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Speaker 3: The information economy has a ride. The world is teeming
Speaker 3: with innovation as new business models reinvent every industry industry.
Speaker 3: Inside Analysis is your source of information and insights about
Speaker 3: how to make the most of this exciting new era.
Speaker 3: Learn more at inside analysis dot com, insideanalysis dot com.
Speaker 3: And now here's your host, Eric Kavanaugh.
Speaker 4: All right, ladies and gentlemen, Hello and welcome to the
Speaker 4: only coast to coast radio show all about the information economy.
Speaker 4: It's called Inside Analysis or True Layer Kavan. I hear
Speaker 4: with my old buddy from ages ago. We haven't seen
Speaker 4: each other in like fifteen years time fliers. We're having fun.
Speaker 4: Mark Pico, he is quite the innovator. He's taught at TDWY,
Speaker 4: He's taught all sorts of different classes, and he's a
Speaker 4: real thinker. And so we were talking earlier about the
Speaker 4: whole concept of value and what is value. People say,
Speaker 4: when you're pushing a project, oh, there's no value? Well
Speaker 4: what do you mean by that? So I'll just throw
Speaker 4: it over to you to kind of dive in on that, Mark.
Speaker 4: Value is this concept that everyone thinks they know, everyone
Speaker 4: thinks they understand, but once you start kind of drilling down,
Speaker 4: it could be hard to define. Right.
Speaker 5: That's so true, Eric, I guess whatgind me thinking with
Speaker 5: this over the last little while. As there's many themes
Speaker 5: of blogs and webinars and presentations that say, we you know,
Speaker 5: the failure rate of data related projects is pretty high.
Speaker 5: Why value wasn't created? I think, what what does that
Speaker 5: actually mean? Value? I'm not an economist, I'm not a
Speaker 5: financial person. I started thinking, how can I think about
Speaker 5: value in a way that unifies people's thinking? And I
Speaker 5: started thinking value is something I care about. And when
Speaker 5: I say I there's different types of people as stakeholders
Speaker 5: that care about different things, So how do we think
Speaker 5: about value from Department AD to Department B to Department C.
Speaker 5: That unifies the flow and connection between these. So I've
Speaker 5: started thinking about thinking about value as something created if
Speaker 5: my objectives are meant it. Those objectives mean I personally
Speaker 5: care about this happening, right, And I started to think
Speaker 5: about what are the different things we care about in companies?
Speaker 5: And I'm taking further from a data driven lens, you know,
Speaker 5: because I've given talks before and you know about data governance.
Speaker 5: So people say, well, where's the value of data governance?
Speaker 5: I think, what are you really asking? What does that mean?
Speaker 5: So I started thinking about conductivity and flow in companies,
Speaker 5: and I started thinking about value as something has a
Speaker 5: life cycle to it and it exists in different kinds
Speaker 5: of stages. And I started thinking about how do I
Speaker 5: organize this in a way that's simple and meaningful. So
Speaker 5: I coined the term that I'm kind of building out
Speaker 5: something called value literacy, right, And value literacy means literacy
Speaker 5: if anything, means I can have a conversation with you
Speaker 5: about it because I know the concepts, I know the vocabulary.
Speaker 5: So this means if I know how to converse with
Speaker 5: my stakeholders, we should have something in common to discuss.
Speaker 5: And if we think about the different kinds of people
Speaker 5: that work in the data field within a company, it's
Speaker 5: very very broad and very very diverse. And I started
Speaker 5: thinking about this. I don't know what organize this in
Speaker 5: a way that's kind of simple yet meaningful, and I
Speaker 5: started creating. My first thinking was, let's think about value,
Speaker 5: like like energy, like charging a battery. When you charge
Speaker 5: a battery, you're creating potential energy. But if that battery
Speaker 5: is never been used for anything, it's there, but nobody's
Speaker 5: using it. But when we use that battery in our
Speaker 5: phone or in a device, maybe something's now coming on.
Speaker 5: And so that's kinetic energy. You can now use your phone.
Speaker 5: It's kinetic value because you're turning the potential into something actionable.
Speaker 5: But people don't really care about having the phone turned on.
Speaker 5: What they care about is booking an order on Amazon
Speaker 5: with it, or communicating with their family or watching a video.
Speaker 5: So what they really care about is the use you
Speaker 5: have because the phone's on, And I call that realized value.
Speaker 5: So you go from potential kinetic to realized. Now potential
Speaker 5: is like raising data quality, building a data product, building
Speaker 5: a report. Kinetic energy or kinetic value happens when you
Speaker 5: use that to drive a process. So if I have
Speaker 5: information to say, how do I scal do my salespeople,
Speaker 5: or how to allocate my resources, I can now turn
Speaker 5: that into a better process. The realized value happens because
Speaker 5: you've done that. It's now you've got happier customers, you've
Speaker 5: got more revenue, and you've turned this into that flow.
Speaker 5: But there's two more building blocks of this. There's something
Speaker 5: I'm calling structural value, which is really what data governance prepares.
Speaker 5: And structural value is the creation of standards and policies
Speaker 5: and they then enable you to then do data quality
Speaker 5: to raise the potential value of potential value data. But
Speaker 5: there's one other thing we care about its companies. That's
Speaker 5: being revolui, dealing with risks, dealing with bad weather events
Speaker 5: with you, technology failing or whatever, or safety issues. So
Speaker 5: the fifth one I've called is resilient value. Now, each
Speaker 5: of these things or value domains are created by something
Speaker 5: called capabilities, but some value is also enabled other capabilities.
Speaker 5: So if you think about the world of data, think
Speaker 5: about data generation as a capability. I get data because
Speaker 5: I acquire it. I enter it into my screens. I
Speaker 5: measured it from my plant, but somehow I generate data.
Speaker 5: Then I advocapability to manage that data. I've got a
Speaker 5: store and provision it, integrate it, protect it, make it
Speaker 5: all the stuff available for others to use. Those are
Speaker 5: both enabled by data governance capabilities in others that I
Speaker 5: can actually because I position data governance as abilit to
Speaker 5: share data right now. If I can share data and
Speaker 5: as provisioned, the next capability there is called data consumption.
Speaker 5: I can use the data that was provisioned because it
Speaker 5: followed the policies, and now I can consume it and
Speaker 5: however form. I'm a data scientist or an analyst or
Speaker 5: whatever I am, and I'm now interpreting the data. I'm
Speaker 5: consuming it to make a recommendation to my boss or
Speaker 5: a decision maker. The last piece, or not really the last,
Speaker 5: but the second last piece, is something called data utilization.
Speaker 5: It's the data and acting on it. So I've scheduled
Speaker 5: my staff at my restaurant better, I've got my salespeople
Speaker 5: more allocated, I've got my plant with more reliability, and
Speaker 5: now my output of my company is better. That's the utilization.
Speaker 5: It creates the realized value. The last piece of the puzzle,
Speaker 5: I believe is something called data leadership capabilities. They're the
Speaker 5: ones that set the tone for why this is so important.
Speaker 5: So data leadership. Without that, these things won't connect. They're
Speaker 5: isolated silos. But if you take a step back, just
Speaker 5: like you're charging your battery so you can run your
Speaker 5: phone so you can order off of Amazon and have
Speaker 5: a nice book to read, that flow, in my view,
Speaker 5: is what value can be modeled as. And so if
Speaker 5: I'm working in it, or I'm working in marketing, I'm
Speaker 5: working in HR, we all see this differently. But yet
Speaker 5: if we all say we're failing because there's no value
Speaker 5: being produced, we need to know which of those five
Speaker 5: types of value are we talking about it? Because the
Speaker 5: word value isn't precise enough, and we just come plane,
Speaker 5: we whine about it, it's not working, and we point
Speaker 5: fingers and we walk away. If we can talk about
Speaker 5: it with a nice, simple picture about how it's connected,
Speaker 5: we can realize it's breaking here because of that interesting
Speaker 5: and that's my premise. And to take this one step further,
Speaker 5: I'm working to models in a graph database. So I
Speaker 5: have some nodes our value items and some nodes or
Speaker 5: capability items, and if we can see how they're connected
Speaker 5: and go to a workshop with our stakeholders, we can
Speaker 5: have conversations around why is it not flowing, where's the bottleneck,
Speaker 5: where's the where's the where's the speed bumps? And that's
Speaker 5: that's where I'm kind of at with this.
