Data Management A0 (47" x 33") synthetitisches Papier (EN)

Why Data Management?

A data strategy defines the possible uses (also called use cases) for your company data. How well your data can be utilized depends not only on the respective application and the tools required for it, but also to a large extent on the quality of the data. The BIBO principle applies: "Bullshit in, bullshit out". Furthermore, the data must also be easily and quickly available. Otherwise, the implementation of the respective application will be time-consuming and costly. Finally, the personal rights of data subjects, such as your customers, must also be respected when their data is processed. In addition to mandatory data protection, data security also plays an important role: without data security there is no data protection. These four areas - data quality, data availability, data protection and data security - together form data management. Data management is an integral part of your data strategy and the Data Management Canvas helps you to define the right data management concept for your company data.

What is Data Management?

The Data Management Canvas is a visual collaboration tool for interdisciplinary teams. Especially the topic data management requires a coordinated approach across departments. Usually several persons or roles are involved: Managers the specialist department, data protection officers, IT managers, data scientists, business anlayst and many more. The Data Management Canvas distinguishes between defensive and offensive measures, which are in conflict with each other.

Defensive:

• Data protection is concerned with the protection of personal data in accordance with the respective data protection laws (in Germany the DSGVO: "Datenschutzgrundverordnung"; in the EU known as "GDPR": "General Data Protection Rule).
• In addition to legal requirements, technical and organizational measures must also be taken into account to ensure data security and thus minimize the risk of data loss, manipulation and misuse.

Offensive:

• Data quality deals with the question of how to improve the database. Various dimensions have to be considered: the quantity, complexity, timeliness, representativeness, correctness and completeness of the data. In addition, care must be taken to ensure that measures to improve data quality do not violate data protection.
• The goal of data availability is to make the data available to employees and especially to decision-makers as easily and quickly as possible. Higher data availability always means a higher risk for data security.

In addition to the four areas, the Data Management Canvas distinguishes three fields per area:

• Measures define one-off projects or recurring activities to improve data protection, data security, data quality and data availability. Examples are in each case: Data protection training for employees, regular data backups, automated data anomaly detection or the introduction of a data warehouse.
• Persons are responsible for the implementation of these measures. Examples are the data protection officer, the IT manager, the data steward or the business intelligence manager.
• Tools are software tools, IT systems, document templates and much more, which help the persons in charge to implement the measures.

How to use Data Management?

How to start.
In the first step you define the data sources for which you want to develop a data management concept. Note the names of the data sources on Stattys Notes and place them in the Data Landscape field in the middle of the template. Then choose one of the four areas (data protection, data landscape, data availability or data quality) you want to start with and work your way from the outside inwards by collecting suggestions from your team for the three fields Actions, People and Tools.

Use the colors.
Write the suggestions on differently colored Stattys Notes. Green notes stand for measures that are already established, for people already working in the company and for tools that are already in use. Red, on the other hand, means that this measure, person or tool is required but not available. Yellow stands for measures, tools and (personnel) positions that are planned or in progress.

Discuss with your colleagues.
For each field you first collect all suggestions without discussing them. Then in the second phase, discussions are explicitly desired: concretize, analyze, evaluate, prioritize and focus the proposals. Dare to take suggestions off the canvas again. At the end of the process a goal-oriented, consistent and above all feasible concept for Data Management should be on the canvas.

You can find more information at Datentreiber.