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.
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.
4. What is the easiest way to find interesting 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:
This morning we kicked off #TC18 with 17,000 data rockstars! 🎉 We shared some exciting announcements including Ask Data, Tableau Prep Conductor, Tableau Developers Program, big news for Tableau Foundation, and more. Learn all about them: https://t.co/CiXWo8qtxOpic.twitter.com/pnWZJzYwma
Anyone can analyze basic social media data in a few steps. But once you’ve started diving into social analytics, how do you bring it to the next level? This session will cover strategies for scaling a social data program. You’ll learn skills such as how to directly connect to your social media data with a Web Data Connector, considerations for building scalable data sources, and tips for using metadata and calculations for more sophisticated analysis.
Here are some key takeaways and links (i.e. additional resources) featured during my TC18 sessions to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Step 1: Understand How to Succeed with Social Media
Apple has officially joined Instagram on 7th August 2017. This isn’t your average corporate account as the company doesn’t want to showcase its own products. Instead, Apple is going to share photos shot with an iPhone:
And there are plenty takeaways for every business:
Wrap your data around your customers, in order to create business value
Interact with your customer in a natural way
Understand your customer and customer behaviour better by analyzing social media data
Step 2: Define Your Social Objectives and KPIs
A previous record-holding tweet: In 2014, actor and talk show host Ellen DeGeneres took a selfie with a gaggle of celebrities while hosting the Oscars. That photo has 3.44 million retweets at the time of writing:
#MakeoverMonday, one of the biggest community endeavors in data visualization, is hosted by Eva Murray and Andy Kriebel. Andy started #MakeoverMonday almost 10 years ago as a weekly blog to document his learning progress on vizwiz.com. In 2016, together with Andy Cotgreave, he turned #MakeoverMonday into a social data project by sharing weekly datasets, providing examples of data visualization best practices, as well as tips and tricks with Tableau. Andy is also Head Coach at The Information Lab Data School and a five-time Tableau Zen Master.
Eva joined #MakeoverMonday in 2017. She loves to blog about Tableau, travel, and triathlon on trimydata.com. Furthermore, Eva is the Head of Business Intelligence at Exasol and a 2018 Tableau Zen Master.
In a few days, Eva’s and Andy’s #MakeoverMonday book will be released. I interviewed both about their data background, where data analytics is heading to, and of course, about #MakeoverMonday!
Alex Loth: Hi Eva, hi Andy, first thank you for the interview. Let’s start with your “data background”. How did you get interested in working with data?
Eva Murray:For me it started at university. I studied Psychology, HR, Accounting and Commercial Law. Psychology was by far the most interesting subject and for some reason I really took a liking to my statistics papers. I had never been very successful in maths during secondary school, but at university something clicked. The right or wrong nature of numbers was satisfying and provided a good balance to the fluffiness of essay writing. I aced all my stats papers and really enjoyed that part of my psychology degree. After university I joined Deloitte as a consultant for Information Management. That let me stumble into data. It was a mix of 80% powerpoint and 20% data analysis and I loved both parts. From there I decided to move into the financial services industry and took on a role as an analyst because I wanted to sharpen those skills.And that’s when things really started because I was surrounded by data every day.
Andy Kriebel:I got interested in numbers from an amazing geometry teacher I had in high school. The beauty of him as a teacher was that he was blind. That’s right, a blind geometry teacher. From there, I had THE BEST professor at university who is still a mentor to me today. As for data itself, I’ve pretty much been involved with data since my career started. My first job was as an underwriter for an insurance company (slimy business that is!) then I went into a revenue planning role at Coca-Cola, where I first found Tableau in 2007.
Alex: What was the first data set you remember working with? What did you do with it?
Eva:The very first one was probably when I was 10 and I collected data about my gerbils. Dad helped me research on the internet to predict the fur color of the gerbil babies that were about to be born. I had quite the breeding operation going on (my biology teacher thought the ones he gave me were brothers, but they turned out to be a couple). My first proper data analysis was done with survey data at university looking at responses, but I don’t really remember the topic. At uni we used SPSS to work with the data and the visualizations we built were typically scatterplots and histograms, focusing on the statistical relevance of the relationships between two metrics.
Andy: My dad was president of the local Little League (youth baseball) for about 20 years or so. I would go to games all weekend and help with scoring games. I would take all of the results and tabulate them by hand to calculate the stats, type them up on a typewriter and post them for any kid in the league to see. I was probably 9 or 10 when I started doing that.
Alex: Was there a specific “aha” moment when you realized the power of data?
