The Analytics Power Hour · 2026-09-15 · 46 min
Key moments - from our scoring
Substance score
62 / 100
Five dimensions, 20 points each
Michael Helbling and Tim Wilson examine the fundamental purpose of analytics with guest Zohar Strinka, whose PhD in industrial and operations engineering informs her consulting work with manufacturers, distributors, and retailers. Rather than jumping into dashboards or models, Strinka advocates for understanding key business trade-offs - like the tension between stockouts and dead inventory, or ad spend ROI versus brand awareness - that reveal what decisions actually matter. She developed the Meta-Problem Method, a hierarchical framework (dilemmas, goals, problem spaces, high-yield problems, problem selection) that forces teams to identify which analytics effort will move the needle rather than pursuing vanity metrics. The conversation touches on real-world examples from COVID and tariff disruptions, where mathematical optimization alone fails without context about lead times, industry dynamics, and true business constraints. Strinka emphasizes that analytics professionals must avoid "performative analytics" - using proxies and metrics that feel data-driven but miss the actual outcome being optimized.
Use Zohar Strinka's Meta-Problem Method hierarchy: start by clarifying the business goal and key trade-offs, then map which problems directly impact that goal, and prioritize high-yield problems - those where solving them will create measurable change in outcomes that matter.
With long lead times (e.g., 8 months for overseas orders), mistakes in order quantity are magnified because you cannot react quickly to market changes; with short lead times (e.g., days from a distributor), the cost of being wrong is much lower, so the decision rules fundamentally shift.
A good metric directly connects to a business outcome you control (conversions, awareness, revenue); a performative metric (like impressions or clicks that don't lead anywhere) feels data-driven but doesn't link back to the actual decision or outcome you're optimizing.
Explicitly state why the reframe matters: explain that if the goal is misunderstood, your recommendation will be wrong; position the questioning as due diligence, not criticism, and show how clarifying goals changes which problem to solve.
Avoid relying solely on historical models or external precedent; instead, identify the key business constraints (cash flow tolerance, lead times, cost of error in each direction) and build frameworks that acknowledge uncertainty rather than predict the future.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode contains several substantive ideas about decision-making in analytics - particularly around identifying true business problems versus proxies, understanding trade-offs, and the inventory overstocking paradox. However, significant portions devolve into filler (extended 'yeah's, repetitive framing), and key frameworks (the Meta-Problem Method hierarchy) are mentioned but never fully explained with worked examples. The guest offers genuine practitioner wisdom about lead times, cost asymmetries, and decision support philosophy, but much ground is covered abstractly rather than concretely.
unless you're going to do something differently after your analysis, which is a decision or an action, that analysis had zero value
the research studies found that people like to over-order inventory. They hate stockouts... the real world experience is go lower, order less inventory
Zohar brings a genuine operations-engineering perspective and usefully challenges the 'analytics = dashboards and insights' default assumption by centering decision-making and problem framing. However, the core thesis - that analytics should support decisions, that you must clarify the problem before analyzing, that trade-offs matter - is not novel in the analytics literature (echoes of operations research, classic BI framing, even Kahneman-style decision theory). The Meta-Problem Method itself is presented as proprietary but never concretely differentiated from existing problem-framing methodologies.
the goal is to help organizations make better and maybe faster decisions
people like to own the decisions. People like to feel like they're making the decision
Zohar Strinka has legitimate credentials (PhD in industrial/operations engineering, independent consultant working with real clients on inventory and supply chain problems) and demonstrates hard-won consulting experience. However, she is positioned more as a thought-leader / method-creator than as an operator who built or scaled a business function herself. Her examples are drawn from consulting engagements, not from running analytics at scale inside an organization. She is credible but not at the tier of a VP Analytics who has managed large teams or a founder who deployed these ideas under existential pressure.
I got into consulting kind of by accident where I graduated with my PhD, which was focused on optimization and these math models of decisions
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
The episode relies heavily on abstraction and generalized frameworks rather than concrete named examples. The Pringles KPI story (sensors, potato moisture, 10% improvement) is mentioned late and briefly, without specifics on implementation, timeline, or actual business impact. The COVID/tariff examples are discussed in principle (long lead times vs. short) but without dollar figures, company names, or measurable outcomes. The marketing spend example ($500K budget) is purely hypothetical. Most claims about client situations lack specificity that would allow listeners to apply or verify the ideas.
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
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
Tim and Michael ask competent, probing questions that push Zohar toward the philosophical and practical center of her thinking (what is a decision? how do you reframe problems without alienating stakeholders?). However, follow-ups are often soft; when Zohar gives abstract answers, the hosts tend to rephrase and affirm rather than demand specificity. The hosts do show vulnerability (Tim's marketing analytics riff, Michael's personal example with his wife) which builds rapport but doesn't deepen the analysis. There are also production issues: long stretches of transcript filled with 'yeah' fillers that suggest poor editing or transcription errors, breaking conversational flow.
Can you do you bridge those uncertainties with what you said early on?
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?
Computed from the transcript - who did the talking, and the words that came up most.
Before there was BI, there were "decision support systems." Somewhere along the way, we seem to have quietly dropped the "decision support" part and just kept the systems. Zohar Strinka, founder of Analytics Strategies and creator of the Meta-Problem Method, joined us to put the point back where it belongs: if nothing is going to be done differently after the analysis, then the analysis had exactly zero effect on the world. But - and this is the part that's easy to miss - that does NOT mean marching up to a stakeholder and demanding, "What decision are you going to make?" People don't want a model that hands them the right answer. They want to understand and weigh the trade-offs themselves, which means good decision support looks a lot more like a really well-informed pro/con list than a score. We got into problem spaces, high-yield problems, the cost of being wrong in each direction, why "we have all this data, so the answer must be in here" is really just a person pulverizing a bag of rocks hoping for diamonds, and, ultimately, peanut butter. This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y .
Transcribed and scored by The B2B Podcast Index.
[Announcer]: Welcome to the Analytics Power Hour. [Announcer]: Analytics topics covered conversationally and sometimes with explicit language. [Michael Helbling]: Hi, everybody. Welcome.
[Michael Helbling]: It's the Analytics Power Hour, and this is episode 306. [Michael Helbling]: You know, sometimes it's good to step back from whatever you're working on [Michael Helbling]: and maybe re-ask the question, well, why do we do analytics in the first place? [Michael Helbling]: I mean, aside from the obvious rock star status. [Michael Helbling]: I mean, I kid, I kid.
[Michael Helbling]: The goal is to help organizations make better and maybe faster decisions. [Michael Helbling]: And the barriers to doing that are, I mean, oftentimes both obvious but also obscure. [Michael Helbling]: So let's talk about it. [Michael Helbling]: My co-host is Tim Wilson.
