree with you a lot of times, business leaders maybe typically aren't data savvy and certainly don't aspire to be data analysts or data scientists. But it's important that they have that strong relationship with the analysts and do take the time to explain the context that business need behind what they're asking for. I think it's so important. So thank you.
- [06:48]
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Sure.
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Wendy, after working with Fortune 100 companies for more than 30 years, what patterns do you see and why analytics and AI projects fail to influence real business decisions?
- [07:00]
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So the obvious answer, which is true, is miscommunication, and the speed and pace of work clearly are part of the problem. So if you don't get the question right, you can't answer the exact piece of information. You can't deliver the piece of information that someone needs to make a decision. But there's a few other patterns. And if you've been around like you say, and I have wandered around in this business for long enough to know that leaders are being pushed by their boards, and boards are saying, look, my friend's company used AI and it eliminated a whole bunch of headcount, and so they're much more profitable. Or you need to have this because the bells and whistles make the valuations go higher. But these requests from boards or this pressure by boards are being made by people who have very little or no expertise in AI or data. So one of those patterns, I think is very difficult for leaders to try and accommodate. To respond to this hearsay is what you can think about it in such a way that they can please the boards. So I've served on publicly traded boards, on startup boards, and too many boards are operating with experts from 30 or 40 years ago. So my advice here is board chairs, if you're listening, update your boards with people who know about the current technologies and the current trends in AI. Now, the second thing is I see companies and leaders thinking that AI is a technology infrastructure change, when actually it's a social, cultural change in addition to a new type of technology. So if they think they can hand it off to it and just snap their fingers and bring in something new, they're going to fail. Because humans have to be part of the design, training, testing, implementation, or AI doesn't work.
- [09:23]
B
Thank you, I appreciate that, really do. And I know there's a lot of pressure. You know, you talked about some of those things that kind of can throw a wrench in, in, in the day, right? The miscommunication, the speed, delivery, the pressures. And you talked about that pressure typically comes from the top or board members. Right. And it's important you highlighted that boards in management include members that have some good, sound technical background and some knowledge to help kind of bridge that gap. And I totally see that. Worked with boards many times and definitely saw a gap there. So I appreciate that.
- [09:56]
A
Yeah, Brian, I think the latest poll that I saw on survey was that 2% of current board members have advanced knowledge of AI.
- [10:11]
B
Yeah. There's another statistic and we need to change that, and that's what we're doing here on the podcast every single day. So thank you, Wendy. Sure. And Wendy, last question of the day. Looking ahead, how do you see the relationship between humans, analytics and AI changing over the next decade? And what mindset shift will matter most for leaders?
- [10:33]
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Well, I, I think that anyone who's been watching AI knows that a decade may be too far for anyone's crystal ball to see. Unlike in the past where you could sort of sense what the changes were going to be, the advancements just in the past 18 months, for example, have been so fast and so massive that 10 years may be a little too far. But I'm going to do my best to talk about what kinds of shifts and preparation leaders could make, even not knowing where 10 years is going to take us. So in the next year, for example, there are going to be significant shifts in the types of jobs that leaders need to fill. And there are going to be a series of successful and unsuccessful AI implementations as people start to get their feet wet. Most of those implementations are going to be in narrow lanes. So all in customer service or all in sales pipeline or all in coding. So we're going to see a big jump in the adoption of narrow implementations. Within five years, there's going to be a huge chasm between the companies who have put the real fundamental work in and those who didn't. And what I mean by that is data infrastructure, data governance and change management with their people, training with their people. So if you don't have your ducks in a row to let AI be helpful as it becomes more and more capable and more and more able to connect disparate parts of the organization, then it's never going to work. So I did see a survey of high level data professionals and 90% of them said that leaders are not paying enough attention to problems with their data or data structure. They quoted saying that one in five have the level of data structure that they need and fewer than one in ten have data integration and interoperability between the different areas to be able to really leverage AI. So beyond that we're going to see some drastic evolution in business that I don't know that any of us can really get our arms completely around. But if you are not understanding how your data will be used, can be used, should or shouldn't be used, if you're not having an AI translator whispering in your ear about what's possible and not possible, then you're going to continue to wander around in the dark while others are leapfrogging forward.
- [13:40]
B
Thank you. Really appreciate that. And you're right, no one has your crystal ball. But we know that AI is advancing rapidly and it's hard to predict beyond probably 3, 4, 5 years at this point with that technology. But we are definitely going to see, as you mentioned, some significant shifts with jobs as AI gets adopted more in organizations. You actually mentioned narrow implementations. It's important to have the fundamentals in place. You know, data infrastructure, data governance, that data structure, employee training, adoption, et cetera. Without the fundamentals, your AI implementation will not be successful. And I would agree with that. So I appreciate your insights today, Wende and Wendy, it was such a pleasure having you on today and I look forward to speaking with you real soon.
- [14:28]
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Great. Well, thanks for having me again, Brian.
- [14:30]
B
Bye for now.