C (3:17)

That's awesome. And I really love that. And that resonates with me. I know it resonates probably with a lot of my audience. You know, there's a gap sometimes with the business side, the technical side, and, and absolutely seen this even as a project manager that I had there for a little while back in my career. Business teams just don't understand what the analytical jargon that is going on sometimes. And I really appreciate you bringing your gift, as I call it, that strong background and as an analyst into the business world. And you're just a great communicator. So I appreciate that.

B (3:50)

Sure.

A (3:51)

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C (4:24)

Wendy, Many organizations invest heavily in analytics and AI but still struggle to turn insights into action. Why does this disconnect between data and decision making persist?

D (4:35)

The short answer, I really believe, is communication and a dedication to intentional human communication between those teams. But if we think about the specifics about it, number one, those two types of people are extremely different. They speak different languages. Like you mentioned, both sides have their own jargon, both sides have their own wings ways of communicating. They both have their own unique perspective about what success means. And I don't mean that we're, that we differ about wanting the company to succeed. I'm not talking about the big macro success, but in terms of what makes a good project or what makes a good result, they think about it differently. And one other aspect is that it's helpful to understand that this mismatch between data and business people is not unique to any one organization. Now, if you're like me, you've gone into different companies and each time they believe that the problem is Fred or Jane or some individual. So the CEO says, oh, we have this head of analytics and he doesn't make any sense. Or the head of data says, yeah, these business leaders, they can't be clear about what they're saying. So they believe that it's a personality, individual thing, but actually it's systemic. And it's pretty much everywhere that we have that struggle. So we need to think about it as systemic rather than as a personality conflict or two teams that just don't happen to get along.

C (6:29)

Thank you. Appreciate that. That's very helpful. Of course. But again, it goes beyond. You talked about that. Right. So a lack of deliberate and planned communication can be a big part of that. It's really good to have a culture where there's a lot of communication up, down, sideways, on diagonal. But additionally, both these groups have their own way of communication you mentioned, and their viewpoint of success obviously differs in their sphere of expertise.

D (6:55)

Right, right, right.

C (6:57)

But ultimately, is what you said I took away, this is a systemic issue. And it's something that, yeah, you can have that deliberate plan communication, but also it needs to be really part of that culture so that we can overcome this systemic breakdown of communication. And I fully agree. So I appreciate that.

D (7:15)

And I don't want to say that there needs to be an initiative where all employees are trained communicators because not everybody's going to be oriented that way and not everybody's going to want that. But what we need is to recognize there can be trained translators in place to help those teams trust and collaborate.

C (7:40)

Thank you. Appreciate you that clarification. And Wendy, communication often becomes the biggest barrier between technical teams and executives. What practical techniques help data professionals present insights in ways that resonate with decision makers?

D (7:55)

Yeah, the first thing is to think about the projects from beginning to end and understand that things go wrong between the two groups most often because they didn't start correctly. When we get to that endpoint and somebody is explaining how they did some really crazy, amazing method to find a particular result, we've already gone way too far and probably gotten off track before that. So the few things that I would suggest is, number one, have a discussion as the project is getting started, and I'm going to advise people to go exactly the opposite direction from what I see. So if you have been in these kinds of projects where analysts have been burned over and over again because they bring a result and the people requesting it said, oh, that's not what I meant, and they have to go back and do it again and they get demoralized and discouraged, the tendency is for Them to get really picky about the criteria and parameters and specific data fields that are being defined. So business leader comes in and says, oh, I want a dashboard. And the way to protect themselves I think is to say, okay, what exact data fields do you need? What exact time frame do you need? What exact visual do you need? And they get really, really specific. My approach and what I train people in, analytic translation is we have to start by going back up to the 30,000 foot purpose and find out how this request will be useful to the requester and the business. Because usually they don't know how to ask for what they need. Not that they are not smart people, they just don't operate in analytics. So if you say something like, so how did this idea come up? Tell me how that will be useful. Tell me about the audiences that are going to use this. Usually by the time they've talked about it for three to five minutes, it morphs into what they really need. And I would say 50, 50 chance they ever really needed a dashboard, but that's the way that they could express it. So that's part of the way we approach it is we go through what I call the discovery process to expand our thinking and get clearer and help them get clearer because they don't know what to ask you. And if you force them into getting really specific, it's probably going to go off track. Even though they quote unquote defined it, it's still not going to give them what they need.

C (10:58)

That's great. Thank you, Appreciate that. And you're right, you think of this project right from beginning, end, what is that going to look like? And having that discussion up front. A lot of times you have a project kickoff, et cetera. But the thing I'm going to highlight is you said it's important to ask the right questions. What's that? Why? Who's your audience? Those types of things will help frame it for the analysts to produce something that the decision maker really wants to see. And I appreciate your insights there.

D (11:27)

Sure.

C (11:28)

Wendy, as we look ahead to the future, how do you see the role of analytic translators evolving as AI and data driven decision making become even more central to business strategy?

D (11:40)

I believe that translators are going to evolve to not just help decision makers define analytics and translate results, but given the way that AI is evolving so quickly, if you're like me, I try and stay on top of it. And even trying to stay on top of it, it's flying by and expanding and changing so fast. So translators, I believe will need to take on this role of helping business leaders not only understand it, but grasp what is possible. And my belief is that every organization that has data involved in it, which pretty much is everybody now, and leaders need an individual that both teams trust that can advocate for both perspectives. And that's what a translator can be, is that person in the middle that can keep things on track, keep things realistic, but also give people the insights that they need.

C (12:50)

Thank you so much. Appreciate that. You're absolutely right. It's important that an analytic translator is a leader, the person in the middle that can really have that empathy, have that eq, be able to understand really both parties and again bridge that gap in the communication. But you talked about AI just real quick is it's evolving so quickly that analytic translators will need to take on that role, not only understanding but what is possible. And I thought that was interesting. So I appreciate you highlighting that, Wendy. It was such a pleasure to have you on today and I look forward to speaking with you real soon.

D (13:29)

Okay. Well Brian, thank you for letting me be a two timer and I hope that the rest of your week goes great.

C (13:36)

Absolutely, my friend. Bye for now.

A (13:48)

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