[Michael Helbling]: Hi, Tim. You've supported a fair few decisions over the years. [Tim Wilson]: We decided to record this episode. [Tim Wilson]: That's right.
[Tim Wilson]: That's the one I'm most proud of ever. [Michael Helbling]: And I'm Michael. [Michael Helbling]: I'm not sure if I've decided yet to fully record this episode, but we'll see how it goes. [Tim Wilson]: Just drop 10 minutes in.
[Michael Helbling]: Yeah, that's right. I'm out. That's right. [Michael Helbling]: All right.
But we actually are excited about this because we also have a guest. [Michael Helbling]: Zohar Strinka is the founder of Analytics Strategies and the creator of the Meta-Problem Method, [Michael Helbling]: a science-based approach to choosing which problems to solve. [Michael Helbling]: She has a PhD in industrial and operations engineering from the University of Michigan. [Michael Helbling]: And today she is our guest.
[Michael Helbling]: Welcome to the show, Zohar. [Zohar Strinka]: Thanks for having me. [Michael Helbling]: Yeah, thanks. [Michael Helbling]: Thanks so much for coming on the show.
[Michael Helbling]: And I think maybe just to get us started, [Michael Helbling]: maybe could you just give us a little more background on the kind of work you do with companies? [Michael Helbling]: I know you more operate in the consulting space like Tim and I do as well. [Michael Helbling]: So just give us a little flavor, some of your background and the types of work you do that [Michael Helbling]: kind of led into some of the work you're doing that we're talking about today.
[Zohar Strinka]: Yeah, happy to. [Zohar Strinka]: So I got into consulting kind of by accident where I graduated. [Zohar Strinka]: I graduated with my PhD, which was focused on optimization and these math models of decisions. [Zohar Strinka]: But I graduated and was trying to figure out what was the career path ahead of me.
[Zohar Strinka]: And a lot of my peers were going into data science. [Zohar Strinka]: So I was looking at opportunities like that. [Zohar Strinka]: But I found I didn't have the machine learning expertise that people were expecting. [Zohar Strinka]: So I applied for jobs and found that in consulting, I asked good questions.
[Zohar Strinka]: And that was an important part of consulting. [Zohar Strinka]: And so I found a job. [Zohar Strinka]: There about a year in, I was laid off. [Zohar Strinka]: 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.
[Zohar Strinka]: 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. [Zohar Strinka]: But I had a different way of approaching analytics that I think was interesting to my clients since then. [Zohar Strinka]: So I've been doing this since 20. [Zohar Strinka]: I've been working as an independent consultant.
[Zohar Strinka]: I've worked with all sorts of different companies. [Zohar Strinka]: I'm often more working with manufacturers or distributors or retailers because I have an inventory background. [Zohar Strinka]: And so this industrial engineering mindset is really extra relevant for those folks. [Zohar Strinka]: 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.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Or what questions to answer or do we need a data warehouse at all? [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.
[Zohar Strinka]: Yes. [Zohar Strinka]: Yeah. [Zohar Strinka]: 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? [Zohar Strinka]: Or they're trying to.
[Zohar Strinka]: 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. [Tim Wilson]: How much is using the math? [Tim Wilson]: This does flash me back to COVID when there were. [Tim Wilson]: It's like I was supporting some companies that were asking, like, tell us what we should do.
[Tim Wilson]: And I'm like, well, this is I don't know that like the historical precedent of 1918 is particularly useful. [Tim Wilson]: It was like you need to kind of think through. [Tim Wilson]: Like. [Tim Wilson]: Can you do you bridge those uncertainties with what you said early on?
[Tim Wilson]: 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? [Tim Wilson]: Is there a redirection that has to happen? [Tim Wilson]: Like it almost I'm trying to read between the lines. [Tim Wilson]: It sounds like I bet you reset every single client within the first week of working with them or no.
[Zohar Strinka]: But. [Zohar Strinka]: It's tricky. [Zohar Strinka]: Because there's so much that goes into each of those decisions. [Zohar Strinka]: The one that I made a mistake on.
[Zohar Strinka]: So I had my book knowledge on inventory. [Zohar Strinka]: I was working with a client and they were trying to deal with the COVID disruptions of these really long lead times. [Zohar Strinka]: What I didn't quite understand is depending on your lead time is how those decisions matter. [Zohar Strinka]: So if you have a short lead time, you can kind of react to those changes in the world really quickly.
[Zohar Strinka]: 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. [Zohar Strinka]: If your lead time is a few days because you're buying from a distributor, you can react much more quickly. [Zohar Strinka]: 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. [Zohar Strinka]: And so working with this client, they had long lead times.
[Zohar Strinka]: And I hadn't like emotionally learned. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
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[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [SPEAKER_UNK]: Yeah.
[SPEAKER_UNK]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
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[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: math can help and highlight, okay, this is a mistake where if we make it, it's fine. Either [Zohar Strinka]: we're getting two weeks of inventory or three months, we can handle that from a cashflow point [Zohar Strinka]: of view.
But if we're talking about either two weeks or 10 years, that becomes a lot more of a [Tim Wilson]: problem. How do you get to finding out what those sort of key factors, like you talk about problem [Tim Wilson]: spaces, you talk about making decisions and there you just sort of talked about like the business [Tim Wilson]: operating environment, like where, where do you start? I guess what's the, and how much of that [Zohar Strinka]: do you have to know before you can start doing math stuff?
It's tricky. So one thing I've learned [Zohar Strinka]: is how much the industry, like industries vary. The math is the same, right? The analytics is the [Zohar Strinka]: same.
So a lot of folks in analytics consulting, especially, [Zohar Strinka]: we come in and we say, I know the math, the data's there, I can figure it out. And there's a risk [Zohar Strinka]: there depending on the industry, right? And so there's, there's some of that of what are the key [Zohar Strinka]: trade-offs to me are a cheat code for navigating a new industry. If you can pull out those key [Zohar Strinka]: trade-offs, what are the goals that you're trying to balance?
Okay. We want to not stock out, [Zohar Strinka]: but we don't want to have, [Zohar Strinka]: we don't want to have a dead inventory. That's a key trade-off and there's got to be a sweet spot [Zohar Strinka]: somewhere. And so for each of those questions, you start to ask yourself, what's the cost of [Zohar Strinka]: getting it wrong either way.
