Speaker 1: Have you ever felt like you're just drowning in data?
Speaker 1: You know, numbers everywhere, charts flashing, reports piling up, but
Speaker 1: you're still thirsty for actual insight.
Speaker 2: Oh. Absolutely, information overload, but insight starvation. It's a common feeling, exactly.
Speaker 1: It's like you're collecting and collecting, but the real aha
Speaker 1: moments feel just out of reach. That clarity you're craving
Speaker 1: just gets lost in the noise. Well, today we're going
Speaker 1: to dive deep into a guy that promises to help
Speaker 1: cut through that clutter. Welcome to the deep dive. Our
Speaker 1: focus is Mastering Tableau twenty twenty one implement Advanced Business
Speaker 1: Intelligence by Meyer and Baldwin.
Speaker 2: Right, and this isn't just a click here, do that
Speaker 2: kind of book. It's about unlocking Tableau's real potential way
Speaker 2: beyond basic dashboards.
Speaker 1: Definitely. So our mission today is pretty clear. We want
Speaker 1: to unpack how Tableau can take raw, sometimes really messy
Speaker 1: data and turn it into genuinely powerful.
Speaker 2: Stories, stories that communicate insights effectively.
Speaker 1: Yes, and we'll look at making sure those stories perform well.
Speaker 1: Nobody likes a slow dashboard.
Speaker 2: H performance, it's key.
Speaker 1: And then we'll get into some pretty cool surprising ways
Speaker 1: to push what tableau can actually do. So let's start
Speaker 1: with this idea of visual storytelling. Why is it more
Speaker 1: than just pretty charts?
Speaker 2: Well, what's really fascinating, and the book highlights this well,
Speaker 2: is how these seemingly small design choices can make a
Speaker 2: huge difference. It's, you know, the difference between your audience
Speaker 2: instantly getting your point or just getting completely lost in
Speaker 2: like visual clutter.
Speaker 1: That's so true. We've all seen those dashboards that are
Speaker 1: just a mess of rainbow explosion or something. The authors
Speaker 1: give some really practical advice to avoid that, like reducing clutter.
Speaker 1: First step, keep font choices simple. Sounds basic, Maybe.
Speaker 2: It does, but it's often overlooked when people try to
Speaker 2: get too creative. Simple is usually clearer, right.
Speaker 1: And they also talk about lines like gridlines.
Speaker 2: Yeah, use them as sparingly. They should be the most
Speaker 2: muted thing on the chart, really faint, or as the
Speaker 2: book says, you might even get rid of them altogether
Speaker 2: if they're not helping. And another neat trick, for say
Speaker 2: tall tables or lots of horizontal bars, use subtle bands
Speaker 2: of color, maybe in groups of three to five.
Speaker 1: Rows, like shading alternate groups exactly.
Speaker 2: It helps guide the eye down The list segments things
Speaker 2: nicely without adding more lines or noise.
Speaker 1: It's clever. Okay, so less clutter. What about color? That's
Speaker 1: another place things can go wrong fast?
Speaker 2: Oh definitely. The book really hammers this. Use color intelligently,
Speaker 2: keep it simple, keep it limited. Limited, maybe three to
Speaker 2: five main hue variations. They quote Alberto Cairo actually pointing
Speaker 2: out our visual working memories pretty limited. Too many colors
Speaker 2: just overload us.
Speaker 1: Makes sense. Our brains can only track so much.
Speaker 2: At once, exactly. And you need to think about the
Speaker 2: psychology of color. You know, red often means stop or bad,
Speaker 2: Green means go or good. Use those conventions and.
Speaker 1: Be colorblind friendly.
Speaker 2: That's crucially critical for accessibility, and make sure everyone can
Speaker 2: read your chart. And they also say use pure, really
Speaker 2: bright colors sparingly. Well. If everything is highlighted, then nothing
Speaker 2: is highlighted, right. Too many bright spots make it hard
Speaker 2: to focus on the key message.
Speaker 1: Got it? So use bright colors for emphasis only precisely.
Speaker 2: And here's a subtle one. Choose color variations over different
Speaker 2: shapes or symbols if you can.
