Challenges and limitations - AWS Prescriptive Guidance

Challenges and limitations

Depending on multiple factors, there are several challenges and limitations to consider when designing and joining data spaces, including the following 10 most observed:

  • Technical complexity – Setting up and maintaining a data space requires some technical expertise, especially in areas such as data integration, data governance, and cybersecurity. Organizations that lack skilled professionals to manage these tasks might struggle to get the full benefit from building a data space.

  • Data quality issues – Data spaces rely on high-quality data to function effectively. However, data quality remains a significant challenge, especially when dealing with legacy systems, disparate data sources, and human error. Ensuring data accuracy, completeness, and consistency across all datasets is crucial but often difficult to achieve.

  • Integration challenges – Combining data from multiple sources into a single, unified view can be a complex task. Different data formats, schemas, and semantics can create integration challenges that require significant time and resources to resolve.

  • Data privacy and security concerns – Data spaces must ensure the privacy and security of sensitive information, especially in industries, such as healthcare or finance, that are subject to strict regulations. Implementing robust security measures and maintaining data confidentiality are essential but not always straightforward.

  • Cultural and adoption barriers – Encouraging collaboration and data sharing across different departments or organizations can be challenging. Some teams or organizations might be hesitant to share their data, citing concerns about intellectual property, competition, or past negative experiences.

  • Scalability limitations – As data volumes continue to grow, data spaces must scale to accommodate the increase. However, scaling can introduce new challenges, such as managing larger amounts of data, ensuring performance, and maintaining data quality. Those limitations can occur on a governance level as well as on a participant level.

  • Cost and ROI – Implementing and maintaining a data space does incur some costs, including infrastructure, personnel, and software expenses. Be sure to project and demonstrate a clear return on investment (ROI) for building a data space, especially in the early stages of implementation.

  • Lack of standardization – The lack of standardization in data formats, schemas, and ontologies can make it difficult for different systems to communicate and share data effectively. Establishing common standards and frameworks can help address these challenges.

  • Change management – Designing or joining a data space requires significant changes to existing workflows, processes, and culture. Managing this change can be challenging, especially in organizations with entrenched habits or resistance to new technologies.

  • Ethical considerations – With the increasing emphasis on data-driven decision-making as well as innovative business models based on data, concern is growing about bias. This includes bias in the data exchanged and in the services offered within data spaces. Ensuring fairness, accountability, and transparency in data spaces is critical, but it requires careful consideration and effort.

By acknowledging and addressing these challenges and limitations, your organization can better understand the potential hurdles  when building or joining data spaces and develop strategies to overcome them.