#306: Decision Support Has Been the Point All Along

00:00:00.00 | Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:14.00 | Michael Helbling: Hi, everybody. Welcome. It’s the Analytics Power Hour, and this is episode 306. You know, sometimes it’s good to step back from whatever you’re working on and maybe re-ask the question, well, why do we do analytics in the first place? I mean, aside from the obvious rock star status. I mean, I kid, I kid. The goal is to help organizations make better and maybe faster decisions. And the barriers to doing that are, I mean, oftentimes both obvious but also obscure. So let’s talk about it. My co-host is Tim Wilson. Hi, Tim. You’ve supported a fair few decisions over the years.

00:00:52.64 | Tim Wilson: We decided to record this episode. That’s right. That’s the one I’m most proud of ever.

00:00:57.70 | Michael Helbling: And I’m Michael. I’m not sure if I’ve decided yet to fully record this episode, but we’ll see how it goes.

00:01:03.17 | Tim Wilson: Just drop 10 minutes in.

00:01:05.35 | Michael Helbling: Yeah, that’s right. I’m out. That’s right. All right. But we actually are excited about this because we also have a guest. Zohar Strinka is the founder of Analytics Strategies and the creator of the Meta-Problem Method, a science-based approach to choosing which problems to solve. She has a PhD in industrial and operations engineering from the University of Michigan. And today she is our guest. Welcome to the show, Zohar.

00:01:27.18 | Zohar Strinka: Thanks for having me.

00:01:28.64 | Michael Helbling: Yeah, thanks. Thanks so much for coming on the show. And I think maybe just to get us started, maybe could you just give us a little more background on the kind of work you do with companies? I know you more operate in the consulting space like Tim and I do as well. So just give us a little flavor, some of your background and the types of work you do that kind of led into some of the work you’re doing that we’re talking about today.

00:01:51.50 | Zohar Strinka: Yeah, happy to. So I got into consulting kind of by accident where I graduated. I graduated with my PhD, which was focused on optimization and these math models of decisions. But I graduated and was trying to figure out what was the career path ahead of me. And a lot of my peers were going into data science. So I was looking at opportunities like that. But I found I didn’t have the machine learning expertise that people were expecting. So I applied for jobs and found that in consulting, I asked good questions. And that was an important part of consulting. And so I found a job. There about a year in, I was laid off. And so I started looking and trying to figure out what I was going to do next and decided to start an independent consulting company. I got some traction, found some clients pretty quickly, which I know is a lot of luck, but also just kind of speaks to the approach I brought. But I had a different way of approaching analytics that I think was interesting to my clients since then. So I’ve been doing this since 20. I’ve been working as an independent consultant. I’ve worked with all sorts of different companies. I’m often more working with manufacturers or distributors or retailers because I have an inventory background. And so this industrial engineering mindset is really extra relevant for those folks. But I’ve worked with a lot of different companies and usually I’m trying to help them figure out what to do next with their analytics, what dashboards to build. Yeah. Yeah. Or what questions to answer or do we need a data warehouse at all?

00:03:32.52 | Michael Helbling: And it sounds like because of the timing, you’ve been involved in two pretty major shifts with COVID in the tariffs in the US.

00:03:41.71 | Zohar Strinka: Yes. Yeah. It’s been interesting because with tariffs especially, it’s just sort of it’s an external factor that a lot of my clients are trying to read the tea leaves and kind of say, do we load up on inventory and hope it’ll be to our benefit? Or they’re trying to. I’m trying to kind of predict the future a little bit, but it’s tricky and a lot of them aren’t using as much math as they could to help them make those decisions.

00:04:08.36 | Tim Wilson: How much is using the math? This does flash me back to COVID when there were. It’s like I was supporting some companies that were asking, like, tell us what we should do. And I’m like, well, this is I don’t know that like the historical precedent of 1918 is particularly useful. It was like you need to kind of think through. Like. Can you do you bridge those uncertainties with what you said early on? You were like, it’s about the questions that are being asked and then squaring that if you get brought in to say, what should we do next with our analytics? Is there a redirection that has to happen? Like it almost I’m trying to read between the lines. It sounds like I bet you reset every single client within the first week of working with them or no.

00:04:56.03 | Zohar Strinka: But. It’s tricky. Because there’s so much that goes into each of those decisions. The one that I made a mistake on. So I had my book knowledge on inventory. I was working with a client and they were trying to deal with the COVID disruptions of these really long lead times. What I didn’t quite understand is depending on your lead time is how those decisions matter. So if you have a short lead time, you can kind of react to those changes in the world really quickly. So if you’re ordering something from overseas and it’s going to take eight months to get to you, you’re in trouble if you misjudge and buy a whole bunch of something that’s not going to sell. If your lead time is a few days because you’re buying from a distributor, you can react much more quickly. And so a bad decision is really just about, yeah, don’t don’t make like an order of magnitude mistake and you’ll be fine. And so working with this client, they had long lead times. And I hadn’t like emotionally learned. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.