Speaker 1: Oh.
Speaker 2: Yeah, apparently our brains find it easier and faster to
Speaker 2: distinguish shades of color than to decode what different symbols mean.
Speaker 2: It reduces that cognitive load we talked about.
Speaker 1: Okay, less mental work for the audience, that's the goal.
Speaker 1: And what about choose the right type of chart? They
Speaker 1: mentioned pie charts?
Speaker 2: Ah, yes, the infamous pie chart. The advice is pretty
Speaker 2: clear use them sparingly.
Speaker 1: Which many people don't, let's be honest.
Speaker 2: True, but the book explains why they don't use screen
Speaker 2: space very well, especially on rectangular screens, and it's often
Speaker 2: genuinely hard to tell which slice is bigger if they're
Speaker 2: close in size. Our eyes aren't great at comparing angles accurately,
Speaker 2: so bar.
Speaker 1: Charts are usually better for comparing amounts.
Speaker 2: Almost always yes, for comparing magnitudes, bars are much clearer.
Speaker 2: So putting it all together, these design tips aren't just
Speaker 2: about making things look nice.
Speaker 1: No, it's about effectiveness, making the data understandable and impactful exactly.
Speaker 2: It elevates the insight itself.
Speaker 1: Okay, so that's the visual side the art, But what
Speaker 1: about the engine behind it? Tableau's magic isn't just skin deep.
Speaker 1: How does it actually handle all that data?
Speaker 2: Great question? And how do we make sure our dashboards
Speaker 2: are fast, not just pretty.
Speaker 1: Yeah, let's get under the hood.
Speaker 2: Okay, So understanding Tableau's core engines is really key here.
Speaker 2: The big one, especially in recent versions, is the Hyper
Speaker 2: Data Handling Engine Hyper. Yeah. Hyper. Think of it as
Speaker 2: a highly specialized database engine. It's designed to do several
Speaker 2: things at once, really efficiently, general database stuff, data ingestion,
Speaker 2: and analytics, all simultaneous.
Speaker 1: WHOA okay, simultaneous. How does it manage that without slowing down?
Speaker 2: It's incredibly efficient. Actually, the book mentions it can use
Speaker 2: like ninety nine percent of available CPUs. It uses this
Speaker 2: technique called morsel driven parallelization. Imagine breaking a huge task
Speaker 2: into tiny little pieces or morsels, and giving each piece
Speaker 2: to a different worker to do at the same time,
Speaker 2: like a super organized team exactly so it can handle transactions,
Speaker 2: load new data, and run complex queries all at once
Speaker 2: incredibly fast.
Speaker 1: That sounds amazing for anyone who's stared at a loading
Speaker 1: spinner for too long. So Hyper processes the data, then
Speaker 1: how does Tableau actually turn that processed data into the
Speaker 1: charts we see?
Speaker 2: Ah, that's where VISCUOL comes in VIZQL Visual Query Language.
Speaker 1: Okay.
Speaker 2: Viscal is the component that translates your actions like dragging
Speaker 2: and dropping fields into data queries and then renders the
Speaker 2: visual results the charts. And it does this entirely in memory.
Speaker 1: In memory, so it's fast, very.
Speaker 2: Fast, and super flexible. The real power is that you,
Speaker 2: the analyst, can change the underlying query just by say,
Speaker 2: drag digging a field from the measures area to the dimensions.
Speaker 1: Area without writing SQL code exactly.
Speaker 2: Viskalel rewrites the query for you on the fly. It
Speaker 2: allows for that really fluid, spontaneous exploration of data. You
Speaker 2: see something interesting, you drag a field and boom new you.
Speaker 1: That drag and drop power is definitely a huge part
Speaker 1: of Tableau's appeal. But okay, powerful engine, great visualization tool.
Speaker 1: What if the data you feed it is.
Speaker 2: Well a mess ah, the perennial problem, garbage in, garbage
Speaker 2: out right right?
Speaker 1: What does the book say about cleaning up that raw
Speaker 1: data before it even gets to hyper or viscoel.
