Increase data quality as well as data availability and ensure data privacy as well as data security.
Data management complements your data strategy by directing your attention to the following questions:
- What is required for data privacy?
- How do we ensure data security?
- How do we increase data quality?
- How do we enable a high level of data availability?
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:
- If you improve data quality by for example buying additional data about your customers or by starting to capture these, you are potentially at risk to breach data privacy regulations.
- If you maximize data availability by for example providing all of your employees with access to all data, you are potentially putting data security at risk because employees could pass sensible data to a third party.
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 breach 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.
- Use green cards for measures, people and tools which are already available in your company.
- Red cards are for resources which are not yet available.
- Yellow means that there are already plans or that there is something in the works respectively.
Data privacy ensures that the rights of customers, employees and partners regarding their personal data are protected by…
- …measures ensuring for example that only those personal data get stored which are necessary (Data Austerity) and that this data does not get used for purposes other than intended (Appropriation) as well as that the concerned person gets informed about the storage and the use of data and has given her / his consent.
- …the fact that people working with personal data are aware of the necessary measures and that there is a data privacy officer.
- …available tools which allow the concerned people to conduct all necessary actions in a simple and legally compliant way for example so that they can follow the obligation of disclosure and deletion.
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 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:
- Measures like for example data backups, versioning of changes, access controls etc..
- People like IT-administrators which are responsible for data backups or the application of respective security settings of databases as well as the implementation of additional security mechanisms.
- Tools like backup solutions, blockchain-based databases or user management solutions.
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:
- Measures like educating employees and the installation of a central business intelligence department.
- People like business analysts who assist in the functional department with analytical problems.
- Tools like data warehouses or self-service BI tools.
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:
- Measures like regular data audits as well as the purchase of external datasets.
- People like data stewards who are holistically responsible for a database.
- Tools which for instance detect anomalies in data (by means of machine learning).
Finally, you should check your data management strategy (data governance) regarding completeness and consistency:
- Did you answer all important questions?
- Which area is only sparsely occupied and why?
- Which measures clash with each other, for example regarding data privacy vs. data quality?
- Which people are at risk to be involved in a conflict of interests, for instance because they are responsible for increasing data security as well as data availability?
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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