[Announcer]: Welcome to the Analytics Power Hour.
[Announcer]: Analytics topics covered conversationally and sometimes with explicit language.
[Michael Helbling]: Hey everybody, it's the Analytics Power Hour.
[Michael Helbling]: This is episode 286.
[Michael Helbling]: It's Tuesday and I know what you're thinking.
[Michael Helbling]: I sure hope revenue and active customers still mean the same thing as they did yesterday.
[Michael Helbling]: A lot of you know firsthand the pain I'm describing.
[Michael Helbling]: There's data ping-pongs around the business taking on shapes and definitions that were never really intended.
[Michael Helbling]: Well, the semantic layer was supposed to take care of all that.
[Michael Helbling]: And to be fair, there are some nice, tidy businesses out there doing a great job.
[Michael Helbling]: But most of us are still trying to figure out where it should live, what it should be written with, and who should own it.
[Michael Helbling]: So I think we should dig into it.
[Michael Helbling]: But first, let me introduce my co-host, Moee Kisss.
[Michael Helbling]: How are you going?
[Moe Kiss]: I'm going great.
[Moe Kiss]: I'm really excited about this.
[Michael Helbling]: I'm excited to, and excited to do the show with you.
[Michael Helbling]: And Tim Wilson, howdy.
[Michael Helbling]: I think it's just all semantics.
[Michael Helbling]: It's all, oh, so good.
[Michael Helbling]: Well, that's an interesting potential cop out.
[Michael Helbling]: Okay, no.
[Michael Helbling]: And I'm like, well, to really get into this topic, I think we found the perfect guest.
[Michael Helbling]: Cindi Howson is the Chief Data and AI Strategy Officer at ThoughtSpot.
[Michael Helbling]: She was previously Vice President at Gartner, along with many other distinguished roles throughout her career.
[Michael Helbling]: She is the host of the Data Chief podcast and has authored many books on BI and data.
[Michael Helbling]: And today she is our guest.
[Michael Helbling]: Welcome to the show, Cindi.
[Cindi Howson]: Thank you for having me, everyone.
[Cindi Howson]: I'm so excited to be here.
[Michael Helbling]: I am excited that you're here too.
[Michael Helbling]: You're kind of, to me, sort of an OG of the data space.
[Michael Helbling]: And so I love people who can provide as much depth and background and historical perspective on all the things we're struggling with in the world of data today that were struggles 20 years ago and still remain with us today, but with different tools and names and things like that.
[Michael Helbling]: But today we're talking about sort of
[Tim Wilson]: Oh, go ahead, Tim.
[Tim Wilson]: Well, I was going to say, I mean, I, having done my little forensic sleuthing that I saw Cindi speak at a TDWI summit back in 2004, which I think is amazing since we're both like 35 years old.
[Tim Wilson]: We just look at him.
[Tim Wilson]: It was like my entry moving from technical writing to Markham, slightly into web analytics, and then it was like my entree into the world of analytics and BI was that conference.
[Tim Wilson]: Yeah, so I just felt like I had an appetite.
[Tim Wilson]: He's a big fat.
[Tim Wilson]: I'm gonna say that.
[Cindi Howson]: He's a big fat.
[Cindi Howson]: It's the summary.
[Cindi Howson]: Tim, now I feel like I have to send you an original BI scorecard black bear that I used to use as giveaways for class participation.
[Cindi Howson]: We'll see if I still have one.
[Tim Wilson]: Now, I couldn't remember the topic, but I feel like I was coming back and I was like, there were some other
[Tim Wilson]: lady who spoke and it wasn't Jill Deshaix and the other person was like the only I cannot remember her name because we wound up actually Claudia Imhoff.
[Tim Wilson]: Yes, like two months later called and had Claudia like come out and just spend two days explaining data warehouses to our team.
[Tim Wilson]: So like doing the conference circuit and as a consultant, you're like, oh yeah, nobody's really just going to call you up.
[Tim Wilson]: Like we literally, she's like, what do you want me to do?
[Tim Wilson]: And we're like, you're smart.
[Tim Wilson]: Please come just sit in a room and answer our questions for two days.
[Tim Wilson]: And it was,
[Tim Wilson]: Our dev team was like thrilled.
[Tim Wilson]: And I'm so glad that you can remember that was who it was.
[Tim Wilson]: Okay.
[Tim Wilson]: Yeah.
[Tim Wilson]: Okay.
[Tim Wilson]: Michael.
[Tim Wilson]: Okay.
[Tim Wilson]: But now we're going to do the episode too.
[Michael Helbling]: All right.
[Michael Helbling]: Fast forward just a few years.
[Michael Helbling]: Yeah.
[Michael Helbling]: So nowadays, no.
[Michael Helbling]: So let's talk about Star Schema.
[Michael Helbling]: No.
[Michael Helbling]: Well, I mean, we can start there.
[Michael Helbling]: So semantic layers, Cindi, obviously everyone talks about those, but there's a history here.
[Michael Helbling]: And maybe just to get us started and for people who aren't as familiar with the concept, maybe just a quick primer on sort of, what does that even mean?
[Michael Helbling]: What are those?
[Michael Helbling]: And we can kind of use that as a launching point.
[Cindi Howson]: Sure.
[Cindi Howson]: So in the simplest terms, a semantic layer provides a representation of the business model in business terms to the physical structures in your whatever, data warehouse, data lake, cloud data platform, whatever you want to call it.
[Cindi Howson]: And it is important that it is in business terms.
[Cindi Howson]: So if I think about, my German language has only served me well when I look at SAP original tables.
[Cindi Howson]: VBAP was the customer table in SAP R3.
[Cindi Howson]: So you could never show a field VBAP to a business person.
[Cindi Howson]: Instead, you would say, this is customer name.
[Moe Kiss]: And I keep hearing the word when people talk about semantic layers of context.
[Moe Kiss]: And that seems like when you say business terms, is that what you mean?
[Moe Kiss]: Like the context of the data, how it relates to each other, what the definition is.
[Moe Kiss]: Is that the same thing?
[Moe Kiss]: Or when you say business terms, are you thinking about something different?
[Cindi Howson]: No, I think it's both because if we get precise, something like revenue, well, revenue in an inventory and supply chain context, I'm going to look at revenue based on when somebody placed an order.
[Cindi Howson]: But if I take it in terms of finance, the Office of Finance, they're going to want to know when that invoice was paid, or if I'm doing, you know, if I'm a cash basis or a cruel basis.
[Cindi Howson]: So the context of that revenue field matters.
