A Fine Slice Of SQL Server
Alon Ohayon makes a report smarter:
Almost every dashboard includes some kind of hierarchy between dimensions. For example:
- Years and Months
- Customers and Campaigns
- Category and Sub-Category
In most cases, we want to show trends across those hierarchies.
This is a good solution. There is an understanding how people want to see their data and it helps show the right information at the right time.
Leave a CommentCole Nussbaumer Knaflic reminds us that visuals should have purpose:
I was facilitating a workshop recently when someone asked one of my favorite questions about a graph on the screen: “So… what are we supposed to take away from this?”
Such a simple—and useful—question.
One challenge was that the graph was attempting to show multiple comparisons at once, so it wasn’t clear what mattered most. To further complicate things, the data in question spanned very different magnitudes, with one category dwarfing the rest.
Click through for a demonstration and how changing the visual layout can affect the message. The challenge I tend to run into is that, when I’m developing a visual for an application or a report, I don’t know what the precise message should be at that moment in time. I have to design with an idea of the data, but what actually emerges will depend upon what data is in there. Tailoring a visual for a specific message at a specific point in time is a lot easier when building a presentation, but it gets tricky when you’re building an application for the long haul.
Leave a CommentValerie Junk covers a pair of options:
In this tutorial, I want to show a small but very practical formatting setting in Power BI.
When we create a table or matrix visual, we sometimes end up with white space on the right side. For example, if you show data by month and you only have 6 months of data so far, but you design the visual to fit 12 months, the table/matrix is already sized for the full year, which leads to a lot of empty space.
In Power BI we have two column header formatting options:
Click through for the two options, where you can find the option, and some important information around both options.
Leave a CommentShittu Olumide visualizes some data:
For data scientists, working with high-dimensional data is part of daily life. From customer features in analytics to pixel values in images and word vectors in NLP, datasets often contain hundreds and thousands of variables. Visualizing such complex data is difficult.
That’s where dimensionality reduction techniques come in. Two of the most widely used methods are Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). While both reduce dimensions, they serve very different goals.
The thing that ultimately soured me on t-SNE is the stochastic nature. You can run the same set of operations multiple times and get significantly different results. It’s really easy to use and the output graphs are really pretty, but if you can’t trust the outputs to be at least somewhat stable, there’s a hard limit to its value.
Leave a CommentMichal Bar shows off a new capability:
A frequent request we receive from dashboard authors is the ability to have greater control over color settings.
Until now, color assignments in real-time dashboards were largely automatic. While this worked for basic scenarios, it often fell short in operational and reporting use cases where color isn’t decoration—it’s meaning. Data Series Colors is a new capability that gives authors direct control over how colors are applied to their visuals.
Read on to see how it works for real-time dashboards.
Comments closedMarco Russo and Alberto Ferrari want more detail:
A common challenge in Power BI reporting is how to display several pieces of information about a single item (such as sales transactions, product details, or customer details) without dedicating a separate column to each attribute. Using individual columns for each detail can consume space, especially for fields that are often empty. This article explores techniques to consolidate multiple fields from a business entity or transaction into a single column in a matrix visual, thus presenting transaction details in a space-efficient way.
They walk through several iterations of the process. The real challenge with displaying those details is that your end users need to understand what’s in the details, as there’s no good way to describe what the information means. But when your users do understand what can be in there, I could see this being quite helpful.
Comments closedPaul Turley thinks about how we think:
You can be the greatest report designer on the planet, but if your report doesn’t meet the needs of the report consumer, it’s all for nothing. In this section, I break down the most important considerations for identifying your audience and their information needs. These are all factors to consider before you jump in and start designing your report.
Paul hits on quite a few of the foundational concepts around how humans visual stimuli and tells some interesting stories along the way.
Comments closedValerie Junk demonstrates a process:
In this tutorial, I will recreate a project I built last year: a mini Power BI dashboard with a toggle button to change its appearance. When you toggle the button, the background color changes, the logo and snow switch images are updated, and the visuals are restyled accordingly (including the color of the trees).
This project combines different tutorials I created in the past, which cover dynamic color changes and the lollipop visual.
At the bottom of this page, you will find a step-by-step video, and you can download the file from the download page.
Click through for instructions, the video, and a zip file to work from.
Comments closedAmy Esselman keeps track of axis:
One of the most common pitfalls in data visualization is manipulating axis scales in ways that distort the story. A frequent example is the use of logarithmic scales where they are not appropriate.
Let’s walk through a case where this choice can mislead, even if unintentionally.
Amy has some good guidance on when you should use log scale, as well as a good example of a case where applying it incorrectly can lead to distorted results.
Comments closedAlexander Arvidsson does some research:
I’ve spent the last few weeks diving deep into something that’s been bothering me for years. Everyone talks about being “data-driven,” but when you actually look at what that means in practice, something doesn’t add up. Companies are knee-deep in data, wading in dashboards, drowning in reports, and yet… nothing changes.
So I went looking for examples. Real examples. Not “we implemented analytics and it was amazing” marketing fluff, but concrete cases where data actually improved outcomes. What I found was fascinating, and not at all what the analytics vendors want you to hear.
This is an interesting article and starts to get to the reason why “data-driven” companies fail to deliver on their promise. It also gets to one of my nag points around dashboards: the purpose of a dashboard is to provide relevant parties enough information, at a glance of the dashboard, to take whatever action is necessary. In order to develop a good dashboard, you need to understand all of that information: who the relevant parties are, what decision points exist, under what circumstances should an individual take action, and (ideally) what action the individual could take. But that’s a lot of information and a lot of effort to tease out the right answers.
Comments closed