00:06:03.25 | SPEAKER_UNK: Yeah. Yeah.

00:06:03.87 | Zohar Strinka: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. math can help and highlight, okay, this is a mistake where if we make it, it’s fine. Either we’re getting two weeks of inventory or three months, we can handle that from a cashflow point of view. But if we’re talking about either two weeks or 10 years, that becomes a lot more of a

00:06:29.86 | Tim Wilson: problem. How do you get to finding out what those sort of key factors, like you talk about problem spaces, you talk about making decisions and there you just sort of talked about like the business operating environment, like where, where do you start? I guess what’s the, and how much of that

00:06:52.81 | Zohar Strinka: do you have to know before you can start doing math stuff? It’s tricky. So one thing I’ve learned is how much the industry, like industries vary. The math is the same, right? The analytics is the same. So a lot of folks in analytics consulting, especially, we come in and we say, I know the math, the data’s there, I can figure it out. And there’s a risk there depending on the industry, right? And so there’s, there’s some of that of what are the key trade-offs to me are a cheat code for navigating a new industry. If you can pull out those key trade-offs, what are the goals that you’re trying to balance? Okay. We want to not stock out, but we don’t want to have, we don’t want to have a dead inventory. That’s a key trade-off and there’s got to be a sweet spot somewhere. And so for each of those questions, you start to ask yourself, what’s the cost of getting it wrong either way. And if the cost is really high one direction, not too bad, the other direction, well, err on the side, that’s not going to be expensive. And so that sort of universal frame up framing of what’s the trade-off, how does my choice impact my outcomes can really help you avoid some of those mistakes. And we’re talking really abstract, but I’m really thinking of like during my PhD, I was focused on inventory questions of how much inventory should you have? And the research studies found that people like to over-order inventory. They hate stockouts. And usually the cost of stocking out, isn’t that crazy? You can expedite, like there’s usually not that high a cost. And so the real world, experience is go lower, order less inventory. You’ll thank yourself later, but that’s like something you have to learn. It’s not in the math by itself. It’s there,

00:08:50.55 | Tim Wilson: but it’s hidden. I’m trying to think coming from kind of more of a, from a marketing analytics background and finding often, I mean, because when you talk inventory and marketing, it’s like, what’s the ad inventory and how much can you spend on, spend your advertising dollars? And the, the risk of not advertising enough is that you’re missing your, your audience. The reality is, is there’s all sorts of money that might as well just go light it on fire. And the, the, the smoke from that will bring more customers than what you’re spending on, you know, crappy ad inventory. I’m trying to like, I’m trying to put myself into a conversation with a senior marketer. I mean, I think it’d be really useful. I’m just trying to imagine how, you know, I’m just trying to imagine how, you know, I’m just trying to imagine how, you know, I’m just trying to imagine how, you know, I’m just trying to imagine how, you know, I’m just trying to imagine how it goes. Like you have, you have half a million dollars that you’re going to spend on advertising and you’re trying to make decisions on where to spend it and like how to, I’m trying to think where the, the, the, the trade-off discussion comes in. I don’t have an,

00:09:59.65 | Zohar Strinka: I don’t have an easy answer. Yeah. And I’ve worked in marketing as well. And so,

00:10:03.98 | Tim Wilson: and then you ran back to inventory. No, sorry. These people are horrible.

00:10:11.68 | Zohar Strinka: It’s, it’s interesting because so much of the marketing world is you do it because you’ve got to do it, right? You, we have to spend money on marketing because we’ve got to be getting our name out there and it can be so hard to trace how that dollar worked or didn’t. And so the fundamental trade-off is what’s a dollar spent versus how much are you going to get back? Right. And so once you’ve decided you’re going to spend half a million dollars, you’ve sort of eliminated some of that trade-off. And now to your point, you’re deciding, okay, where’s the best bang for my buck if I have this fixed budget? So there’s sort of, you could try to make every dollar of ad spend earn itself, but the models are too complicated, right?

00:10:58.75 | Tim Wilson: Michael, where does your marketing data actually live? Oh, everywhere. Google ads,

00:11:03.70 | Michael Helbling: Meta, HubSpot, J4, Snowflake. I think the forecast metrics live exclusively in Steve’s laptop.

00:11:10.70 | Tim Wilson: Well, that, that feels healthy. Oh yeah. Very governed, very mindful. Well, Prism now has connectors that bring those sources together and turn the raw platform data into clean analysis ready tables.

