We recently asked the question on Valid's LinkedIn page: what is the biggest challenge in data-driven work? The outcome was clear: fragmented data sources. A recognizable obstacle for many organizations that want to take the step to data-driven decision-making. In this blog, we take a closer look at what exactly fragmented data sources are, what risks they pose, and most importantly, how to effectively address this problem.

What do we mean by fragmented data sources?

Fragmented data sources occur when data is spread across multiple systems, departments or files that do not communicate well with each other. Think, for example, of financial data in an ERP system, HR data in Excel, customer data in a CRM and project information in all kinds of separate tools. These data sources are often not uniformly structured, difficult to find and do not connect well in terms of content.

What are the risks of fragmented data sources?

Fragmented data sources can hinder an organization on multiple levels. Below we explain the main risks in detail:

1. Inefficiency: When data is in different places, employees have to do a lot of manual work to collect, verify and merge information. This not only takes time, but also increases the likelihood of errors. Consider copying data from Excel files or comparing figures from different systems that contradict each other.
2. Inconsistency: With fragmentation, multiple versions of the truth quickly arise. If department A works with different customer data than department B, confusion arises about which information is correct. This leads to misunderstandings, frustration and, ultimately, declining trust in the data as a whole.
3. Limited decision-making: Without a complete and up-to-date overview of the situation, it is difficult to make informed decisions. Strategic choices are then made based on assumptions or incomplete information, which limits the organization's decisiveness and effectiveness.
4. Obstacle to a data-driven culture: As also described in the blog "Your intuition stands in the way of data-driven decision-making," organizations quickly fall back on gut feeling when reliable data is lacking. This undermines confidence in data-driven work and reduces the use of data in the daily decision-making process.
5. Risk to innovation: Innovative applications such as AI, predictive modeling and process optimization require central, reliable and accessible data. When fragmented, this is difficult to achieve. Without a solid data structure, it is virtually impossible to use advanced technologies in an effective and scalable way. Without central data, it becomes difficult to effectively deploy AI, predictive modeling or process optimization.

How do you address data fragmentation?

Fragmentation is not something you solve with technology alone. It is a combination of strategy, culture, ownership and tooling. To this end, through a variety of projects, we have developed the following approach to efficiently address fragmentation:

1. Map the data situation: Start by mapping all relevant data sources within the organization. This involves finding out exactly where data is stored, which departments are responsible for which data, and in what format this data is available. This inventory provides insight into the degree of fragmentation as well as potential overlap or deficiencies in available information.
2. Designate data owners: Without ownership, there is no structural improvement. For each domain, designate owners who ensure quality, timeliness and accessibility.
3. Choose a central data architecture:A central data architecture - often in the form of a data warehouse - provides a single coherent whole. This allows data from different sources to be stored, cleaned and made available for analysis in a uniform manner. Setting up a "single point of truth" creates consistency and reliability, which is essential for both operational and strategic decision-making. It also avoids having to rebuild analyses each time and provides teams with a shared starting point for collaboration. It also makes future applications with AI or BI tools easier and more scalable.
4. Invest in a data culture: As also mentioned in the blog "Working data-driven at housing associations," culture is at least as important as technology. Leadership must lead by example and encourage data use. It is also important that employees feel space and are motivated to ask questions about the data they use.
5. Start small and scale up: Start with a manageable data project, such as within one department or around one type of process. This facilitates support, reduces risks and allows for rapid learning. Making the results achieved tangible creates enthusiasm in the rest of the organization and makes it easier to take follow-up steps toward broader integration and adoption of data-driven work.