Sven Löffler
23. December 2016 0

Big Data Self Services – Digitization. Simply. Make it happen.

With the motto: „Digitization. Simply. Make it happen“ Telekom has started at this year’s CeBIT. Within the last months, it has shown that topics like Connected Car and Smart Factory become more and more important. For the realization, Telekom has laid two milestones: The Open Telekom Cloud and Microsoft Azure Deutschland. These platforms enable enterprises to easily extend their IT infrastructure in the Cloud. This enables them to implement their innovative ideas in a secure, scalable and high-performance environment.

Big Data Self-Services

For the realization of such projects, suitable data management and analysis tools are essential. The experience has also shown that Data Scientists are not sufficient to answer the difficult questions asked by companies. It often lacks in frequent know-how about the company or domain expertise. Accordingly the demand for self-services, which can be described as self-explanatory applications, is growing. These self-services are designed to enable business users to apply analytical procedures to data and gain rapid insights.

SAS Big Data Analytics Forum

No either-or

At this year’s SAS Big Data Analytics Forum on the 22nd November, Self-Services have been one of the hottest topics. SAS even speaks about the introduction of an analytical culture in companies, the ability of every employee to perform big data analytics. Such an approach has the advantage that every employee is able to try out new ideas and thus promote innovation and disruption in the company.
In order to enable the use of SAS for both, data scientists and business experts, SAS pursues the philosophy “Extend & Embrace”. Data Scientists can use SAS and stay within their familiar environment, e.g. Python or Scala, and business users are provided with graphical interfaces with which they can access for example Hadoop.

Big Data process model

Another important topic is the approach to the implementation of Big Data Analytics projects. A traditional plan-build-run approach is no longer sufficient as data changes with time. Business logic including algorithms and trained models must therefore be regularly adapted and improved. At the conference a process model with two phases is presented:

1. Discovery: Prepare, Explore, Model, Ask
2. Deployment: Implement, Act, Evaluate

Big Data operating model
Source: SAS Big Data Analytics Forum – Bimodal operating model for Big Data, 11/22/2016

For the implementation of this process model a Big Data Lab is suitable for the discovery phase to test and experiment in a very agile environment. The so-called “Analytics Factory” realizes the deployment. This factory integrates the developed models into the business transactions and thus generates added value for the company in the form of sales increases, cost reductions/efficiency increases or new products and business models.

SAS Big Data Lab

To enable companies to make a simple and fast start, T-Systems and SAS offer a SAS Big Data Quickstart Offering as part of their partnership. This complete offer enables beginners to gain experience and to test potential applications effectively, which can later be turned into production. The environment includes the infrastructure for storing, managing and quickly analyzing data, enabling the use of technologies such as Hadoop and In-Memory processing without the need for deep technical know-how.

Now it just means “Big Data. Simple. Make it happen.”

Happy Data,
Sven Löffler

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