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.
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.
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.
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.
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.
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.
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.
For my birthday this year, I’m asking for donations to Alzheimer’s Association. I’ve chosen this nonprofit because their mission means a lot to me, and I hope you’ll consider contributing as a way to celebrate with me. Every little bit will help me reach my goal.
To eliminate Alzheimer’s disease through the advancement of research; to provide and enhance care and support for all affected; and to reduce the risk of dementia through the promotion of brain health.
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.
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:
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