As we approach 2025, the AI, data, and analytics landscape is marked by pivotal shifts in data quality management, AI democratization, and ethical governance. This article delves into these top trends and the accompanying challenges, providing insights from industry leaders ahead of the CDAO Nordics event
The Nordic region’s premier AI and analytics event, CDAO Nordics, is set to take place on November 19th in Stockholm, bringing together leaders from across the industry to discuss the most pressing trends and challenges.
This year’s speakers, representing diverse sectors and some of the most innovative organizations in the Nordics, will detail what to expect in the coming year and beyond in data and analytics.
Ahead of CDAO Nordics, we spoke with key speakers about the top trends and challenges they believe will define AI and analytics in 2024 and 2025.
Top Nordic Trends in AI, Data, and Analytics in 2025
- Data Quality and Observability
Data quality is consistently named as one of the single most important factors in the success of data and analytics initiatives by the data leaders we speak to. As organizations generate and consume more data, the need for robust data quality management and observability tools has never been more important.
According to Krzysztof Saniak, IT Solution Architect at Sii Poland: “Outside the AI world, I see a growing need for better data quality. Enterprises now have a pressing need for things like data catalogs, data lineage, and data quality checks.”
This trend is supported by the increasing complexity of data ecosystems, which require continuous monitoring and refinement to ensure insights are both accurate and actionable.
- Democratization of Data and AI
Democratizing data access within organizations has been on the rise, but the potential for widespread AI adoption is taking this trend to new heights. Speakers at CDAO Nordics are excited about the opportunities this shift creates, allowing employees across various roles to make data-driven decisions.
Charlotte Jansson, Chief Data Officer at Inission, highlighted this as a key priority: “Data democratization empowers individuals across an organization to access, understand, and use data for decision-making.”
By decentralizing data access, organizations are better positioned to foster cross-functional collaboration and unlock insights across all departments.
- Trustworthy and Ethical AI Governance
As AI becomes a strategic pillar in organizations, there’s a growing recognition of the importance of trustworthy and ethical governance.
Ensuring that AI systems are both effective and transparent is essential to maintaining public trust and avoiding bias.
“The need for ethical AI practices to prevent bias and ensure transparency is paramount,” says Murat Acar, Data and Machine Learning Leader at The IKEA Group.
Ethical governance requires organizations to implement tools for bias detection and embed responsible AI practices across their teams, which will also be a central discussion point at CDAO Nordics.
Top Challenges in AI, Data, and Analytics for 2025
- Data Privacy and Regulatory Compliance
With data privacy regulations tightening across Europe, organizations are facing growing challenges to stay compliant. However, meeting regulatory requirements while continuing to innovate is complex.
Charlotte Jansson points out that “data privacy and regulatory compliance” are top concerns, while Murat Acar notes that privacy regulations in the Nordics and beyond present “both investment and operational challenges.” This balancing act is pushing organizations to refine their data management and protection strategies as they adapt to new rules.
- Talent Shortage and Upskilling Needs
The rapid adoption of AI and advanced analytics has outpaced the availability of skilled professionals, creating a talent shortage across the industry.
Charlotte Jansson lists “talent shortage and upskilling needs” as a major challenge, noting the increasing demand for data scientists, AI engineers, and data stewards. This gap emphasizes the need for organizations to focus on talent development and create programs that nurture existing employees’ skills to meet future needs.
- Managing Complexity and Data Trust
With data coming from multiple, often disparate sources, ensuring trust and consistency across the data landscape is a major challenge.
Murat Acar explains that “diverse data sources make consistency and trust harder to maintain,” especially with the rise of synthetic data and generative AI. Organizations are implementing more advanced verification systems to maintain data integrity and ensure consistent, high-quality outputs across all AI applications.
Get Ready to Dive into the Future of AI and Analytics at CDAO Nordics
As these speakers prepare to share their insights at CDAO Nordics on November 19th, attendees can look forward to a lineup of in-depth discussions on these emerging trends and challenges.
From data quality and observability to trustworthy AI governance, the event will be a pivotal opportunity for leaders to collaborate, exchange ideas, and tackle the future head-on.
Join us in Stockholm to explore these transformative trends, discuss solutions to these challenges, and connect with peers who are shaping the future of AI, data, and analytics in the Nordics and beyond.
You can find senior members of our expert community discussing these topics, as well as many others, at CDAO Nordics, click here to register now