And if the cost is really high one direction, not too bad, the other [Zohar Strinka]: direction, well, err on the side, that's not going to be expensive. And so that sort of universal [Zohar Strinka]: frame up framing of what's the trade-off, how does my choice impact my outcomes can really [Zohar Strinka]: help you avoid some of those mistakes. And we're talking really abstract, but I'm really thinking [Zohar Strinka]: of like during my PhD, I was focused on inventory questions of how much inventory should you have?
[Zohar Strinka]: And the research studies found that people like to over-order inventory. They hate stockouts. [Zohar Strinka]: And usually the cost of stocking out, isn't that crazy? You can expedite, like there's usually not [Zohar Strinka]: that high a cost.
And so the real world, [Zohar Strinka]: experience is go lower, order less inventory. You'll thank yourself later, [Zohar Strinka]: but that's like something you have to learn. It's not in the math by itself. It's there, [Tim Wilson]: but it's hidden.
I'm trying to think coming from kind of more of a, from a marketing analytics [Tim Wilson]: background and finding often, I mean, because when you talk inventory and marketing, it's like, [Tim Wilson]: what's the ad inventory and how much can you spend on, spend your advertising dollars? [Tim Wilson]: And the, the risk of not advertising enough is that you're missing your, your audience. [Tim Wilson]: The reality is, is there's all sorts of money that might as well just go light it on fire.
And the, [Tim Wilson]: the, the smoke from that will bring more customers than what you're spending on, [Tim Wilson]: you know, crappy ad inventory. I'm trying to like, I'm trying to put myself into a conversation with [Tim Wilson]: a senior marketer. I mean, I think it'd be really useful. I'm just trying to imagine how, [Tim Wilson]: you know, I'm just trying to imagine how, you know, I'm just trying to imagine how, you know, [Tim Wilson]: I'm just trying to imagine how, you know, I'm just trying to imagine how, you know, I'm just trying to [Tim Wilson]: imagine how it goes.
Like you have, you have half a million dollars that you're going to spend [Tim Wilson]: on advertising and you're trying to make decisions on where to spend it and like how to, [Tim Wilson]: I'm trying to think where the, the, the, the trade-off discussion comes in. I don't have an, [Zohar Strinka]: I don't have an easy answer. Yeah. And I've worked in marketing as well.
And so, [Tim Wilson]: and then you ran back to inventory. No, sorry. [Tim Wilson]: These people are horrible. [Zohar Strinka]: It's, it's interesting because so much of the marketing world is you do it because you've got [Zohar Strinka]: to do it, right?
You, we have to spend money on marketing because we've got to be getting our [Zohar Strinka]: name out there and it can be so hard to trace how that dollar worked or didn't. And so the [Zohar Strinka]: fundamental trade-off is what's a dollar spent versus how much are you going to get back? [Zohar Strinka]: Right. And so once you've decided you're going to spend half a million dollars, [Zohar Strinka]: you've sort of eliminated some of that trade-off.
And now to your point, you're deciding, okay, [Zohar Strinka]: where's the best bang for my buck if I have this fixed budget? So there's sort of, you could try [Zohar Strinka]: to make every dollar of ad spend earn itself, but the models are too complicated, right? [Tim Wilson]: Michael, where does your marketing data actually live? Oh, everywhere.
Google ads, [Michael Helbling]: Meta, HubSpot, J4, Snowflake. [Michael Helbling]: I think the forecast metrics live exclusively in Steve's laptop. [Tim Wilson]: Well, that, that feels healthy. [Tim Wilson]: Oh yeah.
Very governed, very mindful. [Tim Wilson]: Well, Prism now has connectors that bring those sources together and turn the raw platform data [Tim Wilson]: into clean analysis ready tables. [Michael Helbling]: Wait, so it does not just connect to the data and say, good luck with these 183 columns. [Tim Wilson]: Correct.
Ask.Y has prebuilt marketing analytics recipes that model the raw data [Tim Wilson]: and keep it refreshed automatically. [Tim Wilson]: Google ads, [Tim Wilson]: Meta, HubSpot, GA4. [Tim Wilson]: You betcha.
Plus Slack, Snowflake, Power BI, and more. I'm not sure about Steve's laptop. [Michael Helbling]: Well, fewer exports means fewer mystery spreadsheets and fewer moments where someone says, [Michael Helbling]: I thought that refreshed automatically. [Michael Helbling]: That is exactly the idea.
[Michael Helbling]: Uh, I would like to personally nominate CSV attachments for retirement. [Tim Wilson]: Just kick them out the door. [Tim Wilson]: We'll click the link in the show description and sign up for the Ask.Y waitlist.
[Tim Wilson]: Use code APH and they will move you to the top. [Tim Wilson]: Top of that list. [Michael Helbling]: Connect the data and disconnect from the nonsense. [Tim Wilson]: Michael, I have a new rule for AI.
[Tim Wilson]: Ooh, this should be good. [Tim Wilson]: If a task involves clicking through six menus just to confirm something I already suspect, AI can have it. [Michael Helbling]: Hey, that's pretty much the idea behind Stape's AI Assistant. [Michael Helbling]: It handles routine measurement tasks directly inside your Stape's account.
[Michael Helbling]: Like what? [Michael Helbling]: Well, ask which containers are connected, check server-side GTM usage, review existing tags, [Michael Helbling]: and check server-side GTM usage. [Michael Helbling]: Review existing tags and check if they are connected. [Michael Helbling]: Check if you have any tags and triggers.
[Michael Helbling]: Uh, inspect your GTM configuration or confirm that events are being collected in GA4. [Tim Wilson]: And it can actually make changes? [Michael Helbling]: Yeah, for supported tasks, yes it can. [Michael Helbling]: It can create Stape's and server-side containers and help set up tags and triggers in GTM.
[Michael Helbling]: And the best part is, you aren't using your own LLM tokens to do it. [Tim Wilson]: So the AI handles some of the repetitive navigation and I get to spend more time checking whether [Tim Wilson]: the implementation actually makes sense. [Michael Helbling]: Yeah, which feels like a much better division of labor. [Tim Wilson]: Yeah.
[Tim Wilson]: So, if you're looking for a better way to get started with Stape's AI Assistant, click [Tim Wilson]: the link in the show description. [Tim Wilson]: I think that's the challenge is that it winds up being performative analytics because you [Tim Wilson]: can find something that claims that from the Google ecosystem or the meta ecosystem or [Tim Wilson]: your digital analytics platform. [Tim Wilson]: And that's like the, I don't know if that winds up, I mean, this is also going to sound [Tim Wilson]: philosophical that it's like, well, it's performative.