Speaker 2: That's where Tableau Prep Builder comes into play. It's described
Speaker 2: as a newer member of the Tableau family specifically designed
Speaker 2: for data preparation. Prep Builder, and the great thing about
Speaker 2: prep is that it's visual, just like Tableau Desktop. You
Speaker 2: don't write scripts in the dark. You actually see each
Speaker 2: step of your data cleaning process laid out visually.
Speaker 1: So you can literally watch the data transform like a flow.
Speaker 2: Chart pretty much. Yeah, you connect steps for cleaning, joining tables, pivoting,
Speaker 2: data aggregating, even running scripts if you need to. It
Speaker 2: makes that whole process much more transparent and intuitive.
Speaker 1: That sounds incredibly useful because data prep can take forever.
Speaker 2: It really can. The book mentions data prep can account
Speaker 2: for up to sixty percent, zero percent of the entire
Speaker 2: data mining effort sometimes.
Speaker 1: Wow, okay, so streamlining that with a visual tool like
Speaker 1: prep builder.
Speaker 2: Is huge, absolutely invaluable, says massive amounts of time, potential errors.
Speaker 1: All right, So let's assume we've used prep, We've got
Speaker 1: clean data, we've got hyper and visquil working their magic.
Speaker 1: We're building our dashboard and Tableau desktop. But it's still slow.
Speaker 1: Users are complaining. What can we do? Then?
Speaker 2: Yeah, performance tuning crucial step. The book offers several good strategies.
Speaker 2: One big one is using data extracts effectively.
Speaker 1: Extras like saving a local copy.
Speaker 2: Of the data exactly. Tableau extracts are often much faster
Speaker 2: to query than connecting live to a database, especially a
Speaker 2: complex one. The book points out that extracts are always flattened.
Speaker 1: Flattened meaning what.
Speaker 2: It means Tableau pre processes the data, handling any joins
Speaker 2: or complexities, and stores it in a single optimized table
Speaker 2: structure within the extract file. This makes querying super fast.
Speaker 2: The advice is maybe start building your dashboard using a
Speaker 2: small extract, just a sample of the data, so it's
Speaker 2: really responsive while you design.
Speaker 1: Ah, so you're not waiting for the full data set
Speaker 1: to load every time you tweak something right.
Speaker 2: Then once you're happy with the design, you can point
Speaker 2: it to the full extract or the live connection if needed.
Speaker 1: Smart What about filters they always seem to slow things down.
Speaker 2: They can, but it depends on the type of filter.
Speaker 2: This is a key distinction the book makes okay. Dimension
Speaker 2: filters and measure filters, the most common types, actually improve
Speaker 2: performance usually now because they limit the amount of data
Speaker 2: pulled from the source before Tableau does the visualization work.
Speaker 2: Less data processed means faster results.
Speaker 1: Makes sense.
Speaker 2: But there's another type called table calculation filter. These are different.
Speaker 2: They run after all the data has been retrieved and
Speaker 2: calculated AH.
Speaker 1: So they don't reduce the initial data load exactly.
Speaker 2: They just hide marks that are already calculated, so they're
Speaker 2: useful for analysis, but they don't give you that initial
Speaker 2: performance boost. Knowing the difference is important.
Speaker 1: Definitely good tip. Anything else on a dashboard design itself
Speaker 1: for speed.
Speaker 2: Yeah, keep it simple, basically, avoid overcrowding every single chart.
Speaker 2: Every filter adds to the calculation.
Speaker 1: Load less it's more again pretty much.
Speaker 2: Also, fixing the dashboard size setting it to a specific
Speaker 2: resolution rather than automatic can help prevent Tableaux from having
Speaker 2: to constantly recalculate layouts for different screens. Okay, and maybe
Speaker 2: software advice, but important. Set expectations with your users. If
Speaker 2: a complex dashboard will take a few seconds to load,
Speaker 2: let them know.
Speaker 1: Manage expectations. Yeah. And hardware does it matter much?
Speaker 2: It does play a role.
Speaker 1: Yeah.
