Schlagwort: Data Strategy

Leadership: Stagnation Kills Your Business, 3 Times

Petronas Twin Towers
Petronas Twin Towers held the title of the tallest building in the world for six years (Flickr)

Your business has been thriving, and the pipeline is well filled with work? Don’t get comfortable too soon. Stagnation kicks in fast nowadays, and kills your business‘ innovation, its growth, and its people.

1. Innovation 

The speed of transformation that we are currently witnessing is challenging all of us to think differently. Every industry can be disrupted, there is no safe habour. So we need to deliver our products and services in a way that is relevant today, not yesterday.

Minor product iterations do not work forever. We need to prepare product revolutions, not just iterations on what we were selling since ten years. Establishing an innovation lab engages our employees to think in innovative ways and lift our business to new heights.

2. Growth

We need to adjust our business development plan to drive business growth. This should not be a static document. Customer and market definition are changing fast and we need to adopt this in our sales strategy and in our product development.

This does not work without data. Therefore we need to implement a data strategy. Our data strategy guides our entire business how to collect and analyze data, and how to generate the insights that we use for our decisions. If we do not take advantage of data, our competition will do.

3. People

Sushi Google DoodleIt is essential for leaders to recognize that you cannot possibly manage everything. We need to employ great people. But recruiting is not the end. We need to keep your talents, empower them, and motivate them to take initiative in their roles. Stagnation will rotate employees out of the company.

Offering free sushi and laundry service might impress our new hires from university. Experienced and independent employees will be more interested in career perspectives. We should establish personal development plans for everyone, not just for the designated managers.

Although it is important for leaders to set such a career framework, we should not forget to invest in your employees‘ training. Both, hard skills (technical) and soft skills (non-technical), are mandatory because they each play very important roles in the development of our employees.

In Summary

Yesterday’s innovations are tomorrow’s commodities. We need to invest in innovations, otherwise we will face disruptive competition. Creating insights from data is important for the iterative adjustments on our business development plan. Offering personal development plans and proper training avoids stagnation for your employees, which is causing brain drain.

This post is also published on LinkedIn.

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.

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

Data Strategy: Erstickt Innovation zwischen Berichtswesen und Data Discovery?

Abbildung 4: Interaktives Dashboard zur Darstellung von variablen Abhängigkeiten in Tableau
Abbildung 4: Interaktives Dashboard zur Darstellung von variablen Abhängigkeiten mit TensorFlow in Tableau

Der erste Schritt auf dem Weg zu besserer Entscheidungsfindung im Unternehmen, ist zu verstehen, wie gute (oder schlechte) Entscheidungen zustande kamen. Genau wie manche Unternehmen formale Prozesse für Aktivitäten haben, wie z. B. What-if-Analysen, prädiktive Wartung und Bestimmung von Abhängigkeiten in Korrelationen (siehe Abbildung 4), so müssen sie formale Prüfprozesse für Entscheidungen im gesamten Unternehmen einführen. Dies soll jedoch keinesfalls dazu dienen, die an schlechten Entscheidungen Beteiligten zu bestrafen, sondern den Entscheidungsfindungsprozess und -stil des Unternehmens im Allgemeinen verbessern.

Die Rolle der IT nähert sich hierbei wieder ihren Wurzeln an und statt eine Berichtefabrik für den Rest des Unternehmens zu unterhalten, wird die IT wieder zum Dienstleister und Partner, der die Infrastruktur für eine Data Discovery bereitstellt. IT-Mitarbeiter werden entlastet und erhalten den Freiraum, ihre professionelle Energie und Kreativität in den Dienst der Innovation zu stellen, und die Mitarbeiter in den Abteilungen sehen ihre Datenfragen nicht am Flaschenhals Berichtswesen verhungern. Nur so lassen sich die Investitionen in Business Intelligence und Analytics optimal in den Dienst der strategischen Ziele des Unternehmens stellen.

Abbildung 5: Anforderungen, Fähigkeiten und Ziele einer Datenstrategie (TC17-Präsentation)
Abbildung 5: Anforderungen, Fähigkeiten und Ziele einer Datenstrategie (TC17-Präsentation)

Moderne Unternehmen sehen sich vielen analytischen Anforderungen (siehe Abbildung 5) gegenüber, und diese Anforderungen werden unweigerlich schneller wachsen, als Unternehmen sie bedienen können. Es ist daher unerlässlich, Analytics als lebenswichtigen Teil der eigenen Datenstrategie zu verstehen und entsprechend zu planen.

Dabei ist ein umfassender Betrachtungswinkel sinnvoll, denn die wachsende Nachfrage nach Analysen und Erkenntnissen wird mehr und mehr von den kundenbezogenen Abteilungen wie Marketing oder Support ausgehen. Dementsprechend wird auch das Budget für Analytics verstärkt aus diesen Abteilungen kommen, statt aus einem zentralisierten IT- oder BI-Budget. Dort, wo viele Kundendaten vorhanden sind, wird der CMO bald mehr für Analytics ausgeben als der CIO. Und dort, wo Mitarbeiter über gut integrierte, intuitive Werkzeuge für komplexe Analysen verfügen, können gute Instinkte und datenbasierte Entscheidungen Hand in Hand für den Erfolg sorgen.

Dieser Beitrag ist der fünfte Teil der Datenstrategie-Serie:

Teil 1: Die Notwendigkeit einer modernen Datenstrategie im Zuge der digitalen Transformation
Teil 2: Steigern smarte Erkenntnisse den Business Impact?
Teil 3: 10 BI & Analytics Trends, die in keiner Datenstrategie fehlen dürfen
Teil 4: Wie unterstützen Analysen Ihre Entscheidungsfindung?
Teil 5: Erstickt Innovation zwischen Berichtswesen und Data Discovery?

TC17 Data Strategy Title Slide Möchten Sie mehr zu den neuesten Trends im Bereich Datenstrategie erfahren? Dann freue ich mich, wenn Sie an meinem Vortrag „Building an Enterprise Big Data & Advanced Analytics Strategy“ auf unserer Tableau Conference TC17 (9.-12. Okt., Las Vegas) teilnehmen.