Speaker 4: This is very interesting. I like the fact that you're
Speaker 4: trying to dig into a better understanding of what value
Speaker 4: really is because typically when people think value, they think, Okay, ROI,
Speaker 4: what's the return on investment for this data governance project?
Speaker 4: And the answer would be something like, well, our customer
Speaker 4: satisfaction has gone on twenty percent. Our upsell capability has
Speaker 4: gone up because the customers are more satisfied. So that's
Speaker 4: the case where you can kind of track some monetary
Speaker 4: value from the blocking and tackling work you did, which
Speaker 4: is not very fun improving your data quality. That's true, ROI.
Speaker 4: But to a deeper point, I think you are enabling
Speaker 4: much more productive conversations about what to do next, because
Speaker 4: that's really what every business person is trying to figure out,
Speaker 4: what do I do next in order to get new
Speaker 4: business satisfy the business I have. Whatever the case may be,
Speaker 4: it's always pretty darn unwieldy, whether it is trying to
Speaker 4: figure out a new market to approach, trying to figure
Speaker 4: out a new product to roll out, a new service
Speaker 4: to roll out. These are deep gritty questions that you
Speaker 4: don't get answers to very easily, very quickly. And then
Speaker 4: the other side of the equation is you kind of
Speaker 4: never know. Like I remember what I learned the concept
Speaker 4: of opportunity costs. I was like, oh, wow, yeah, that's
Speaker 4: pretty interesting. So if I'm working on this, I'm not
Speaker 4: working on that. There's a cost of not working on
Speaker 4: something as opposed to cost of working on something. So
Speaker 4: what is the opportunity cost? What are you missing? All
Speaker 4: these things come out, and I'll give you an interesting example.
Speaker 4: I learned a risk management conference years ago. I was
Speaker 4: talking to these big bankers and they're all trying to
Speaker 4: figure out how to hedge their bets, how to move
Speaker 4: forward in a safe but effective manner in a very
Speaker 4: dynamic world, which is financial services. And I said, you know,
Speaker 4: don't you find that sometimes, like voting a certain way
Speaker 4: will cause personal tension, Like people take things personally and
Speaker 4: they get upset about stuff. And he goes guy said,
Speaker 4: that's a very good point. That's why on our boards
Speaker 4: we either concur or don't concur. So it's like we
Speaker 4: don't even really say yes or no. We either concur
Speaker 4: with the assessment that you've made, or we don't concur.
Speaker 4: So it's a softer way of kind of getting around
Speaker 4: the rough spots of conversations. And I kind of feel
Speaker 4: like what you're building is that softening but clarifying structure,
Speaker 4: almost like an emulsifier or something. What do you think?
Speaker 5: I agree? I think it's stimular to have a conversation.
Speaker 5: I think if you don't have literacy on a topic,
Speaker 5: you can't converse about it. I'm a real basketball fan,
Speaker 5: so if I'm talking to a person about basketball, or
Speaker 5: they don't understand what's important in basketball, they may know
Speaker 5: the words, but they don't have a conversation because they
Speaker 5: don't know what's important. Right. And so you can literacy
Speaker 5: on any topic, and you need concepts and vocabulary and
Speaker 5: some sort of an underlying model that kind of connects them.
Speaker 4: Right, that's cool. That's cool. So now you're building out
Speaker 4: will this be something you'll give speeches about and consult
Speaker 4: with organizations? Essentially? Is that the vision.
Speaker 5: My goal is that I've been working on this now
Speaker 5: on a part time basis, make you since late last year,
Speaker 5: maybe around November I started. I've been thinking about it
Speaker 5: for a long time, but I got kind of really
Speaker 5: serious about it late last year. My goal is to
Speaker 5: build maybe workshop content where we can assess and diagnose
Speaker 5: your values trapped and why. So rather than complain about
Speaker 5: values not happening, let's assess in the term of what
Speaker 5: to do about it there. So I'd like to turn
Speaker 5: it into workshop material. I've already given a presentation last
Speaker 5: week to a group of CEOs to kind of position
Speaker 5: the idea. So that's my goal is to build education
Speaker 5: content right now. Maybe it's some consulting and definitely workshop
Speaker 5: content kind of things right, But I also parallel one
Speaker 5: more thing I think of I think of the word
Speaker 5: data literacy is kind of too narrow. I think of
Speaker 5: the word data storytelling was too narrow. I do a
Speaker 5: lot of work in data storytelling, but I think it
Speaker 5: is better talking about value storytelling because if we can
Speaker 5: talk about value, we can have a conversation at a
Speaker 5: higher level. Data is simply a component. It's not the
Speaker 5: be all and end all. It's the value we're trying
Speaker 5: to create by as it flows through the company. So
Speaker 5: that's that's my premise is to visualize this in simple
Speaker 5: diagrams that show where it's stuck, where it's broken, and
Speaker 5: where it's not working, and ultimate where it does work,
Speaker 5: and so we can kind of see how to, how to,
Speaker 5: how to fix problems.
Speaker 4: Yeah, I think it's absolutely fascinating. And you know, I'll
Speaker 4: throw out another interesting tidbit here, which is, at the
Speaker 4: end of the day, companies consist of people, processes, objects, assets,
Speaker 4: and it's all driven by ideas. And you look at
Speaker 4: some of the most successful companies, they pivoted hard at
Speaker 4: some point in time. You look at Amazon, they were
Speaker 4: all about being a book delivery service basically, and they
Speaker 4: figured out, wow, we just built a fantastic supply chain
Speaker 4: system essentially, or point as saler or everyone to describe it,
Speaker 4: and they just pivoted to be selling whatever you want
Speaker 4: to sell and creating an interface that anyone could come
Speaker 4: along and sell. And that's because they obviously had a
Speaker 4: culture where ideas could flourish. You could share an idea
Speaker 4: and you can really get somewhere with it. And I
Speaker 4: think your approach to understanding value can help organizations get
Speaker 4: that kind of atmosphere in that kind of culture, because
Speaker 4: that's when you succeed. I mean, if you've got a
Speaker 4: company of even ten people, that's a whole lot of knowledge.
Speaker 4: It's a whole lot of moxie and savoaf fare and
Speaker 4: problems and issues and personnality whatever it is. There's a
Speaker 4: lot of bad stuff, for sure, But if you can
Speaker 4: work around the bad stuff and get to the good stuff,
Speaker 4: that happens in honest conversations in the moment, referencing real assets,
Speaker 4: real data, real ideas, real things that are needed. And
Speaker 4: to do that in a collaborative way, in a collegial way,
Speaker 4: that's the key. And I view your work in this
Speaker 4: value matrix as being very facilitating to that goal.
Speaker 5: What do you think, Absolutely, my whole point is to
Speaker 5: enable communication, and you can't communicate if you don't have literacy.
Speaker 5: So where do you start? What are the concepts, what's
Speaker 5: the vocabulary? How are they connected? And now starting that
Speaker 5: to have a conversation in the space, we have some
Speaker 5: building blocks to work on. Now this is a work
Speaker 5: in progress. It's early days, so it's not refined yet.
Speaker 5: There's probably other kinds of value not considering right, But
Speaker 5: again I'm not an economist, I'm not a financial analyst.
