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The key principles of data governance are ownership, responsibility, and accountability for data. This includes sharing information and consulting with relevant stakeholders on how the data can be used, processed, and stored. Setting up the appropriate data domains per product or business unit helps to manage information and maintain data integrity.
"Garbage in, garbage out." If an organization has the ambition to implement predictive analytics in the future, this is the first important step towards achieving that. When Machine Learning (ML) models are applied to data that is not of good quality, the chances of obtaining good results from these analyses are low. As this step is crucial, we develop appropriate controls to identify errors in the data and to make proposals to supplement or resolve them.
Data lineage is the journey that data takes from the moment it is created. If all data flows are aligned and aligned with each other, any errors and changes in the data can be more easily detected and adjusted.
Data architecture is a set of (policy) rules, models, and standards that determine how data is collected, stored, managed, and integrated in relation to database systems.
A good architecture enables us to truly rely on master data. Having a single source of truth is essential for analyses, business intelligence, repeatable automated processes, and machine learning/AI models.
Based on the points identified for improving data quality, a plan for implementation can be developed. One of the biggest challenges is determining what good data quality is for a particular application. This requires a deep understanding of the organization's data needs and requirements and the specific application.
In addition, obtaining good data quality can be challenging due to the diversity and complexity of data sources and types, the need for data integration and transformation, and the limited resources available for data management.
In recent years, the number of applications generating data has increased. Many organizations find unlocking data from these different sources to be complex. In addition to the technical complexity of unlocking data, it is important to create data execution plans that ensure that the data sources do not create timing issues.
Once all sources are unlocked and refreshed, the data can be linked together. The enterprise's data landscape determines the approach. If the data sources generate a lot of data and the enterprise's information needs are significant, there must be a central place where the data can be linked and transformed. This also applies when the enterprise wants to link several simple Excel files together and automate the analysis. When new data is obtained, it can be viewed in one place.
With your information needs as a guide, we prepare the data in such a way that we can use scripts to convert, combine, transform, or store the data in a central environment. This allows us, together with you, to build fast and thorough dashboards that answer your information needs.