00:11:21.74 | Michael Helbling: Wait, so it does not just connect to the data and say, good luck with these 183 columns.

00:11:28.24 | Tim Wilson: Correct. Ask.Y has prebuilt marketing analytics recipes that model the raw data and keep it refreshed automatically. Google ads, Meta, HubSpot, GA4. You betcha. Plus Slack, Snowflake, Power BI, and more. I’m not sure about Steve’s laptop.

00:11:45.24 | Michael Helbling: Well, fewer exports means fewer mystery spreadsheets and fewer moments where someone says, I thought that refreshed automatically. That is exactly the idea. Uh, I would like to personally nominate CSV attachments for retirement.

00:11:58.56 | Tim Wilson: Just kick them out the door. We’ll click the link in the show description and sign up for the Ask.Y waitlist. Use code APH and they will move you to the top. Top of that list.

00:12:07.68 | Michael Helbling: Connect the data and disconnect from the nonsense.

00:12:11.87 | Tim Wilson: Michael, I have a new rule for AI. Ooh, this should be good. If a task involves clicking through six menus just to confirm something I already suspect, AI can have it.

00:12:22.72 | Michael Helbling: Hey, that’s pretty much the idea behind Stape’s AI Assistant. It handles routine measurement tasks directly inside your Stape’s account. Like what? Well, ask which containers are connected, check server-side GTM usage, review existing tags, and check server-side GTM usage. Review existing tags and check if they are connected. Check if you have any tags and triggers. Uh, inspect your GTM configuration or confirm that events are being collected in GA4.

00:12:42.20 | Tim Wilson: And it can actually make changes?

00:12:43.70 | Michael Helbling: Yeah, for supported tasks, yes it can. It can create Stape’s and server-side containers and help set up tags and triggers in GTM. And the best part is, you aren’t using your own LLM tokens to do it.

00:12:54.74 | Tim Wilson: So the AI handles some of the repetitive navigation and I get to spend more time checking whether the implementation actually makes sense.

00:13:01.60 | Michael Helbling: Yeah, which feels like a much better division of labor.

00:13:04.60 | Tim Wilson: Yeah. So, if you’re looking for a better way to get started with Stape’s AI Assistant, click the link in the show description. I think that’s the challenge is that it winds up being performative analytics because you can find something that claims that from the Google ecosystem or the meta ecosystem or your digital analytics platform. And that’s like the, I don’t know if that winds up, I mean, this is also going to sound philosophical that it’s like, well, it’s performative. It makes people, it just makes you feel good because you’ve got something that shows I spend a dollar and I got $2. Yeah. I’m not going to be able to get the numbers back, even if the math is horribly flawed, I feel like marketing will uniquely say, well, that’s okay, because I just want to keep spending more and I want to justify it. I’m sorry, I’m just going to have an existential crisis, like right in the middle of, you know, 15 minutes into the episode.

00:13:51.76 | Zohar Strinka: I mean, it’s something where I think that is a challenge we run into in analytics when you’re trying to make these decisions and you want it to be data-based, but you don’t have the data. And so you make up a proxy. Yeah. Yeah. And so one of the questions I start asking is, are those good proxies? Right? So, so our impression, right? I started working on a marketing project when they still sort of cared about impressions a little bit, but they had mostly switched over to clicks. And so there’s sort of this, what’s the metric we can see and how does it connect to the things we care about? Which is really, I think one of the most valuable things we can do is keep coming back to what are you trying to do with it? Like that marketing dollar? Are you trying to just bring awareness? Are you trying to get people to click something? Well, a radio ad will never get someone to click something. Okay. So, so you can sort of start to break down what are the decisions you can make and how they’re connecting to the things you care about.

00:14:53.52 | Michael Helbling: So when, when people come to you, like, do you frequently have to go through a refinement process to the problem itself? Like this is pretty common and on the marketing side where people are like, I think I have this problem. You’re like, well, you technically have a sort of different problem and you have to kind of go through that. Like, how do you kind of go through that process of like helping refine or redefine the problem? And how do you avoid alienating your stakeholder in that process? Cause like a lot of times that that’s a negotiation in a way.

00:15:25.17 | Zohar Strinka: Yeah, that one’s really tricky. And one of my answers is my favorite clients, the ones that get the most out of me and I enjoy working with the most.

00:15:34.79 | SPEAKER_UNK: Yeah.

00:15:31.79 | Zohar Strinka: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.