[Cindi Howson]: Did that answer your question, Moe?
[Cindi Howson]: Yeah, absolutely.
[Cindi Howson]: Okay, totally.
[Tim Wilson]: Not to head, I struggle with
[Tim Wilson]: And revenue is a great example because it's...
[Tim Wilson]: part of the challenge, it's like we're trying to find a technology or a tool or a process to solve for something where the business, the person in finance, when they think revenue, they're always thinking in a revenue recognition world.
[Tim Wilson]: And when somebody's an inventory, they're always thinking of it another way and don't even necessarily, and they both may complain that I see reports from the other department and they have revenue and it's wrong.
[Tim Wilson]: It becomes a,
[Tim Wilson]: business understanding challenge that data processing technique mechanism is trying to solve.
[Tim Wilson]: Or am I just being cynical about that?
[Cindi Howson]: No, it is.
[Cindi Howson]: I could get annoying and say, yes, it is semantics, Tim.
[Cindi Howson]: But this is where, let's say, and sometimes people conflate data literacy with technical literacy, which is wrong.
[Cindi Howson]: But we're really talking about what does the data mean in a business context?
[Cindi Howson]: And where does the data originate from?
[Cindi Howson]: And if I'm talking about an order system versus an invoicing system, sometimes that's different.
[Cindi Howson]: And so a finance person is always going to assume I am talking about when it was invoiced.
[Cindi Howson]: A salesperson is going to come from the context of when is my commission going to get paid.
[Cindi Howson]: And so we come to the data already thinking about the data through our own lens, our own business function.
[Cindi Howson]: And yet they may have very, very different meanings.
[Cindi Howson]: Even somebody that I was working with
[Cindi Howson]: I won't name him, but it was hysterical.
[Cindi Howson]: We're both working off the same dataset you would have thought.
[Cindi Howson]: I'm like, why are your numbers different than mine?
[Cindi Howson]: Here's what I thought the number was, and you're coming up with a different number.
[Cindi Howson]: Yet, in his dataset, he only included software licensing and did not include professional services.
[Cindi Howson]: I was like, why would you exclude that?
[Cindi Howson]: I'm really just looking for total revenues related to this particular segment.
[Moe Kiss]: Can I then ask, do we risk?
[Moe Kiss]: I'm going to definitely come full circle on this because it's definitely been a topic that's on my mind a lot.
[Moe Kiss]: One of the things that I've seen play out is this very precise, I don't want to say business domain, but this very specific interpretation of a metric by a particular area of the business.
[Moe Kiss]: And I'm going to give the typical example in my world, which is like, let's say you have 12 different products.
[Moe Kiss]: And so then one team is like, well, we're going to talk about
[Moe Kiss]: video MAUs.
[Moe Kiss]: And another team is like, we're going to talk about search MAUs.
[Moe Kiss]: And then another area of the business is, I don't know, template MAUs.
[Moe Kiss]: I'm making up all stuff that's relevant to my world, of course.
[Moe Kiss]: And then we come up with this fundamental problem of if you summed every department's version of their metric, we would never end up with all MAUs.
[Moe Kiss]: But we also end up with these very precise definitions that might work within the business context that they're in.
[Moe Kiss]: But then like,
[Moe Kiss]: I think the thing that I struggle from that viewpoint is sometimes I feel like we over orchestrate things for a specific domain.
[Moe Kiss]: Then we can't roll up and think about what's the bigger picture across the whole company.
[Moe Kiss]: When we say, MAU, what do we mean?
[Moe Kiss]: Because they might have had interactions with lots of different products, for example.
[Moe Kiss]: I feel like semantic layers in this are like there is an overlap here.
[Moe Kiss]: I'm sure we'll get to.
[Moe Kiss]: But do you see that problem playing out a lot?
[Moe Kiss]: And is that part of why semantic layers are becoming the new hot topic of the moment?
[Cindi Howson]: Well, let's go back and say, you just used a term, Moe.
[Cindi Howson]: If I was a new employee at Canva or at any kind of SaaS startup, what the heck is it?
[Moe Kiss]: Is it MAU?
[Cindi Howson]: Or what the hell?
[Moe Kiss]: Oh, fair.
[Cindi Howson]: A male.
[Cindi Howson]: A male active user.
[Cindi Howson]: Well, and maybe even really going to split hairs here and say, well, if I only clicked on the video and so it was a one-second interaction, are you going to count me there as a user or should I have actually watched at least two minutes of the content?
[Cindi Howson]: So we can parse these definitions a lot of different ways.
[Cindi Howson]: But I want to come back to why did semantic layers start more than 30 years ago and why are they coming back now?
[Cindi Howson]: 30 years ago, prior to semantic layers and really business objects patented and won in courts, the first semantic layer and Cognos at the time within Promptu had to actually pay a license fee to them.
[Cindi Howson]: And prior to this, you had to code your own SQL.
[Cindi Howson]: You would have to say some cryptic name,
[Cindi Howson]: by VBAP L333 from this table, and that was terrible.
[Cindi Howson]: The semantic layer gave report writers a way to click on business terminology to generate the SQL.
[Cindi Howson]: That was the first purpose of the semantic layer.
[Cindi Howson]: Now, as the industry moved to, let's say, in memory tools,
[Cindi Howson]: With the likes of Click and Tableau, there's a whole generation of let's say 10 years, maybe 15 years, where people didn't think about this.
[Cindi Howson]: They just loaded their data, did maybe one big SQL extract.
[Cindi Howson]: loaded it into an in-memory file.
[Cindi Howson]: And so they were only working with their subset of data.
[Cindi Howson]: And so, of course, the MAUs meant what I wanted it to mean.
[Cindi Howson]: And there was this loss of knowledge about what semantic layers are.
[Cindi Howson]: Now, here we are in 2025, and we're all trying to build agentic AI systems.
[Cindi Howson]: And what we're learning is that without this context or clear business definitions, we have hallucinations.
[Cindi Howson]: We get incorrect results.
[Cindi Howson]: So the more context you give the LLM, the more accurate
[Cindi Howson]: your answers will be.
[Cindi Howson]: And that is why I think, well, I think semantic layers have become more important because of agentic AI.
[Cindi Howson]: But also, let's say before that, cloud data platforms and the whole modern data stack have given rise
[Cindi Howson]: to, hey, I don't have to subset my data.
[Cindi Howson]: I don't have to load just a small data set into an in-memory engine.
[Cindi Howson]: Let me get to all of it, whether it's in Snowflake or Databricks or Google BigQuery.
