Using the Data Management Canvas you determine all measures, tools and people required to improve data quality and data availability while at the same time ensuring data privacy and data security. With this visual collaboration tool you have a structured template allowing you to bring together all responsible people and departments.
The Data Management Canvas complements the Data Strategy Canvas as a tool to concretize the data strategy. Whereas the Data Strategy Canvas is focusing on the utilization of data as part of concrete use cases, the Data Management Canvas deals with the following fundamental questions:
Depending on the respective data landscape (please also see corresponding canvas) different concrete measures can be derived which in turn have different requirements regarding necessary staff and tools.
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.
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 properly utilize 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 breach or suffer economical damage because of data leaks. Do not treat your data like a waste product, instead rather consider it as a corporate asset that 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.
Step 1 of 4
Data privacy ensures that the rights of customers, employees, and partners regarding their personal data are protected by…
Whilst data privacy deals with the legal aspect of data and is limited to personal data, data security targets all data and particularly defines technical and organizational measures. Adequate data privacy always demands sufficient data security.
Step 2 of 4
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:
Step 3 of 4
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.
Step 4 of 4
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 (data governance) 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 claims neither to be complete nor correct in every detail.
Here you can find further documentation:
Data Thinker Group (LinkedIn)
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