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In the digital era, the importance of data is becoming increasingly evident, especially in terms of being able to bring together, refine and build on data from different systems within the organization. This means that there are requirements both for the data itself and for the systems used to create, store and make it available. Most importantly, there are requirements for master data, which is a description of key concepts and is used by the entire organisation, such as customers, products and employees. In this age of digitisation, data and its availability is becoming a critical success factor.
Data quality and its dimensions
For businesses and organizations, ensuring that the data they have access to is of high quality is of great importance. To achieve this, there are different dimensions that the data must fulfill. Below are some examples:
1. Accuracy:
Data must have correct values for attributes and be understandable to users.
2. Completeness:
Data must contain all the necessary concepts and attributes required for the business.
3. Accuracy:
Data must have appropriate granularity (level of aggregation) and precision for values, such as the number of decimal places used to store values.
4. Consistency:
The rules for data and its context must be clear and consistent both within and across systems. This includes having unique and identifiable representations for entities and maintaining both structural and semantic consistency. Structural consistency is about data models, formats, and references, while semantic consistency is about everyone dealing with the concepts having a common understanding of their meanings and naming.
5. Accessibility:
The right data must be available quickly enough, both in terms of timing of access and response time to queries. It also means that data must be available in the right way and to the right recipients.
By maintaining these dimensions, companies and organizations can ensure that their data is of high quality and thus contribute to better decision-making and operational efficiency.
The key to success
An important key to success is collaboration between different parts of the organization. To achieve this, the business needs to develop clear definitions and test them in collaboration with IT. Experienced data modelers and data warehouse developers can quickly identify gaps in the definitions and the complex situations that challenge the business's perception of reality.
IT also plays a crucial role by providing technologies that are tailored to the needs of the business. However, it is important to remember that technology in itself has no intrinsic value, but is only a tool to meet business needs. Technology can mainly solve accessibility problems, but if the data does not meet the aforementioned criteria, accessibility does not matter. The model of the business data is central and the maintenance of this model is of utmost importance.
All this may seem obvious, but applying it often challenges an organization. Different views may surface and need to be resolved to create a common canonical model that systems can use to communicate with each other, even if the internal definitions differ. Moving from a collection of systems with their own definitions to a set of systems that can communicate through a canonical data model is an investment. But not making this investment means that data held by the systems will remain locked up and not be used in the best way.
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