[Cindi Howson]: Let me get to all of it.
[Cindi Howson]: And so people don't want to move the data, but they do want to trust it, no matter if they're doing agentic or not.
[Tim Wilson]: Well, so this whole notion of context and using agentic AI as an example, is it moving down the path?
[Tim Wilson]: Will a semantic layer help AI demand some explicit context?
[Tim Wilson]: If I ask for, tell me how many customers
[Tim Wilson]: we had last month, will a semantic layer start to say, that's not enough?
[Tim Wilson]: I know, Tim, what role you're in, I can guess what your definition of a customer is, but I'm going to require that you give me more business context in order for me to find the right
[Tim Wilson]: to pull the right information.
[Tim Wilson]: Is that a feature of the semantic layer or is that something that's got to be built in the intermediary tool that's using the semantic layer to interface with a business user?
[Cindi Howson]: Yeah, I follow you, Tim.
[Cindi Howson]: And this is where I think what people want
[Cindi Howson]: is one semantic layer to rule them all.
[Cindi Howson]: And I just think that's a fallacy.
[Cindi Howson]: Will I ever see that?
[Cindi Howson]: I don't ever see that.
[Cindi Howson]: What the industry, at least right now, is trying to get to, and I will also say this is the second attempt, maybe the third attempt in the industry.
[Cindi Howson]: With snowflakes open semantic interchange, is it least let there be a common set of standards so that everyone can interoperate?
[Cindi Howson]: And that already would be a huge sea change.
[Cindi Howson]: Otherwise, everyone's building proprietary integrations.
[Cindi Howson]: Even, I mean, I will say, working for ThoughtSpot.
[Cindi Howson]: ThoughtSpot integrates with the Looker metrics layer in LookML.
[Cindi Howson]: ThoughtSpot integrates with the DBT semantic layer, and that has changed different incarnations.
[Cindi Howson]: There's a few others.
[Cindi Howson]: that some have built integrations with KubeJS, some have built integrations with AtScale, there's others.
[Cindi Howson]: But let's just take those.
[Cindi Howson]: Well, those are all point solutions.
[Cindi Howson]: We have to keep up with what is DBT's latest protocol, what is Looker's latest protocol.
[Cindi Howson]: And it would be great if we all just say, here are the approaches that we're going to use.
[Cindi Howson]: And so it's all common rather than point solutions.
[Cindi Howson]: So that is the vision and the hope of snowflakes open semantic interchange.
[Cindi Howson]: However, so this is a very long-winded answer, but we will have separate incarnations.
[Cindi Howson]: And that, I have to say, like every customer conversation I've had about this in the last month, they're like, we only have to have one instantiation.
[Cindi Howson]: And I'm like, no, you don't, you do need separate instantiations because every downstream tool
[Cindi Howson]: and even backend database, they have their own limitations.
[Cindi Howson]: So if I'm going to create something, a metric called top 10 customers, well, there's some databases that don't support a ranking function.
[Cindi Howson]: So even like Denoto virtualization tried to do this for a while.
[Cindi Howson]: And it's like, great.
[Cindi Howson]: In ThoughtSpot, we have an object called top 10.
[Cindi Howson]: Well, if I hit the Snowflake database, it's working.
[Cindi Howson]: If on the back end it's hitting, I'm going to forget which database didn't support it, some variation of SQL server or whatever didn't support it.
[Cindi Howson]: Well, then Denoto is like not working, not giving an answer.
[Cindi Howson]: Or in Looker, we have a very cool visualization, my favorite visualization, a KPI chart.
[Cindi Howson]: it's too complicated for the looker metrics layer.
[Cindi Howson]: So you're always going to have these separate instantiations of a semantic layer because nobody is going to want to dumb down their semantic layer for the least common denominator.
[Cindi Howson]: Okay.
[Moe Kiss]: I've got to make sure I'm following this though.
[Moe Kiss]: Okay.
[Moe Kiss]: So what we're saying
[Moe Kiss]: is that I guess the thing is like what I'm observing is that folks seem to want to be pulling their semantic layer further and further up in the chain, right?
[Moe Kiss]: So like you want it to sit less in a downstream tool and more like internally and I obviously have a biased view.
[Moe Kiss]: But wanting to bring things like semantic layer in-house so that you also have your options open about which way you go with whatever AI you choose to leverage.
[Moe Kiss]: But what you're saying is it's unavoidable that we're going to end up with a semantic sandwich or cake or whatever you want to call it, where you might have to have something at one layer.
[Moe Kiss]: And then when you go to a BI tool or some other type of tool or integration, you might end up having
[Moe Kiss]: to have a second layer just because they have different features or attributes that you want to leverage.
[Moe Kiss]: Am I hearing that correctly?
[Cindi Howson]: Yes.
[Cindi Howson]: And if by downstream and upstream you mean the database, people want it closer to the database because that's where the data lives.
[Cindi Howson]: But then as you get closer to the business decisions, you're going to have derivations and metrics
[Cindi Howson]: and context that may not exist in the database.
[Cindi Howson]: And I would also say, we also have to think about how these things get defined.
[Cindi Howson]: So working with one team, they're like, okay, we're going to build everything out in the database.
[Cindi Howson]: I'm like, great.
[Cindi Howson]: So your DBA is going to do all this.
[Cindi Howson]: Or here I have a really strong SME.
[Cindi Howson]: And if we bring data mesh operating principles and domain ownership, so I have this great marketing person and they know the differences between a video, MAU, or a web click MAU.
[Cindi Howson]: And I'm gonna want them to add a little more context to it.
[Cindi Howson]: So I'm going to want an easier interface.
[Cindi Howson]: And guess what?
[Cindi Howson]: That interface does not exist in the one that was designed for the DBA.
[Moe Kiss]: OK, but I don't want to take things in a totally different direction, but I'm like dying a little bit.
[Cindi Howson]: I feel like a dymo.
[Moe Kiss]: I opened another can of worms, I can tell.
[Moe Kiss]: The thing I'm really struggling with with this whole discussion about semantic layers
[Moe Kiss]: It doesn't feel new, and I feel like what you've written about it makes that very clear.
[Moe Kiss]: But part of me is also really grappling with, is it actually the fact that it's not net new?
[Moe Kiss]: Is it the fact that the way we want to use agentic AI on top of our data?
[Moe Kiss]: Or is it the fact that we have gone towards this data mesh approach with less, I don't know if structured is the right word.