Speaker 5: So some people say this is too simplistic, but I'm
Speaker 5: thinking if it helps break the dead luck of conversation,
Speaker 5: then it's got value using that word again, using it's
Speaker 5: got value to people because you can now solve problems
Speaker 5: without being an e communist or a financial analyst.
Speaker 4: Right when I like to do do you do this
Speaker 4: analogy to energy? Like I remember learning potential energy and
Speaker 4: kinetic energy, So you've got potential value, you've got kinetic value,
Speaker 4: and then you've got real life value. Right, then you
Speaker 4: also had resilience value and leadership value. Is that right?
Speaker 5: Structure structural the person.
Speaker 4: Structural yet and go back on that again when it's that.
Speaker 5: So, structural is laying the frameworks, the policies, the things
Speaker 5: that kind of keep us structured.
Speaker 4: In the company data governance program, the primary.
Speaker 5: Output of governance to me is structural value. It then
Speaker 5: enables data management to raise the quality based on that
Speaker 5: structural policies and standards. And so when people say what's
Speaker 5: the value of governance, it produces structural value. Right, And
Speaker 5: if you know what that is, let's talk about it.
Speaker 5: Because that's that that tell if it's successful, right, things
Speaker 5: downstream of that will depend on it.
Speaker 4: Right well, And that's you know, what's the old expression,
Speaker 4: the car broughts from the head down right? So who
Speaker 4: is ever at the top they're if they're bad, if
Speaker 4: they're doing bad things and bad decisions, that's going to
Speaker 4: reflect all the way through the organization, and that's going
Speaker 4: to lead you to an unpleasant place. But the key,
Speaker 4: I think is for extant organizations to leverage this kind
Speaker 4: of thinking to understand where are we, where can we
Speaker 4: go next? Realistically? What's plausible with our assets, with our people,
Speaker 4: with our resources, what can we realistically hope to accomplish.
Speaker 4: That's a big question these days, and especially it's a
Speaker 4: big question because now with AI and all these technologies
Speaker 4: and all the data that's available to us, you can
Speaker 4: spin up a business model quickly. And the cloud used
Speaker 4: to be that you had to hit ten twelve people.
Speaker 4: That they can do with two people. So you can
Speaker 4: spin up ideas quickly, but how do they scale?
Speaker 6: Well?
Speaker 4: In order to scale, you need that structural value right correct.
Speaker 5: You know, if you're building anything, you need in structure
Speaker 5: to connect things, to have standard ways of doing things,
Speaker 5: and if you don't standardize, you can't scale, right. You know,
Speaker 5: that enables the scaling to have some way you're doing
Speaker 5: things in a standard way.
Speaker 4: Right, standard processes, standard procedures, standard file formats. I mean,
Speaker 4: if you get all the way down to where rubber
Speaker 4: meets the road, standard nomenclature for columns and things of
Speaker 4: this nature. If you do things properly, it's easier down
Speaker 4: the road to integrate into mix and match, right.
Speaker 5: And I think one of my big goals here is
Speaker 5: also to get people out of their silos. Yeah, I
Speaker 5: think one of the biggest barriers, and I kind of
Speaker 5: attribute why we have these problems is, I mean this
Speaker 5: is not new to anybody, but siloed thinking. Right, one
Speaker 5: department things are successful and the next one down the
Speaker 5: hall doesn't. Right, And well, then are we one company
Speaker 5: or we two companies?
Speaker 7: Right?
Speaker 4: Right?
Speaker 5: And that's the whole goal here, is to connect the dots,
Speaker 5: get rid of silos, and understand that how things flow
Speaker 5: should flow right independent of your org structure, because that
Speaker 5: can change.
Speaker 4: That's right. Well, And I'm reminded of something I learned
Speaker 4: a long time ago by moreen Clary and Kelly Gilmore
Speaker 4: of connect the knowledge network. They used to teach here
Speaker 4: at TDWY all the time, and she taught me a
Speaker 4: lot about organizational hierarchies and structures and talking to each other.
Speaker 4: And what you want are all the managers talking to
Speaker 4: each other across divisions. This sort of top down communications
Speaker 4: structure that we've all become aftimated to. Its kind of
Speaker 4: going out the window. And you're gonna need to be
Speaker 4: talking left and talking right to people near you and
Speaker 4: around you. In addition to understanding what your boss wants,
Speaker 4: what your customers need, you really want to get that
Speaker 4: context from around you. That's in the structural value, it's
Speaker 4: in the systemic value. Well, look this gentleman up online,
Speaker 4: Mark peakel Peco. He is a twy instructor. Will be
Speaker 4: right back. You're listening to Inside and OUTSIM.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All right, folks back here on Inside and olicis. And
Speaker 4: now I've got Josh Hicks with me from a cool
Speaker 4: company called Jumpmine. They're right down the street from me.
Speaker 4: I'm in Pittsburgh. They're in Columbus, Ohio, doing really innovative stuff.
Speaker 4: They're as old as a DM radio too. Seventeen years old,
Speaker 4: and they do a lot of data replication. It's just
Speaker 4: one thing they do, but they do data replication extremely well.
Speaker 4: We're not going to mention any company names. We're talking
Speaker 4: thousands of stores, real time updates. And Josh, you were
Speaker 4: telling me one of the real challenges with data replication
Speaker 4: is when things go wrong, like a network goes down,
Speaker 4: a store closes because of a hurricane. Well, these are
Speaker 4: all real serious issues. When you're trying to track thousands
Speaker 4: of data points and get them information about all their
Speaker 4: products like updates and pricing or whatever. To be able
Speaker 4: to do that seamlessly, that is a real magic bullet
Speaker 4: tell us about jump mine on what you guys are
Speaker 4: working on.
Speaker 6: Yeah, so yeah, Eric, Like Eric said, specializing data replication
Speaker 6: a lot of different endpoints, a lot of different platforms now,
Speaker 6: but it started with a lot of retailers having the
Speaker 6: problem of being able to move this data between all
Speaker 6: these locations and dealing with things that happen network issues
Speaker 6: and communication problems and low bandwidths and bad networks and
Speaker 6: things like that. And so the happy path of replication
Speaker 6: can be pretty straightforward and a lot of people can
Speaker 6: probably work through it. Basically, on their own with a
Speaker 6: little bit of tech experience, but when you're dealing with
Speaker 6: a lot of the thing unexpected outages, and when you
Speaker 6: have a note offline, you could have conflicts. So dealing
Speaker 6: with conflicts and not causing backlogs in the replication and
Speaker 6: being able to be resilient to networks that are down
Speaker 6: and stores that are offline, or sites that are offline,
Speaker 6: or databases that are offline. Because you're not really focused
Speaker 6: anymore on just the retail platform. It's pretty much used
Speaker 6: everywhere data is needed and moved and migrated, and with
Speaker 6: so many platforms out there now, it's not just relational
Speaker 6: databases we're dealing with anymore. It's a lot of different
Speaker 6: plays platforms with the nose sequels in the righthouses and
Speaker 6: the cloud came along, right, So there's a lot of
Speaker 6: benefits to utilizing lots of these data sources for different reasons,
Speaker 6: and so moving that data between them is kind of
Speaker 6: where we come.
Speaker 4: In well, right, And we were talking about the history
Speaker 4: of the database industry and how you know, twenty years
Speaker 4: ago there were like five choices you could make. Now
Speaker 4: there are four hundred and twenty five choices you can make.