[Tim Wilson]: It makes people, it just makes you feel good because you've got something that shows I [Tim Wilson]: spend a dollar and I got $2. [Tim Wilson]: Yeah. [Tim Wilson]: I'm not going to be able to get the numbers back, even if the math is horribly flawed, [Tim Wilson]: I feel like marketing will uniquely say, well, that's okay, because I just want to keep spending [Tim Wilson]: more and I want to justify it. [Tim Wilson]: I'm sorry, I'm just going to have an existential crisis, like right in the middle of, you know, [Tim Wilson]: 15 minutes into the episode.
[Zohar Strinka]: I mean, it's something where I think that is a challenge we run into in analytics when [Zohar Strinka]: you're trying to make these decisions and you want it to be data-based, but you don't [Zohar Strinka]: have the data. [Zohar Strinka]: And so you make up a proxy. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: And so one of the questions I start asking is, are those good proxies? [Zohar Strinka]: Right? [Zohar Strinka]: So, so our impression, right? [Zohar Strinka]: I started working on a marketing project when they still sort of cared about impressions [Zohar Strinka]: a little bit, but they had mostly switched over to clicks.
[Zohar Strinka]: And so there's sort of this, what's the metric we can see and how does it connect to the [Zohar Strinka]: things we care about? [Zohar Strinka]: Which is really, I think one of the most valuable things we can do is keep coming back to what [Zohar Strinka]: are you trying to do with it? [Zohar Strinka]: Like that marketing dollar? [Zohar Strinka]: Are you trying to just bring awareness?
[Zohar Strinka]: Are you trying to get people to click something? [Zohar Strinka]: Well, a radio ad will never get someone to click something. [Zohar Strinka]: Okay. [Zohar Strinka]: So, so you can sort of start to break down what are the decisions you can make and how [Zohar Strinka]: they're connecting to the things you care about.
[Michael Helbling]: So when, when people come to you, like, do you frequently have to go through a refinement [Michael Helbling]: process to the problem itself? [Michael Helbling]: Like this is pretty common and on the marketing side where people are like, I think I have [Michael Helbling]: this problem. [Michael Helbling]: You're like, well, you technically have a sort of different problem and you have to [Michael Helbling]: kind of go through that. [Michael Helbling]: Like, how do you kind of go through that process of like helping refine or redefine the problem?
[Michael Helbling]: And how do you avoid alienating your stakeholder in that process? [Michael Helbling]: Cause like a lot of times that that's a negotiation in a way. [Zohar Strinka]: Yeah, that one's really tricky. [Zohar Strinka]: And one of my answers is my favorite clients, the ones that get the most out of me and I [Zohar Strinka]: enjoy working with the most.
[SPEAKER_UNK]: Yeah. [SPEAKER_UNK]: [SPEAKER_UNK]: [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[SPEAKER_UNK]: [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah. [Zohar Strinka]: when I change the problem on them. [Zohar Strinka]: So they say, I want to figure out [Zohar Strinka]: which ads are working well.
[Zohar Strinka]: Like what's the click-through rate? [Zohar Strinka]: And you sort of say, okay, [Zohar Strinka]: tell me a little bit more about [Zohar Strinka]: why you care about click-through rate. [Zohar Strinka]: Like what outcomes is that driving at? [Zohar Strinka]: And that sort of question of [Zohar Strinka]: how do the things that we control [Zohar Strinka]: link to the things that you want [Zohar Strinka]: is where I've had a lot of success [Zohar Strinka]: throughout my consulting career.
[Zohar Strinka]: But now I'm telling clients more about it. [Zohar Strinka]: I'm saying, I'm asking because [Zohar Strinka]: if we got this wrong, [Zohar Strinka]: then it's going to change [Zohar Strinka]: which problem we should solve. [Zohar Strinka]: If there are different outcomes you care about, [Zohar Strinka]: I'm going to go recommend something terrible [Zohar Strinka]: if I'm working to the wrong goal. [Zohar Strinka]: So if you care about clicks [Zohar Strinka]: because that leads to conversions [Zohar Strinka]: in the marketing world, [Zohar Strinka]: like, okay, we care about conversions then.
[Zohar Strinka]: That's the goal. [Tim Wilson]: And so you have like within the, [Tim Wilson]: and you have the website, [Tim Wilson]: so it's pretty easy for people to go look [Tim Wilson]: and see the high level [Tim Wilson]: because you have kind of the hierarchy of dilemma goals, [Tim Wilson]: problem space, high yield problems, [Tim Wilson]: problem selection. [Tim Wilson]: Like, I think it might be useful like to, [Tim Wilson]: we're not going to get a full complete primer [Tim Wilson]: on the entire method.
[Tim Wilson]: But the fact that it's this, [Tim Wilson]: anytime I see a hierarchy, I'm like, [Tim Wilson]: well, this is sort of forcing decisions at each point [Tim Wilson]: because it's like a tree, I imagine, [Tim Wilson]: that goes down and you're like, [Tim Wilson]: yeah, you can't do everything. [Tim Wilson]: So which branch on the tree are we following? [Tim Wilson]: So maybe you can talk a little bit about like, [Tim Wilson]: nailing down the goal, [Tim Wilson]: which like you just, [Tim Wilson]: I think you just talked about is conversion.
[Tim Wilson]: How do you get from that to a problem space [Tim Wilson]: versus high yield problems, I guess? [Zohar Strinka]: Yeah. [Zohar Strinka]: And the first question, [Zohar Strinka]: I like the dilemma idea [Zohar Strinka]: or the question you're starting with is your scope. [Zohar Strinka]: This is where I've gotten in the most trouble [Zohar Strinka]: where someone says, [Zohar Strinka]: we need to manage this.
[Zohar Strinka]: We need to maximize conversions. [Zohar Strinka]: I'm like, but why? [Zohar Strinka]: You've already worked on conversions. [Zohar Strinka]: Let's work on something else [Zohar Strinka]: that I think is going to go better.
[Zohar Strinka]: And if you make that mistake, [Zohar Strinka]: if you do that change on them [Zohar Strinka]: and that wasn't what they wanted, [Zohar Strinka]: you're going to run into challenges. [Zohar Strinka]: And so that's where I've made those mistakes. [Tim Wilson]: Well, do you have, [Tim Wilson]: because I have been dropped in where, [Tim Wilson]: if you just asked, what is the dilemma? [Tim Wilson]: I would hear, we have all this data [Tim Wilson]: and we don't know what to do with it.
[Tim Wilson]: Or we know we have all the right data, [Tim Wilson]: but it's not giving me insights, [Tim Wilson]: which to me, all sorts of, [Tim Wilson]: and not alarm bells. [Tim Wilson]: I'm like, okay, there's coaching. [Tim Wilson]: I'm not going to swoop in and look at your data. [Tim Wilson]: I've got to get you to the actual, [Tim Wilson]: I guess, business dilemma.
