Speaker 1: It's a It's a really common challenge, isn't it. You
Speaker 1: know your organization needs to leverage data, I mean properly
Speaker 1: leverage it, but just figuring out where to start. How
Speaker 1: do you even build that capability? It can feel well,
Speaker 1: completely paralyzing sometimes.
Speaker 2: Yeah, like staring at this huge ocean. You know there's
Speaker 2: something valuable out there, but the map seems missing.
Speaker 1: Exactly that feeling of being overwhelmed. The potential is massive,
Speaker 1: we all see that, but the actual path yeah, not
Speaker 1: always clear.
Speaker 2: It's understandable. There's so much noise, so many options, it's
Speaker 2: easy to get, you know, bogged down.
Speaker 1: And that's exactly what we're diving deep into today. We
Speaker 1: actually had a request from one of you asking for
Speaker 1: a practical guide. Ah yeah, someone looking to strategically build
Speaker 1: data science and analytics functions, but crucially without needing this
Speaker 1: like huge upfront investment or getting lost in planning forever.
Speaker 2: Right, makes sense.
Speaker 1: So we're going to unpack the key insights from data
Speaker 1: science and analytics strategy an emergent design approach. It's by
Speaker 1: Kayla Shawadi.
Speaker 2: And Alex good Choice. It offers a really useful perspective.
Speaker 2: Our goal here is basically to pull out the actionable
Speaker 2: advice from the book give you a clearer, maybe step
Speaker 2: by step feel for how you can develop these capabilities.
Speaker 2: And the key thing to remember really is that this
Speaker 2: isn't about reaching some perfect end state overnight.
Speaker 1: No, definitely not.
Speaker 2: It's much more of a journey, you know, learning, adapting,
Speaker 2: growing as you go.
Speaker 1: Okay, so let's get into it. The book kicks off
Speaker 1: by looking at why those traditional, very rigid data strategies
Speaker 1: often just don't work out.
Speaker 2: Yeah, they often fail to deliver.
Speaker 1: It even points to that Harvard Business Review article is
Speaker 1: data scientists still the sexiest job of the twenty first century?
Speaker 1: Remember that one?
Speaker 2: Oh? Yeah, that highlights some of the let's say, traps
Speaker 2: for the unwary when setting up these functions.
Speaker 1: So what are those main traps? What goes wrong with
Speaker 1: the old school approach?
Speaker 2: Well, one big one is rushing in hiring data scientists,
Speaker 2: buying the flashy tech before really nailing down the business
Speaker 2: problems they're supposed to solve.
Speaker 1: This sexy java lure takes.
Speaker 2: Over exactly, it distracts from the groundwork. And then there's
Speaker 2: this great concept the book introduces, the corporate immune system.
Speaker 1: The corporate immune system tell meymore.
Speaker 2: It's that, you know, inherent organizational resistance to big changes,
Speaker 2: like your body fighting off a cold.
Speaker 1: Okay, I can picture that.
Speaker 2: These big, top down, prescriptive strategies, they often trigger that
Speaker 2: resistance because they feel disruptive, maybe even threatening, imposed from above.
Speaker 1: Right. Yeah, we've probably all felt that pushback at some point.
Speaker 1: So if that traditional, heavily planned way hits this wall,
Speaker 1: what's the alternative? What does emergent design offer.
Speaker 2: Emergent design is well, it's much more flexible, more responsive,
Speaker 2: instead of these huge detailed plans that are probably outdated
Speaker 2: the minute you print them. Sure, it focuses on setting
Speaker 2: a clear direction and then learning and adapting as you go.
Speaker 1: Iteratively, Well, that's about mapping every single step, more about
Speaker 1: setting a course and then adjusting based on what you find.
Speaker 2: Precisely, it's how much more evolutionary.
Speaker 1: The book uses this really interesting analogy. It talks about
Speaker 1: the strategist being more like a midwife rather than an expert.
Speaker 1: What does that actually mean in practice?
Speaker 2: It means, especially early on, your role is more about facilitation,
Speaker 2: less about coming in saying I'm the expert, here are
Speaker 2: the answers, and more about helping the business give birth
Speaker 2: to its own solutions if you like. It's about engaging
Speaker 2: with different departments, facilitating conversations to really understand their core challenges.
Speaker 1: So it puts a big emphasis on problem finding.
Speaker 2: Huge emphasis really digging deep to uncover what keeps people
Speaker 2: up at night, and.