Speaker 4: I mean, it is that diverse out there, and let's
Speaker 4: face it, individual companies can have any number of technologies
Speaker 4: in play. But one thing I've always loved about effective
Speaker 4: information architectures is that companies figure out where we're going
Speaker 4: to keep data, how we're going to move data, how
Speaker 4: we're going to use data. And you have these layers
Speaker 4: of abstraction where something in the middle can handle any
Speaker 4: source and handle any target. And that's the idea. That's
Speaker 4: where you are. You're the intermediate almost like the middleware
Speaker 4: as we've called it over the years, where you got
Speaker 4: all these source systems. I got an Oracle database, I
Speaker 4: got a Postgrass database, I got a Cybe database, and
Speaker 4: then I have all these point solutions, all these targets
Speaker 4: where I want to get information to the stores, to
Speaker 4: the manufacturers, whatever. If you try to do point to
Speaker 4: point for every one of those, you're going to jump
Speaker 4: off a cliff, like you are not going to be
Speaker 4: that's the unhappy path. Yeah you talk about.
Speaker 6: Right, yeah, And I mean and being able to merge
Speaker 6: all that together and not be vendor locked into a
Speaker 6: specific product. Right, You're having a tool that has the
Speaker 6: cross platform flexibility like we do. We don't really care
Speaker 6: what platforms you're using. We just want to be an
Speaker 6: option and we want to work with all those options
Speaker 6: that you define how your data should move, where it
Speaker 6: should move to what platforms you choose. Right, and that
Speaker 6: is that that becomes a big deal of keeping more
Speaker 6: than one or two of them in sync, because now
Speaker 6: you have to keep the order of how those transactions
Speaker 6: occurs very important, and some might not be available when
Speaker 6: others are. So keep being all that status ying and
Speaker 6: history of what data has moved and what hasn't moved,
Speaker 6: and then might be performant it because.
Speaker 4: Exactly right managing state, say, I try to explain state
Speaker 4: to people. It's a very interesting concept. The easy example
Speaker 4: I give is when you are online. Let's see on Amazon,
Speaker 4: you're shopping for stuff and you're searching around, and then
Speaker 4: you do the magical thing, which is I want to
Speaker 4: go ahead and buy this stuff, and so you hit
Speaker 4: go to Kart, and now the state is okay, I'm
Speaker 4: in the state of getting ready to take the money. Right,
Speaker 4: and if you say, oh no, wait, I want to
Speaker 4: go find something, you're getting out of that state. Now
Speaker 4: you're going back into your searching state to find things
Speaker 4: to build your cart. But the point is when you
Speaker 4: are in that cart and about to hit the purchase button,
Speaker 4: that is a certain state. And what you talk about
Speaker 4: makes a lot of sense of when things go down.
Speaker 4: So if I'm logged in, I'm about to hit purchase
Speaker 4: and the system goes down, my power goes out, or whatever,
Speaker 4: where is that state captured and persistent such that I
Speaker 4: can log back in and get to where I was. Right,
Speaker 4: And that's what you're talking about. Right, when things go down,
Speaker 4: you need some ledger basically or matrix that's monitoring where
Speaker 4: things were at the time of the failure so you
Speaker 4: can pick back up and not miss a beat and
Speaker 4: not miss something right.
Speaker 6: Yeah, The nature the nature of how the product fundamentally
Speaker 6: works is it will retain changes until they're committed at
Speaker 6: their target I see, and now obviously commit being more
Speaker 6: of a database term, but we deal with some other
Speaker 6: platforms like the kafkas and things like that. But being
Speaker 6: able to make sure that that is committed before you
Speaker 6: move on, and that's very important because we must retain
Speaker 6: that and keep that state and know that where that
Speaker 6: state of that information is until we've gotten a full
Speaker 6: acknowledgement that it's committed. Right, and we have processes that
Speaker 6: purge that stuff out. Then once that state is that's resolved,
Speaker 6: and there's all kinds of bells and whistles to turn,
Speaker 6: and you can set different retention periods for how long
Speaker 6: you keep history of that. But in a happy path,
Speaker 6: you know, keep it. You don't need to necessarily keep
Speaker 6: it around real long. If things are moving but something
Speaker 6: comes offline, you need to be able to maintain that
Speaker 6: state and playback hours potentially.
Speaker 4: Rice right right, the unhappy path. Now, that's such a
Speaker 4: good that's such a good point. And you know, I'm
Speaker 4: reminded again that if you have a good architecture and
Speaker 4: you know where your marshaling area is for moving things around,
Speaker 4: that allows you the flexibility to bring on different source
Speaker 4: systems and different target systems. And I did want to
Speaker 4: explain the commit, like a two phase commit is something
Speaker 4: I think a lot of people can understand. In the
Speaker 4: banking world. Because I send you one hundred dollars through Venmo,
Speaker 4: that the request goes through, take one hundred dollars out
Speaker 4: of my account, put it in his account. I need
Speaker 4: to get a message back that says, Okay, we got it.
Speaker 4: It's now in his account. That is a two phase
Speaker 4: commit right.
Speaker 6: Yeah, very very common that EF.
Speaker 4: We're gonna understand, I now have the money, and you
Speaker 4: know that I have the money, right, as opposed to, well,
Speaker 4: the money's out of my account, but it's not in
Speaker 4: your account. That's a problem. That's the unhappy path for
Speaker 4: money man.
Speaker 6: With relational databases, yeah, we are an asynchronous because a
Speaker 6: lot of people say, you know, I want a synchronous solution,
Speaker 6: but didn't really know what that means. Because you don't
Speaker 6: necessarily want to open a connection on your source and
Speaker 6: wait until it's committed on the target. That could hold
Speaker 6: up your user experience. So making that asynchronous is a
Speaker 6: big difference, and it's actually more performance for the user
Speaker 6: because it allows them to finish the user experience on
Speaker 6: the source, gather the data asynchronously, processes right target. Now,
Speaker 6: those it could be very fast and feel like it's synchronous,
Speaker 6: but it's important that if there is any failure along
Speaker 6: that pipeline, you're not holding up the user experience, right,
Speaker 6: And that's a big deal. It comes to data application right,
Speaker 6: not to affect the user. That's a lot of times
Speaker 6: what the first question is, how is this going to
Speaker 6: affect my users.
Speaker 4: Well right, well, I mean everyone has experienced either in
Speaker 4: the store or they're on the phone and customer service center.
Speaker 4: And what does the person say, Oh, our systems are
Speaker 4: running slow today? Well, why is that? It Could be
Speaker 4: the database, It could be netwight, could be data replication,
Speaker 4: it could be any number of things. And boy, the
Speaker 4: more interconnected we get in the cloud, the more things
Speaker 4: that are that can go wrong. I mean there are
Speaker 4: all sorts of things in between.
Speaker 6: As we've evolved in our experience with replication, we've we've
Speaker 6: gathered more and more information and stats along the way,
Speaker 6: which is really beneficial for pinpointing when there's a problem.
Speaker 6: Where is the bottleneck is?
Speaker 4: You know?
Speaker 6: Is it the extraction? Is it the transfers at the load?
Speaker 6: Is it somewhere in between?
Speaker 5: Is it? You know?
Speaker 6: What failure points? What retries? Did we have ryan gathering
Speaker 6: all of those statistic points, which helped put the bigger
Speaker 6: picture now and again back to the happy path. Those
Speaker 6: may look real inear and be real smooth, but when
Speaker 6: you start to have h maybe you add more bandwidth,
Speaker 6: you have more stores, you have more offices, you have
Speaker 6: more data, and you start pushing more through the pipe.
Speaker 6: You'll start to expose where the bottlenecks are. Maybe there
Speaker 6: weren't bottlenecks first quarter, right second quarter, helping the amount
Speaker 6: of users, you've added customers, you've added more data through
Speaker 6: your pipeline, and being able to quickly analyze where those
Speaker 6: fragments are and where those performance breakdowns are allows you
Speaker 6: to get in and make adjustments well.
