Quality Checks for your Power BI Visuals
For more formal enterprise Power BI development, many people have a checklist to ensure data acquisition and data modeling quality and performance. Fewer people have a checklist for their data visu…
Data Visualization
2022 Week 26 | Power BI: Polar Plots with Charticulator
17th Century Watercolors
Work: 17th Century Watercolors
Pulse | LinkedIn
Its about the clusters. I have created a scatter chart with the measure of Profit Margin and Revenue of y and x axis respectively.
Pulse | LinkedIn
Creating a calendar is not as complicated as you think. It requires some creativity and you could achieve it too.
Aryng Data Literacy Test
Data literacy is the ability to read, understand, and use data to drive decisions. Find your data literacy persona by taking this assessment.
The next chapter in analytics: data storytelling | MIT Sloan
Presenting data with empathy and context leads business users to better decision-making.
Charticulator Visual
Creating custom visuals using the Charticulator in Power BI is not easy. So here are some some Charticulator resources I've used to skill up.
Using Tree Map as legend on a page in Power BI - Prathy's Blog...
I recently worked on two projects where the client wanted to show multiple metrics sliced by the same categorical data. For example, seeing how various metrics are performing over different…
“Out of One, Many”, or: Making Switchable Charts in Power BI with Deneb
When Personalize Visuals is too personalized: an experiment in switching displayed charts using a dimension attribute.
How to create a custom visual in Power BI using Charticulator
Show full category text on Y-axis of a bar chart in Power BI
Is It OK To Guide the Reader in Visualization?
I was listening to Cole Nussbaumer Knaflic interviewing Steven Franconeri a few days ago (excellent episode!) and I was struck by something they mentioned towards the end regarding the illusion of being neutral when visualizing data. Many seem reticent to editorialize their data visualizations for fear of being perceived as biased. Cole was mentioning strategies she advocates for that bring more clarity and focus but necessarily leads to more direct steering of the reader’s attention from the visualization designer’s side (highlighting, annotations, simplification, etc.)
The reason is that by making the message explicit at the design stage of visualization, there are many choices one can make to facilitate the communication of that message (Cole has some great ones in her book). Without a specific communicative intent, we are left with this abstract idea of using the “right” visualization for the data we have, which is not particularly effective
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The reason is that by making the message explicit at the design stage of visualization, there are many choices one can make to facilitate the communication of that message (Cole has some great ones in her book). Without a specific communicative intent, we are left with this abstract idea of using the “right” visualization for the data we have, which is not particularly effective.
4 Keys To Better Data Interpretation Skills For Any Manager
Many managers are uncomfortable with interpreting the numbers in their reports and dashboards. If this common data literacy problem isn't addressed across management levels, companies will continue to struggle in their efforts to develop data-driven cultures. In this article, I cover four key areas of data interpretation that are essential for all managers who are striving to make data-informed decisions.
Using the art of visualization to communicate and share data. 📊
A good way to think of data visualization (data viz) is that it can be the difference between utter confusion and really grasping an issue.
Insight Literacy: Why We Need To Clarify What Insights Really Are
In today's data economy, the word 'insight' is used frequently in business. However, in many cases, it is misused without a clear understanding what the word means. We need to enhance insight literacy, which starts with developing a clearer definition of what insights really are.
10 Reasons Why Your Organization Still Isn't Data-Driven
According to a NewVantage Partners survey, 99% of firms have invested in data initiatives and 92% reported the pace of investment is accelerating. However, only 24% reported having a data-driven organization. This article looks at 10 reasons why your company may not have achieved this goal yet.
Why Data Storytelling Requires a Mindset Shift
To become an effective data storyteller, you must shift your mindset from being reporting centered to embracing a different storytelling approach. In this article, I introduce the Insight Funnel and how reports/dashboards complement data stories. Then I explore the six key approach differences between reports/dashboards and data stories.
Shifting From 'What' To 'Why': How Data Storytelling Unlocks Your Data's Full Potential
The most common way of sharing data with employees has been through dashboards and reports, but they mainly focus on the 'what.' What executives really crave is the 'why'--interpreting the numbers to help inform decisions. A recent survey reveals how data stories can address this crucial need.
Data Literacy And Data Storytelling: How Do They Fit Together?
Data literacy is a key focus of many organizations as they seek to help their employees take full advantage of their data. While data storytelling is often included in data literacy training, the question is whether it is a required skill to be data literate. In this article, I explore the minimum viable skills someone needs to qualify as being data literate. I then share where data storytelling fits in the data literacy landscape.
Every Good Data Story Has An Insight At Its Core
Today, more and more people realize there is more to data storytelling than just data visualization. If you were to evaluate what makes a data story "good," you'd discover one crucial ingredient that's necessary--a meaningful insight. In this article, I review what insights are and the critical role they play in data stories. I also share thoughts from a recent journal article by Sorin Adam Matei and Lucas Hunter on the importance of insights to data storytelling in science communication and data journalism.
