Sharing knowledge and double it.

Results Analytics Priorities – Web Poll 12/2016

„Easy to understand“, „interactive dynamic dashboards“ und „Multiple Sources“…

Reports und Analysen müssen
– einfach zu erfassen sein, eine
– interaktive Komponente besitzen und darüber hinaus mehr
– unterschiedliche Datenquellen einbeziehen. Das ist das Ergebnis der Umfrage auf TheDigitalization.com 12/2016 (siehe Grafik in Survey).

Data Integration

Interessant ist der Aspekt der Integration unterschiedlicher Datenquellen. Sind die vorhandenen ETL Prozesse schnell und flexibel genug, um den Informationshunger gerecht zu werden? Oder kommen diesbezüglich Änderungen auf die Reporting- und Analyseteams der Unternehmen zu?

Die Faktoren Business Support und datenbasierte Entscheidungen sind als weniger wichtig eingeschätzt worden. Erstaunlich, denn der Zweck von Analytics sind so genannte „Business Values“ (data driven decisions und business support).

Daraus ergibt sich die Frage, ob die funktionale Ausprägung wie einfache interaktive Darstellung und das Einbinden verschiedener Datenquellen mit dem Business Values (business support + data driven decisions) in Zusammenhang stehen. Und wenn ja, wie ist dieser Zusammenhang zu bewerten? Diese Erörterung lässt sich aber auf Basis des Polls nicht vornehmen.

Die Auswahl der 5 Schlagworte wurden mittels Twitter-Stichproben über die Hashtags
#digitalization, #smartanalytics, #nosql und #bigdata vorgenommen (Anfang 2016).

Die circa 100.000 Tweets sind mit einem ELK-Stack (elastic search, logstash und kibana), erfasst, analysiert und in Beziehung zueinander gesetzt worden.

Die Ergebnisse der Umfrage kamen durch 106 Teilnehmer zustande.

Data Scientists and business value…

Business direction moves you ahead

Digitalization and analytics is somehow a pair of shoes. You probably need it, when you move forward with your business. Often the point of having data-science-value is underestimated or has a technology-over-exposure.

David Stephenson (pls. see link below) compiled an easy-to-understand summary about the business value of data scientists.

The article points out the underlying problem. And that’s the beauty of this article, without referring to any tool, product or solution.

David is figuring exactly out why the linkage between data science and business value often isn’t given or is missing.

The article is a big contribution for those
– who want to start with analytics (which is differently from BI) or
– already started with analytics and missing results.

In a way Daivid points out how to circumvent of running into typical analytics mishaps – not focussing on technical details.

David raised the question of proven business value in a room filled with 150 data scientists – congratulations!

 

https://medium.com/@Stephenson_Data/150-data-scientists-and-still-no-business-value-65a997fca24f#.cpj7v51jv

NoSQL Datenbanken sind auf dem Vormarsch …

… und das aus gutem Grund. Der Beitrag geht der Klärung verschiedener Fragen zum Thema NoSQL Datenbanken nach. Zum Beispiel, wie unterscheiden sich NoSQL Datenbanken von relationalen Datenbanken und warum steigt geraden jetzt die Bedeutung von NoSQL Datenbanken.

Neben einem kurzen Überblick zu Typen / Arten von NoSQL Datenbanken werden Themen wie Transaktionssicherheit, Datenmodellierung, Integration relationaler Schemata und Skalierbarkeit beschrieben.

Hier ist der Link zum Beitrag

http://it-governance.dpunkt.de/archiv/2015/beitrag2015-22-b5.php

What is the digital economy?

Accentures Mark Knickrehm, Bruno Berthon and Paul Daugherty made a pass to digital growth by defining digital economy and pinning down some other remarkable ideas.

Quote:

“The digital economy is the share of total economic output derived from a number of broad “digital” inputs. These digital inputs include digital skills, digital equipment (hardware, software and communications equipment) and the intermediate digital goods and services used in production. Such broad measures reflect the foundations of the digital economy.”

Please find the link to the article below.

https://www.accenture.com/t20160119T085449__w__/us-en/_acnmedia/PDF-4/Accenture-Strategy-Digital-Disruption-Growth-Multiplier.pdf#zoom=50

Results Q4/2015 Poll – Corporate Strategy Analytics, the implementation and practise

The Q4/15 web-poll tried to catch a glimpse about the concurrence between
– corporate analytics strategy the
– implementation of the strategy and the
– practice and expectation of so called unstructured data?

Results of the poll showing

1.    Analytics is a strategic cornerstone. The analytics implementation roadmap raises some points, as the majority of participants don’t see a realistic and visible scope.

2.    The systematic and machine based analysis of unstructured data (a.k.a. NoSQL data) isn’t really practiced and usually has no decision-making role.

3.    A significant change in the role of unstructured data analysis in the near future is expected.

Why the linkage between analytics strategy and unstructured data?

Looking from a corporate perspective analytics is an investment and should deliver business values. Analytics are typically helpful to
– identify cost savings or/and increase revenues,
– gain understanding in the substantial re-design of business processes,
– find out or invent completely new/different business models…
… in other words to compete.

In the same way corporate applications mostly provide structured data and usually running in the range of the applications in a given situation. In addition the focus on data is rather processing than generating data-driven decisions or creating new business based on data. If companies want to move ahead in a qualified and reasonable pace it comes down to other data like web-based, legacy systems embedding, supplier data, customer centric data or data which are beyond the line right now. Each sort of data carries its own structure – and that’s the point. They don’t fitting into a given and well-balanced corporate data model, therefore they’re called unstructured.

If companies want to integrate the above type of data into
– research,
– sales,
– customer support or
– product development or
– other departments
the traditional way it would take years to put them into corporate processing framework.
NoSQL data technologies provide capabilities to integrate, query and process unstructured data very easy and sufficiently into corporate data life-cycle.
That’s why a serious analytics strategy should have a reasonable NoSQL element.