Dear Data Scientists – ease your job!

Data Science Framework – YUNA elements now available for download

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Reasons why data science projects are not always successfull

10 Gründe für YUNA

How to build data science platforms – Part 5: Data visualization and reliable results

How to build data science platforms – Part 4: Database scalability and business models

How to Build Analytics Platforms – Part 3: Customizable Workflows and Dashboards

How to Build Data Science Platforms – Part 2: Intelligent User and Role Concept

What does a modern analytics platform need to offer companies real added value?

Why is the administration of user and role rights a factor not to be underestimated when using analytics platforms? In the previous article, we showed how important an intuitive user interface and an open user group concept are for the company-wide use of data science. Now we are developing the idea further – without intelligent user and role management, this concept simply cannot work.

Rights and roles as the first step to new functions!

As a rule, platforms for the use of data science have an almost incomprehensible database with which the respective analyses work and from which reports are generated then. Analytics platforms need the ability to assign individual rights at user and group level in order to process the database efficiently. Only in this way, it is possible to use the capabilities in a targeted manner and at the same time guarantee the security of the database. Not every individual needs complete access. In addition, it is not helpful to equip a single user group with all the extensive admin rights.

It would be even more intelligent if user and role rights would refer to individual components, such as filters or result views. In this way, a single view can display different information without having to provide a separate view for each group. Analytics projects can therefore be implemented more quickly, as they can be reused and extended by adding new components and simultaneously giving new users access via the respective roles without stopping ongoing operations. Thinking one step further again, additional security precautions can be built in when different roles on a platform work together on a project. Role X could then set and customize the analysis. Role Y could see the analysis script, but could not influence it, however, it could process the results accordingly. This point becomes even more important in the area of data discovery, i.e. the recognition of patterns and correlations.

Another important point in the context of role concepts is meaningful integration into existing structures. Ideally, the corresponding analytics platforms are also technically structured in such a way that they function as a supplement to an existing security concept of the database. This means that they can be easily inserted into the cycle of authentification, authorization and authentication. It shows that the “user and role concept” point, intelligently implemented, is the basis for other important factors in the use of analytics platforms.

CONCLUSION: Different user groups contribute to using data science as profitably and company-wide as possible. The next logical step is an intelligent concept consisting of granular and gradual authorizations on the role and component level and thus becomes the basis for other functions.

Outlook

In the next part: How important are customizable workflows and flexible, reusable dashboards?

How to build analytics platforms – Part 1: UI & Teams

Are you [R]eady for production? The Data Science Event 2019 – Register now!

[R]eady for production – The Data Science Event 2019. In the upcoming week, eoda and RStudio invite the German speaking R-community to the event for the productive use of R. On 13th June 2019, you can learn how to seamlessly implement your analysis solutions with the optimal IT infrastructure into your business processes. Moreover, exciting topics like Shiny App development, Plumber-APIs and much more will be in focus on this day.

Discover best practice approaches in productive data science architectures, RStudio solutions for the professional application of R, and get important impulses for your own analysis environment with the help of experienced experts from RStudio and eoda.

Inspiring networking, exclusive insights into RStudio’s leading products and the know-how of eoda as one of the leading R integrators make this event obligatory for Data Scientists and Solution Engineers.

Look forward to a practical success story, first-hand RStudio news and the BarCamp in the afternoon, where you will have the opportunity to discuss exciting infrastructure topics of your choice with eoda and RStudio experts in small groups.

What you can expect: Agenda of the Data Science Event 2019

09:30-10:00 | Registrierung
10:00-10:45 | R in Produktivumgebungen: Das richtige Zusammenspiel zwischen Data Science und IT
10:45-11:00 | Success Story: Aufbau einer Analyseumgebung bei der REWE International AG
12:00-13:00 | Mittagessen
13:00-14:00 | RStudio what’s next mit Andrie de Vries (Solutions Engineer | RStudio)
14:00-14:30 | Kaffee & Snacks
14:30-16:30 | BarCamp: [R]eady for production

Location: Historischer Festsaal Frankfurt am Main

Register now and secure your free participation in the Data Science Event 2019 of eoda and RStudio.

To the registration.