00:15:32.53 [SPEAKER_UNK]:

00:15:32.53 | Zohar Strinka: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. when I change the problem on them. So they say, I want to figure out which ads are working well. Like what’s the click-through rate? And you sort of say, okay, tell me a little bit more about why you care about click-through rate. Like what outcomes is that driving at? And that sort of question of how do the things that we control link to the things that you want is where I’ve had a lot of success throughout my consulting career. But now I’m telling clients more about it. I’m saying, I’m asking because if we got this wrong, then it’s going to change which problem we should solve. If there are different outcomes you care about, I’m going to go recommend something terrible if I’m working to the wrong goal. So if you care about clicks because that leads to conversions in the marketing world, like, okay, we care about conversions then. That’s the goal.

00:16:56.32 | Tim Wilson: And so you have like within the, and you have the website, so it’s pretty easy for people to go look and see the high level because you have kind of the hierarchy of dilemma goals, problem space, high yield problems, problem selection. Like, I think it might be useful like to, we’re not going to get a full complete primer on the entire method. But the fact that it’s this, anytime I see a hierarchy, I’m like, well, this is sort of forcing decisions at each point because it’s like a tree, I imagine, that goes down and you’re like, yeah, you can’t do everything. So which branch on the tree are we following? So maybe you can talk a little bit about like, nailing down the goal, which like you just, I think you just talked about is conversion. How do you get from that to a problem space versus high yield problems, I guess?

00:17:48.51 | Zohar Strinka: Yeah. And the first question, I like the dilemma idea or the question you’re starting with is your scope. This is where I’ve gotten in the most trouble where someone says, we need to manage this. We need to maximize conversions. I’m like, but why? You’ve already worked on conversions. Let’s work on something else that I think is going to go better. And if you make that mistake, if you do that change on them and that wasn’t what they wanted, you’re going to run into challenges. And so that’s where I’ve made those mistakes.

00:18:17.59 | Tim Wilson: Well, do you have, because I have been dropped in where, if you just asked, what is the dilemma? I would hear, we have all this data and we don’t know what to do with it. Or we know we have all the right data, but it’s not giving me insights, which to me, all sorts of, and not alarm bells. I’m like, okay, there’s coaching. I’m not going to swoop in and look at your data. I’ve got to get you to the actual, I guess, business dilemma. Like if you run into that where they’re bringing, because it was like an early example you had was that you’re like, well, they’re bringing me in to say, do some analysis. I’m like, well, that’s not right.

00:18:54.03 | Zohar Strinka: I have run into that. The thing I tell myself today is, okay,

00:18:59.00 [SPEAKER_UNK]:

00:18:59.00 | Zohar Strinka: You’re not going to be able to do that. You might think the data magically is going to have some answers for you. But what I find is often they know there’s certain data they’re not using to make a decision. And so that’s what I want to hone in on. So I started asking, okay, why do you think the data will have answers for you? Well, we’re not doing forecasting. Okay. That’s now something that analytics makes sense to bring to the problem. And so I’m usually trying to navigate those questions, like go from the world of everything where, okay, I don’t know your business. I’m not an expert in whatever domain it is. You need to give me something more than that. Where should I be looking? And so that’s been my approach is to really ask more questions about, okay, what opportunities do you think there are? And that’s the high yield problems to some extent. So this is jumping ahead a little bit in the method. I start with goals because, you know, I find that we people think there’s an opportunity, right? Like, so we say that something’s a problem because we think there’s a solution out there. We think that conversion could be higher. It’s a 3% industry standard says 5%. Okay. That’s an opportunity. So they often have that reason somewhere in the group bringing you in, or they wouldn’t bring you in, right? But you have to kind of suss it out because they sort of, there’s this belief that the data is just going to reveal the answers. And that’s really not true.

00:20:33.31 | Michael Helbling: I’ve literally been sat down and told, like, find something in the data that we can do, like optimize. And it’s like, do you have something you’d like to accomplish or you just know the data is going to tell us?

00:20:46.46 | Tim Wilson: I had a client where there was an analytics center of excellence. And the guy who ran that, he said, look, we meet with the brand managers every two weeks and we have to find something to bring to them. And I’m like, well, what’s keeping them up at night? Like, what are they trying to do? It’s like, I don’t like the first time we did it, we found all sorts of stuff. They didn’t act on any of it. We found some other stuff two weeks later. They didn’t act on any of that either. And it was like, but you’re trying to blame them when you haven’t actually asked them what they care about. I mean, I didn’t that did not go well. It was a long, that was engagement that felt a lot longer than it was.

00:21:18.91 | Zohar Strinka: It’s a misunderstanding of what we do with analytics, right? It’s really this belief that somewhere in the data is going to be some answer that we can just, like, go solve something. And it might be there, right? When you’re an analyst, if you’re good at what you do, you’re asking those questions. You’re trying to say, what would increase profit? What would increase conversion? It’s why I like certain dashboard tools, because you can explore the data more easily and just say, hey, is there any, like, relationship here that we could do something about? Is there some action we could take? So I’ve enjoyed, that was how I actually got started in analytics. I did a lot.