[Tim Wilson]: Like if you run into that where they're bringing, [Tim Wilson]: because it was like an early example you had [Tim Wilson]: was that you're like, [Tim Wilson]: well, they're bringing me in to say, [Tim Wilson]: do some analysis. [Tim Wilson]: I'm like, well, that's not right. [Zohar Strinka]: I have run into that. [Zohar Strinka]: The thing I tell myself today is, okay, [SPEAKER_UNK]: [SPEAKER_UNK]: [SPEAKER_UNK]: [Zohar Strinka]: You're not going to be able to do that.
[Zohar Strinka]: You might think the data magically [Zohar Strinka]: is going to have some answers for you. [Zohar Strinka]: But what I find is often they know there's certain data [Zohar Strinka]: they're not using to make a decision. [Zohar Strinka]: And so that's what I want to hone in on. [Zohar Strinka]: So I started asking, okay, [Zohar Strinka]: why do you think the data will have answers for you?
[Zohar Strinka]: Well, we're not doing forecasting. [Zohar Strinka]: Okay. [Zohar Strinka]: That's now something that analytics makes sense [Zohar Strinka]: to bring to the problem. [Zohar Strinka]: And so I'm usually trying to navigate those questions, [Zohar Strinka]: like go from the world of everything where, okay, [Zohar Strinka]: I don't know your business.
[Zohar Strinka]: I'm not an expert in whatever domain it is. [Zohar Strinka]: You need to give me something more than that. [Zohar Strinka]: Where should I be looking? [Zohar Strinka]: And so that's been my approach is to really ask more [Zohar Strinka]: questions about, okay, [Zohar Strinka]: what opportunities do you think there are?
[Zohar Strinka]: And that's the high yield problems to some extent. [Zohar Strinka]: So this is jumping ahead a little bit in the method. [Zohar Strinka]: I start with goals because, you know, [Zohar Strinka]: I find that we people think there's an opportunity, right? [Zohar Strinka]: Like, so we say that something's a problem [Zohar Strinka]: because we think there's a solution out there.
[Zohar Strinka]: We think that conversion could be higher. [Zohar Strinka]: It's a 3% industry standard says 5%. [Zohar Strinka]: Okay. [Zohar Strinka]: That's an opportunity.
[Zohar Strinka]: So they often have that reason somewhere in the group [Zohar Strinka]: bringing you in, or they wouldn't bring you in, right? [Zohar Strinka]: But you have to kind of suss it out because they sort of, [Zohar Strinka]: there's this belief [Zohar Strinka]: that the data is just going to reveal the answers. [Zohar Strinka]: And that's really not true. [Michael Helbling]: I've literally been sat down and told, like, [Michael Helbling]: find something in the data that we can do, like optimize.
[Michael Helbling]: And it's like, do you have something you'd like to accomplish [Michael Helbling]: or you just know the data is going to tell us? [Tim Wilson]: I had a client where there was an analytics center of excellence. [Tim Wilson]: And the guy who ran that, he said, look, we meet with the brand managers [Tim Wilson]: every two weeks and we have to find something to bring to them. [Tim Wilson]: And I'm like, well, what's keeping them up at night?
[Tim Wilson]: Like, what are they trying to do? [Tim Wilson]: It's like, I don't like the first time we did it, we found all sorts of stuff. [Tim Wilson]: They didn't act on any of it. [Tim Wilson]: We found some other stuff two weeks later.
[Tim Wilson]: They didn't act on any of that either. [Tim Wilson]: And it was like, but you're trying to blame them [Tim Wilson]: when you haven't actually asked them what they care about. [Tim Wilson]: I mean, I didn't that did not go well. [Tim Wilson]: It was a long, that was engagement that felt a lot longer than it was.
[Zohar Strinka]: It's a misunderstanding of what we do with analytics, right? [Zohar Strinka]: It's really this belief that somewhere in the data is going to be some answer [Zohar Strinka]: that we can just, like, [Zohar Strinka]: go solve something. [Zohar Strinka]: And it might be there, right? [Zohar Strinka]: When you're an analyst, if you're good at what you do, [Zohar Strinka]: you're asking those questions.
[Zohar Strinka]: You're trying to say, what would increase profit? [Zohar Strinka]: What would increase conversion? [Zohar Strinka]: It's why I like certain dashboard tools, because you can explore the data more [Zohar Strinka]: easily and just say, hey, is there any, like, relationship here [Zohar Strinka]: that we could do something about? [Zohar Strinka]: Is there some action we could take?
[Zohar Strinka]: So I've enjoyed, that was how I actually got started in analytics. [Zohar Strinka]: I did a lot. [SPEAKER_UNK]: [SPEAKER_UNK]: [Zohar Strinka]: I know a lot. [Zohar Strinka]: I know a lot.
[Zohar Strinka]: I know a lot. [Zohar Strinka]: I know a lot. [Zohar Strinka]: I know a lot. [SPEAKER_UNK]: [SPEAKER_UNK]: [SPEAKER_UNK]: [SPEAKER_UNK]: [Zohar Strinka]: I know a lot.
[Zohar Strinka]: I know a lot. [Zohar Strinka]: I know a lot more. [Zohar Strinka]: Like, I called it clever averages at the time, but it turns out that it has a real [Zohar Strinka]: name of diagnostic analytics. [Zohar Strinka]: And just trying to see, is there something in there?
[Zohar Strinka]: Is there a pattern that we can do something about? [Tim Wilson]: Do you run into, I guess, yeah, this, we're heading down the, we're heading down [Tim Wilson]: the path of, like, the assumption is I have all this data, therefore there must [Tim Wilson]: be value in it. [Tim Wilson]: I sometimes think about it, like, in an ad, it's like somebody taking a bag of [Tim Wilson]: rocks and saying, oh, I don't know. [Tim Wilson]: I heard somebody once busted up their bag of rocks and found some diamonds in it.
[Tim Wilson]: So I'm just going to keep pulverizing and testing these rocks because I have a bag of [Tim Wilson]: rocks. [Tim Wilson]: So I'm going to just keep pounding it until I find value. [Tim Wilson]: And I feel like I run into cases where really they're best suited to actually gather some [Tim Wilson]: new data, often through an experiment. [Tim Wilson]: It's like, I know you have this massive.
[Tim Wilson]: Wonderful snowflake environment with all of these integrated data sources. [Tim Wilson]: But what you really should do is design an experiment and run it for two months. [Tim Wilson]: And that'll give you, that will actually give you a stronger answer. [Tim Wilson]: And that data is never going to live in the warehouse and you're not going to find it.