[Moe Kiss]: Cindi, you can definitely insert better terminology because you are the queen of exceptional terminology.
[Moe Kiss]: But we used to have such structured datasets.
[Moe Kiss]: We had store schemas, they had context.
[Moe Kiss]: Is part of this just our own doing because we wanted to move faster and have less structure in our data?
[Moe Kiss]: And so this is just the consequence.
[Cindi Howson]: So that was a two-part question.
[Cindi Howson]: So is it new?
[Cindi Howson]: Is the semantic layer new?
[Cindi Howson]: It's not new.
[Cindi Howson]: It has gotten more robust over time.
[Cindi Howson]: And not all semantic layers are created equal.
[Cindi Howson]: So I can show you one semantic layer, and it only supports a single star schema.
[Cindi Howson]: Or maybe even worse, it only supports one big table.
[Cindi Howson]: I can show you another semantic layer and it supports multiple fact tables, different design approaches, star schema, snowflake schema.
[Cindi Howson]: It even includes capabilities
[Cindi Howson]: for aggregate table navigation or query compilation so that the most efficient query path is taken.
[Cindi Howson]: So not all semantic layers are created equal.
[Cindi Howson]: And I do think that has changed over time.
[Cindi Howson]: And for sure, the openness has changed over time.
[Cindi Howson]: So if I go back to the original query tools, whether again, business objects, Cognos, whatever, those were largely closed.
[Cindi Howson]: Some boutique consultancies had open APIs to access them.
[Cindi Howson]: OBI EE, their model was open and nobody used it.
[Cindi Howson]: You could expose it as an ODBC connector to other BI tools, but nobody used it.
[Cindi Howson]: Performance was not good.
[Cindi Howson]: So what we have now is definitely more openness, but I do believe it is the agentic part of why we're demanding, why we need them more.
[Cindi Howson]: It'll just make AI better.
[Cindi Howson]: The second part of your question was then, are we decentralizing these things?
[Cindi Howson]: And yes, I think that's part of it too.
[Tim Wilson]: This makes me feel like there are
[Tim Wilson]: This is either going to be just so obvious that it's dumb.
[Tim Wilson]: Are there people out there who, if some generic person came in and looked at it, they would say, you have built a wonderful semantic layer, and the people who built it would say, I built something that functioned for what I need.
[Tim Wilson]: I didn't know that's what it was called.
[Tim Wilson]: On the flip side, that has me thinking that semantic layer, it sounds cool.
[Tim Wilson]: It gets treated as this binary.
[Tim Wilson]: If you have one, things are good.
[Tim Wilson]: If you don't have one, they're bad.
[Tim Wilson]: It sounds like what you're saying is,
[Tim Wilson]: you could try to boil the ocean with one grand semantic layer, and it would probably be bad.
[Tim Wilson]: We treat it as though there's this label, and if you have it, then things are fixed.
[Tim Wilson]: But there's always the gradations of whether you do it well, well-architected and appropriately.
[Tim Wilson]: That probably happens with everything that gets a fancy new label.
[Cindi Howson]: Yeah.
[Cindi Howson]: So I don't, I don't know, Tim, like, do I want one mega semantic layer?
[Cindi Howson]: Oh, please not.
[Cindi Howson]: So because it becomes overwhelming to maintain and, and it becomes now maybe, maybe if I'm just using natural language, um, to ask questions, I don't care what it's hitting on the back end, but I, I,
[Cindi Howson]: I would be skeptical that that would work.
[Cindi Howson]: There is a belief that in the industry we're going to go towards verticalization of some of these semantic layers.
[Cindi Howson]: So there will be
[Cindi Howson]: And maybe this is, I kind of bristle it.
[Cindi Howson]: We throw new terms out there, ontologies.
[Cindi Howson]: Well, can we just talk about domains?
[Cindi Howson]: That makes more sense to me, and that aligns with the data mesh.
[Cindi Howson]: But could we have an insurance industry semantic layer?
[Cindi Howson]: Could we have a marketing web analytics semantic layer?
[Cindi Howson]: I think we could.
[Cindi Howson]: We would get to common metrics.
[Cindi Howson]: The physical pointers ultimately back to which table is it hitting might change a little bit, but I think that business representation, we could possibly get to that.
[Moe Kiss]: Okay, one thought, just asking for a friend, of course, that has been on my mind is we can approach this from like a business domain perspective, just like the examples you gave, right?
[Moe Kiss]: So like, you might have one that's more like marketing and acquisition, more the one that's like, I don't know, finance or whatever, and so whatever else business domain.
[Moe Kiss]: And the thing that I kind of keep wrestling with though is,
[Moe Kiss]: Are we just doing this again where we're overlaying our thoughts about like what a domain is versus the business user and how they want to interact with data?
[Moe Kiss]: And what I mean by that is like, if I'm a business user, what's my business question?
[Moe Kiss]: What are the questions that I want to ask?
[Moe Kiss]: And let's say the theme might be I want to ask a question about our users or I want to ask a question about
[Moe Kiss]: I don't know.
[Moe Kiss]: Now I'm going to like struggle to think of a comparative example.
[Moe Kiss]: I might want to ask something about, someone help me with an answer.
[Moe Kiss]: A marketing channel.
[Moe Kiss]: Sure.
[Moe Kiss]: A marketing channel or, yeah, like I want to understand something about how experiments have done.
[Moe Kiss]: Like, are we doing a thing where we're trying to make
[Moe Kiss]: semantic layer is representative of business domains that make maybe business sense, but don't reflect the way that users and our business users want to interact with data when they have questions.
[Cindi Howson]: Well, so to me, if you build a semantic layer that doesn't work that way, what is the point?
[Cindi Howson]: Like, go home.
[Cindi Howson]: Because you know SQL, you want to code your SQL, you don't need a semantic layer.
[Cindi Howson]: You might want it for some reusability, but the semantic layer gives the business user the ability to ask the questions without knowing SQL.
[Cindi Howson]: And then it gives the LLM more context to generate better SQL.
[Cindi Howson]: So all these companies that have tried to do text to SQL without a semantic layer, they're largely failing.
[Cindi Howson]: And guess what?
[Cindi Howson]: They're adding semantic layers so that they work.
[Cindi Howson]: So semantic layers bring reusability.
[Cindi Howson]: That was the original purpose.
[Cindi Howson]: And then it is a business-friendly interface.
[Cindi Howson]: And now in agentic AI, it's the context for the LLMs to ensure accuracy.