Speaker 4: And you bring up an excellent point about the history
Speaker 4: and the tenure that jump Mind has because over time
Speaker 4: you do gather data, and so you'll understand when this
Speaker 4: system connects to that system, we've had some problems. When
Speaker 4: that system connects over here, we've had some problems. I mean,
Speaker 4: there was the whole disastrous CrowdStrike crash a few months ago,
Speaker 4: which was apparently because there was an update pushed and
Speaker 4: they hadn't done a full null scan and there was
Speaker 4: one of those little formula in there that just zapped
Speaker 4: the systems and brought it crashing down. I mean, that's
Speaker 4: the kind of thing that should not have happened to
Speaker 4: be blunted me. I still haven't gone to the bottom
Speaker 4: of what really happened there, but the point is that
Speaker 4: if you have been around for a while, as you
Speaker 4: guys have you can capture lots of metadata and understand
Speaker 4: what is a reasonable amount of time for this process
Speaker 4: to execute.
Speaker 6: And actually a feature that's kind of interesting that we've
Speaker 6: added to the product and about the last year and
Speaker 6: a half. We call them insights, and what it's doing
Speaker 6: is there's several of these that are running in the
Speaker 6: background and they're watching trends. They're watching those. So we
Speaker 6: started by capturing the metrics because that's the first paint point,
Speaker 6: is to be able to gather certain metrics along the way.
Speaker 6: But now these insights are running and trying to fire
Speaker 6: and do a kind of like an AI analysis. Hey,
Speaker 6: we noticed this normally takes this much time to push
Speaker 6: this much data. You're up one hundred and fifty percent.
Speaker 4: Right, what's going on?
Speaker 6: Yeah, it can bring it to your forefront and say
Speaker 6: these trends are happening, and then in some of these insights,
Speaker 6: we actually can provide you and approof. But it might
Speaker 6: be we want to durn a nile during a dial
Speaker 6: behind the scenes, we want to up some time out,
Speaker 6: we want to up a threshold, increase your connection pool.
Speaker 6: Maybe you've involved class and there's so some of those
Speaker 6: metrics we have settings, but we you know, it might
Speaker 6: involve in the past, that might involve consulting, right, go
Speaker 6: in and analyze this right now with these insights, they're
Speaker 6: kind of bringing it to your attention. Here, we noticed
Speaker 6: something that's abnormal. Improve We're going to tweak this dial
Speaker 6: for you and adjust it. You know, maybe you appreci
Speaker 6: your connection.
Speaker 4: Pool right now.
Speaker 6: Some people may or may not know what that means,
Speaker 6: but you know, at least it puts it in front
Speaker 6: of them to say, hey, this is what we'd like
Speaker 6: to do.
Speaker 4: Well, you know the old expression what gets measured gets
Speaker 4: managed exactly and what I love, especially because these systems
Speaker 4: are so complex and so interdependent. Now, yes, and there
Speaker 4: are so many things that can go wrong, and you're
Speaker 4: always like, is it my network? Is it me? I'm
Speaker 4: the one who's doing the speed test, Like, maybe it's
Speaker 4: me the other team. Right, it's like playing the tool, right,
Speaker 4: that's the old pickers playing the tool. But when you
Speaker 4: can see and you and guess what, that also educates
Speaker 4: the user, so now they know a little bit more
Speaker 4: about what's actually happening. And it's like I found that
Speaker 4: people will be very reasonable as long as they know
Speaker 4: what's happening. It's when you don't know what's happening that
Speaker 4: it gets very dicey.
Speaker 6: And one of the key features we've always and the
Speaker 6: owners have always brought to the table when they formed
Speaker 6: jump Line was the support we've worked hard to We
Speaker 6: don't outsource any support. It's all the engineers that have
Speaker 6: worked on the product.
Speaker 4: Wow.
Speaker 6: We typically try to get through about a six month
Speaker 6: learning curve where they if they hire new that we
Speaker 6: bring them on because the support is everything we want
Speaker 6: to be able. We've all worked in it. As an engineer.
Speaker 6: I'm still on call at times, and we've all been
Speaker 6: in crisis situations and there's nothing worse than having not
Speaker 6: having the proper support change yeah yeah, yeah yeah. And
Speaker 6: like you mentioned earlier, it might involve multiple teams, so
Speaker 6: we want the data side of things in the replication
Speaker 6: to always have the metrics and stuff that we can
Speaker 6: provide is best support we can for it, because we
Speaker 6: know what it's like to have an auditgen in the
Speaker 6: middle of the right right and oftentime we're dealing with
Speaker 6: an outage, could increase increase a backlog very quickly because
Speaker 6: it is replication, right, so immediately a small problem becomes
Speaker 6: a big one and thinks backlog while so the support
Speaker 6: has been a huge model. And quite frankly, I think
Speaker 6: people are just moving a lot more data. We didn't
Speaker 6: have to ty years ago. It wasn't as it was
Speaker 6: a big deal and what your storage was. Storage was expensive,
Speaker 6: data was expensive. Now we run into groups and I'm like,
Speaker 6: do you even know what these tables are, right, because
Speaker 6: they just have copies of everything, right, So that's it's
Speaker 6: kind of fascinating seeing the progression of how much data
Speaker 6: is moving now.
Speaker 4: Right, Well, and you actually just hit on one of
Speaker 4: my classic talking points. And folks who've listened to these
Speaker 4: shows multiple times have heard me say this multiple times.
Speaker 4: But the dynamics have changed so much. I mean, I'm
Speaker 4: a big fan of constraint based design because your constraints
Speaker 4: are reality and you have to either address them or
Speaker 4: work within them. Right, And back in the day, processors
Speaker 4: were slow, storage was expensive. Networks speed was.
Speaker 6: Slow on the forefront of everybody's mind.
Speaker 4: Right Now, processors are fast and you could do multi core,
Speaker 4: you could do GPU versus CPU, the the pipes are fat,
Speaker 4: storage is cheap, So all those dynamics that that drove
Speaker 4: the design of old systems are different now yeah, and
Speaker 4: are gone. So what do we do now?
Speaker 8: Now?
Speaker 4: There is this concept of code bloat remember learning now
Speaker 4: we got about a minute left, and like code bload
Speaker 4: is when the processors got fast that the developers got lazy. Yeah,
Speaker 4: who cares? Just go write more script to get it done.
Speaker 6: It seems like it's.
Speaker 4: Kind of a wave right right, right, it goes up
Speaker 4: right and we're going back down again. Well, closing thoughts,
Speaker 4: where can someone learn more about jump mind? And we
Speaker 4: didn't talk too much about It's called symmetric.
Speaker 6: Das Yeah, for symmetric data synchronization. Right, you can find
Speaker 6: us on jumpline dot com and it's under our data.
Speaker 6: There's a data menu, right, and there's a trial. It's
Speaker 6: a look at it. And then obviously if you email
Speaker 6: us myself for one of the other engineers, are happy
Speaker 6: to kind of walk it through and give a demo.
Speaker 4: I love it.
Speaker 6: We're very big on making sure we're not maybe the
Speaker 6: right fit for every use case, So we love to
Speaker 6: talk to people. Hear what their story is, what their
Speaker 6: use case is. We want to find a good fit.
Speaker 6: It's not a good fit, it's a waste of both
Speaker 6: their time. Yeah, yeah, yeah, that's that's been a key.
Speaker 4: Well, that's that's a great signed folks. I can tell
Speaker 4: you that's a look up jump mind. And I remember
Speaker 4: one of the coolest companies I knew. In the same sentence,
Speaker 4: a guy would say, well, here's what we do and
Speaker 4: here's what we don't do, right, and it's really important
Speaker 4: to understand that. Well, folks, don't touch it out with
Speaker 4: right back, you're listening to Inside Analysis.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All right, folks, back here on Inside Analysis at TDWY
Speaker 4: in Las Vegas, my old haunt. I'm hanging out with
Speaker 4: some of my old buddies, and I'm here now with
Speaker 4: Patrick O'Halleran. He is with the company called wear Escape.