Speaker 1: It actually suggests asking that specific question, right, what keeps
Speaker 1: you up at night? Go ask sales operations HR yes, get.
Speaker 2: Them to articulate their main pain points. Sandra Hogan, who's
Speaker 2: quoted in the book, even suggests starting right at the top,
Speaker 2: ask the.
Speaker 1: CEO, I understand the being picture first.
Speaker 2: Exactly, get the CEO's vision the main organizational challenges, and
Speaker 2: then you can tailor those conversations with other leaders to
Speaker 2: make sure everything aligns so.
Speaker 1: You ensure the data stuff you do actually tackles the
Speaker 1: most critical business needs.
Speaker 2: Right and crucially, the book stresses showing value early tangible
Speaker 2: results through what they call proofs of value, not just
Speaker 2: proofs of concept, not just technical feasibility no Criicknapier makes
Speaker 2: this point well in the book. You need to demonstrate
Speaker 2: actual business benefit quick wins that show this approach works.
Speaker 1: Okay, that makes a lot of sense. So you found
Speaker 1: some key problems using this collaborative method. Now you need
Speaker 1: the tools, the infrastructure, the team. The book talks about
Speaker 1: building the data analytics stack. Can you break that down?
Speaker 1: What are the basic pieces?
Speaker 2: Sure, at a high level, you've got a few key stages.
Speaker 2: First is data ingestion getting the data in from all
Speaker 2: the different source systems okay. Then data storage keeping it
Speaker 2: safe and accessible. Then data processing that's the cleaning transforming,
Speaker 2: getting it ready for analysis.
Speaker 1: A messy part, often often the messiest.
Speaker 2: Then the analysis itself, finding the insights, answering the questions,
Speaker 2: and finally data consumption how those insights get to the
Speaker 2: people who need them, reports, dashboards, apps, whatever it is.
Speaker 1: And the book really hammers home the incrementally part of
Speaker 1: building this right. Start with what you've got.
Speaker 2: Absolutely, leverage existing tools, existing infrastructure as much as you can. Initially,
Speaker 2: don't feel you need the shiniest new thing right away,
Speaker 2: so resist.
Speaker 1: The urge to buy everything immediately.
Speaker 2: Definitely, And a key principle is matching your data ingestion
Speaker 2: frequency how often you pull data in with what you
Speaker 2: actually need for analysis.
Speaker 1: Meaning if you only report weekly, maybe you don't need
Speaker 1: real time data streams for everything exactly.
Speaker 2: Batch processing might be fine, but there's an important caveat.
Speaker 2: The book mentions operational alerts think manufacturing IoT sensors.
Speaker 1: Ah, right, where you need to know now if something's wrong.
Speaker 2: Yes, for those critical operational systems, real time data is
Speaker 2: obviously essential.
Speaker 1: Got it. And then there's the team structure. Sure, the
Speaker 1: book mentions BI developers, analysts, data scientists, data engineers, architects.
Speaker 1: Sometimes those lines feel a bit fuzzy.
Speaker 2: They really can be, especially in smaller teams or organizations
Speaker 2: just starting out. But broadly, think of BI folks as
Speaker 2: focused on the what.
Speaker 1: Happened reporting dashboards right looking back.
Speaker 2: Data scientists are more about the why and what might
Speaker 2: happen next. They dig deeper, build predictive.
Speaker 1: Models, forecasting, understanding drivers exactly.
Speaker 2: And data engineers or architects they're the builders. They create
Speaker 2: and maintain the underlying infrastructure, the data pipelines, making sure
Speaker 2: everything is reliable and performs well.
Speaker 1: The foundation, the foundation.
Speaker 2: But yeah, the book acknowledges it's often blurry. People wear
Speaker 2: multiple hats.
Speaker 1: That clarification helps. And you mentioned predictive models. The book
Speaker 1: touches on supervised versus unsupervised machine learning. Quick explanation for
Speaker 1: US Dragon free.
Speaker 2: If possible, Okay, let's try. Supervised learning is like like
Speaker 2: learning with a teacher or flash cards. You have data
Speaker 2: that's already labeled with the right.
Speaker 1: Answer, like emails marked spam or not spam.
Speaker 2: Perfect example. The algorithm learns from those labels to classify
Speaker 2: new emails or regression. Another supervised type predicting a continuous
Speaker 2: number like a house price or sales figures based on
Speaker 2: labeled historical data.
Speaker 1: So you know what you're aiming for.