[Cindi Howson]: So if you're going to give me a semantic layer that is just a bunch of cryptic names, technical names, and it's not giving it to me in a way that the business sees it, it's a waste of time.
[Cindi Howson]: It's a poorly architected semantic layer.
[Moe Kiss]: So hypothetically, if you just took all your YAML descriptions, that probably wouldn't be good enough because it's been written by a data scientist in their domain for their own specific domain for use by someone who deeply understands their area.
[Cindi Howson]: Well, if they deeply understand their area, there might be a lot of useful context in there.
[Cindi Howson]: But if it's a lot of code,
[Cindi Howson]: and techno babble, then I think it's going to be less useful.
[Tim Wilson]: Back on the, I may blend two things together.
[Tim Wilson]: The referencing snowflakes open semantic, what is it?
[Tim Wilson]: Open semantic interchange does feel like
[Tim Wilson]: That brings to mind the XKCD cartoon about people complaining that there are 13 different standards.
[Tim Wilson]: We need one standard, and then the next panel is, well, now we have 14 competing standards.
[Tim Wilson]: There does need to be a first
[Tim Wilson]: mover or a dominant, is there a race to say, obviously, Snowflake wants to be the owner, the driver of that?
[Tim Wilson]: I guess the same thing when you talk about verticalization,
[Tim Wilson]: say something like digital analytics, and you're like, let's just have one common marketing digital analytics.
[Tim Wilson]: Well, now you're going to have the players in that are all going to say, yes, the way that we think about that data is the way that the industry.
[Tim Wilson]: the effort to try to have some sense of standards not lead to self-interested competition to sort of pull the market towards whoever's on point for defining the standard.
[Tim Wilson]: Or maybe my third example would be the W3C.
[Tim Wilson]: I mean, we go back 30 years trying to define what HTML is supposed to do.
[Tim Wilson]: And Microsoft doesn't even conform to the W3C standards because
[Cindi Howson]: Tim, you just answered that question.
[Cindi Howson]: Microsoft would love us to revert to MDX instead of SQL for the most part.
[Cindi Howson]: But it is true.
[Cindi Howson]: So look at who is not part of that.
[Cindi Howson]: effort.
[Cindi Howson]: Was Databricks invited to the party?
[Cindi Howson]: Was Google BigQuery invited to the party?
[Cindi Howson]: Will they invite themselves?
[Cindi Howson]: Will they become part of it?
[Cindi Howson]: Standards get adopted based on who leads it, but then also who uses it and who asks for it.
[Cindi Howson]: So that's where when I look at how we prioritize our product strategies, we are very much listening to the customers.
[Cindi Howson]: And sometimes we've gone down rabbit holes.
[Cindi Howson]: And I'm like, why did we build that integration?
[Cindi Howson]: So I won't say which integrations to me were a waste of time.
[Cindi Howson]: But some of them, I'm like, why did we do that?
[Cindi Howson]: Because we were trying to, we thought something would have legs to it.
[Cindi Howson]: We listened to the customer and it never really took off.
[Cindi Howson]: And then some will change strategies.
[Cindi Howson]: So, you know, we thought DBT's initial effort would take off and instead then, you know, they're on version two and now
[Cindi Howson]: So Snowflake, hugely influential in the industry.
[Cindi Howson]: We're very proud to be part of the committee defining these standards, but we have to see how broadly adopted they are.
[Cindi Howson]: The market will decide.
[Michael Helbling]: And certainly right now, AI is kind of a forcing function for the industry where maybe that hasn't been or there hasn't been an imperative like that for a lot of companies.
[Michael Helbling]: Does that seem fair?
[Cindi Howson]: I think that seems fair.
[Cindi Howson]: Yeah.
[Cindi Howson]: And there's more willingness to be open and to focus on where you add the value in this data to insight to action change.
[Moe Kiss]: That actually triggers an interesting thought.
[Moe Kiss]: One of the things that I've observed is this push for semantic layers.
[Moe Kiss]: I feel like it's come out of left field.
[Moe Kiss]: I don't know if that's fair or not.
[Moe Kiss]: It just seems to have swirled very quickly.
[Moe Kiss]: And the products maybe aren't at a state of maturity where they need to be for what people.
[Moe Kiss]: I almost feel like a lot of companies are building as they're gathering requirements as customers are trying to build out with them.
[Moe Kiss]: Do you think that's a fair representation?
[Moe Kiss]: Has this happened before with a particular tool that's had to develop very quickly because of the pressure?
[Moe Kiss]: And I feel like AI is the pressure of like,
[Moe Kiss]: Everyone suddenly needs these semantic layers to make AI quote unquote work.
[Moe Kiss]: Do you feel like that's happened with the product development before or is this like a net new thing that data companies are trying to deal with where they're trying to build at pace while customers are wanting to already leverage and use it?
[Cindi Howson]: Yeah.
[Cindi Howson]: So I don't want to sound like a commercial and you can edit this out afterwards.
[Cindi Howson]: All semantic layers are not created equal.
[Cindi Howson]: Now, fortunately, because ThoughtSpot, whether it was purposeful or luck, ThoughtSpot always generated SQL on the backend.
[Cindi Howson]: So the semantic layer was always super robust.
[Cindi Howson]: So, did we get lucky or was it intentional?
[Cindi Howson]: And the cloud data warehouse and agentic AI has just helped that.
[Cindi Howson]: Others have only just started to embark on natural language processing.
[Cindi Howson]: and a gentek AI.
[Cindi Howson]: And they tried to do it without a semantic layer.
[Cindi Howson]: And that's why now they're dabbling in it.
[Cindi Howson]: And they're like, oh, it takes a lot to build this.
[Cindi Howson]: And some of them, they're starting out simple.
[Cindi Howson]: You know one big table.
[Cindi Howson]: That's all they can handle and code based.
[Cindi Howson]: So and I think about I think about a blog actually that our co-founder Amit Prakash wrote about
[Cindi Howson]: four years ago, I think it was, and it was the metrics layer, which is just a subset of the semantic layer.
[Cindi Howson]: The metrics layer has some growing up to do.
[Cindi Howson]: And even as a former Gartner research vice president, I have to give credit to Gartner, they still say that the time to maturity for metrics layers is five to 10 years.
[Cindi Howson]: That's a long time.
[Tim Wilson]: Yeah.
[Tim Wilson]: But so how unfair is this parallel to point to master data management as something that I remember having a moment that was, oh, things are getting fragmented.
[Tim Wilson]: We need to just do an MDM initiative.