[Tim Wilson]: Like, so do you, if you start at that level and you get to like a high yield problem, [Tim Wilson]: do you come across like, I'm going to constrain myself to the data that we have? [Tim Wilson]: Or do you say, if there's. [Tim Wilson]: If there's some other low cost, whatever that means, data, that's the best way to solve it. [Tim Wilson]: You're looking in the wrong space.
[Zohar Strinka]: It can be high cost data, right? [Zohar Strinka]: If you're thinking about going and running your whole business strategy and you're a decent [Zohar Strinka]: sized company, like the data could be high cost. [Zohar Strinka]: There's reasons those third party data providers exist. [Zohar Strinka]: But yeah, I run into it.
[Zohar Strinka]: It comes up a lot that they sort of think that, well, we have so much data that the answer must be in here. [Zohar Strinka]: So to your point. [Zohar Strinka]: Yeah. [Zohar Strinka]: Yeah.
[Zohar Strinka]: Yeah. [Zohar Strinka]: So I think that you do need to really bring that modeling point of view. [Zohar Strinka]: 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. [Zohar Strinka]: So you take the inputs and you can decide exactly.
[Zohar Strinka]: The future is uncertain. [Zohar Strinka]: 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. [Zohar Strinka]: Right. [Zohar Strinka]: And so there's always this.
[Zohar Strinka]: Question of what do I need to commit to today? [Zohar Strinka]: And what do I want to put off till tomorrow? [Zohar Strinka]: Or what do I want to learn before I commit to a decision? [Tim Wilson]: I remember my mind being blown in a.
[Tim Wilson]: 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. [Tim Wilson]: But now we said like decision two or three times in the last 60 seconds. [Tim Wilson]: So I've got to go like the easy. [Tim Wilson]: 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.
[Tim Wilson]: I was like, oh, I guess people still say decision support systems. [Tim Wilson]: You're you're framing is like we're fundamentally trying to help people make decisions. [Tim Wilson]: But it does. [Tim Wilson]: It feels like to me, we've lost that somewhat.
[Tim Wilson]: There's a lot of times the business like, yeah, I want to decide the best way to spend my money. [Tim Wilson]: And that's like that somehow feels too nebulous. [Tim Wilson]: And I don't know. [Tim Wilson]: Where do you you have thoughts on decisions?
[Tim Wilson]: I know from pre-show back and forth. [Tim Wilson]: Where do you land on that? [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. [Zohar Strinka]: It had no effect on the world.
[Zohar Strinka]: And so. [Zohar Strinka]: Fundamentally, any analysis we do has to be informing a decision or. [Zohar Strinka]: It's kind of a waste of time. [Zohar Strinka]: That's my my initial shot across the bow is decisions have to be what we're aimed at.
[Zohar Strinka]: The second part of that is people like to own the decisions. [Zohar Strinka]: People like to feel like they're making the decision. [Zohar Strinka]: So this is what I ran into initially with inventory. [Zohar Strinka]: So I have these great inventory models.
[Zohar Strinka]: I can make the optimal inventory decision. [Zohar Strinka]: And. [Zohar Strinka]: You go do that and they're like, oh, yeah, the data is wrong in these ways. [Zohar Strinka]: I'm just going to ignore that and make my own decision the way I'm comfortable with it.
[Zohar Strinka]: 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. [Zohar Strinka]: And so I think that's the core of it is really we often think of a model telling you the right answer. [Zohar Strinka]: People don't want a right answer.
[Zohar Strinka]: They want to understand and figure out how to balance these tradeoffs. [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. [Michael Helbling]: And so I think that's the core of it is really we often think of a model telling you the right answer. [Michael Helbling]: Because if they're sort of like, yeah, I need support.
[Michael Helbling]: I've got these decisions to make. [Michael Helbling]: Give me as much material as possible to inform me versus I already pretty much know what I'm doing. [Michael Helbling]: And maybe you'll come up with something. [Michael Helbling]: Maybe you won't.
[Michael Helbling]: And I'll spend all my time picking at your answers and then just go make my own choice. [Michael Helbling]: Like, OK, well, why am I working with you? [Michael Helbling]: Like, I don't need to do this for you. [Michael Helbling]: You already know everything.
[Michael Helbling]: So it's not a big deal. [Michael Helbling]: But it goes back to like, yeah, how and like in the. [Michael Helbling]: 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. [Michael Helbling]: And like, what is data culture?
[Michael Helbling]: 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? [Michael Helbling]: And I'm endlessly fascinated by that because it's. [Michael Helbling]: Actually more of a human problem than a data problem. [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?
[Tim Wilson]: And they're kind of pat themselves on the back because they're like, oh, they asked for this data. [Tim Wilson]: And I told them, what decision are you going to make? [Tim Wilson]: And I'm like, that's that's wrong. [Tim Wilson]: And then I see things that are like a dashboard that doesn't lead to decisions is a worthless dashboard.
[Tim Wilson]: I don't like that either. [Tim Wilson]: What I like about if you with your perspective on the decisions. [Tim Wilson]: And. [Tim Wilson]: Instead of having a framework and maybe this is me projecting what I feel like is effective is you've got to start.
[Tim Wilson]: 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. [Tim Wilson]: They were involved with it. [Tim Wilson]: They were bought in. [Tim Wilson]: 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.
[Tim Wilson]: So just give me all the data and that would give me the answer. [Tim Wilson]: You're like, it's it. [Tim Wilson]: 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. [Tim Wilson]: You have a problem.
[Zohar Strinka]: And I'm going to push this a little further. [Zohar Strinka]: So for both of you, imagine you're standing in. [Zohar Strinka]: The grocery store and you're looking at all the different kinds of peanut butter. [Zohar Strinka]: How do you decide which one to buy?
[Zohar Strinka]: It's a data informed decision. [Michael Helbling]: That's so it's not. [Michael Helbling]: It's whichever one my wife, Maria, told me to buy. [Michael Helbling]: So it's good.
[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. [Zohar Strinka]: And I said, what decision are you trying to make? [Zohar Strinka]: Well, we often don't. [Zohar Strinka]: We don't really know which decisions we're making or what we really care about until we really try to make it right.
[Zohar Strinka]: It's really when you're trying to make that decision that you either have an answer or you don't. [Zohar Strinka]: 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. [Zohar Strinka]: These are all the goals I have. [Zohar Strinka]: And that's how two alternatives typically rank on the goals.
[Zohar Strinka]: Now I can sort of weigh these different factors in my head and see I like option A better than option B. [Zohar Strinka]: To me, that's the mindset we should be taking into analytics to help inform decisions. [Zohar Strinka]: It's more pro and con lists and less. [Zohar Strinka]: Here's the score.