Speaker 4: I've known wear Escape for about fifteen years or so.
Speaker 4: Old Michael Whitehead he launched it and sold it to
Speaker 4: a Idea a long time ago, or a number of
Speaker 4: years ago, I guess. But they're great for data warehouse
Speaker 4: documentation because guess what, you know, how many developers like
Speaker 4: to do documentation? None, No, developers like to do documentation.
Speaker 4: Slightly less than one, slightly less than one, somewhere between
Speaker 4: zero and one. Of the developers out there like to
Speaker 4: do documentation. That's important, but they do a lot of
Speaker 4: other stuff too, and it's an excellent tool for being
Speaker 4: able to leverage the power of a snowflake for example,
Speaker 4: or I guess at data bricks or any of these guys. Right,
Speaker 4: So tell us a bit about what you're working on
Speaker 4: with wear Escape red these days. Sure, for sure.
Speaker 9: So as you say, wear Escape's been around for quite
Speaker 9: a while, and we started twenty five years ago in
Speaker 9: New Zealand as a consulting company building data warehouses.
Speaker 5: We're doing a lot of.
Speaker 9: Repetitive tasks and rise that those could be automated with
Speaker 9: technical tools.
Speaker 4: Right.
Speaker 9: Those tools became very powerful and very popular, and some
Speaker 9: people started asking if they could start buying those tools.
Speaker 9: So at that point Whereescape stopped as a consulting company
Speaker 9: began as a software company. We've got a few thousand
Speaker 9: customers around the world. Wow, in Asia, US, North America,
Speaker 9: South America, Europe, Middle East Africa.
Speaker 4: Wow. And if you still in New Zealand, right, Well,
Speaker 4: that's a cool path to start as a consultancy, figure
Speaker 4: something out and then build the software. I see that
Speaker 4: quite often because as a consultant you're on the front lines,
Speaker 4: you're trying to solve things, and to your point, you
Speaker 4: keep running into these repetitive tasks and you're like, wait
Speaker 4: a minute, why don't we just automate this task or
Speaker 4: that task? Documentation being one of those things to say, okay,
Speaker 4: what do we do? And that's one thing I love
Speaker 4: about the cloud is that I think a lot of
Speaker 4: the best practices around things like log in, things like
Speaker 4: tracking what's been done, are just sort of baked into
Speaker 4: the architecture of cloud. And now we see that with
Speaker 4: a lot of these products too, right to bake in
Speaker 4: things like documentation and testing. I mean, automated testing is
Speaker 4: one of the real keys.
Speaker 9: Right, Yeah, So let's talk about documentation first. I'm a programmer,
Speaker 9: you know, one of those guys that started coding on
Speaker 9: IBM PCs at twelve years old, Neucurabo, Pascal and eighty
Speaker 9: eighty six assembly and all that stuff. And like you said,
Speaker 9: documentation is one of those phases. I describe it as
Speaker 9: the phase of a project at the end where someone
Speaker 9: runs around and grabs all the posted notes and types
Speaker 9: it into word and as soon as the.
Speaker 5: Hit file save, it's obsolete. That's fun.
Speaker 9: So The thing about Wearscape and automation tools, the Warscape specifically,
Speaker 9: is that we're capturing what you're doing as you're doing it.
Speaker 9: So it's not like you're writing a bunch of code
Speaker 9: then we have to go figure out what that code means.
Speaker 9: But through Wizards, through other automated procedures, intelligence built into it,
Speaker 9: not just in code generation, but in design and architecture
Speaker 9: as well. We captured the key points where you're doing things.
Speaker 9: Something as simple as data lineage, you've got to say
Speaker 9: a factor, a dimension at the end of a long
Speaker 9: chain of transformations, and a data analyst looks at it
Speaker 9: and says, where does this number come from? Anytime you've
Speaker 9: got a new dashboard, a new report, the question is
Speaker 9: always do you trust these numbers? So the nice thing
Speaker 9: about that kind of documentation is I can very very
Speaker 9: quickly go back online and see exactly where that number
Speaker 9: came from, what transforms it went through, what calculations it
Speaker 9: went through. Maybe it came from data combined from three
Speaker 9: or four different source systems. That's something that's virtually impossible
Speaker 9: to do manually. Track back diagrams be able to present
Speaker 9: documentations that the users. Something as simple as when we're
Speaker 9: profiling a source system to pull data in. If there's
Speaker 9: extended attributes like in SQL server, if there's any kind
Speaker 9: of extra information, they get a data catalog. If there's
Speaker 9: things we can feed in from the data governance tool
Speaker 9: or data cataloging tool, purview or whatever it is, that
Speaker 9: can work its way all the way downstream into the
Speaker 9: documentation as well. Wow, the documentation is literally a couple
Speaker 9: of clicks of the button. And if you're in a
Speaker 9: dev environment at QA environment, every morning you can create
Speaker 9: this large p has all the necessary documentation in it
Speaker 9: and then push that out to a shared drive or
Speaker 9: someplace we can get to it. So it's not just
Speaker 9: having a docutation that's having to get accessible as.
Speaker 4: Rights right, having it accessible. And you know, when I
Speaker 4: think about documentation, I think about automatic documentation log files,
Speaker 4: and log files are baked into many of these enterprise
Speaker 4: systems as a way of knowing what happened, as a
Speaker 4: way of knowing what the system did and thus being
Speaker 4: able to go back and trace it. But there are
Speaker 4: so many of them that you need to have a
Speaker 4: process around analyzing the log files, and that's been a
Speaker 4: whole thing over I don't know the last twelve fifteen years,
Speaker 4: I've seen as companies figure out, hey, that log file
Speaker 4: is a source of truths. Let's pay attention to that
Speaker 4: and watch for changes and patterns in the log files.
Speaker 4: And that's how you can understand what happened, what went wrong,
Speaker 4: and then go back and remediate something. Right.
Speaker 9: Yes, Spunk is a good example thought, right, So that's
Speaker 9: what they do, right, Yeah, they've read in parcelog files.
Speaker 9: We're even a step beyond that because pretty much everything
Speaker 9: that's going into the log files is already in our
Speaker 9: bedded data. So if you're running jobs to work your
Speaker 9: data from the source system through all the transforms, through
Speaker 9: your data foundation layer into you know, if you're doing bronze, silver,
Speaker 9: gold layers or presentation layer or semantic layer, whatever it is,
Speaker 9: all that data, all that processing is already on our metadata. Okay,
Speaker 9: it's wide open, worse kept to a wide open product.
Speaker 9: We don't publish a format of the metadata. But we've
Speaker 9: got plenty of customers, plenty of service partners that have
Speaker 9: built scripts queries to go look at it. Maybe they've
Speaker 9: got jobs that are processing more data or less data
Speaker 9: than they used to. Maybe you've got jobs that are
Speaker 9: taking longer, and you've got to measure throughput those kinds
Speaker 9: of troubleshooting things which you used to have to go
Speaker 9: through and look through, and logs you can pull directly
Speaker 9: from our metadata. We've got a sister company called yellowfin.
Speaker 4: I know phone Yeah, yeah, I know, guys.
Speaker 9: Okay, we're adding an analytics package to to our tool itself,
Speaker 9: our scheduler that's going to be built into the tool
Speaker 9: using their technology, so that you can see the operations
Speaker 9: of your data warehouse and how things are working and
Speaker 9: when they're slowing down, when they're speeding up, where things work,
Speaker 9: where things don't work, to help you not just identify
Speaker 9: as a it's a problem, but to identify exactly where
Speaker 9: that problem is to help you fix it faster.