[Tim Wilson]: And I guess to my earlier point, it was kind of a binary, if we do MDM,
[Tim Wilson]: all these problems get solved.
[Tim Wilson]: The companies that were already built where they had MDM under the hood anyway because they'd architected their setup well,
[Tim Wilson]: could do MDM, the ones that had built kind of a hot mess and were then trying to just apply a whole bunch of duct tape and bailing wire to do MDM like never really got there.
[Tim Wilson]: Is that a fair parallel or am I too much of a stretch?
[Cindi Howson]: Yeah, well, so is it a fair parallel?
[Cindi Howson]: I would just say it's valid.
[Cindi Howson]: It's valid.
[Cindi Howson]: And I remember, so the first eight years of my life in this industry were at Dow Chemical.
[Cindi Howson]: And we had a master data management system called INCA.
[Cindi Howson]: I don't even remember what it stands for.
[Cindi Howson]: It was homegrown.
[Cindi Howson]: And then I worked at Deloitte, and I was like, wait, you don't have clean product codes?
[Cindi Howson]: You don't have a single product hierarchy?
[Cindi Howson]: You don't have clean customer data?
[Cindi Howson]: It was a foreign concept to me that not everyone had clean master data.
[Cindi Howson]: So I would just say that
[Cindi Howson]: And Moe, you asked this earlier, so I wanted to come back to this point.
[Cindi Howson]: Semantic layers right now are mainly for the structured data.
[Cindi Howson]: But I think there's a time in the not too distant future that it will encompass also the semi-structured data.
[Cindi Howson]: And I would say this data
[Cindi Howson]: is a hot mess, frankly, because we've never applied all of these data governance and data management disciplines that we have been applying to the structured data.
[Cindi Howson]: So I think organizations that had the organizations that are best positioned for the agentic AI era got to cloud.
[Cindi Howson]: had clean data, had good master data, and then of course the culture and the people change management.
[Cindi Howson]: If they already did that, they're already, they have such a leg up.
[Cindi Howson]: Now we're throwing generative AI, agentic AI, semi-structured data, a lot more data that we couldn't get to before.
[Cindi Howson]: And yeah, it's not that easy.
[Michael Helbling]: It's nice to know we'll continue to have jobs going into the future though.
[Cindi Howson]: Yeah.
[Cindi Howson]: That's why I'm like, what's everyone worried about not having jobs?
[Cindi Howson]: They, they just will be different jobs, different jobs.
[Cindi Howson]: Yeah.
[Tim Wilson]: I got one more.
[Tim Wilson]: This is like, this could be a complete non sequitur, but it, but I feel like Cindi could tee off on this and I want more color because it was, it was from the post that you'd written where the quote was, our industry has also now raised the generation of data analysts who never learned proper data modeling.
[Tim Wilson]: And I kind of wanted you to elaborate on that.
[Cindi Howson]: Well, I'm going to say first, tell me if you disagree or not.
[Cindi Howson]: But tell me if you disagree or not.
[Cindi Howson]: But I follow the work of people like Joe Rice and Sonny Rivera, a snowflake superhero.
[Cindi Howson]: And yeah, it's, and I work with a lot of, let's say visualization experts who are just used to
[Cindi Howson]: one-offs, let me load the data and let me visualize it.
[Cindi Howson]: They never really learned proper data modeling techniques.
[Tim Wilson]: I guess my question is that a way of saying that there are analysts who aren't really actually thinking about the structure of the data and the ramifications for how the data fits together.
[Tim Wilson]: They're just trying to get to
[Tim Wilson]: and output.
[Tim Wilson]: I don't know if I agree or disagree.
[Tim Wilson]: I probably agree because I'm just generally negative and that's like a negative statement.
[Cindi Howson]: Let's not take it negative.
[Cindi Howson]: Yeah.
[Cindi Howson]: Let's challenge these people.
[Cindi Howson]: Let, to me, empower them.
[Cindi Howson]: So you know what?
[Cindi Howson]: You're great at visualization and you're great at building dashboards.
[Cindi Howson]: But if you want to continue to have a career on this space, in this space, I want you to learn some data modeling fundamentals.
[Cindi Howson]: And I don't care which methodology you follow.
[Cindi Howson]: Learn some data modeling.
[Cindi Howson]: That's on the technical side.
[Cindi Howson]: But also, we talk about data literacy.
[Cindi Howson]: We also need to bring in business literacy.
[Cindi Howson]: And so to me, it's not just about
[Cindi Howson]: Where is the data coming from?
[Cindi Howson]: It is also, how is it used?
[Cindi Howson]: And that there really might be two different definitions.
[Cindi Howson]: I mean, when I talk to somebody in airlines, I don't even, I'm like, oh wow, I think of on time performance.
[Cindi Howson]: Did it leave the gate on time?
[Cindi Howson]: or did it arrive on time?
[Cindi Howson]: Which one is really more important to you?
[Cindi Howson]: And by the way, when you're crossing international date lines, that it gets a little more complicated still.
[Cindi Howson]: So I would say I want these analysts to learn both the skills.
[Moe Kiss]: I have one last question.
[Moe Kiss]: Just hypothetically, if you were implementing a semantic layer, what would be the top three things you'd want to avoid?
[Cindi Howson]: The top three things?
[Cindi Howson]: Okay, well, I'm going to start with the first thing I would want to do, so I'd have to flip it, avoid it, or what do I want to do?
[Moe Kiss]: Or you can do the top three things to make it successful, either way, whichever your brain works.
[Cindi Howson]: You want to avoid bringing in absolutely everything in the physical storage and exposing that to mere mortals because that'll be overwhelming.
[Cindi Howson]: So I always start with who is going to use this.
[Cindi Howson]: And what are the top questions they're going to want to be able to ask of it?
[Cindi Howson]: Not because I'm going to hard code that, but that I'm going to get an idea of the context in which they're operating.
[Michael Helbling]: Cindi, wow.
[Michael Helbling]: So cool to talk to you.
[Michael Helbling]: Thank you so much.
[Michael Helbling]: This has been really, really good.
[Michael Helbling]: I've got a ton of notes that I've been writing down.
[Michael Helbling]: So I know that our listeners probably also get gaining a lot from this episode.
[Michael Helbling]: All right, well, let me switch gears really quickly because I need to talk about a quick break with our friend Michael Kaminsky from ReCast.
[Michael Helbling]: The media makes marketing and GeoLift platform helping teams forecast accurately and make better decisions.
[Michael Helbling]: Michael's been sharing with us bite-sized marketing science lessons over the last couple of months, and they'll help you measure smarter.
