Join my Social Media Analytics sessions at Tableau Conference #data18

Are you ready for Tableau Conference 2018? Don’t miss my Social Media Analytics sessions!

Why do we need Social Media Analytics?

Social Media Analytics transforms raw data from social media platforms into insight, which in turn leads to new business value.

What will your learn in this sessions?

Once you dive into Social Media Analytics, how do you bring it to the next level? Social data can offer powerful insights right away. In this session, you will learn how to build a mature social data program from that foundation and strategies for scaling a social data programme, as well as how to connect directly 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.

Where and when are the sessions?

Do you want to learn more about Social Media Analytics with Tableau? Meet me at the 2018 Tableau Conferences in London or New Orleans and join my sessions:

Anything to prepare?

Yes, I’m glad that you ask! Kindly take this survey if you plan to attend the session: https://goo.gl/forms/MlfsatGptvR0X6Yc2

Digitale Banken: Welche Digitalisierungstrends bewegen die Finanzbranche 2018?

Immersive und interaktive Analyse von Finanzdaten mit Argumented Reality
Immersive und interaktive Analyse von Finanzdaten mit Argumented Reality (Blockchain-Dashboard)

Jedes Jahr (2015, 2016, 2017 und 2018) stelle ich Digitalisierungstrends vor, die der Finanzbranche ein großes Potenzial bieten. Dabei geht es vor allem um einen Überblick darüber, welche Trends und Technologien zukünftig eine größere Rolle spielen werden oder könnten.

Im Folgenden habe ich die fünf Digitalisierungstrends identifiziert, die für Banken und Versicherungen in Zukunft besonders spannend sein dürften:

1. Maschine Learning

Maschine Learning und Deep Learning werden im Investment Banking angewandt, um Unternehmensbewertungen schneller und zuverlässiger durchzuführen. Mehr Daten denn je können hinzugezogen werden. Eine Gewichtung der Daten erfolgt komplett autonom. Da manuelle Analyse weitgehend entfällt, werden Entscheidungsprozesse drastisch beschleunigt. Investoren, die mit konventionellen Werkzeugen arbeiten, haben das Nachsehen.

2. Künstliche Intelligenz

Durch Künstliche Intelligenz gesteuerte Chatbots vermitteln den Kunden eine menschlichen-ähnliche Betreuung. Chatbots werden darüber hinaus in existierende Cloud-basierende Assistenten, wie Alexa oder Siri, eingebunden und sind in der Lage mittels Natural Language Processing, auch komplexere Anfragen zu verstehen. Recommender-Systeme liefern maßgeschneiderte Lösungen, die speziell auf die Bedürfnisse der Kunden abgestimmt sind.

3. Internet of Things

Wearables und in Kleidung eingearbeitete Sensoren (Internet of Things, IoT) liefern ausreichend Daten, um den Lebensstil der Kunden vollständig zu vermessen. Dadurch können individuelle Raten für Versicherungen und Finanzprodukte berechnet werden. Außerdem bieten die IoT-Daten eine weitere Datenquelle für die Recommender-Systeme.

4. Blockchain

Verträge werden kostengünstig, fälschungssicher und irreversibel in der Blockchain gespeichert. Die Blockchain dienst sogenannten Smart Contracts als dezentrale Datenbank. Darüber hinaus liefern Blockchain-Implementierungen, wie Ethereum, das Ausführen von Logik, die beispielsweise monatliche Zahlungen prüfen und ggf. auch die Erfüllung von Vertragsbestandteilen (z.B. im Schadenfall) steuern.

5. Argumented Reality

Arbeitsplätze werden mit Technik ausgestattet, die Argumented Reality ermöglicht. Lösungen wie Microsoft’s Hololense ermöglichen Analysten und Händlern eine immersive und interaktive Analyse von Finanzdaten in Echtzeit. Insbesondere fällt dadurch auch die Zusammenarbeit mit Kollegen leichter, da Plattformen zur visuellen Kollaboration traditionelle Meetings weitgehend ablösen.

Welcher ist der 6. Trend?

Helfen Sie den 6. Digitalisierungstrend zu benennen? Nehmen Sie hierzu an der Twitter-Umfrage teil. Selbstverständlich freue ich mich auch über Kommentare und eine spannende Diskussion.

Hyper Kickoff Event: 5th Frankfurt Analytics + Tableau User Group Meetup

Tableau Hyperfest: Hyper Kickoff Event at Tableau's Frankfurt office
Tableau Hyperfest: Hyper Kickoff Event at Tableau’s Frankfurt office

We’d like to invite you to the 5th Frankfurt Analytics + Tableau User Group Meetup.

Join us for the global launch of Tableau’s super fast data engine, Hyper! Hyper brings faster data refreshes and query performance to Tableau extracts, plus increased scalability in a platform-wide update.

This is your opportunity to get to know the Hyper dev team, hear from Tableau beta customers about their hands-on Hyper experience, and participate in live Q&A. Best of all, learn more about Hyper’s patent-pending technology as well as some of the other features headed your way in 10.5. (Viz in Tooltip, anyone?)

Tableau is hosting the Hyperfest meetup – come and celebrate with the community and the world on the upcoming release of Hyper. In addition to the Hyper presentation, we will also have food, drinks and Tableau swag, so don’t miss it!

