Tag: Tableau

Visual Analytics with Tableau: Book is Now Available

Visual Analytics with Tableau book cover
Visual Analytics with Tableau book cover

My book Visual Analytics with Tableau is now available:

From the back cover:

A 4-COLOR JOURNEY THROUGH A COMPLETE TABLEAU VISUALIZATION FOR NON-TECHNICAL BUSINESS USERS

Tableau is a popular data visualization and analytics tool favored by financial analysts, marketers, statisticians, business and sales professionals, data scientists, developers, and many others who need to explore insights and present visual, easy-to-understand data. Visual Analytics with Tableau is an accessible, step-by-step introduction to the world of visual analytics. This up-to-date guide is ideal for both beginners and more experienced users seeking a practical introduction to the fields of data analysis and visualization. Through hands-on examples and exercises, readers learn how to analyze their own data and clearly communicate the results.

This guide covers everything you need to get started with Tableau, from the first steps of connecting to data, creating different types of charts, and adding calculation fields to more advanced features such as table calculations, forecasts, clusters, and R, Python, and MATLAB integration for sophisticated statistical modelling. User-friendly instructions for existing options within the Tableau ecosystem–Tableau Desktop, Tableau Prep, Tableau Server, Tableau Online, and Tableau Public–enable you to integrate, clean, and prepare your data and share your work with others. Visual Analytics with Tableau:

  • Covers the newest versions of Tableau 2018.3 and 2019.1 plus Tableau Prep, Tableau’s brand-new data integration application
  • Requires no background in mathematics nor any programming experience
  • Focuses on the visual analytics functionality of Tableau rather than complex statistical programming
  • Offers expert guidance from popular Tableau Germany employee and visualization expert Alexander Loth
  • Discusses advanced Tableau functionality and working with different data structures
  • Provides easy-to-follow instructions, full-color illustrations, learning tools, online resources, and more

If you’re getting started with visual analytics and Tableau, this book will teach you everything you need to know to build the foundations and understand how and why to explore your data visually. Alexander has created a fantastic resource that guides you step by step through the process of preparing your data, using Tableau Desktop to analyse it and finding insights.
–Eva Murray, Head of BI and Tableau Zen Master at Exasol

If you’re keen to go from beginner to expert in Tableau, Alexander’s excellent book gives you everything you need to know. With a crisp and clear style, he talks the reader through all aspects of Tableau, from data cleaning through data analysis and into sharing insight with others.
–Andy Cotgreave, Author of Big Book of Dashboards and Technical Evangelist at Tableau Software

Visual Analytics with Tableau – an easy to understand book by Alexander Loth, one of Tableau’s very first employees based in Germany, a recognized speaker on countless conferences, and a Tableau Jedi. It contains both the basics and advanced Tableau features. If you ask me: there is nothing more you need to get started with Tableau!
–Klaus Schulte, Professor at Münster School of Business & 2019 Tableau Zen Master

Thank you all who helped me to complete this book!

Visual Analytics with Tableau
Visual Analytics with Tableau
  • Alexander Loth
  • Publisher: Wiley
  • Edition no. 1 (01.08.2019)
  • Taschenbuch: 288 pages
35,99 EUR
Buy on Amazon

How China is winning in the Age of Artificial Intelligence

Alibaba Campus
Alibaba Campus

Currently, I’m on a 4-week China trip, visiting many cities. In Hangzhou, I met CEIBS peers who work for Alibaba. While the Alibaba campus is quite impressive, I got even more impressed by Alibaba’s leadership culture, which is encouraging its employees to innovate as intrapreneurs.

If you start your own project (a new mobile app, a patent, a scientific paper, etc.), you’re doing it in your own pace, you’re not being micro-managed and you’ll receive a bonus based on success. Intrapreneurship at Alibaba is just one of many examples where we (Europeans) can learn a lot from China!

Yue and me, Hangzhou West Lake

While traveling in China I was reading AI Superpowers: China Silicon Valley, and the New World Order by Kai-Fu Lee, a book that is a must-read to get an idea where China’s AI ambitions are heading to. What matters most for AI innovation these days, the author argues, is access to vast quantities of data—where China’s advantage is overwhelming.