[Michael Helbling]: Okay, over to you, Michael.
[Michael Kaminsky (Recast)]: Multicollinearity strikes fear into the hearts of many analysts and executives, but it's also one of the most commonly misunderstood concepts in analytics.
[Michael Kaminsky (Recast)]: Some amount of correlation across variables is expected in most real-world analyses, so it's critical to understand what multicollinearity is, why it causes issues, and whether or not it's a problem for your particular analysis.
[Michael Kaminsky (Recast)]: Multicollinearity means that two of your variables share some of the same signal.
[Michael Kaminsky (Recast)]: This causes problems for a regression model, which will not know how to allocate credit between the two variables.
[Michael Kaminsky (Recast)]: This can cause challenges when it comes to interpreting the results of your regression.
[Michael Kaminsky (Recast)]: Let's imagine you're modeling the drivers of home prices in some geography, and you want to include home square footage and the number of bedrooms as predictors.
[Michael Kaminsky (Recast)]: These two variables share some amount of signal, namely about the bigness of the house.
[Michael Kaminsky (Recast)]: If you include both variables in a simple linear regression, you'll often get strange results, where one of the two variables is highly impactful with a large coefficient, and the other might be very small or even negative.
[Michael Kaminsky (Recast)]: Slightly different data sets might even cause the variables to flip, which one is positive and which one is negative.
[Michael Kaminsky (Recast)]: This happens because the model doesn't know how to apportion credit for bigness, which is present in both variables.
[Michael Kaminsky (Recast)]: So you get these strange results.
[Michael Kaminsky (Recast)]: So the core problem of multicollinearity is that when there's shared information across variables, a simple regression won't know how to apportion credit between them.
[Michael Kaminsky (Recast)]: This means that you either need to accept more uncertainty in results, or try to change the variables you're using to account for the shared information.
[Michael Helbling]: Thanks, Michael.
[Michael Helbling]: And for those who haven't heard, our friends at ReCast just launched their new incrementality testing platform, GeoLift, by ReCast.
[Michael Helbling]: It's a simple, powerful way for marketing and data deems to measure the true impact of their advertising spend, and even better, you can use it completely free for six months.
[Michael Helbling]: Just visit getrecast.com slash geolift to start your trial today.
[Michael Helbling]: Okay, well, we've got that done.
[Michael Helbling]: One thing we'd love to do is go around the horn and share something we call last call, something of interest that might be of interest to our listeners.
[Michael Helbling]: Cindi, you're our guest.
[Michael Helbling]: Do you have a last call you'd like to share?
[Cindi Howson]: Well, I want to ask a question if I can on the last call.
[Cindi Howson]: And when you think about how quickly our industry is moving and innovating, what do you see as your best method media to keep up?
[Cindi Howson]: Is it listening to podcasts, reading, substack or medium articles, or how do you feel about books?
[Michael Helbling]: Are we supposed to answer that?
[Cindi Howson]: Well, I'm looking for feedback because you know, even though I'm a podcast host, I'm a writer at heart and yet is the industry moving too quickly for another book?
[Moe Kiss]: Yeah.
[Moe Kiss]: I mean, I can speak for myself.
[Moe Kiss]: I listen to podcasts and host a podcast.
[Moe Kiss]: That's a big part of how I stay up to date.
[Moe Kiss]: But I also, I love books.
[Moe Kiss]: I'm a book person.
[Moe Kiss]: Probably books more than articles.
[Moe Kiss]: But you listen to a lot of the books, right?
[Moe Kiss]: Yes, I do, but that's just because of my life stage of being time poor.
[Moe Kiss]: I end up listening to books on Audible a lot.
[Moe Kiss]: Yeah, for sure.
[Michael Helbling]: What about you, House?
[Michael Helbling]: I would say my number one source is articles.
[Michael Helbling]: So in my day-to-day travels, I'll run across an article and then bookmark it and read it later.
[Michael Helbling]: So I'll do that.
[Michael Helbling]: I buy a lot of books and then don't read them.
[Michael Helbling]: Oh boy.
[Michael Helbling]: In fact, that's...
[Michael Helbling]: Right behind me.
[Michael Helbling]: Michael, have you finished the book?
[Michael Helbling]: I have not finished your book, Tim.
[Michael Helbling]: Oh, well, you haven't finished that either.
[Michael Helbling]: Uh, so yeah, but I, so I don't, cause for me reading is sort of like an enjoyable pastime.
[Michael Helbling]: And I, unlike Moe, I can't pay attention if someone's reading it aloud or audio books.
[Michael Helbling]: So I have to sit down and read it.
[Michael Helbling]: And then when I do finally get a chance to read, I end up reading like sci-fi or fantasy novels instead of business books.
[Michael Helbling]: So it's, it's a tough one.
[Michael Helbling]: And then of course, of course podcasts are very important.
[Michael Helbling]: I have to believe that, right?
[Michael Helbling]: So there you go.
[Cindi Howson]: This feels like confessions of a podcast host.
[Michael Helbling]: Yeah, that's right.
[Michael Helbling]: Exactly.
[Michael Helbling]: What do you think Tim?
[Michael Helbling]: I listen to a ton of podcasts.
[Michael Helbling]: Yeah, he does.
[Tim Wilson]: I listen to a ton of podcasts and very few of them are business or data analytics related.
[Tim Wilson]: So I am very much the subscribe to, I mean, a medium substack.
[Tim Wilson]: daily weekly newsletter fiend, which starts to feel a little overwhelming.
[Tim Wilson]: But yeah, so with the occasional book.
[Tim Wilson]: The books feel like a chore, though.
[Tim Wilson]: Well, I feel like if someone else is doing a good job.
[Tim Wilson]: So cool.
[Tim Wilson]: I'm just going to be clear.
[Tim Wilson]: So I don't listen to the podcast, even though I make one.
[Tim Wilson]: And I don't tend to read.
[Tim Wilson]: I struggle to read the books, even though I wrote one.
[Tim Wilson]: So yeah, I'm the worst.
[Cindi Howson]: Wow.
[Cindi Howson]: So I think Tim summed it up.
[Cindi Howson]: Wait, are you telling me two-third of our time spent is like a waste of time?
[Cindi Howson]: Why am I writing books and why am I hosting a podcast?
[Cindi Howson]: I'm just gonna get on with building stuff.
[Cindi Howson]: Okay.
[Michael Helbling]: I don't like the data that we've uncovered here.