[Zohar Strinka]: The score says this is the decision to make because it's the most accurate. [Zohar Strinka]: It's the best. [Zohar Strinka]: But instead, sort of turning it more into that multifaceted set of things we care about. [Tim Wilson]: Although it also sounds.
[Tim Wilson]: It feels like it works into a confusion matrix world where you were talking about the cost of making the wrong. [Tim Wilson]: And decisions aren't necessarily binary, but what's the cost of missing in this direction? [Tim Wilson]: What's the cost of trying to quantify or clarify what those trade-offs and what the upside and downside. [Tim Wilson]: Risk.
[Tim Wilson]: Like it just feels like it starts with. [Tim Wilson]: You got to capture it and get it out of people's brains and then figure out. [Tim Wilson]: Can we bring it into focus? [Tim Wilson]: Like, is there data to help focus that?
[Tim Wilson]: 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. [Tim Wilson]: And then you can move down to saying, OK, given all of that now. [Tim Wilson]: 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. [Tim Wilson]: Like once I do this thing, the decision will become.
[Tim Wilson]: Self-evident, even though there's going to be uncertainty in it still. [Tim Wilson]: Wow. [Tim Wilson]: I'm feeling hand wavy theoretical now. [Zohar Strinka]: Well, and this is what I've struggled with as I've been trying to put pen to paper on this.
[Zohar Strinka]: Right. [Zohar Strinka]: It's. [Zohar Strinka]: It is really like it's this abstract thought process of. [Zohar Strinka]: For me, it just felt like I always knew which next question I wanted to ask my client because I was driving at something.
[Zohar Strinka]: I thought there was an opportunity over here. [Zohar Strinka]: And so I'd ask them about it and then they'd give me an answer. [Zohar Strinka]: And OK, I'd ask a different question based on that answer. [Zohar Strinka]: And so this problem space idea, you've mentioned it a couple of times.
[Zohar Strinka]: I haven't defined it. [Zohar Strinka]: But it's this idea that you you can sort of say, OK. [Zohar Strinka]: 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? [Zohar Strinka]: 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.
[Zohar Strinka]: 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? [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right. [Zohar Strinka]: Right.
[Tim Wilson]: Right. [Tim Wilson]: Right. [Tim Wilson]: Right. [Tim Wilson]: Right.
[Tim Wilson]: Right. [Tim Wilson]: Right. [Tim Wilson]: smart and inquisitive person who's asking some really good questions that making this making [Tim Wilson]: them think is it on you to then say like synthesize it and play it back to them so that they start to [Zohar Strinka]: get clearer in their thinking so that's what i i did innately when i started at consulting [Zohar Strinka]: it turns out to actually drive benefits or value you know to use the consulting speak [Zohar Strinka]: you have to actually make decisions eventually and so feeling like you understand your business [Zohar Strinka]: better you understand where the opportunities are is only say half the battle it's the half that i [Zohar Strinka]: i started with but to your point at some point you have to say all right this is the problem [Zohar Strinka]: we should go after and solve and this is the benefit of doing so and that's another of those [Zohar Strinka]: areas where i have to hold myself accountable to not keep exploring because there's always [Zohar Strinka]: more interesting questions right if you're a curious person in analytics that sort of guides [Zohar Strinka]: you in all these discussions and oh what if we could go do something over there well at some [Zohar Strinka]: point you have to rein yourself in and sort of say okay what can we do here what what can we [Zohar Strinka]: what decisions can we inform or what analysis can we do there's like this reckoning moment where you [Zohar Strinka]: have to just say okay the world's uncertain but we've gotta move this [Tim Wilson]: direction but do you find yourself like almost like that that becomes a living like this is the [Tim Wilson]: this is the space and other stuff will come up and i'm gonna have the discipline to not [Tim Wilson]: pursue it but i'm gonna throw it into the into my i don't know what is the problem space [Tim Wilson]: map or whatever to have it so there's always something to come back to that's how my brain [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 [Zohar Strinka]: 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 [Zohar Strinka]: 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 [Zohar Strinka]: of me because new things always come up right that might change the right approach to take and [Zohar Strinka]: that's good if you've gotten somewhere if you've delivered something but now you really really have [Zohar Strinka]: to like i i enjoy proof of concept projects and mvp kind of projects because we're just trying [Zohar Strinka]: to prove it works and someone else can go scale it now so that's that's for me like just a self [Zohar Strinka]: awareness thing like that that initiative is a self-awareness thing and i think that's a good [Zohar Strinka]: version that proves it makes sense okay let's now go on to the next thing it out of that bucket of [Zohar Strinka]: potential problems okay we solved that thing what's next boy that seems like another devil [Tim Wilson]: watch clients like people get so excited that something's going to happen and they're like [Tim Wilson]: well let's just either it's let's build the whole thing so it becomes way bigger and it hasn't been [Tim Wilson]: proved out or it's let's build the proof of concept and say cool so now can you just push [Tim Wilson]: it into production it's like well no this was built it's got it's got manual [Tim Wilson]: data refresh loads that aren't going to be sustainable and we need to [Tim Wilson]: to throw it out but you can do 10 of those for one full-on implementation wrote an article that [Zohar Strinka]: touched on that because it is like it's a challenge we technical folks have where we get [Zohar Strinka]: to decide do you build it enterprise ready and then nine-tenths of them get shelved or do you [Zohar Strinka]: build it as a true poc and then get burned the one time that they actually want to go [Zohar Strinka]: go launch it and it's like i i feel like that's one of the things in the technical space where [Zohar Strinka]: we have to educate the business partners better about these trade-offs of okay are we building [Zohar Strinka]: this as a real poc and what does that mean right do you have to do the the man do you have to build [Zohar Strinka]: a database or can we prove it out in python like do we what choices do we need to make and [Zohar Strinka]: how much what parts of the [Zohar Strinka]: question are sort of questions and which ones are known we can build the technology okay if that's [Zohar Strinka]: not a concern don't worry about the technology side right if it's a data question and so to me [Zohar Strinka]: there's an element of choosing the right parts of the problem to to test it's really the things [Zohar Strinka]: where you have the least the biggest questions if this works we're good if it doesn't work it [Zohar Strinka]: doesn't okay just test that part of the project it's funny because i've also used this in kind [Michael Helbling]: of a sort of like a [Michael Helbling]: circular fashion where as an analyst i'm keeping track of how i'm approaching solving these [Michael Helbling]: problems and then what's working and what's not working and then going back and reiterating on [Michael Helbling]: that process as time goes on so like you can even take your method and apply it just to even how you [Michael Helbling]: approach