Speaker 2: The book mentions things like having enough ram at least
Speaker 2: eight gigle bay for Mac for example, and a decent
Speaker 2: graphics card like an GPU can help with rendering speed
Speaker 2: using accelerated graphics. And one for the developers, remember the
Speaker 2: run update command F nine on Windows. You can pause
Speaker 2: automatic updates, make a bunch of changes, and then hit
Speaker 2: F nine to refresh only when you're ready. Saves constant
Speaker 2: re rendering.
Speaker 1: Nice practical tips. Okay, so we've covered design, data prep,
Speaker 1: the engine performance. Let's push further. How does Tableau help
Speaker 1: us get deeper insights beyond the basics.
Speaker 2: Yeah, moving into more advanced analytics. It's one thing to
Speaker 2: see totals or averages, but what if you need to
Speaker 2: compare a value to say, a category average that isn't
Speaker 2: even shown on.
Speaker 1: Your charge exactly? Those more complex comparisons.
Speaker 2: That's where level of detailed calculations or LODs come in.
Speaker 2: They are incredibly powerful.
Speaker 1: Alu D's I've heard of those, but they can seem
Speaker 1: a bit intimidating.
Speaker 2: They can be at first. Yeah, but the core idea
Speaker 2: is this. Normally Tableau calculates measures based only on the
Speaker 2: dimensions you have physically dragged into your view.
Speaker 1: Right, sales per region if region is in the view exactly.
Speaker 2: But LOD calculations, let you Tellcablo hey for this calculation,
Speaker 2: calculate it at a different level in detail than what's
Speaker 2: currently shown.
Speaker 1: Ah, so you can override the default behavior precisely.
Speaker 2: The book gives a handyamonic for the three main types fixed,
Speaker 2: include and exclude. Fixed lets you specify exactly which dimensions
Speaker 2: to use for the calculation, regardless of the view. In
Speaker 2: slury d lets you add dimensions to the calculation that
Speaker 2: aren't in the view, and excluid lets you remove dimensions
Speaker 2: from the calculation that are in the view.
Speaker 1: Okay, so you can do things like calculate the total
Speaker 1: sales for an entire product category and show that value
Speaker 1: next to the sales for each individual subcategory.
Speaker 2: Exactly even if the main category isn't a dimension in
Speaker 2: your chart. Or you could calculate the average customer order
Speaker 2: value ignoring the specific products in the view. It unlocks
Speaker 2: really sophisticated analysis that.
Speaker 1: Sounds super flexible, a real game changer for asking complex questions,
Speaker 1: definitely and beyond calculations within Tableau itself. The book talks
Speaker 1: about extending Tableau's capabilities, right, Yeah, connecting it to other tools.
Speaker 2: Absolutely. Tableau isn't just a closed box anymore. It's becoming
Speaker 2: more of an open platform. One way is through the
Speaker 2: Tableau Extensions API API.
Speaker 1: So connecting other software.
Speaker 2: Yeah, it allows third party developers or even your own
Speaker 2: company to build tools that run directly inside a Tableau dashboard.
Speaker 1: Like what kind of tools.
Speaker 2: The book mentions things like a sinky chart extension. Sinki
Speaker 2: diagrams are complex to build manually in Tableau, so an
Speaker 2: extension can save a ton of time or maybe custom
Speaker 2: rate that capabilities or specialized statistical analysis tools.
Speaker 1: Okay, so extending the functionality. What about AI and machine learning?
Speaker 1: That's everywhere now.
Speaker 2: Right, and Tableau integrated that with Einstein Discovery, which the
Speaker 2: book flags is a major feature introduced around the twenty
Speaker 2: twenty one point one version.
Speaker 1: Einstein Discovery from Salesforce.
Speaker 2: Yeah, leveraging Salesforce's AI capabilities, it essentially brings built in
Speaker 2: machine learning and real time prediction directly into your Tableau workflow.
Speaker 1: How would that work in practice?
Speaker 2: Well, imagine you have a supply chain dashboard. You could
Speaker 2: point Einstein Discovery at your data and it might automatically
Speaker 2: identify the key factors driving delivery times. Okay, and not
Speaker 2: just identify them, but actually build a predictive model on
Speaker 2: the fly and even suggest actions you could take based
Speaker 2: on the model to improve shipment times. It's bringing predictive
Speaker 2: power to the business user.