Speaker 4: Well, and you just picked up on one of the
Speaker 4: hottest topics in the world of data today, which is finops, right,
Speaker 4: because if you can understand what's happening in there, you
Speaker 4: can understand what it costs you. Right, So running certain reports,
Speaker 4: doing certain projects, what does it really cost? I mean
Speaker 4: a lot of times it's very nebulous. I Mean, you
Speaker 4: can see what the line item is on your budget
Speaker 4: when it comes through, when the bill comes in but
Speaker 4: actually being able to have fine grain views into which
Speaker 4: which query cost how much? All that kind of stuff
Speaker 4: is extremely valuable.
Speaker 9: Right, Yeah, I'm sure with you know, cod based consumption
Speaker 9: costs being the big cost for everybody. I won't mention
Speaker 9: a specific cloud vendor, but there's a certain one, certain
Speaker 9: one that's getting a cold shoulder sometimes, right, because a
Speaker 9: lot of them cussion costs are unexpected. Right, So when
Speaker 9: you can use analytics, I'm sure it can be added
Speaker 9: tooryally package to go query the cloud vendor and find
Speaker 9: out what is it costing?
Speaker 5: What does it costing me to do this? Right?
Speaker 9: And it's it's I used to be in big an
Speaker 9: application performance management, and the ninety percent of doing a
Speaker 9: good job is knowing where to focus. You can take
Speaker 9: something that's very slow but doesn't run very often and
Speaker 9: improving it doesn't really give you much of a much
Speaker 9: of an improvement. Where you can take something that's just
Speaker 9: a little bit slow but runs a lot more frequently
Speaker 9: and work on that and get a bigger improvement. So
Speaker 9: maybe you've got one particular sequel statement that's incredibly costly
Speaker 9: but it runs once a week that may pop to
Speaker 9: the top of some list saying, hey, pay attention to me.
Speaker 9: Need I need, I need some attention. But really what
Speaker 9: you want to pay attention to is the total cost.
Speaker 9: So yeah, having analytics packages to be able to slice
Speaker 9: and dice that for you and give you, like I said,
Speaker 9: someplace to focus so you can you can you can
Speaker 9: get more bang for your buck.
Speaker 4: Right well, and to understand the inner workings of these things,
Speaker 4: how they've come together, what value you're getting from them.
Speaker 4: We just talk to a gentleman, Mark Pico, all about value,
Speaker 4: understanding what value is. He's got some really interesting concepts there.
Speaker 4: But you know, in the data warehouse automation world, let's
Speaker 4: face it, there are lots of tedious tasks that come
Speaker 4: together to create a data warehouse and then to use
Speaker 4: the data warehouse and a wear escape. You are really
Speaker 4: the first that I came across to start focusing on
Speaker 4: automating some of these tasks. Right, people just think of it,
Speaker 4: get the data in and do the analysis and get
Speaker 4: the analysis out. There's a lot of other stuff that
Speaker 4: happens in between to make that all take place. And
Speaker 4: you guys will sit on top of any environment, right
Speaker 4: like data bricks or snowflake or whatever, right.
Speaker 9: Yeah, there's about say seventeen eighteen different targets we support.
Speaker 9: So we talked about this earlier today, you and I
Speaker 9: did about how Wearescape over the years has evolved from
Speaker 9: a very specific product that works for SQL server and
Speaker 9: works for.
Speaker 5: Work and works for serio data.
Speaker 9: And as the tool grew and use and grew an expansion,
Speaker 9: it's become what I would call framework.
Speaker 4: This is very interesting, it's a very interesting concept.
Speaker 9: I said to my Unix backgrounds, and I told you
Speaker 9: it's been long enough ago that I still call it
Speaker 9: Unix not Linux. That the best program is the program
Speaker 9: that doesn't do anything. It's everything's been externalized into scripts
Speaker 9: and to configurations and things like that. Warescape has really
Speaker 9: been turned into a framework where you apply packages what
Speaker 9: we call enablement packs or rules or other things to
Speaker 9: really configure it and how you need to use it.
Speaker 9: And I gave you the example of data bricks. We
Speaker 9: had about two years ago. We had several prospects come
Speaker 9: to us asking about whether wear escape supports data bricks,
Speaker 9: and the first three times we said no, and the
Speaker 9: fourth time we said, we need a different answer. So
Speaker 9: one of our engineers took the Snowflate and Snowflake enablement pack,
Speaker 9: which we had and in about two or three weeks
Speaker 9: had it working for data bricks. So now we technically
Speaker 9: support data bricks. But then we went through some beta
Speaker 9: testing and refined it because the enablement packs are geared
Speaker 9: towards a specific technology, and within about four months we
Speaker 9: had a productized release of supported data bricks. So we
Speaker 9: went from zero to sixty in four months.
Speaker 4: Maybe that's not something to brag about. Yeah, No, zero
Speaker 4: sixty in a few seconds is good. That's not too bad.
Speaker 4: That's not too bad. Well, folks, we've got a podcast
Speaker 4: bonus segment coming up here. Next, we're talking with Patrick
Speaker 4: O'Halleran from wear Escape, the guys who really kind of
Speaker 4: invented data warehouse automation way back when. And now I
Speaker 4: got seventeen different targets, a lot of stuff going on.
Speaker 4: We'll be right back. Don't touch that down.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
Speaker 4: All right, folks, back here on Inside Analysis, talking to
Speaker 4: Patrick O'Halleran of war Escape. And you just mentioned in
Speaker 4: the break there the design side, you're not just the
Speaker 4: code generator, but you're helping in the design of the
Speaker 4: data warehouse and helping organizations know how to get the
Speaker 4: most of whichever their target system is. Right, let's say
Speaker 4: it's Snowflake, which, let's face it was. I watched the
Speaker 4: whole had dup Era come and go, and I was
Speaker 4: one of the ones scratching my heads about thinking, oh,
Speaker 4: I don't think you guys are missing something here. I
Speaker 4: don't think it's going to solve all the world's problems.
Speaker 4: Then what happened. Snowflake comes out, figures out how to
Speaker 4: separate compute from storage. And the other thing they did
Speaker 4: was very clever, is they figured out that you know what,
Speaker 4: in the old world at data warehousing, the sign of
Speaker 4: the schema was really important and really constraining because once
Speaker 4: you had it, you weren't going to go changeing. I
Speaker 4: want to change the skin, like what are you crazy?
Speaker 4: Don't even sales words to me again? And they figure
Speaker 4: out how, look, just tear it down, change the schema
Speaker 4: and build it back up. Right, wasn't that one of
Speaker 4: their big that's a pretty big innovation. But you guys
Speaker 4: can help in that process, right, sure?
Speaker 9: Sure, I'd say a lot of our customers have come
Speaker 9: to us a lot of people aren't familiar with what
Speaker 9: their data warehouse automation is or just data automation, so
Speaker 9: we wind up doing a lot of education on that,
Speaker 9: explaining where the benefits are, where the time savings comes in,
Speaker 9: and where their performance improvements come from. And it's not
Speaker 9: just code generation. That's one level of automation. I did
Speaker 9: a webinar with Ken Graziano.
Speaker 4: Yeah, not too old, the data Warrior.
Speaker 9: Yeah, about different levels of automation. And think about cruise
Speaker 9: control in your car. Okay, my motorcycle, it's cruise control.
Speaker 4: Was a throttle lock.
Speaker 9: That's that's automation.
Speaker 4: That's funny.
Speaker 9: Then you've got cruise control, which keeps a constant speed. Okay,
Speaker 9: that's maybe another level of automation. Then you've got adaptive
Speaker 9: cruise control, which is keeping a space between you and
Speaker 9: the front of you and it's suggesting it's it's speed
Speaker 9: that way. Those are all levels of automation. Well, Wearescape
Speaker 9: has and data warehouse automation has different levels of automation.