00:21:57.87 [SPEAKER_UNK]:

00:21:57.87 | Zohar Strinka: I know a lot. I know a lot. I know a lot. I know a lot. I know a lot.

00:21:58.07 [SPEAKER_UNK]:

00:21:58.07 | Zohar Strinka: I know a lot. I know a lot. I know a lot more. Like, I called it clever averages at the time, but it turns out that it has a real name of diagnostic analytics. And just trying to see, is there something in there? Is there a pattern that we can do something about?

00:22:11.50 | Tim Wilson: Do you run into, I guess, yeah, this, we’re heading down the, we’re heading down the path of, like, the assumption is I have all this data, therefore there must be value in it. I sometimes think about it, like, in an ad, it’s like somebody taking a bag of rocks and saying, oh, I don’t know. I heard somebody once busted up their bag of rocks and found some diamonds in it. So I’m just going to keep pulverizing and testing these rocks because I have a bag of rocks. So I’m just going to keep pounding it until I find value. And I feel like I run into cases where really they’re best suited to actually gather some new data, often through an experiment. It’s like, I know you have this massive. Wonderful snowflake environment with all of these integrated data sources. But what you really should do is design an experiment and run it for two months. And that’ll give you, that will actually give you a stronger answer. And that data is never going to live in the warehouse and you’re not going to find it. Like, so do you, if you start at that level and you get to like a high yield problem, do you come across like, I’m going to constrain myself to the data that we have? Or do you say, if there’s. If there’s some other low cost, whatever that means, data, that’s the best way to solve it. You’re looking in the wrong space.

00:23:35.77 | Zohar Strinka: It can be high cost data, right? If you’re thinking about going and running your whole business strategy and you’re a decent sized company, like the data could be high cost. There’s reasons those third party data providers exist. But yeah, I run into it. It comes up a lot that they sort of think that, well, we have so much data that the answer must be in here. So to your point. Yeah. Yeah. Yeah. So I think that you do need to really bring that modeling point of view. I mentioned that I have this optimization background, which basically says, if you have the right information, it’ll tell you the best decision to make. So you take the inputs and you can decide exactly. The future is uncertain. And often the best decision is to put off a decision and see what happens in the world before you commit to something or you buy flexibility. Right. And so there’s always this. Question of what do I need to commit to today? And what do I want to put off till tomorrow? Or what do I want to learn before I commit to a decision?

00:24:37.21 | Tim Wilson: I remember my mind being blown in a. I don’t remember what the class was when like options theory was like, there’s value in an option, so you don’t have to like make if there’s a way to make a small decision that learns and moves you forward. But now we said like decision two or three times in the last 60 seconds. So I’ve got to go like the easy. Hacky rant that I have was my understanding of like the history of the BI came out of decision support systems and even prepping for this show. I was like, oh, I guess people still say decision support systems. You’re you’re framing is like we’re fundamentally trying to help people make decisions. But it does. It feels like to me, we’ve lost that somewhat. There’s a lot of times the business like, yeah, I want to decide the best way to spend my money. And that’s like that somehow feels too nebulous. And I don’t know. Where do you you have thoughts on decisions? I know from pre-show back and forth. Where do you land on that?

00:25:38.75 | Zohar Strinka: So where I land on decisions is unless you’re going to do something differently after your analysis, which is a decision or an action, that analysis had zero value. It had no effect on the world. And so. Fundamentally, any analysis we do has to be informing a decision or. It’s kind of a waste of time. That’s my my initial shot across the bow is decisions have to be what we’re aimed at. The second part of that is people like to own the decisions. People like to feel like they’re making the decision. So this is what I ran into initially with inventory. So I have these great inventory models. I can make the optimal inventory decision. And. You go do that and they’re like, oh, yeah, the data is wrong in these ways. I’m just going to ignore that and make my own decision the way I’m comfortable with it. And so you run into this this challenge where people really want to feel in control of the decision and decision support that isn’t adapted to how they think about it is just going to be ignored because it needs to support the way they think about these tradeoffs and these problems and these decisions. And so I think that’s the core of it is really we often think of a model telling you the right answer. People don’t want a right answer. They want to understand and figure out how to balance these tradeoffs.

