Data analysis and authorization management

Why data and authorization management? Answers from practice.

Hand holding modern tablet or mobile device with analytics dashboard for sales, marketing, accounting, controlling department to check revenue, sales and business KPIsSince switching from file folders to digital storage, companies have often been driven by the motto that everything is stored and stored.

Since then, this mentality has been consistently in the heads and is being passed on to the company.

As a result, it is now considered a constant in file server data that the volume of data grows each year by 20% - 40%. The data in their structures usually exist for many years in an unchanged form. On the file server must be cleaned up at some point, if you do not want to lose the overview one day completely. After all, in addition to the data that you really still use productively, especially the part of the data that is without any productive value for the company grows.

The Data Mountain: 2 / 3 Dark Data,
much of the remainder of RED Data.

In the past, strategies were regularly developed on how to get more and more data into the storage systems in less time. The problem is that it until very few cases there are real "exit" strategies for the data, that is, regulations that determine how and, above all, when data leaves the system again. As a result, have become average in the companies between 5.000 and up to 25.000 files per capita to a true one Datenberg accumulated.

If one analyzes these unstructured data, the so-called Dark Data (data whose potential use is completely unknown to the company) two-thirds of the total data. Of the remaining third of the labeled or qualified data, roughly half are to be classified as RED Data, ie data thatRedundant, Obsolet or Trivial "are.

Who wants to clean up the file server today has two main problems: Lack of transparency and unclear responsibilities

Create transparency as a basis for decision-making
The biggest problem with the unstructured data on the file server is the missing information. While every dataset can be tracked and evaluated in a database, that keeps Filesystem only very few helpful information about the data located there, In contrast, the file system is particularly easy to use and almost "random". It is usually limited only by the finite storage space. So how should one find out which data are relevant?

Create Responsibilities: Integrate Data Owner
In the full directories usually also no employee knows so well. It would be helpful, the Data Owner to ask for support, but that is not known. So what to do?

In order to solve these problems, data must first be collected for the analytical examination of the current situation and evaluated clearly. On the basis of the many projects we carry out, we know which parameters are relevant in order to optimally prepare the subsequent steps. These include, for example, the movement data of users about whom Identify and discard "dead" shares to let. Other parameters can be used to make a potential Identify data owners for specific directories or at least to identify users who use them regularly.

Relevant information is the prerequisite for making meaningful decisions when dealing with data.

In addition, the specialist departments must be involved. Because often sick projects because the process of voting runs badly. Simply asking the departments to design something new and clean up old structures can only fail if there are no reasonable data for the departments to make decisions on. In addition to the provision of information, the departments should additionally work by a easy to use interface which they can make changes directly in their respective area.

Of course, a one-time cleaning up makes only limited sense, if not at the same time the course is set for the future to handle the data differently. These include the topics:

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