#Tableau #datamodel in my Bridges to Prosperity #MakeoverMonday2020 #Week4 challenge

-
For this month I thought that I would try my hand at a selected MakeoverMonday challenge. For this one I picked Week 4, the data set is available here.

I actually did this using the Tableau 2020.2 Beta (I have early access!) and my viz utilised Tableau's new #DataModel





Do you ever have to blend data at varying levels of detail (LoD)? This is a frequent challenge faced by analysts working in the field: you might get a transactional sales analysis of each and every sale across the organisation, but you might get a year-end debtors analysis that aggregates to major customer accounts. Reconciling between the two can get very time consuming, and a lot of the typical analyst's time is spent (nay, wasted?) in detailed reconciling of different records and manually transforming data sets in Excel.

Tableau's latest release tries to do away with this by introducing the "Data Model" (also known by us affectionately in the community as the "Data Noodle"), and this functionality supersedes somewhat the old functionality of joins and blends.

In this data set, I had one set of data aggregated to the country level (country populations and socio-demographics) and another data set at the detailed level of bridges (which rolled up into the country level). The Tableau Data Model made it very easy to define the relationship between the levels of detail and Tableau just "automagically" visualised the details, reconciling between the different LoD.

So after teaching myself a little bit about implementing the new Data Model, I downgraded the version that I uploaded to Tableau Public just so that I can demonstrate the use of Stories and Dashboards. Let me know what you think!

Comments

Post a Comment

Popular posts from this blog

Define: artificial intelligence

-
Image
Alas, that term "artificial intelligence" gets bandied about a lot in business and non-business circles. So what exactly do we mean by "AI"? And to make things more complicated - what do people mean when they say "machine learning"?  A no-nonsense guide to AI Quite simply: AI is a general label for all of the computerised systems that perform tasks in an approximation of human intelligence. You know that magic rice cooker? It might have a heuristic (fancy way of saying "a rule of thumb") on "how long to keep the water on the boil". It's not quite as sexy as "machine learning", but this approximation of how grandma used to cook the rice is as simple as saying, "use two cups of water for every one cup of rice".  The next level down is "machine learning", and I actually prefer to split this down into supervised learning, unsupervised learning and reinforcement learning. These are statistical methods where
Read more

Inductive vs Deductive approaches #statisticalThinking @muralCo #imagine2020

-
Image
In this past month I've had a bunch of interesting interactions with strategists, consultants and statisticians. Quite tongue-in-cheek, but I thought I'd share this insight on #inductive vs. #deductive approaches to statistics : Settling the debate between induction vs deduction once and for all. #StatsTikTok pic.twitter.com/U7xSuBFrTL — Daniël Lakens (@lakens) May 17, 2020 Inductive vs Deductive But more seriously, what does it mean to be inductive or deductive? I'm sharing my notes here, because it was a chance for me to experiment with the Mural service, having attended some of Mural #Imagine2020 . I'm planning to use #Mural on my next data science project and I can quickly see how this can be a great facilitation tool to work with problem owners and business leaders to bring design thinking and a clear analytical thought process to their ill-defined business problems. Figure 1: Inductive thinking Let's have a hypothetical situation of a manufacturer of unique p
Read more

Can you do this analysis in Tableau?

-
Image
If all you know how to use is a hammer, everything looks like a nail! I frequently get asked, "Izam, can you use Tableau to create a chart that looks like this ..." or "Izam, can you use Tableau to calculate this ..." Lately, I've been thinking about how the tools we use shape the way that we go about solving problems. And the above adage comes to mind: if all you know how to use is a hammer, everything looks like a nail! Excel and PowerPoint are great for writing documents and reports. Reports which go about answering the: "WHAT"  are we planning to do?  "HOW MUCH"  are profits? or "WHEN"  does it need to be done by? Tableau on the other hand, shines when we use it to explore the question ... "WHY"  is this trend happening? "How much are profits?" For example, with the Superstore sample data set, an analyst might be interested in building out a table that compares the key performance indicators of
Read more