Speaker 9: So beyond just generating code, beyond just you know, generating
Speaker 9: physical Python scripts and DDL and email and all that
Speaker 9: kind of stuff, Warescape helps with the design as well,
Speaker 9: so I can create a conceptual model of my sources
Speaker 9: of my data warehouse, either pull one in if I've
Speaker 9: already got one created, or created one for tratch. And
Speaker 9: we also always tell people that's a business process. Creating
Speaker 9: your data warehouse is a business process, not an IT process. Yeah,
Speaker 9: there's certainly technology involved in it, but it solves a
Speaker 9: business problem. Okay, So you create your large conceptual model
Speaker 9: independent of your source systems, addressing business issues and business
Speaker 9: objects and xsts. You define all the attributes of all
Speaker 9: those entities that you want to.
Speaker 5: Track and report on.
Speaker 9: Then you can go and you can profile all your
Speaker 9: source systems, find out what data you have, assign attributes
Speaker 9: to those attribute types, run profiles not just about the structures,
Speaker 9: but the data itself, and then our rules engine can
Speaker 9: do that. Sounds kind of silly about anything with that,
Speaker 9: So PI, for example, you can bring in data, flag
Speaker 9: it as PII. Our rules engine can then do something
Speaker 9: with it. What do you want to do? Do you
Speaker 9: want to mask it? Do you want to drop it?
Speaker 9: Do you want to encrypt it? Do you want to
Speaker 9: put it into a separate object and then shut that
Speaker 9: off to a separate schema. That's lockdown, You decide what
Speaker 9: you want to do, you create the rules, and you
Speaker 9: can be sure that anything you've defined as PII in
Speaker 9: your source system is handled appropriately. Data quality checks, data
Speaker 9: governance checks, things like that being able to grab an
Speaker 9: object and say I want this to be a I
Speaker 9: want this to be a type two dimension data vault
Speaker 9: is built for automation. If you're familiar with data vaults
Speaker 9: the whole concept, it's a very pattern based structure for
Speaker 9: creating what is an abstract, complicated foundation with ub satellites
Speaker 9: and links where things are broken apart in strange ways,
Speaker 9: linked and strange ways. Can you build a data vault
Speaker 9: with without automation?
Speaker 5: I've never seen one.
Speaker 9: I would say two thirds of our databault customers have
Speaker 9: come to us because they've tried that and failed.
Speaker 4: Wow.
Speaker 9: But the idea that I can create my conceptual model
Speaker 9: and my logical model independent of my physical model, bed
Speaker 9: all that within wear escape, attach my source systems to it,
Speaker 9: and then push that all out to our builder tool
Speaker 9: that will then create all like I said, the DDL,
Speaker 9: the DML, the Python scripts and everything else I can build.
Speaker 9: I do it in demos all the time. I can
Speaker 9: build a star scheme leable effect in a couple of
Speaker 9: dimensions from two or three different source systems, and have
Speaker 9: a physical model up in snow Lake or data bricks
Speaker 9: or a SEQL server or whatever, and I can do
Speaker 9: all that in twenty thirty minutes and have the documentation.
Speaker 4: Wow, that's crazy. Yeah, Well, it's like it's almost like
Speaker 4: you've got to make an analogy to painting. You've got
Speaker 4: a tool that allows someone to imagine what they want
Speaker 4: that painting to look like, and then you hit the
Speaker 4: nentwer button and it goes b let it sprays it
Speaker 4: all on the screen. So the original tool we had
Speaker 4: was our builder tool. It's a bottom up approach. You
Speaker 4: defined load tables and stage tables, and then all the
Speaker 4: objects you want to have in your warehouse. I always
Speaker 4: describe that as you've got a bunch of legos and
Speaker 4: then you've built your dinosaur or your spaceship or whatever
Speaker 4: it is.
Speaker 5: The design tool on top of that is top down.
Speaker 9: Do you tell it what you want to build. I
Speaker 9: want to build a spaceship, a dinosaur, a castle or
Speaker 9: whatever it is, and it then figures out all the
Speaker 9: individual legos that has to produce to build that interesting
Speaker 9: and we have customers that do both and do both successfully.
Speaker 4: So you can do top down and bottom up at
Speaker 4: the same time. And yeah, yeah, don't see how they
Speaker 4: meet together.
Speaker 9: I talk about the design tool as being like a
Speaker 9: an architect for a new house. They've got a blueprint
Speaker 9: to hand that blueprint off to the general contractor who's
Speaker 9: going to build the house. Great, Now, what happens in
Speaker 9: the real world is the architect didn't understand a certain
Speaker 9: zoning rule, or there's a big rock that they can't move,
Speaker 9: or the homeowner decides they want the staircase moved. The
Speaker 9: GC will just go ahead and do those things right.
Speaker 9: The nice thing about our tool is that you can
Speaker 9: do both. You can build the blueprint, you can have
Speaker 9: the builder tool building things, but then you you can
Speaker 9: communicate information back so your design never goes out a date.
Speaker 5: Your mind is always up to date, which is a
Speaker 5: huge feature. That's interesting.
Speaker 4: Yeah, So, I mean people can go buy snowflake and
Speaker 4: they don't have to use something like wear escape. But
Speaker 4: it makes a lot of sense to do that because
Speaker 4: any I was talking about this today with a couple
Speaker 4: other people. Because you've abstracted out the design and build
Speaker 4: layer from the actual management of the warehouse. And one
Speaker 4: thing I love about Snowflake is how they'd say, you know,
Speaker 4: we're going to control your data, you'll do everything around
Speaker 4: it basically, and they have a very clean line of
Speaker 4: demarcation between what they're doing what the partners are doing,
Speaker 4: which I thought it like it's central to their success,
Speaker 4: but what do you think?
Speaker 5: Yeah.
Speaker 9: The other nice thing is because you're working at an
Speaker 9: abstract level, at a conceptual and logical level, you can
Speaker 9: switch targets very very quickly. We had a customer, i'll
Speaker 9: just call them, a large insurance company in the Midwest
Speaker 9: that had been using wear Escape in Terra Data for
Speaker 9: years and they decided they wanted to move have up
Speaker 9: a pilot project in Snowflake. Okay, so they said, what's
Speaker 9: it going to take to recreate our data warehouse and Snowflake. Well,
Speaker 9: we already had all the metadata, we had everything the
Speaker 9: information we needed to generate it because they've been using Wearescape.
Speaker 9: So we went to our offshore team and said, what's
Speaker 9: it going to take to convert this data from Terra
Speaker 9: Data to Snowflake and then create data warehouse And they
Speaker 9: gave me a number, and since I was the manager
Speaker 9: of professional services. I bumped it up ten percent. I
Speaker 9: bumped it up to forty hours, all right, forty hours
Speaker 9: and in a week, in a week, they had their
Speaker 9: physical data warehouse replicated from Terra Data to Snowflake.
Speaker 5: Why, including all the objects, all the procedures and everything else.
Speaker 4: That's crazy.
Speaker 5: That's an exception. But that's that's a real case.
Speaker 4: Well, it's because you had the recipe in wear Escape,
Speaker 4: you had the metaday, you already knew what the structure
Speaker 4: would look like, you had the sort of matrix, the
Speaker 4: blueprint to use your term, and then you could just
Speaker 4: go connect snow flicking build it up.
Speaker 9: Yeah, and if they had customizations and things, that might
Speaker 9: have slowed down. But you know, even if you get
Speaker 9: eighty percent of the way there, right, push up a button.
Speaker 4: Right, great, Wow, this is fantastic. Well look this gentleman
Speaker 4: up online folks, Patrick o'hallerin. So it's a good Polish name,
Speaker 4: of kidding, it's an Irish name. So yeah, look up
Speaker 4: wear Escape, w H E R E Escape. They're part
Speaker 4: of Idea these days. I D E R A and
Speaker 4: we'll talk to you next time. You've been listening to
Speaker 4: Inside Analysis.
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