Welcome to the tech section of my blog, where I share my thoughts and insights on the latest developments in the world of digital, data, and AI. As someone who is passionate about the transformative power of technology, I believe that staying up to date with the latest trends and innovations is crucial to success in this field.
In this section, you’ll find a wide range of articles and posts on topics such as digital transformation, data privacy, artificial intelligence, and more. I’ll share my perspectives on current events, emerging trends, and the future of technology, with a focus on how these developments will impact businesses, society, and individuals.
Whether you’re a technology professional, business leader, or just someone interested in the world of tech, you’ll find plenty of valuable insights and knowledge in these posts. My goal is to make complex topics accessible and easy to understand so that everyone can benefit from the incredible advances happening in the tech world today.
You can also find a lot of technical product reviews on my Amazon reviews page. These are mostly in German.
So, join me on this journey as we explore the cutting-edge world of digital, data, and AI together, and discover new ways to harness the power of technology for a better tomorrow.
Interactive dashboard displaying the most important variables for determining the Sales measure in Tableau 10.0 (click screenshot to enlarge)
During the Q&A session of a recent talk on Data Strategy, I was challenged with a rather technical question: I was asked how to identify the variables that are heavily influencing a certain measure – with an interactive solution that matches a modern data strategy as suggested in my presentation.
Of course, this could be done by executing a script. The result however would be static and it would be not convenient for a Business Analyst to run it over and over again. Instead of applying a script every time the data changes, it would be much more innovative to get the answer immediately with every data update or interactivity such as a changed filter.
So why not solve this with Tableau? The magic underneath this easy-to-use Tableau dashboard is a nifty R script, embedded in a calculated field. This script calls a statistical method known as Random Forest, a sophisticated machine learning technique used to rank the importance of variables as described in Leo Breiman’s original paper.
The Tableau Packaged Workbook (twbx) is available here. Do you have more ideas or use cases? Feel free to leave a comment or send me an email: aloth@tableau.com
Three core elements of a Data Strategy Plan for the telecommunications industry
Does your enterprise manage data as a corporate asset? Many companies don’t. Here’s how to get started with the three core elements for your Data Strategy Plan.
1. Data
The ongoing digital transformation of our environment has created an enormous amount of data about just every aspect of what we are doing. Every website we visit, every link we click, every search engine term, every purchase is recorded associated either with our online identity if we have logged in, or in a system that saves our session through cookies or digital fingerprinting.
Once gathered, data across the enterprise are typically stored in silos belonging to business functions (vertical silos), business units (horizontal silos), or even different projects within the same division (segmented silo). Making this data a valuable and useful asset will require to break down the silos. This may not be so easy to accomplish, due to ownership issues, regulatory concerns, and governance practices.
2. Analytics
Collecting data alone does not generate value. The completeness of your Advanced Analytics stack and the complexity of the applied models determine how „smart“ your insights will be and therefore how deep the level of business impact will get. Prescriptive and Semantic Analytics might be tough to implement, especially, if you need to find a way to classify semi-structured data, such as social media streams.
While you look to apply sophisticated models, you should not forget to collect the low-hanging fruits and see if you put in all your quantitative information, such as revenue data, to scale out your diagnostic capabilities.
3. Decision-support Tools
Now you need intuitive tools that integrate data into sustainable processes and apply your analytic models to generate information that can be used for your business decisions. Depending on the stakeholder, the outcome might be presented as a self-service web front end, such as a Network Performance Monitor that allows predictive maintenance, or an Executive dashboard that provides your CFO the latest numbers for upcoming M&A.
An important consideration for your decision-support tools is user acceptance. Decision-support tools should be easy to use and should not make processes more complicated. Instead, consider adding buttons that trigger actions directly from the user interface.
This content is part of the session „3 Essential Components to building a Data Strategy“ that I delivered at Telekom Big Data Days 2016. Have a look at my upcoming sessions!
Twitter Sentiment Analysis: klicken, um interaktives Dashboard zu öffnen
Ist der Einsatz sozialer Netze in Ihrem Unternehmen auf Marketing beschränkt, und lässt dadurch Chancen ungenutzt?
Noch immer schöpfen viele Unternehmen in Deutschland die Möglichkeiten von Social Media nur unzureichend aus. Die meisten Firmen nutzen Social Media lediglich als Marketinginstrument, senden zum Beispiel in Intervallen die gleichen Inhalte. Wesentlich weniger Unternehmen setzen Social Media dagegen in der externen Kommunikation, in Forschung und Entwicklung, zu Vertriebszwecken, oder im Kundenservice ein.