AI Superpowers: China, Silicon Valley, and the New World Order
AI Superpowers: China, Silicon Valley, and the New World Order
  • Kai-Fu Lee
  • Publisher: Houghton Mifflin Harcourt
  • Gebundene Ausgabe: 272 pages
24,78 EUR
Buy on Amazon

A quite entertaining book focusing on the new mindset of China’s young generation is this one: Young China: How the Restless Generation Will Change Their Country and the World by Zak Dychtwald.

Young China: How the Restless Generation Will Change Their Country and the World
Young China: How the Restless Generation Will Change Their Country and the World
  • Zak Dychtwald
  • Publisher: ST MARTINS PR
  • Gebundene Ausgabe: 304 pages
20,13 EUR
Buy on Amazon

[Update 2 May 2019]: Which other cities in China did I visit? Check out my Tableau Public viz:

Data Operations: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

#dataops: Folgen Sie der Diskussion auf Twitter
#dataops: Folgen Sie der Diskussion auf Twitter

Sind Sie mit der Geschwindigkeit Ihrer Datenanlyse unzufrieden? Oder haben Ihre Dashboards lange Ladezeiten? Dann können Sie bzw. Ihr Datenbank-Administrator folgenden Hinweisen nachgehen, die sich je nach Datenquelle unterscheiden können.

Allgemeine Empfehlungen zur Performance-Optimierung

Möchten Sie die Geschwindigkeit der Analyse verbessern? Dann beachten Sie folgende Punkte:

  • Benutzen Sie mehrere »kleinere« Datenquellen für individuelle Fragestellungen anstatt einer einzigen Datenquelle, die alle Fragestellungen abdecken soll.
  • Verzichten Sie auf nicht notwendige Verknüpfungen.
  • Aktivieren Sie in Tableau die Option »Referentielle Integrität voraussetzen« im »Daten«-Menü (siehe Abbildung 2.20). Wenn Sie diese Option verwenden, schließt Tableau die verknüpften Tabellen nur dann in die Datenabfrage ein, wenn sie explizit in der Ansicht verwendet wird*. Wenn Ihre Daten nicht über referentielle Integrität verfügen, sind die Abfrageergebnisse möglicherweise ungenau.
Aktivierte Option „Referentielle Integrität voraussetzen“ im „Daten“-Menü
Abbildung 2.20: Aktivierte Option »Referentielle Integrität voraussetzen« im »Daten«-Menü

* So wird beispielsweise der Umsatz anstatt mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] S INNER JOIN [Product Catalog] P ON S.ProductID = P.ProductID lediglich mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] ermittelt.

Empfehlungen für Performance-Optimierung bei Dateien und Cloud-Diensten

Achten Sie insbesondere beim Arbeiten mit Dateiformaten, wie Excel-, PDF- oder Textdateien, oder Daten aus Cloud-Diensten wie Google Tabellen zusätzlich auf folgende Punkte:

  • Verzichten Sie auf Vereinigungen über viele Dateien hinweg, da deren Verarbeitung sehr zeitintensiv ist.
  • Nutzen Sie einen Datenextrakt anstatt einer Live-Verbindung, falls Sie nicht mit einem schnellen Datenbanksystem arbeiten (siehe Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden).
  • Stellen Sie sicher, dass Sie beim Erstellen des Extrakts die Option »Einzelne Tabelle« wählen, anstatt der Option »Mehrere Tabellen« (siehe Abbildung 2.21). Dadurch wird das erzeugte Extrakt zwar größer und das Erstellen des Extrakts dauert länger, das Abfragen hingegen wird um ein Vielfaches beschleunigt.
Ausgewählte Option „Einzelne Tabelle“ im „Daten extrahieren“-Dialog
Abbildung 2.21: Ausgewählte Option »Einzelne Tabelle« im »Daten extrahieren«-Dialog

Empfehlungen für Performance-Optimierung bei Datenbank-Servern

Arbeiten Sie mit Daten auf einem Datenbank-Server, wie Oracle, PostgreSQL oder Microsoft SQL Server, und möchten die Zugriffszeiten verbessern? Dann achten Sie bzw. der dafür zuständige Datenbankadministrator zusätzlich auf folgende Punkte:

  • Definieren Sie für Ihre Datenbank-Tabellen sinnvolle Index-Spalten.
  • Legen Sie für Ihre Datenbank-Tabellen Partitionen an.

