The central role of data in the age of digitalization

In the digital era, the importance of data is becoming increasingly apparent, especially in terms of being able to bring together, refine and build on data from different systems within the business. This means that there are requirements both for the data itself and for the systems used to create, store and make it available. Most important of all are the requirements for master data, which constitutes a description of key concepts and is used by the entire organization, such as customers, products and employees. In this era of digitalization, data and access to it becomes a crucial factor for success.

Data quality and its dimensions

It is of great importance for businesses and organizations to ensure that the data they have access to is of high quality. To achieve this, there are various dimensions that data must meet. Below are some examples:

1. correctness:

Data must have correct values for the attributes and be understandable to users.

2. completeness:

Data must contain all the necessary concepts and attributes required for the business.

3. Accuracy:

Datamust have the appropriate granularity (level of aggregation) and precision for values, such as the number of decimals used to store values.

4. consistency:

The rules for data and its context must be clear and consistent both within and between 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 time of access and response time to queries. It also means that data must be available in the right way and to the right recipient.

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' perception of reality.

IT also plays a crucial role by providing technologies that are adapted 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 primarily solve accessibility problems, but if the data does not meet the aforementioned criteria, accessibility does not matter. The business data model 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 opinions can surface and need to be resolved to create a common canonical model that systems can use to communicate with each other, even if their 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 the data held by the systems remains locked up and is not put to best use.