00:27:13.51 | Michael Helbling: This is resonating with me because what I’ve observed is that how people approach this, like both through personality as well as positionality influences how analytics works for them or it doesn’t. And so I think that’s the core of it is really we often think of a model telling you the right answer. Because if they’re sort of like, yeah, I need support. I’ve got these decisions to make. Give me as much material as possible to inform me versus I already pretty much know what I’m doing. And maybe you’ll come up with something. Maybe you won’t. And I’ll spend all my time picking at your answers and then just go make my own choice. Like, OK, well, why am I working with you? Like, I don’t need to do this for you. You already know everything. So it’s not a big deal. But it goes back to like, yeah, how and like in the. In the world of analytics, we talk about this term like data literacy or numeracy or the ability to use data to make decisions and this kinds of things. And like, what is data culture? And all of that kind of stems around sort of this concept of like, yeah, how do you take analysis and put it in front of somebody so they’ll actually consume it into the process? And I’m endlessly fascinated by that because it’s. Actually more of a human problem than a data problem.

00:28:30.47 | Tim Wilson: I will metaphorically throttle analysts who say when somebody comes to them and ask them a question, they say, what decision are you going to make? And they’re kind of pat themselves on the back because they’re like, oh, they asked for this data. And I told them, what decision are you going to make? And I’m like, that’s that’s wrong. And then I see things that are like a dashboard that doesn’t lead to decisions is a worthless dashboard. I don’t like that either. What I like about if you with your perspective on the decisions. And. Instead of having a framework and maybe this is me projecting what I feel like is effective is you’ve got to start. You’ve got to start understanding their problem and their mindset, build the trust, get that focus, do a little bit of educating along the way so that then whenever whatever happens to be done with the numbers, they were on board with it. They were involved with it. They were bought in. And you were also kind of gently educating them that it isn’t it’s not effective for them to say, I got to decide whether I spend on A or spend on B. So just give me all the data and that would give me the answer. You’re like, it’s it. The fact that you’re kind of framing it as saying, let’s really be clear on what our goal is, because if you’re trying to make a decision and you don’t really have that articulated, you’re in a problem. You have a problem.

00:29:52.63 | Zohar Strinka:/strong> And I’m going to push this a little further. So for both of you, imagine you’re standing in. The grocery store and you’re looking at all the different kinds of peanut butter. How do you decide which one to buy? It’s a data informed decision.

00:30:07.03 | Michael Helbling: That’s so it’s not. It’s whichever one my wife, Maria, told me to buy. So it’s good.

00:30:16.37 | Zohar Strinka: So this is where I go when what to your point, Tim, of like thinking of that analyst saying, oh, they asked for the data. And I said, what decision are you trying to make? Well, we often don’t. We don’t really know which decisions we’re making or what we really care about until we really try to make it right. It’s really when you’re trying to make that decision that you either have an answer or you don’t. And so one of the things that I think is really powerful that we do as individuals making decisions is pro and con lists, because it lets you sort of say these are the things I care about. These are all the goals I have. And that’s how two alternatives typically rank on the goals. Now I can sort of weigh these different factors in my head and see I like option A better than option B. To me, that’s the mindset we should be taking into analytics to help inform decisions. It’s more pro and con lists and less. Here’s the score. The score says this is the decision to make because it’s the most accurate. It’s the best. But instead, sort of turning it more into that multifaceted set of things we care about.

00:31:26.86 | Tim Wilson: Although it also sounds. It feels like it works into a confusion matrix world where you were talking about the cost of making the wrong. And decisions aren’t necessarily binary, but what’s the cost of missing in this direction? What’s the cost of trying to quantify or clarify what those trade-offs and what the upside and downside. Risk. Like it just feels like it starts with. You got to capture it and get it out of people’s brains and then figure out. Can we bring it into focus? Like, is there data to help focus that? And then maybe because it may be like, oh, yeah, we know that the downside risk is huge and the upside risk upside benefit is small or whatever. And then you can move down to saying, OK, given all of that now. What we’re trying to figure out is actually quite narrow and tight and everybody we’ve iterated on it enough that everybody’s on board. Like once I do this thing, the decision will become. Self-evident, even though there’s going to be uncertainty in it still. Wow. I’m feeling hand wavy theoretical now.

00:32:50.92 | Zohar Strinka: Well, and this is what I’ve struggled with as I’ve been trying to put pen to paper on this. Right. It’s. It is really like it’s this abstract thought process of. For me, it just felt like I always knew which next question I wanted to ask my client because I was driving at something. I thought there was an opportunity over here. And so I’d ask them about it and then they’d give me an answer. And OK, I’d ask a different question based on that answer. And so this problem space idea, you’ve mentioned it a couple of times. I haven’t defined it. But it’s this idea that you you can sort of say, OK. Do we want to maximize conversions or do we want to maximize clicks or do we need to have better marketing ads or do we need to have different platforms? And it’s sort of this question, the series of questions as you’re trying to figure out where’s the opportunity, where’s the decision I can make differently that’s going to have the results I want. And so to me, problem space is really you start somewhere and then you ask a question of, well, is this really what I’m after or is there something better over there? Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right. Right.