-> Sign Up <-

Tableau Hyperfest meetup event page
Sign up for free at the Hyperfest meetup event page

Agenda

9:00pm: Doors Open

9:30pm: Presentations:

10:30pm: Drinks & Networking

11:00pm: Live Hyperfest Viewing Party

Midnight: Event Concludes

Livestream: Follow us on Twitter @FraAnalytics and check for the livestream and additional content!

Feedback and ideas: Let us know if you’d like to discuss a particular topic or if you want to become one of our future speakers – email or twitter.

5 Takeaways from Tableau’s Hybrid Transactional/Analytical Processing

What makes Hyper so fast?
The Future of Enterprise Analytics: Hyper can handle both OLTP and OLAP simultaneously. In the future it will address NoSQL and graph workloads.

1. What is Hyper’s key benefit?

Hyper is a Hybrid transactional/analytical processing (HTAP) database system and replaces Tableau Data Extracts (TDE). The change will be mostly transparent for end users, other than everything being faster. Hyper significantly improves extract refresh times, query times and overall performance.

2. What is Hybrid transactional/analytical processing?

As defined by Gartner:

Hybrid transaction/analytical processing (HTAP) is an emerging application architecture that “breaks the wall” between transaction processing and analytics. It enables more informed and “in business real time” decision making.

The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load (ETL) data staging and excessive resource consumption due to maintaining two separate information systems.

3. Does Hyper satisfy the ACID properties?

Hyper, initially developed at the Technical University of Munich and acquired by Tableau in 2016, can handle both OLTP and OLAP simultaneously. Hyper possesses the rare quality of being able to handle data updates and insertions at the same time as queries by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. Hyper is an in-memory database that guarantees the ACID properties (Atomicity, Consistency, Isolation, Durability) of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot.

4. What makes Hyper so fast?

The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100,000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. This would support real-time streaming of data in future releases of Tableau. These performance increases come from the nature of the Hyper data structures, but also from smart use of contemporary hardware technology, and particularly nvRam memory. Additional cores provide a linear increment in performance.

5. What does this mean for Tableau?

With Hyper now powering the Tableau platform, your organization will see faster extract creation and better query performance for large data sets. Since Hyper is designed to handle exceptionally large data sets, you can choose to extract your data based on what you need, not data volume limitations. Hyper improves performance for common computationally-intensive queries, like count distinct, calculated fields, and text field manipulations. This performance boost will improve your entire Enterprise Analytics workflow.

Join our “The Future of Enterprise Analytics” events and get a sneak peak at upcoming features and the Tableau Roadmap: 14th of November in Düsseldorf and 6th of December in Frankfurt.

[Update 20 Dec 2017] Hyper Kickoff Event: Join us for the Hyper Kickoff Event at the 18th of January 2018 in Tableau’s Frankfurt Office.

Data Science Toolbox: How to use Julia with Tableau

R allows Tableau to execute Julia code on the fly
R allows Tableau to execute Julia code on the fly

Michael, a data scientist, who is working for a German railway and logistics company, recently told me during an FATUG Meetup that he loves Tableau’s R and Pyhton integration. As he continued, he raised the raised the question for using functions they have written in Julia. Julia, a high-level dynamic programming language for high-performance numerical analysis, is an integral part of newly developed data strategy in the Michael’s organization.

Tableau, however, does not come with native support for Julia. I didn’t want to keep Michael’s team down and was looking for an alternative way to integrate Julia with Tableau.

This solution is working flawless in a production environment since several months. In this tutorial I’m going to walk you through the installation and connecting Tableau with R and Julia. I will also give you an example of calling a Julia statement from Tableau to calculate the sphere volume.

1. Install Julia and add PATH variable

You can download Julia from julialang.org. Add Julia’s installation path to the PATH environment variable.

2. Install R, XRJulia and RServe

You can download base R from r-project.org. Next, invoke R from the terminal to install the XRJulia and the RServe packages:

> install.packages("XRJulia")
> install.packages("Rserve")

XRJulia provides an interface from R to Julia. RServe is a TCP/IP server which allows Tableau to use facilities of R.

3. Load libraries and start RServe

After packages are successfully installed, we load them and run RServe:

> library(XRJulia)
> library(Rserve)
> Rserve()

Make sure to repeat this step everytime you restart your R session.

4. Connecting Tableau to RServe

Now let’s open the Help menu in Tableau Desktop and choose Settings and Performance >Manage External Service connection to open the External Service Connection dialog box:

TC17 External Service Connection

Enter a server name using a domain or an IP address and specify a port. Port 6311 is the default port used by Rserve. Take a look on my R tutorial to learn more about Tableau’s R integration.

5. Adding Julia code to a Calculated Field

You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Julia code in Tableau, such as this simple snippet that calculates sphere volume:

6. Use Calculated Field in Tableau

You can now use your Julia calculation as an alternate Calculated Field in your Tableau worksheet:

Using Julia within calculations in Tableau (click to enlarge)
Using Julia calculations within Tableau (click to enlarge)

Feel free to download the Tableau Packaged Workbook (twbx) here.

Further reading: Mastering Julia