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