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Was sucht Deutschland zur Wahl?

Welche Begriffe werden am häufigsten in Verbindung mit den Spitzenkandidat/innen der sieben meistgesuchten Parteien auf Google gesucht? Finden Sie es heraus! www.2q17.de

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Schöne Visualisierung vom Google News Lab in Zusammenarbeit mit Truth & Beauty — Moritz Stefaner, Dominikus Baur und Christian Laesser. Den vertikalen Zeitstrahl auf einem Gerät mit wenig horizontaler Displayfläche finde ich besonders gelungen. 

Chartmaker Directory

Over the past 5+ years, during which time I have delivered more than 200 data visualisation training events to over 4500 delegates, the question I unquestionably get asked the most is ‘which tool do you need to make that chart?’.

It is a question I often find hard to answer elegantly as it is often weighed down with the classic baggage of “it depends”. Above that, there is such variety in the ways of expressing data visually and arguably an even broader variety of tools offering the means to do so, ranging from simple solutions to the more complicated. It is a large, complex and ever-changing landscape to have to make sense of.

With my training and, by extension, my book primarily emphasising the importance of critical thinking and the underlying craft of data visualisation - the ‘what’ and the ‘why’ - I have been seeking to substantiate this content with solid guidance about the critical matter of ‘how’.

This is what motivated the development of the The Chartmaker Directory: an attempt to gather and organise a useful catalogue of references that will offer people a good sense of what charts can be made using which tools and, where necessary, how.
— Andy Kirk

Andy Kirk startet mit einem Chartmaker Directory. Ein toller Anlaufpunkt für Beispiele und Tutorials, geordnet nach Diagrammklassen und Applikationen.

Source: http://www.visualisingdata.com/2017/07/new...

Kontext ist wichtig!

I use a Misfit activity tracker to count my steps. The Misfit app does a decent job of showing me step counts per day and every month, misfit also sends me a summary of the previous month’s activity. Unfortunately, the numbers in that summary are presented without any context, making that summary almost entirely useless.
— Robert Kosara

Robert Kosara schreibt über die Wichtigkeit von Kontext. Gerade im Business Intelligence und Planungs -umfeld sollte Kontext allgegenwertig sein. Beispielsweise ist eine monatliche Hochrechnung weniger aussagekräftig, wenn der historische Kontext oder die Zielerreichung fehlt. Daten ohne Einheiten, Kontext oder Detailinformationen sind leider nicht viel mehr als Dekoration—so wie in Robert's Fitness Tracker App.

Source: https://eagereyes.org/blog/2017/the-import...

Warum werden so viele Kinder um 8 Uhr morgens geboren?

Based on the stories we share, it would be easy to imagine that when a baby is born is random. In the U.S., however, weeks in September have 5 to 10 percent more births than weeks in January. Twelve thousand babies are born on a typical Tuesday compared with 8,000 on a typical Saturday. Sixty percent of babies are born during the day, between 6 A.M. and 6 P.M. And, 3.5 times as many babies are born at exactly 8:00 A.M., the most common minute to be born, than at the least common, 3:09 A.M.

Zan Armstrong | blogs.scientificamerican.com

Why Are so Many Babies Born around 8:00 A.M.?

Die schön gemachte Analyse von Zan Armstrong für "Scientific American", der sich Datenvisualisierungsunterstützung bei Nadieh Bremer geholt hat, hat es sogar über den Atlantik in die "Zeit" geschafft.

Source: Why Are so Many Babies Born around 8:00 A.M.?

Paper: Erzählstruktur für Data Stories

Instead, I simply ignored all the bad stories and looked at just a very small number of the good ones. What do they have in common? It turns out, there is a common pattern for some of them. And I believe it's a very useful one: make a claim, provide evidence, conclude by tying the evidence back to the claim.

Robert Kosara | eagereyes.org

Paper: An Argument Structure for Data Stories

Robert Kosara verfolgt für seine Data Stories den klassischen Ansatz einer Interpretation: "Make a point and proof it". Eine Data Story ist keine Geschichte mit Einleitung, Hauptteil und Schluss.

Source: Paper: An Argument Structure for Data Stories

What I learned on misleading data and visualizations at Alberto Cairo’s #visualTrumpery event

Trumpery is not linked to a famous politician. It means something flashy or beautiful, but without content. This is how Doctor Alberto Cairo started his talk in Barcelona last Friday on data visualizations and the challenge of truthful information.

Maria Crosas | dinfografia.wordpress.com

Fünf Grundsätze von Alberto Cairo, die dazu führen sollen, dass Zielgruppen Datenvisualisierungen besser verstehen.

Source: What I learned on misleading data and visualizations at Alberto Cairo’s #visualTrumpery event

How to measure success in data journalism and other tips

Last month we put 45 data journalism experts from around the world in a room in central London and got them talking about today’s challenges in the world of data-driven storytelling. This article is a roundup of what we’ve learned at this one-day event hosted by the BBC.

Marianne Bouchart | medium.com

How to measure success in data journalism and other tips from experts at the Data Journalism Unconference 2017

Quelle: medium.com

Die Ergebnisse des Workshops ist nicht nur für Datenjournalisten interessant.

Your reports must be racetracks

A bar or a line remains geometry that has to be decoded. To understand numbers, we need the left side of our brain that is made for abstract and logic thinking. Seeing profit and loss in bars and lines brings the two sides of our brain in conflict.

Dr. Nicolas Bissantz | blog.bissantz.de

Your reports must be racetracks I

Dr. Nicolas Bissantz mit einem interessanten Ansatz. Anstelle einer visuellen Tabelle mit Incell Grafiken bzw. Microcharts zu verwenden, schlägt er vor, die Zahlen selber zu formatieren. Wichtiges soll ausschließlich über Größe und Farbe hervorgehoben werden. Er greift dabei auf Erkenntnisse aus dem Rennsport und der Neurologie zurück.

Source: Your reports must be racetracks I

Putting The B Back Into BI

Filip Doušek darüber, warum es wichtig ist, sich wieder auf das Business in BI zu fokussieren:

I was planning to write about putting the intelligence back into business intelligence. About making data tools smarter than 90’s style drilling and 00’s style charting. About massive analytics and graph DBs and dealing with big data. And how BI should be more AI. That would be my inner geek’s blogpost. It would be about the promise of BI - making companies smarter through the use of data.

But when I put my business hat on, I don’t care about the intelligence. […]

The whole point of BI is (or should be) business, not intelligence. And that is the real problem with BI. Not missing the I, but missing the B.

Und, was aus seiner Sicht nicht funktioniert:

What Doesn’t Work: Dashboards. Reports. Exploration. Self-service. Why? Because digging around increasingly large datasets is not what you want your people to spend time on. It doesn’t directly increase profits or speed up execution. Quite on the contrary. Executives today are swamped with information, 95% of which is noise. Digging through that noise distracts them from work, burdens their minds and clots their decision-making.

Es geht Ihm, wie auch bei der Datenvisualisierung, um das Vermeiden von Störungen, Ablenkungen und um Priorisierung. Die richtigen Fragen zu kennen, und auch zu beantworten. Ein guter Ratschlag, sich weniger um die Tools zu kümmern, sondern um die Business Prozesse und deren Unterstützung. 

Source: https://www.linkedin.com/pulse/putting-b-b...