Eva:Yes, definitely. It wasn’t until a bit later after finishing uni. I was working on a project which basically involved an IT audit, looking at individual line items of spending on various hardware. Having to manage, analyse and find insights in the huge amount of data in Excel was a massive challenge but it showed me just a snippet of the type of data that’s out there, ready to be taken apart. Finding insights and creating data stories became something that fascinated me. Another couple of years later when I got my hands on Tableau and was able to make data visible so much more easily, data started to really come to life for me.
Andy:Absolutely! It was the day I found Tableau. Getting insight into the data in a few minutes after downloading the software totally blew me away. I showed that to the Director of our group and we immediately began using it to measure our sales teams.
Alex: How important is data in your personal life?
Eva: As an endurance athlete, data is very important for me. I track my sleep, my weight, my training, distances ridden/run/swum, elevation conquered and the effort it took to get me there. I’m often fascinated by what the human body is capable of and having a way to put it in words and numbers through data is something I really enjoy. Of course I also try to learn something from the data so I can improve my performance.
I like using data to identify patterns which in turn helps me build good habits and behaviours and stop the bad ones (at least I try to)
Andy: I’d say it’s less important than it used to be. I track a lot of quantified self data, but I don’t do much with it. I found that I became too obsessed with tiny things that didn’t really matter in my life, like step counts, weight, etc. I exercise enough to not worry about those things, so why worry about that? Just about the only thing I do now is create art with my fitness data.
Alex: Thank you for sharing. Next, let’s talk a little about helpful resources and where you think data analytics is heading to. What is the book (or books) that have greatly influenced the way your work with data?
Eva:I have to say that for me it wasn’t a book in particular. When I work with data, I sit in front of a screen, so my go-to-resources are typically blogs and forums to find the answers. In the early days of my Tableau journey, I heavily relied on the Tableau forums for help. Once I had a better understanding and knew what I was looking for, I shifted to blogs, such as the one Andy writes. Quite honestly, if I need an answer, I ask google first and based on my knowledge of people in the community, I then quickly pick from the results based on the names that pop up.
Andy:If I had to pick one book, I’d say #MakeoverMonday :-). But to answer your question less selfishly, here are some books that influenced me:
Alex: What advice would you give to a student about to enter the “analytics world”? What advice should they ignore?
Eva: Don’t think you need to have a computer science or statistics background to be successful. Yes, it can help, but if you’re someone with curiosity, you’re well on your way. Different disciplines play into the field of data analysis. Here are some that come to mind for me:
Thinking and researching like a journalist, finding sources of information, checking them, building a story and sharing it effectively
Analysing and challenging the data like a researcher, not just taking it at face value but testing different hypotheses, running through different scenarios and checking the statistical validity of your conclusions
Structuring your results like an attorney, making sure you have solid foundations for your arguments, you have proof and facts to back up your claims
Looking at data like a graphic designer, making sure the story becomes visible in a beautiful and impactful visualization, using colors, white space, text and charts in the most effective way to elicit emotion in your audience and to draw them into your data story.
Andy: I’d agree with Eva, don’t let your “degree” get in the way of your enjoying a career in data analytics. If you love numbers, jump right in. Find your niche, practice relentlessly, build a portfolio.
When approaching any project, try to answer five key questions: When? What? Where? Who? Why?
Alex: What are bad recommendations you hear in the area of analytics?
Eva:I don’t think they’re necessarily recommendations I hear but a phenomenon I have been witnessing is the almost compulsive move by everyone to do a Masters degree. Sure, if you’d like to do one after you graduate, go right ahead. Don’t feel like you have to do it, however, to be successful. If you instead spend those 12-18 months working, learning and applying your knowledge to real-life scenarios and gaining experiences in the real business world, you’ll probably benefit more than just financially. Having experience in applying your knowledge to client scenarios, finding solutions to problems and helping your organisations save money, improve processes, make greater contributions to their communities, etc. will probably be more exciting than spending more time at university for another certification to hang on your wall. No one ever asked me for my missing Masters title and getting my hands ‘dirty’ instead by working, learning on the job, seeking opportunities and pursuing them, has helped me greatly. Everyone should find their way and if you’re unsure whether or not you should stay another year or so at university, please don’t feel like you don’t have options or should do it because everyone is doing it.
Andy:That’s a good question. I often hear of people giving bad advice for how to approach data analysis and data visualization. People give advice that can be too complicated, which leads to frustration and kills someone’s interest. The most important thing anyone can do is keep it simple.
Alex: How does the future of analytics look like?
Eva:In my opinion, we’ll see a shift for analysts towards greater requirements for data science knowledge and skills. A lot of standard reporting will be automated but the stories we can tell with data will still come from human beings, from analysts who work with data and understand the human connection within the numbers.
We’ll hopefully see a lot fewer silos and much more collaboration within and across organisations. Data will become the lifeblood of humanitarian causes with volunteers and nonprofits using data and analytics to drive change at scale to improve living conditions for and the wellbeing of millions of people around the world because they know when to act, what resources to send and how to most effectively deploy the right people, machinerie and processes in different parts of the world.
Andy:I’m hoping that the future of data leads to a better world to live in. I hope we can get through all of the noise and lies by using data and facts to educate people. I hope data is used to improve education and health, especially for those that don’t have the best access to those resources now. Maybe I’m living in a utopian world, but one can dream and I promise I’ll do my best to make it happen.
Alex: Very insightful. Finally, let’s talk about your initiative #MakeoverMonday. How did you come to found #MakeoverMonday?
Eva: I’ll let Andy answer the question on how Makeover Monday came about. He brought it to life, I joined him in 2017 and injected my own personality and ideas into the initiative. It’s been so much fun to see the project grow to hundreds of regular participants and to follow people’s growth and development.
Andy:As of this writing, I’ve done 224 vizzes for “Makeover Monday”, but really, I’ve been doing makeovers since my first blog post in August 2009. Credit for the name “Makeover Monday” goes to Emily Kund. She saw that I tended to do the makeovers on Mondays, and came up with the alliteration. It looks like my first official Makeover Monday was on April 28, 2014. #MakeoverMonday the community project started in January 2016 with me and Andy Cotgreave. Eva replaced Andy in January 2017 and the project has really taken off since.
Alex: What specific problem is #MakeoverMonday trying to solve? How would you describe it to someone who is not familiar with it?
Eva:Our mission is to improve the way we visualize and analyze data – one chart at a time. ‘We’ in this case is everyone. Not just Andy and me. Not just the Makeover Monday community. There are so many people in the world who work with data and there are countless examples of bad data visualizations. We want to change that. Beyond beautiful charts we want to help people create truthful, easy-to-understand representations of the data which bring various topics to their audiences in a way that resonates with them. There is so much knowledge in the world and to make it accessible, we need to find easy ways to distill complex scenarios into clear, simple representations.
The way Makeover Monday works is that every week, Andy and I provide a visualization and the accompanying data to our community. We ask participants to create an improved visualization of the same data. To support the community, we run a weekly 75min feedback webinar where we help people with their questions, provide recommendations for improvement and explain why some visualizations work better than others to represent the data at hand. We also write a weekly blog post with lessons learned, provide feedback on social media, have a gallery with each week’s favorite vizzes and have written a book that distills everything into a paper-version people can use for reference
Andy:Ultimately, we’re helping people learn, not only technically, but with data literacy and communication. There are way too many charts that communicate poorly and we’re hoping people can use #MakeoverMonday to improve on those charts, take what they learn into their day job, and ultimately find the career they’ve always wanted. It’s quite simple when you think about it.
Alex: What should we know about your new #MakeoverMonday book?
Eva:Our book has been a labor of love, bringing together lessons learned from thousands of Makeover Monday visualizations, close to 150 data sets, over 100 hours of webinar content we created and showcasing the work from our community since the project started almost three years ago.
It puts the essentials into your hands, focusing on the foundations every analyst should build when it comes to their analysis and visualization skills. It is packed with over 300 examples from the community and can be read cover to cover or referred to as and when needed.
The book has been a very personal project, as we worked closely with our participants, as well as friends to create the final version. We had great support from the team at i-for-ideas.com who helped create a design that reflects the essence of what we and this project are all about.
Andy:I’m quite proud of how Eva and I pulled the book together so quickly. It’s a culmination of everything we have learned through the project. We’re taking the most frequently discussed lessons and turn them into a practical guide for anyone with an interest in data visualization. I don’t want to give away too many spoilers.
Alex: What has been the most surprising insight you have found while writing the #MakeoverMonday book?
Eva: It wasn’t as much a surprise as it was a very nice realisation: Andy and I are very good at teamwork and playing to our strengths. We didn’t argue once about who would work on which tasks. We simply created a plan with everything we needed to do, split up the jobs according to our interests and preferences and got to work. When one person was pressed for time, the other would take on a couple of extra jobs to ease the pressure and that’s how we went from book proposal to finished manuscript in 120 days. While I’m not sure Andy is keen on a second book at this point, I’d be happy to write another one with him :-).
Andy:I was surprised at how little time it took. Don’t get me wrong, we spent countless nights and weekends writing, but it wasn’t nearly as bad as people had led me to believe. I quite enjoyed the writing too; it helped reinforce my personal learning and I find writing therapeutic.
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