Speaker 2: Yes, you have a target. Unsupervised learning is more like exploring.
Speaker 2: You don't have labels. The algorithm's job is to find
Speaker 2: hidden patterns or structures in the data itself.
Speaker 1: Like grouping customers together based on behavior exactly.
Speaker 2: Customer segmentation is a classic example, finding groups you didn't
Speaker 2: even know existed. The book also gives a nod to
Speaker 2: deep learning, especially for unstructured stuff like text and images, right,
Speaker 2: the complex stuff, but it also wisely points out it's
Speaker 2: not always the magic bullet, especially for standard you know,
Speaker 2: tabular data. Sometimes simpler models work better.
Speaker 1: Okay, useful distinction. So you're building the tech, assembling the team,
Speaker 1: but the book is very clear technology isn't the whole story.
Speaker 1: You need to cultivate a data driven culture. What does
Speaker 1: that actually look like?
Speaker 2: It means creating an environment where using data to make
Speaker 2: decisions isn't just like a special project, It's just how
Speaker 2: things are done everywhere, ingrained in the day to day exactly.
Speaker 2: Technology is just the tool. The real shift is in
Speaker 2: people's mindset and behavior, and the book suggests several ways
Speaker 2: to nurture this. One is finding and growing talent.
Speaker 1: Internally, not just hiring externally.
Speaker 2: Right, maybe offer introductory courses, say in Python, across different departments.
Speaker 2: You might uncover hidden talent, people who are really keen
Speaker 2: on data but never have the chance upscill your existing folks.
Speaker 1: I like that, democratizing it a bit. The book also
Speaker 1: talks about internal hackathons like it Lassians ship it days,
Speaker 1: but maybe smaller scale to start.
Speaker 2: Yeah, even mini hackathons can be fantastic, great for learning,
Speaker 2: trying new things, getting different teams, collaborating on data problems.
Speaker 2: Plus they can be fun, create some buzz definitely. And
Speaker 2: then there are things things like communities of practice, do
Speaker 2: heat a catapo shares experiences on this, or data champions networks.
Speaker 2: Kyl Ash mentions this ways to connect people interested in
Speaker 2: data across the organization, spreading the knowledge and enthusiasm precisely.
Speaker 2: It all feeds into building broader data literacy, which the
Speaker 2: book highlights as absolutely critical.
Speaker 1: And not just for the data team right. Ian Jackman
Speaker 1: and zanvan Wick emphasize.
Speaker 2: This, No, absolutely not. Data literacy is for everyone. It's
Speaker 2: about understanding data, being able to interpret it, question it,
Speaker 2: think critically about its quality and how it's being used.
Speaker 2: Sandra Hogan calls it empowering the business community.
Speaker 1: And there's that key phrase. Data doesn't make decisions.
Speaker 2: People do crucial point data informs, but humans decide, which
Speaker 2: brings in the need for critical thinking skills across the board.
Speaker 2: Tim van Gelder's definition is great the art of being right,
Speaker 2: partly by considering how you might.
Speaker 1: Be wrong, thinking critically about the data itself. Okay, makes
Speaker 1: sense now, shifting gears slightly, actually choosing the technology. That
Speaker 1: landscape can feel incredibly complex. Any practical guidance from the book.
Speaker 2: Yeah, it offers some solid advice. First, maybe start with
Speaker 2: cloud providers. Your organization is already.
Speaker 1: Familiar with less friction that way usually. Yeah.
Speaker 2: It mentions the Big three AWS, AZURE, GCP and notes
Speaker 2: that since many orgs use Microsoft Tools. Azure often feels
Speaker 2: like a natural starting point for the bi side of things,
Speaker 2: but a really important theme is interoperability. Design things to
Speaker 2: be modular, API driven avoid getting locked into one vendor.
Speaker 2: Ian Jackman really stresses this.
Speaker 1: Future proofing essentially exactly needs change.
Speaker 2: De Weeeda Katapou gives an example of her org moving
Speaker 2: from Redshift to Snowflake because Redshift struggled with their Jason
Speaker 2: data needs at the time. You need that flexibility, good example.
Speaker 2: What else, usability for the team is key. Heima Prosade
Speaker 2: makes this point. If the tools are clunky or hard
Speaker 2: for your data folks to use, adoption just won't happen.
Speaker 1: Makes sense, Keep the users happy.
