Data Management complements your Data Strategy by directing your attention to the following questions:
There is something like an area of conflict between the defensive topics of Data Privacy and Data Security, in contrast to the offensive topics like Data Quality and Data Availability:
For more information, see Data Strategy Design.
By the way, you can order a print version (DIN A0) from Stattys.
Data Management is a laborious and virtually never-ending task. Hence, you should consider carefully which data you would like to capture at all. An individual Data Strategy helps you when choosing the data sources.
On the other hand, you cannot realize your data’s value without active Data Management. On top of that and among other potential conflicts, you are putting yourself at risk to get prosecuted because of a Data Security breaches or to suffer economical damage because of Data Leaks. Do not treat your data like a waste-product, instead rather consider it as a corporate asset which needs to be maintained as well as protected.
Select a data source from your Data Strategy or your Data Landscape and place this card in the middle of the template where you see the term Data Landscape. Then you run through the four areas of Data Management (Data Privacy, Data Security, Data Availability and Data Quality) from the outer to the inner section by working through the three areas Measures, People and Tools.
Data Privacy ensures that the rights of customers, employees and partners regarding their personal data are protected by…
Whilst Data Privacy is concerned with the legal aspect of data and limited to personal data, Data Security targets all data and especially defines technical and organizational measures. Adequate Data Privacy always demands for sufficient Data Security.
Data Security aims at minimizing the risk of data loss, data manipulation or the misuse of data. In order to guarantee that, the following is required:
On the opposite side of Data Security, there is Data Availability. Data Availability is dealing with how employees can get access to all necessary data in an easy and fast way. Means for that matter are:
Data Availability can only be successful if the Data Quality is sufficient. If employees need to deal with error prone, incomplete or sparse data, they cannot draw enough value from the data.
Data Quality management is among other things about how to consistently ensure in the long-term that data is up-to-date as well as to continuously retain and improve the variety, completeness, correctness and representativeness of data. The following means ensure this:
Finally, you should check your Data Management strategy regarding completeness and consistency:
Eventually you can transfer the results of your Data Management template into your Data Strategy.
The provision of the Data Management canvas does not represent any kind of legal advice and does not claim to be neither complete nor correct in every detail.
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