Nachfolgend betrachten wir die Twitter-Kommunikation von vier Social-Media-affinen Unternehmen etwas näher, und zeigen anhand sieben Fragestellungen was sie anders machen und wo die übrigen Nachholbedarf haben.
1. Wann und wie werden Tweets gesendet?
Ein Blick auf das Histogram lässt auf reichlich Interaktion schließen (Tweets und Replies), während das Weiterverbreiten von Tweets (Retweets) eher sporadisch auftritt:
2. Wie umfangreich sind die Tweets?
Wie es scheint, reitzen die meisten Tweets die von Twitter vorgesehenen 140 Zeichen aus – oder sind zumindest nahe dran:
3. An welchen Wochentagen wird getweetet?
Am Wochenende lässt die Kommunikation via Twitter nach. Die Verteilung der Emotionen bleibt dabei gleich, unterscheidet sich aber von Unternehmen zu Unternehmen:
4. Zu welcher Tageszeit wird getweetet?
Auch nachts werden weniger Tweets verfasst. Bei Lufthansa kommt es dabei recht früh zu einem Anstieg durch Pendler-Tweets, etwas später tritt dieser Effekt bei der Deutschen Bahn ein:
5. Welche Art der Kommunikation herrscht vor?
Der hohe Anteil an Replies bei Telekom, Deutsche Bahn und Lufthansa impliziert, dass diese Unternehmen Twitter stark zum Dialog nutzen. Unter den Tweets der Deutsche Bank ist hingegen der Anteil an Retweets – insbesondere bei jenen mit Hashtag – deutlich höher, was auf einen höheren Nachrichtengehalt schließen lässt:
6. Welche User sind besonders aktiv?
Nun betrachten wir die Twitter-User, welche die entsprechend Twitter-Handles der Unternehmen besonders intensiv nutzen:
Je nach Emotion und Kontext ist es vor allem für das adressierte Unternehmen von Interesse rechtzeitig und angemessen zu reagieren. So lässt sich eine negative Stimmung frühzeitig relativieren, und so Schaden an der Marke abwenden. Positive Nachrichten können hingegen durch Weiterreichen als Multiplikator dienen.
Until now the sentiment package for R only worked with English text. Today, I released version 1.0 of the sentiment package on GitHub that features multi-language support. In order to perform sentiment analysis with German text, just add the parameter language="german" as shown in this example:
Demo showing Multi-Language Sentiment Analysis with English and German text
The new code allows you to add any language. So far, I started to prepare German sentiment files. French and Spanish sentiment files are on my to-do list.
The main goal of Advanced Analytics is to help organizations make smarter decisions for better business outcomes.
Only a few years ago, Advanced Analytics was based almost entirely on a complex tool chain and plenty of scripting in Gnuplot, Python and R. Today, Tableau enables us to analyze our data at the speed of thought, to connect to our data sources in seconds, to add dimensions and measures on the canvas by dragging and dropping, and to get insights faster than ever before.
However, R still comes in very handy when we want to enrich Tableau’s Visual Analytics approach with advanced features that enables us to ask questions along the entire Analytics stack:
Descriptive Analytics describes what happened, characterized by traditional business intelligence (BI). E.g. visualizations and dashboards to show profit per store, per product segment, or per region.
Diagnostic Analytics, which is also known as Business Analytics, looks into why something is happening, and is characterized by reports to further „slice and dice“ and drill-down data. It answers the questions raised by Descriptive Analytics, such as why did sales go down in a particular region.
Predictive analytics determines what might happen in future („What might happen?“), and needs larger domain expertise and tool set (i.e. Tableau + R). E.g. regression analysis, and forecasting which product segments are likely to perform better in next quarter.
Prescriptive Analytics identifies the actions required in order to influence particular outcome („What should I do?“). E.g. portfolio optimization, and recommendation engines to answer which customer segment shall be targeted next quarter to improve profitability.
Semantic Analytics examines data or content to identify the meaning („What does it mean?“), and suggests what you are looking for and provides a richer response. E.g. sentiment analysis and Latent Semantic Indexing to understand social media streams.
Do you want to learn more about Advanced Analytics and how to leverage Tableau with R? Meet me at the Tableau Conference in Munich (5-7 July) where I deliver the session „R You Ready For Advanced Analytics“.
„Analytics is essential for any competitive strategy“ (further reading: data science + strategy)
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