00:34:06.42 | Tim Wilson: Right. Right. Right. Right. Right. Right. smart and inquisitive person who’s asking some really good questions that making this making them think is it on you to then say like synthesize it and play it back to them so that they start to

00:34:38.40 | Zohar Strinka: get clearer in their thinking so that’s what i i did innately when i started at consulting it turns out to actually drive benefits or value you know to use the consulting speak you have to actually make decisions eventually and so feeling like you understand your business better you understand where the opportunities are is only say half the battle it’s the half that i i started with but to your point at some point you have to say all right this is the problem we should go after and solve and this is the benefit of doing so and that’s another of those areas where i have to hold myself accountable to not keep exploring because there’s always more interesting questions right if you’re a curious person in analytics that sort of guides you in all these discussions and oh what if we could go do something over there well at some point you have to rein yourself in and sort of say okay what can we do here what what can we what decisions can we inform or what analysis can we do there’s like this reckoning moment where you have to just say okay the world’s uncertain but we’ve gotta move this

00:35:50.42 | Tim Wilson: direction but do you find yourself like almost like that that becomes a living like this is the this is the space and other stuff will come up and i’m gonna have the discipline to not pursue it but i’m gonna throw it into the into my i don’t know what is the problem space map or whatever to have it so there’s always something to come back to that’s how my brain

00:36:10.70 | Zohar Strinka: works i i uh yeah it’s something we’re trying to set that aside is my challenge is to sort of focus on the things that are in front of me and i’m like okay well i’m gonna go do this and i’m gonna do this and i’m gonna do this and i’m gonna do this and i’m gonna do this and i’m gonna do this and of me because new things always come up right that might change the right approach to take and that’s good if you’ve gotten somewhere if you’ve delivered something but now you really really have to like i i enjoy proof of concept projects and mvp kind of projects because we’re just trying to prove it works and someone else can go scale it now so that’s that’s for me like just a self awareness thing like that that initiative is a self-awareness thing and i think that’s a good version that proves it makes sense okay let’s now go on to the next thing it out of that bucket of potential problems okay we solved that thing what’s next boy that seems like another devil

00:37:02.74 | Tim Wilson: watch clients like people get so excited that something’s going to happen and they’re like well let’s just either it’s let’s build the whole thing so it becomes way bigger and it hasn’t been proved out or it’s let’s build the proof of concept and say cool so now can you just push it into production it’s like well no this was built it’s got it’s got manual data refresh loads that aren’t going to be sustainable and we need to to throw it out but you can do 10 of those for one full-on implementation wrote an article that

00:37:31.59 | Zohar Strinka: touched on that because it is like it’s a challenge we technical folks have where we get to decide do you build it enterprise ready and then nine-tenths of them get shelved or do you build it as a true poc and then get burned the one time that they actually want to go go launch it and it’s like i i feel like that’s one of the things in the technical space where we have to educate the business partners better about these trade-offs of okay are we building this as a real poc and what does that mean right do you have to do the the man do you have to build a database or can we prove it out in python like do we what choices do we need to make and how much what parts of the question are sort of questions and which ones are known we can build the technology okay if that’s not a concern don’t worry about the technology side right if it’s a data question and so to me there’s an element of choosing the right parts of the problem to to test it’s really the things where you have the least the biggest questions if this works we’re good if it doesn’t work it doesn’t okay just test that part of the project it’s funny because i’ve also used this in kind

00:38:49.18 | Michael Helbling: of a sort of like a circular fashion where as an analyst i’m keeping track of how i’m approaching solving these problems and then what’s working and what’s not working and then going back and reiterating on that process as time goes on so like you can even take your method and apply it just to even how you approach the business to work on different problems anyways this conversation has been awesome really really uh right up our alley thank you so much zohar one of the things we’ll be talking about is the last call something that might be of interest to our listeners so har you’re our guest uh do you have

00:39:27.48 | Zohar Strinka: a last call you’d like to share well i have two if that’s okay that’s fine yeah all right um so the first is i do have a udemy course on the metal problem method that i’ve just built so if you’re interested in learning more i have the website that’s just out there as well but i’ve tried to make it a little bit more hands-on the second one is i live in this manufacturing and inventory and all that space and so i follow a blog that’s on operations management and a couple weeks ago they had a blog on pringles kpis so they were using advanced analytics to deal with the fact that you know when you have a new batch of potatoes the moisture will be a little bit different and so they were using sensors and all that information to adjust how they made pringles and what they were reporting was a 10 percent rate of improvement in the metal problem and so i’m going to talk a little bit more about that and things like that and they’re talking about maybe we don’t have to be so picky about which potatoes we buy because apparently they buy exactly one variety and so it was just an