Dieser Beitrag ist der dritte Teil der Data-Operations-Serie:

Teil 1: Daten für die Analyse optimal vorbereiten
Teil 2: Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden
Teil 3: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

Außerdem basiert dieser Blog-Post auf einem Unterkapitel des Buches “Datenvisualisierung mit Tableau“:

Datenvisualisierung mit Tableau
Datenvisualisierung mit Tableau
  • Alexander Loth
  • Publisher: mitp
  • Edition no. 2018 (31.07.2018)
  • Broschiert: 224 pages
29,99 EUR
Buy on Amazon

How to research LinkedIn profiles in Tableau with Python and Azure Cognitive Services

Tableau is using Python to access the Web Services API provided by Microsoft Azure Cognitive Services
Tableau is using Python to access the Web Services API provided by Microsoft Azure Cognitive Services

A few weeks after the fantastic Tableau Conference in New Orleans, I received an email from a data scientist who attended my TC18 social media session. She had a quite interesing question:

How can a Tableau dashboard that displays contacts (name & company) automatically lookup LinkedIn profile URLs?

Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…

1. Python and TabPy

We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.

2. Microsoft Azure Cognitive Services

One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + “linkedin”. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.

3. Calculated Field in Tableau

Let’s wrap our Python script together and create a Calculated Field in Tableau:

4. Tableau dashboard with URL action

Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.

LinkedIn demo on Tableau Public

Is this useful for you? Feel free to download the Tableau workbook (don’t forget to add your API key), leave a comment and share this tweet:

The Empathy Machine: Are Digital Technologies the Best Bet in Telling about your Cause?

The panel discussion “The empathy machine: are digital technologies the best bet in telling about your cause?” took place on the opening day of the 2018 Fundamental Rights Forum (FRA). This forum was organized by the European Union Agency for Fundamental Rights, and took place at the METAStadt Vienna 25-27 September 2018.

In this panel discussion Kadri Kõusaar (a Oscar nominated film director), Fanny Hidvegi (European Policy Manager) and me discussed if digital technologies really are the “empathy machine” and how innovative applications can help human rights defenders to achieve some challenging goals such as a change in public attitudes or meeting tough fund-raising targets. The panel discussion was moderated by the virtual reality artist Dr. Frederick Baker.

In this blog post I want to share some of the panel’s questions, which I answered:

1. How do algorithms interfere with human rights?

When algorithms make certain decisions, these algorithms  tent to mirror what they are shown with training sets. This is especially apparent for issues such as bias and machine discrimination. Both might be the result of the content of the training data, which reflects existing inequalities.

2. So, it’s about the data? What else makes data so important today?

The effective use of data is vital for our understanding of fundamental issues, such as human rights violations and political instability, for informing our policy-making, and for enhancing our ability to predict the next crisis. Furthermore, the scope, complexity and life-changing importance of the work being done on topics like these across the European Union has made it more important than ever for everyone participating in the public conversation and in demographic decision-making to have access to and to be able to derive insights from key data sources.

3. Where is data coming from and how can people benefit?

Every time we google something, send a tweet, or just browse a website, we create data. With the rise of visual analytics we can benefit from this vast amount of information. Visual analytics is a hands-on approach to interacting with data that does not require any programming skills. Furthermore, communicating with data, is seen as one of the most relevant skills in today’s information age.

Global Refugee Crisis visualization on Tableau Public

4. What is the easiest way to find interesting data?

I would check out the Google’s new search engine for datasets that was just released recently! Tableau Public is a good source for existings visualizations. Many of these are based on public data.

5. What is required to enable organizations to use data for good?

Data can be used for the good of society, but private- and public-sector firms, nonprofits and NGOs still lack analytics resources and expertise. Data and analytics leaders must cross traditional boundaries to use data for good, to better compete for limited talent, and to foster an ethical culture. VizForSocialGood and Tableau Foundation are good examples.

6. How can the private sector contribute for good?

Some private sector organizations are making data open and available to researchers, nonprofits and NGOs. Examples include:

  • Mastercard anonymizing credit card data to be analyzed in smart city initiatives.
  • Google making search data available to hospitals to predict infection disease outbreaks such as flu and dengue fever.
  • Insurance companies providing anonymized healthcare data to improve patient outcomes and prevention strategies.
  • Yelp providing ratings data to cities to prioritize food safety inspectors.

The panel discussion was followed by workshops in the afternoon: