Schlagwort: Tableau

Welcome to the Tableau section of my blog, where I share my knowledge and experience with one of the world’s most popular data visualization tools. As someone who has worked with and used Tableau for years, I’m passionate about helping others master this powerful tool and make the most of its capabilities.

In this section, you’ll find a variety of tutorials on topics such as building visualizations, creating dashboards, and using Tableau’s advanced features. I’ll also share stories and insights from my time working with Tableau, including tips and tricks for getting the most out of the tool.

Whether you’re new to Tableau or an experienced user looking to take your skills to the next level, these tutorials and stories will give you the practical knowledge and insight you need to succeed. I believe Tableau has the power to change the way we think about and interact with data, and I’m excited to share that vision with others.

So join me on this journey as we explore the world of Tableau together and discover new ways to unlock the full potential of your data.

Social Media and the Customer-centric Data Strategy #data17 #resources

Social media marketing mix
Do you analyze your social media marketing mix? | Photo Credit: via Richard Goodwin

With over 3 billion active social media users, establishing an active presence on social media networks is becoming increasingly essential in getting your business front of your ideal audience. These days, more and more consumers are looking to engage, connect and communicate with their favorite brands on social media.

Adding social media to your customer-centric data strategy will help boost brand awareness, increase followership, drive traffic to your website and generate leads for your sales funnel. In 2017, no organization should be without a plan that actively places their brand on social media, and analyzes their social media data.

Once you’ve started diving into social media analytics, how do you bring it to the next level? This session covers a customer-centric data strategy for scaling a social media data program.

Here are the links (i.e. additional resources) featured during the session to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:

The Data Opportunity

TC17 Social Media Slides: The Data Opportunity

Focus on relevant metrics for your strategy

TC17 Social Media Slides: Sentiment Analysis

How to get Social Media in Tableau?

TC17 Social Media Slides: 3rd Party Platform Talkwalker

Tips to Level Up

TC17 Social Media Slides: Unshorten URLs in Tableau with R

Tutorials and Slide Set

The slides and tutorials presented at Tableau Conference on Tour in Berlin are also available on SlideShare, and on YouTube in English and German.

English Tutorials

German Tutorials

Slide Set

It’s My 10 Year Blogging Anniversary!

10-year blogging anniversary: 2007 Hampi, a temple town in South India recognised as UNESCO World Heritage Site
10-year blogging anniversary: 2007 Hampi, a temple town in South India recognized as UNESCO World Heritage Site (Flickr)

A Milestone Blogging Anniversary

It’s hard to believe, but I’m celebrating my 10-year blogging anniversary! I’m filled with gratitude for those who have read, encouraged, and inspired me throughout this adventure. Without you, blogging would be only half the fun. Now, let’s take a moment to reflect on this incredible decade…

The Early Years: SAP and India (2007-2009)

It all started in 2007 when I decided to explore an internship abroad in India. I was studying Computer Science and decided to go for an internship abroad. China and India were on my shortlist. I decided for India, applied for a scholarship, and asked some companies for interesting project work. Before starting the adventure, I published my very first blog post to keep family and friends in the loop.

For the next seven months, I lived in Bangalore and worked for SAP Labs India to develop prototypes for mobile BI apps. I spent plenty of weekends exploring India and surrounding countries. After returning from India, I continued to work for SAP at their headquarters while finishing my degree in Karlsruhe.

CERN: Data and Discovery (2009-2012)

Surrounded by snow-capped mountains and Lake Geneva, CERN grabbed my attention at the end of my studies. CERN has tons of data: some petabytes! Challenge accepted. CERN is known for its particle accelerator Large Hadron Collider (LHC). We applied machine learning to identify new correlations between variables (LHC data and external data) that were not previously connected. This chapter was all about embracing the challenges of big data.

Capgemini and MBA: Expanding Horizons (2012-2015)

Back in Germany, my focus shifted to bringing Big Data Analytics to companies. To one company? No, to many companies! So instead of getting hired as Head of BI for an SME, I started to work for Capgemini. I had fantastic projects, designed data-driven use cases for the financial sector, and gave advice for digital transformation initiatives.

In order to keep in balance with all the project work, I dedicated many of my weekends to studies and got enrolled in Frankfurt School’s Executive MBA program. During my studies, I focused on Emerging Markets and visited a module at CEIBS in Shanghai.

Tableau and Futura: Innovation and Entrepreneurship (2015-201?)

I knew Tableau from my time as a consultant. It is an awesome company with a great product and a mission: help people see and understand their data. That’s me! Joining Tableau allowed me to assist organizations in transitioning from classic BI to modern self-service analytics by developing data strategies so that data can be treated as a corporate asset. This includes education, evangelism, and establishing a data-driven culture.

In the evenings I’m working for Futura Analytics, a fintech startup, which I co-founded in 2017. Futura Analytics offers real-time information discovery and transforms data from social media and other public sources into actionable signals.

What’s Next? A Glimpse into the Future

Currently, I’m excited to present my Data Strategy talk on TC17, accompanied by a TensorFlow demo scenario. I’m also learning Mandarin, the predominant language of business, politics, and media in China and Taiwan, for quite a while. Let’s see if that is going to influence my next steps… 🙂

Follow my ongoing journey in data analytics and more on Twitter and LinkedIn.

Tableau Conference TC17 Sneak Peek: Integrating Julia for Advanced Analytics

Demo: using Julia within calculations in Tableau (click to enlarge)
Demo: using Julia calculations within Tableau (click to enlarge)

We have already seen some love from Tableau for R and Python, boosting Tableau’s Advanced Analytics capabilities.

So what is the next big thing for our Data Science Rockstars? Julia!

Who is Julia?

JuliaJulia logo is a high-level dynamic programming language introduced in 2012. Designed to address the needs of high-performance numerical analysis its syntax is very similar to MATLAB. If you are used to MATLAB, you will be very quick to get on track with Julia.

Compared to R and Python, Julia is significantly faster (close to C and FORTRAN, see benchmark). Based on Tableau’s R integration, Julia is a fantastic addition to Tableau’s Advanced Analytics stack and to your data science toolbox.

Where can I learn more?

Do you want to learn more about Advanced Analytics and how to leverage Tableau with R, Python, and Julia? Meet me at the 2017 Tableau Conferences in London, Berlin, or Las Vegas and join my Advanced Analytics sessions:

Will there be an online tutorial?

Yes, of course! I published tutorials for R and Python on this blog. And I will also publish a Julia tutorial soon. Feel free to follow me on Twitter @xlth, and leave me your feedback/suggestions in the comment section below.

Further reading: Mastering Julia

A German translation of this post is published on the official Tableau blog: Tableau Conference On Tour Sneak Peek: Julia-Integration für Advanced Analytics

Update 11 Oct 2017: The Julia+Tableau tutorial blog post is now published.

Price and Sentiment Analysis: Why is Bitcoin Going Down?

Bitcoin Price and Sentiment Analysis with variable Moving Average: click to open interactive Tableau dashboard with annotations
Bitcoin Price and Sentiment Analysis with variable Moving Average: click to open interactive Tableau dashboard with annotations

Bitcoin has become one of the trendy investment assets in the recent years. Whenever bitcoin prices approach historical highs, every investor should watch the currency closely. Bitcoin rallied by more than 20% in the first days of 2017, crossing the $1000 mark for the first time since November 2013.

As many experienced bitcoin traders will remember, the first $1000 peak was a case of obvious over exuberance. Bitcoin was hot, plenty of money was pouring into it. Bitcoin investors got too excited, causing a price surge. Prices then rebounded and suffered a long-term collapse shortly after.

Moving Average Convergence/Divergence Indicator

Many traders rely on a Moving Average Convergence/Divergence (MACD) indicator. The MACD is a measure of the convergence and divergence between two EMAs (usually 12 and 26 days) and is calculated by subtracting the two of them. The signal line is constructed by creating an EMA (usually 10 days) of the signal line.

The signal line crossing the MACD from above is a buy signal. The signal line crossing the MACD from below is a sell signal. Relying only on momentum-based indicators (such as the MACD) and optimization-based models, however, will most certainly fail to indicate heavy price drops, as the drop in late 2016.

Predicting Fear with Sentiment Analysis

In late 2016 a lot of people began to pour money into bitcoin again. This time because they were worried that stock markets and other assets were due for a drop. For investors, it is essential to figure out whether or not these fears are actually founded. However, such „safe assets“ are prone to suffering from bubbles. People get scared, get invested into gold, or bitcoin, then realize that their fears were unfounded. As a result bitcoin prices could plummet.

So how to catch emotions such as fear in advance? Twitter is a valuable source of information and emotion. It certainly influences the stock market and can help to predict the market. Sentiment analysis can lead price movements by up to two days. Negative sentiment, however, is reflected in the market much more than positive sentiment. This is probably because most people tweet positive things about bitcoins most of the time. Even more positive news occurred after breaking the $1000 barrier.

This content is part of the session “Price and Sentiment Analysis: Why is Bitcoin Going Down?” that I deliver at the Frankfurt Bitcoin Colloquium. Have a look on my upcoming sessions!

[Update 14 Jun 2017]: Axis for Moving Average adjusted. Relative Date selector added with last 6 month as default. Screenshot updated.

Feel free to share the Bitcoin Price and Sentiment Analysis dashboard, which is also featured as Viz of the Day on Tableau Public:

Monkey Business: Always be Ready to Demo

The famous Tableau Superstore demo data set
The famous Tableau Superstore demo data set

Usually, I really don’t like looking on the screens of other passengers. On this early morning train from Frankfurt to Cologne, however, the screen of my seatmate caught my attention. Where have I seen the logo on his slide deck before? Two coffee sips later, it came to me: it was the logo of Monkey 47, a very delicious gin, distilled in the heart of the Black Forest. So I asked my neighbor: “Is that the Monkey 47 logo?”

He was happy that I recognized his brand and we had a small chat about gin and the Black Forest. Turns out his name is Thomas, and he is the head of Sales and Marketing for Monkey 47. Thomas mentioned that his team is planning a tour to promote Monkey 47 in a number of cities. That sounds similar to what we are doing with the Tableau Cinema Tour, so I showed him our Cinema Tour landing page and explained briefly who we are and what our mission is.

I asked him how he is organizing his data. Thomas revealed that he lives in Excel hell: “spreadsheets with thousands of rows and way too many columns”. This also sounded familiar. I opened up our Superstore.xlsx in Excel and asked: “Do your Excel sheets look like this?” Thomas replied: “Yes!”

Here we go! I drag’n’dropped the file on my Tableau desktop icon and paced through a 7-minute-demo ending with an interactive dashboard. Thomas was flabbergasted. To polish things off, I showed him the interactive Twitter Sentiment Dashboard embedded in my blog. Thomas grabbed his jacket and gave me a business card, saying: “We need Tableau!”

Monkey 47 business card (back side)
Monkey 47 business card (back side)

This story was originally written for Tableau’s EMEA Sales Newsletter. I think it’s a good read for the holidays, and wish you all Merry Christmas!