Speaker 2: And don't forget the ongoing effort to keep the lights
Speaker 2: on or ktlo work needed to maintain any data product
Speaker 2: or platform that needs factoring in.
Speaker 1: Right, it's not just build and forget and the process
Speaker 1: for choosing, it's just.
Speaker 2: The structured way brainstorm your decision criteria collectively, get input
Speaker 2: from everyone affected. Then maybe use something like pair wise comparison,
Speaker 2: comparing options side by side on each specific criterion to
Speaker 2: help make a rational choice.
Speaker 1: Okay, methodical approach. So we've got tech team culture selection.
Speaker 1: What about governance and ethics that feels increasingly important, hugely important.
Speaker 2: The book frames data, governance and ethics not as nice
Speaker 2: to have as anymore, but absolute, must have, non negotiable
Speaker 2: pretty much. Governance provides the rules of the road. The
Speaker 2: processes rolls responsibilities for managing data well, ensuring quality, security compliance.
Speaker 2: Think DAMA definitions, managing availability, usability, integrity. Security sounds potentially
Speaker 2: massive that it can be, But the advice, echoing fvirus
Speaker 2: Hamden's experience is start small. Build governance based on the
Speaker 2: problems you actually have right now. Don't try to boil
Speaker 2: the ocean on day one. Establish data stewards, maybe data
Speaker 2: councils to manage specific areas. The road organically, yes, and
Speaker 2: ethics is woven through this. Especially with AI, bias and
Speaker 2: fairness are critical. You have to minimize bias to build trust.
Speaker 2: There's an IBM stat mentioned about how crucial trust is
Speaker 2: for AI adoption.
Speaker 1: Bias can creep in easily.
Speaker 2: Very easily. The book uses a great phrase data does
Speaker 2: not speak for itself. It depends on perspective, how it
Speaker 2: was collected, how it's interpreted. The example given is how
Speaker 2: different Sydney suburbs might look depending on which data points
Speaker 2: you choose to highlight right.
Speaker 1: Context matters hugely.
Speaker 2: Plus, you have regulations like GDPR, data privacy rights, the
Speaker 2: right to be informed, access erasure and so on. These
Speaker 2: have to be built in.
Speaker 1: So responsible AI needs thinking about from the start.
Speaker 2: Absolutely. Doctor Lean's point in the book is about building
Speaker 2: in diversity from the beginning to encourage ethical discussions. Frame
Speaker 2: it using the language your organization understands. Maybe it's risk management,
Speaker 2: maybe it's core company values, maybe it's sustainable development goals.
Speaker 2: Find the hook to embed ethical thinking.
Speaker 1: Okay, that brings a lot together. So wrapping this up,
Speaker 1: what's the single biggest takeaway from this whole emergent design
Speaker 1: approach to building data capabilities?
Speaker 2: I think the core message is it's a process. It's
Speaker 2: not a project with a neat finish line. It's this
Speaker 2: ongoing emergent journey of learning, adapting, and constantly collaborating between
Speaker 2: the tech side and the business side.
Speaker 1: A continuous evolution exactly. And for someone listening feeling maybe inspired,
Speaker 1: but still a bit unsure where to start, what's one
Speaker 1: concrete next step you'd suggest based.
Speaker 2: On this, Start with the conversations, use the what keeps
Speaker 2: you up at night. Question Talk to different teams, Find
Speaker 2: one specific nagging business problem that data might.
Speaker 1: Help with just one to start just one.
Speaker 2: Then focus on delivering a small, targeted proof of value
Speaker 2: for that problem. Show a tangible win, however small, that
Speaker 2: builds momentum.
Speaker 1: Great advice, and finally, a last thought for our listener
Speaker 1: to maybe chew on after this deep dive.
Speaker 2: Maybe reflect on that idea of the ever changing reality
Speaker 2: borrowing from Claudiosubora. Things will change the market, the business,
Speaker 2: the tech. An emergent approach accepts that.
Speaker 1: It embraces the flux right.
Speaker 2: So think about how you can foster that ongoing dialogue,
Speaker 2: that iterative adaptation in your organization to make sure your
Speaker 2: data efforts stay aligned and keep delivering value as things
Speaker 2: inevitably shift.
Speaker 1: A really powerful way to think about it, Fantastic Insights,
Speaker 1: Thanks so much for unpacking all of that with us today,
Speaker 1: My pleasure. We really hope this deep dive gives you,
Speaker 1: our listener, a clearer path forward on your own data journey.
Speaker 1: Thanks for joining us