Building (Easy-To-Adopt) Software while Doing Visualization Research
Should Ph.D. students focus more on building software people will use?
Asking the right data questions and asking the data questions right
In my last post I lamented the fact that there is too little focus on data thinking in data science and data visualization, and I described how much of a struggle it’s been for me to figure out how to learn and teach these skills. When I teach my academic visualization course, the thing the most exemplifies this need is the way I struggle to teach how to formulate good
P110 mackinlay
About that weird Georgia chart
Visualization social media has been busy mocking the following char t by the Georgia Department of Public Health. Pay attention to its horiz...
Why Shouldn't All Charts Be Scatter Plots? Beyond...
A central concept in information visualization research and practice is the
notion of visual variable effectiveness, or the perceptual precision at which
values are decoded given visual channels...
Beyond Precision: Expressiveness in Visualization
Using precision as guidance for visualization design is powerful and yet limited in many different ways. Expressiveness may help.
Visualization is about mapping data values to visual channels;
While precision is a useful factor to keep in mind, it’s neither sufficient nor necessary to create effective visualizations.
First, it’s not always true that visualizations that use position are “better” than those that do not. Second, position can be expressed in multiple ways, so the guidelines leave us unable to discern between visualizations that use position differently.
The problem with guidelines based on precision is that visualization is not really about precision.
But visualization is less about precision, and much more about what the visual representation expresses.
data contains information that we want to communicate, and that information is what we want to “express” with visual representations
Good visualizations stem from a good matching between what we want to express and what the visual representation expresses (something highlighted in the foundational work of Jock Mackinlay, who I believe introduced the term in the first place).
Expressiveness is much more about finding a good match between visual properties and “concepts” than precision or accuracy.
diverging color scale is not a matter of precision or accuracy (in fact a diverging color scale may even be less precise!), it’s a matter of good semantic matching; a matter of expressiveness.
The book divides visual channels into two large groups, magnitude channels and identity channels, according to whether they are more appropriate for quantitative or categorical information. For example, if I want to express a quantity, I shouldn’t use color hue, because humans do not perceive hues as ordered). And if I want to express categories, I shouldn’t use symbol size, because we naturally associate sizes with quantities, not categories.
Let’s look at other things one may want to express. Without any pretense to be exhaustive, here are some examples off the top of my head:
Directionality: can the values be positive or negative? Is there a zero value or a meaningful threshold?
Part-to-whole: do the data objects represent the part of a whole?
Order: are the objects organized in a meaningful order?
Grouping: are the objects organized around a set of meaningful groups
Space/time: do the objects represent space or time or space-time phenomena?
But position expresses information in so many other important ways! Think about position on a single axis versus an orthogonal pair of axes; position in polar coordinates; position on a map; position to express grouping and alignment; etc.
Pursuing expressiveness may ultimately lead to inelegant or irrelevant findings, who knows. But it may also open the doors to a much richer description of how visualization works and to much better guidance.
we have to keep in mind that expressiveness is not only useful in figuring out how to express the information and ideas we want to communicate, but it is equally useful in making sure we do NOT accidentally convey information we did not intend to express.
We can work towards having a better and more complete characterization of what information and concepts we typically want to communicate with data and, similarly, develop a better understanding of what visual properties exist and what they express.
The more granular we go, the harder it is to generalize. In addition to that, the impulse to be able to describe every possible situation may backfire by making things more complicated than they need be.
Once we have a better description of data concepts and visual features, how do we build guidelines that can help people think more productively about visualization design?
Finally, if we want to approach this problem scientifically, we will also need to verify that guidelines and models based on expressiveness have a measurable impact on visualization effectiveness. For instance, it would be useful to explore if, among two competing visual representations, the one having a better match in terms of expressiveness leads to a difference in comprehension (which is not easy to measure, but that’s a different story).
We do not visualize data, we visualize ideas based on data, and we need a better mental model (and maybe even a theory) to design visualizations.
Set Power BI Data Color: All Visuals to Follow Same Color for the Same Data Point
Learn how you can set the same color for the same data value across multiple visuals and pages in a Power BI report using examples.
Fundamentals of Data Visualization
A guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Create Microsoft Power BI Custom Visuals Interactively | PBI VizEdit
Create Power BI custom visuals with PBIVizEditBuild your Power BI custom visuals easily under 15 minutes with mouse clicks without any coding. Only pay for visuals when you are satisfied with the …
PureViz Infographic for Power BI
PureViz Infographic custom visual allows turning your creative ideas into super-fast & animated Power BI visuals. Start from Power Point and bring your data to life!