00:40:31.20 | Tim Wilson: interesting question of the pringle dashboard i feel like walt hickey’s numlock news had a reference that rings a bell that like the like pringles innovation because i mean they’re such a odd little chip with cocaine or whatever crack something that’s baked into them because

00:40:49.48 | Michael Helbling: can he just one i technically i don’t think they’re even called a potato chip anymore but you know that’s not relevant to the to the question all right tim what about you

00:41:01.17 | Tim Wilson: what’s your last call um i will pop a good old gary angel he wrote a post on his medium site of educating for ai the skills grads need in an ai enabled world around aren’t what anyone thinks and he doesn’t pretend to have a good old gary angel and he doesn’t pretend to have a good a good old gary angel and he doesn’t pretend to have a good old gary angel and he doesn’t pretend to have the answer and you’re like oh this is going to be some it’s very thoughtful had some really clever takes on that so i recommend that what about you michael what’s your last call

00:41:31.28 | Michael Helbling: well i recently ran across an old short story by isaac asimov they wrote in 1957 called profession and you can find it online in a couple places but basically the story goes that every person gets into their career after high school by basically basically their knowledge of that profession being wired directly into their brain and follows the story of a young man who is basically set outside of that process and he takes him a long time to figure out why he’s not being able to be in a profession but has to figure out how to do things on his own and learn things on his own and what that means for him anyways fascinating on a couple levels especially because we’re at a time where we’re basically kind of letting ai be our brain in a lot of ways our versus business Genbie products could work very well if they should play around with different choices one way or another right now when all this happens them um i i feel like when i talk about familial Romantic arts i feel like every star bearers i feel like ways and what that might mean for thinking and human enfeeblement and things like that. So,

00:42:24.85 | Tim Wilson: anyways, it’s a fun short story. Without the three laws of robotics applied to it.

00:42:28.98 | Michael Helbling: Dang it. Not directly. Yeah. No, Asimov is just so great. Like his whole brain, like what was he doing in 1957 thinking like this? Anyway, it’s really good. All right. Zohar, again, thank you so much for coming on the show. This has been a great conversation and one that’s near and dear to our hearts. And so, I appreciate your take on it and taking the time to chat with us. And if people want to learn more, you have a website for the Meta Problem Method, which is meta-problem.com, which I highly recommend people take a look at. And as you’ve been listening, you probably have your own thoughts and we would love to hear from you. And so, yeah, please reach out to us. You can do that as a listener through the Measure Slack chat group or you can reach out to us on the Meta Problem Method website. And we would love to hear from

00:43:16.34 [SPEAKER_UNK]:

00:43:16.34 | Michael Helbling: you. And as you’re listening, also feel free to leave reviews, ratings, and comments on whatever episode or whatever platform you listen to the episode on. We’d love to hear from you. All right. I think we did it. We did the whole thing. And that’s awesome. I don’t know. You know, it’s funny because I love a scientific approach to this, because it feels super daunting when you first start tangling with this problem. And I’ve watched a lot of analytics people basically bash their brains out in a sort of way on this problem where it’s sort of like you hear this again and again. Nobody ever listens to me or I do all this work and it never gets anything done. And I’m never attached to the decisions that ends up getting made. And so, it’s like, well, how do we get better at this? And so, I love so hard that you’re tackling this problem and you’re thinking, well, how do we get better at this? And I think it’s not only helpful for businesses, but it’s helpful for everybody in our industry. So, thank you very much. And I think I speak for my co-host, Tim Wilson. No matter how big your meta problem that you’re

00:44:29.32 | Announcer: trying to solve, keep analyzing. Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at at Analytics Hour, on the web at analyticshour.io, our LinkedIn group, and the Measured Chat Slack group. Music for the podcast by Josh Crowhurst. So, smart guys wanted to fit in. So, they made up a term called analytics. Analytics don’t work.

00:44:56.30 | Charles Barkley: Do the analytics say go for it no matter who’s going for it? So, if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition. I’m fighting out in real time some of my responsibilities.

00:45:13.40 | Michael Helbling: Stop. That’s good. We’re super…

00:45:16.82 | Tim Wilson: I’ve been on Slack like a week and a half ago. We’re super buttoned up.

00:45:18.98 | Michael Helbling: The test designer said this is great. Let’s start arguing after we start the show, Tim. Come on. No, I’m just kidding. All right. All right.

00:45:38.07 | Tim Wilson: Rock, flag, and peanut butter decisions. Decisions.