Speaker 1: Wow, that is powerful. Moving from just reporting the past
Speaker 1: to actively predicting and influencing the future.
Speaker 2: That's the goal.
Speaker 1: And for users who are comfortable with coding like data scientists,
Speaker 1: can they bring their own models in.
Speaker 2: Yes, definitely. Tableau has integrations with R and Python, two
Speaker 2: of the most popular languages for data science. This lets
Speaker 2: you run R or Python scripts directly from Tableau calculations,
Speaker 2: so you can perform really advanced analyzes that go way
Speaker 2: beyond Tableau's built in.
Speaker 1: Function like what sort of things?
Speaker 2: All sorts? The book examples like calculating sentiment analysis on
Speaker 2: techt data. They actually use dialogue from Lord of the
Speaker 2: Rings as a fun example data set for that.
Speaker 1: Ah, really that's cool.
Speaker 2: Yeah, Or generating random numbers for simulations, performing complex statistical tests,
Speaker 2: running sophisticated machine learning models like linear regression for prediction. Basically,
Speaker 2: anything you can code in R or Python you can
Speaker 2: potentially integrate into your Tableau visualization.
Speaker 1: So it really opens the door to highly specialized, cutting
Speaker 1: edge analytics right within the Tableau environment.
Speaker 2: It really does. It bridges the gap between visual analytics
Speaker 2: and advanced statistical modeling.
Speaker 1: Wow, what incredible journey we've been on today. We've really
Speaker 1: covered a lot of ground. We certainly have started with
Speaker 1: the art, you know, visual storytelling, making sure dashboards are clear,
Speaker 1: clutter free, using color wisely mm hmm.
Speaker 2: Getting those foundations right.
Speaker 1: Then we looked under the hood at the engines Hyper
Speaker 1: for speed, vizquil for flexibility so important.
Speaker 2: Absolutely understanding how it works helps you use it better.
Speaker 1: Than tackling messy data with Tableau prep builder. That visual
Speaker 1: workflow seems.
Speaker 2: Key, save so much time and headache.
Speaker 1: And optimizing performance using extracts smartly understanding those filter types,
Speaker 1: keeping dashboards lean.
Speaker 2: Crucial for user adoption. Slow dashboards just don't get.
Speaker 1: Used, definitely not. And finally diving into the really advanced
Speaker 1: stuff led calculations for deeper analysis and pushing boundaries with
Speaker 1: extensions Einstein Discovery, AI and even R and Python integration.
Speaker 2: Yeah, it's quite the toolkit, it really is. You know,
Speaker 2: if you connect all these pieces together, what we've really
Speaker 2: described isn't just a list of software features. It's more
Speaker 2: like a complete ecosystem. Ecosystem I like that. Yeah, an
Speaker 2: ecosystem designed to empower you not just to see your data,
Speaker 2: but to really understand it, clean it up, properly, analyze
Speaker 2: it deeply, and even use it to predict what might
Speaker 2: happen next.
Speaker 1: So it moves you from just passively looking at reports
Speaker 1: to actively discovering insights and making informed.
Speaker 2: Decisions exactly, active data discovery, actionable intelligence. That's the promise.
Speaker 1: So thinking about our listeners, what does all this mean
Speaker 1: for you, the person wanting to get better with data?
Speaker 1: The book itself says something interesting near the end that
Speaker 1: data mining isn't really a project you finish.
Speaker 2: Right says it does not cease upon the completion of
Speaker 2: a particular project, but continues for the life of the business.
Speaker 2: It's an ongoing process.
Speaker 1: An ongoing process. So the question for you is what
Speaker 1: data story is waiting inside your data? What insights are
Speaker 1: you hoping to uncover, either for work or just out
Speaker 1: of curiosity, And how can these powerful but as we've seen,
Speaker 1: pretty accessible tableau features help you find that story, shape it,
Speaker 1: and share it in a way that really makes an impact.
Speaker 1: Something to think about