[Tim Wilson]: I mean, I get a lot of value out of hosting the podcast because we get to have excuses to say, hey, why don't you come on and explain semantic layers to us?
[Michael Helbling]: So yeah, that is actually doing a podcast is one of the ways I learn new things.
[Michael Helbling]: So that's something you could add to the mix.
[Michael Helbling]: Yeah.
[Michael Helbling]: So when is your next book coming out?
[Cindi Howson]: I don't know.
[Cindi Howson]: Can I take a break from the podcast or stop something?
[Cindi Howson]: I don't know.
[Michael Helbling]: This is what I was trying to figure out.
[Cindi Howson]: What should I do next?
[Cindi Howson]: Yeah.
[Michael Helbling]: Fair point.
[Michael Helbling]: All right.
[Michael Helbling]: Tim, what about you?
[Michael Helbling]: What's your last call?
[Tim Wilson]: Well, I guess follow on.
[Tim Wilson]: There is a sub-stack that I discovered a couple of months ago from somewhere that is We Have the Data.
[Tim Wilson]: It's kind of silly.
[Tim Wilson]: It's kind of data visualization candy, but it's WeHaveTheData.net.
[Tim Wilson]: I think it's a couple of times a week, and it's just kind of a
[Tim Wilson]: It's like NOMLAC news, but data visualizations instead.
[Tim Wilson]: So they're pretty lengthy.
[Tim Wilson]: They're a collection of often kind of trivial data visualizations, but it's kind of a fun scroll in my inbox.
[Michael Helbling]: Outstanding.
[Michael Helbling]: All right, Moe, what about you?
[Moe Kiss]: I want to do a plug for Cindi's podcast.
[Moe Kiss]: I was lucky enough to be a guest back in October and it's called The Data Chief.
[Moe Kiss]: And as you can tell, I ended up hanging out after the show and picking Cindi's brain for like another 30, 40 minutes about all of these topics, which is why she's here today.
[Moe Kiss]: And she just has such a range of like really incredible guests.
[Moe Kiss]: It's a really different format to our show.
[Moe Kiss]: So really encourage you to go check out The Data Chief podcast.
[Michael Helbling]: I'm standing in, and yeah, we'll put a link to that in our show notes as well, so people can find it.
[Tim Wilson]: You're supposed to hand her at the beginning.
[Michael Helbling]: It's fine.
[Michael Helbling]: We'll hand her all over the place.
[Michael Helbling]: What's your last call?
[Michael Helbling]: Well, I'm so glad you asked him.
[Michael Helbling]: So a good friend of mine, Mary Gates, actually made me aware of this.
[Michael Helbling]: So Informs, which I'm sure we're all familiar with, they have an initiative called Pro Bono Analytics.
[Michael Helbling]: So I'm a big fan of any analytics initiatives that I've been able to be part of them over the years that help nonprofits and allow people to give of their skills and data and analytics to nonprofits and mentorship and things like that.
[Michael Helbling]: Pro Bono Analytics is an initiative run by Informs.
[Michael Helbling]: And so I just wanted to give that a shout out.
[Michael Helbling]: I was not familiar with this before, but it looks like a very cool organization.
[Michael Helbling]: And so if you're a nonprofit and you're listening, that might be an amazing place to partner with them to get help with data initiatives.
[Michael Helbling]: And if you're a professional in working in data and you want to find a way to give back, that might be an amazing way to do that.
[Michael Helbling]: So we'll put a link to that in the show as well.
[Michael Helbling]: OK.
[Michael Helbling]: As you've been listening about on this topic of semantic layers, I'm sure you have thoughts.
[Michael Helbling]: I'm sure you have questions.
[Michael Helbling]: We would love to hear from you.
[Michael Helbling]: Go ahead and reach out to us.
[Michael Helbling]: And there's three main ways you can do that.
[Michael Helbling]: You can do that through LinkedIn or the measure slack chat group, or you can email us at contact at analyticshour.io.
[Michael Helbling]: And yeah, we'd love to hear from you.
[Michael Helbling]: Cindi, once again, this has been a very information-rich and awesome episode, and primarily because your deep knowledge and expertise in this field.
[Michael Helbling]: So thank you again so much for joining.
[Cindi Howson]: Thank you for having me.
[Cindi Howson]: I feel like we should do this over a cup of coffee or a glass of wine at some point.
[Michael Helbling]: Yes, I wholeheartedly agree.
[Michael Helbling]: That's how this whole podcast started was because we're all drinking at an analytics conference and said, we should put this on the radio.
[Michael Helbling]: That's a great idea.
[Michael Helbling]: That's right.
[Michael Helbling]: Another drunken, great ideas.
[Michael Helbling]: All right.
[Michael Helbling]: Also, if you are somebody who puts and is not directed at you, Cindi, this is back to the audience.
[Michael Helbling]: If you're someone who puts stickers on your laptops or whatever, we do have stickers and we'd love to send you one.
[Michael Helbling]: You can actually request one on our website so you can go and do that.
[Michael Helbling]: And then
[Michael Helbling]: Obviously, no show would be complete without saying a huge thank you to all of you listeners who go out and share ratings and reviews with us and tell us how you're enjoying the show.
[Michael Helbling]: So please continue to do that.
[Michael Helbling]: We look forward to that feedback.
[Michael Helbling]: We appreciate it very much.
[Michael Helbling]: All right.
[Michael Helbling]: As we wrap up, I know that no matter if you're trying to
[Michael Helbling]: Build one ring to rule them all type of semantic layers, or if you're spreading it out across verticals.
[Michael Helbling]: I know both of my co-hosts, Tim and Moe, would agree with me.
[Michael Helbling]: You should keep analyzing.
[Announcer]: Thanks for listening.
[Announcer]: Let's keep the conversation going with your comments, suggestions, and questions on Twitter at @analyticshour, on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group.
[Announcer]: Music for the podcast by Josh Grohurst.
[Announcer]: Those smart guys wanted to fit in, so they made up a term called analytics.
[Announcer]: Analytics don't work.
[Charles Barkley]: Do the analytics say go for it, no matter who's going for it?
[Charles Barkley]: So if you and I were on the field, the analytics say go for it.
[Charles Barkley]: It's the stupidest, laziest, lamest thing I've ever heard for reasoning in competition.
[Michael Helbling]: We'll just do our best with it.
[Michael Helbling]: It's why we have an audio engineer.
[Michael Helbling]: Hi, Tony.
[Tim Wilson]: Rock flag and semantic layers are 30 years old.