the business to work on different problems anyways this conversation has been [Michael Helbling]: awesome really really uh right up our alley thank you so much zohar one of the things we'll be talking about is the [Michael Helbling]: last call something that might be of interest to our listeners so har you're our guest uh do you have [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 [Zohar Strinka]: first is i do have a udemy course on the metal problem method that i've just built so if you're [Zohar Strinka]: interested in learning more i have the website that's just out there as well but i've tried to [Zohar Strinka]: make it a little bit more hands-on the second one is [Zohar Strinka]: i live in this manufacturing and inventory and all that space and so i follow a blog that's on [Zohar Strinka]: operations management and a couple weeks ago they had a blog on pringles kpis so they were using [Zohar Strinka]: advanced analytics to deal with the fact that you know when you have a new batch of potatoes [Zohar Strinka]: the moisture will be a little bit different and so they were using sensors and all that information [Zohar Strinka]: to adjust how they made pringles and what they were reporting was a 10 percent [Zohar Strinka]: rate of improvement in the metal problem and so i'm going to talk a little bit more about that [Zohar Strinka]: and things like that and they're talking about maybe we don't have to be so picky about which [Zohar Strinka]: potatoes we buy because apparently they buy exactly one variety and so it was just an [Tim Wilson]: interesting question of the pringle dashboard i feel like walt hickey's numlock news had a [Tim Wilson]: reference that rings a bell that like the like pringles innovation because i mean they're such a [Tim Wilson]: odd little chip with cocaine or whatever crack something that's baked into them because [Michael Helbling]: can he just one i technically i don't think they're even called a potato chip anymore [Michael Helbling]: but you know that's not relevant to the to the question all right tim what about you [Tim Wilson]: what's your last call um i will pop a good old gary angel he wrote a post on his medium [Tim Wilson]: site of educating for ai the skills grads need in an ai enabled world around aren't what anyone [Tim Wilson]: thinks and he doesn't pretend to have a good old gary angel and he doesn't pretend to have a good [Tim Wilson]: a good old gary angel and he doesn't pretend to have a good old gary angel and he doesn't pretend to [Tim Wilson]: have the answer and you're like oh this is going to be some it's very thoughtful had some really [Tim Wilson]: clever takes on that so i recommend that what about you michael what's your last call [Michael Helbling]: well i recently ran across an old short story by isaac asimov they wrote in 1957 called profession [Michael Helbling]: and you can find it online in a couple places but basically the story goes that every person [Michael Helbling]: gets into their career after high school by basically [Michael Helbling]: basically their knowledge of that profession being wired directly into their brain [Michael Helbling]: and follows the story of a young man who is basically set outside of that process and he [Michael Helbling]: takes him a long time to figure out why he's not being able to be in a profession but has to figure [Michael Helbling]: out how to do things on his own and learn things on his own and what that means for him anyways [Michael Helbling]: fascinating on a couple levels especially because we're at a time where we're basically kind of [Michael Helbling]: letting ai be our brain in a lot of ways our versus business Genbie products could work very well if they should [Michael Helbling]: play around with different choices one way or another right now when all this happens them [Michael Helbling]: um i i feel like when i talk about familial Romantic arts i feel like every star bearers i feel like [Michael Helbling]: ways and what that might mean for thinking and human enfeeblement and things like that.
So, [Tim Wilson]: anyways, it's a fun short story. Without the three laws of robotics applied to it. [Michael Helbling]: Dang it. Not directly.
Yeah. No, Asimov is just so great. Like his whole brain, [Michael Helbling]: like what was he doing in 1957 thinking like this? Anyway, it's really good.
All right. Zohar, [Michael Helbling]: again, thank you so much for coming on the show. This has been a great conversation and one that's [Michael Helbling]: near and dear to our hearts. And so, I appreciate your take on it and taking the time to chat with [Michael Helbling]: us.
And if people want to learn more, you have a website for the Meta Problem Method, which is [Michael Helbling]: meta-problem.com, which I highly recommend people take a look at. And as you've been listening, [Michael Helbling]: you probably have your own thoughts and we would love to hear from you. And so, yeah, [Michael Helbling]: please reach out to us.
You can do that as a listener through the Measure Slack chat group [Michael Helbling]: or you can reach out to us on the Meta Problem Method website. And we would love to hear from [SPEAKER_UNK]: [Michael Helbling]: you. And as you're listening, also feel free to leave reviews, ratings, and comments on whatever [Michael Helbling]: episode or whatever platform you listen to the episode on. We'd love to hear from you.
All right. [Michael Helbling]: I think we did it. We did the whole thing. And that's awesome.
I don't know. You know, it's [Michael Helbling]: funny because I love a scientific approach to this, [Michael Helbling]: because it feels super daunting when you first start tangling with this problem. [Michael Helbling]: And I've watched a lot of analytics people basically bash their brains out in a sort of [Michael Helbling]: way on this problem where it's sort of like you hear this again and again. Nobody ever listens [Michael Helbling]: to me or I do all this work and it never gets anything done.
And I'm never attached to the [Michael Helbling]: decisions that ends up getting made. And so, it's like, well, how do we get better at this? [Michael Helbling]: And so, I love so hard that you're tackling this problem and you're thinking, [Michael Helbling]: well, how do we get better at this? And I think it's not only helpful for businesses, [Michael Helbling]: but it's helpful for everybody in our industry.
So, thank you very much. [Michael Helbling]: And I think I speak for my co-host, Tim Wilson. No matter how big your meta problem that you're [Announcer]: trying to solve, keep analyzing. Thanks for listening.
Let's keep the [Announcer]: conversation going with your comments, suggestions, and questions on Twitter at [Announcer]: at Analytics Hour, on the web at analyticshour.io, our LinkedIn group, and the Measured Chat Slack [Announcer]: group. [Announcer]: Music for the podcast by Josh Crowhurst. [Announcer]: So, smart guys wanted to fit in.
So, they made up a term called analytics. Analytics don't work. [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, [Charles Barkley]: the analytics say go for it.
It's the stupidest, laziest, lamest thing I've ever heard for [Charles Barkley]: reasoning in competition. I'm fighting out in real time some of my responsibilities. [Michael Helbling]: Stop. That's good.
[Michael Helbling]: We're super... [Tim Wilson]: I've been on Slack like a week and a half ago. [Tim Wilson]: We're super buttoned up. [Michael Helbling]: The test designer said this is great.
[Michael Helbling]: Let's start arguing after we start the show, Tim. Come on. [Michael Helbling]: No, I'm just kidding. All right.
[Michael Helbling]: All right. [Tim Wilson]: Rock, flag, and peanut butter decisions. [Tim Wilson]: Decisions.
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