C-suite Exchange: Building an agile business data strategy in 2025

Agile business data strategies

In this virtual C-suite exchange, senior technology and business leaders came together to tackle one big question: What is the biggest challenge to extracting the most value from your data in 2025? Discover more insights from the virtual C-suite Exchange.

Data is the fuel that powers decision-making, innovation and competitive advantage. Yet, for many organisations and their C-suite, data remains fragmented, locked within silos that hinder agility, efficiency and value creation. As businesses strive to leverage AI, enhance operational efficiency and improve customer experiences, the quality and accessibility of data are paramount. 

As leaders are beginning to understand, challenges in AI value generation are in fact legacy data challenges businesses previously failed to address. Building an agile business data strategy in 2025 is the first step in becoming an AI-ready organisation.

To offer leaders the opportunity to share learnings, HotTopics and Confluent brought business and technology leaders together for a virtual C-Suite Exchange. The following insights, drawn from this discussion, explore the key challenges of fractured data silos, data quality and organisational culture when considering business agility. 

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Fractured data silos: A business bottleneck

One of the most persistent challenges the leaders on the call are facing is the fragmentation of data across various systems, departments and platforms. CIOs and data leaders repeatedly encounter disjointed systems, where legacy applications that do not effectively communicate effectively with modern cloud-based solutions. A lack of integration was also touched upon: data locked in different tools, requiring manual workarounds like spreadsheets, slows things down. And departmental ownership conflicts, where teams manage their own data without a cohesive enterprise strategy, can have real consequences on time to market.

The impact? Inefficiencies, duplication of efforts and a lack of a single source of truth, make it difficult to extract meaningful insights.

Blockers to an agile business data strategy

Even as many organisations are eager to embrace AI today, teams still struggle with poor data quality. The C-Suite Exchange highlighted:

• Incomplete or inaccurate data: Leading to unreliable insights and misinformed decisions.
• Lack of standardisation: Different teams using varied formats, definitions, and structures.
• Cultural resistance to data governance: A reluctance to take ownership of data hygiene.

Successful organisations prioritise data quality at the source, applying validation mechanisms, automation and governance frameworks. Leaders are advocating for a “quality by design” approach, ensuring data is captured, structured and maintained properly from the outset.

Data ownership: Where’s the knowledge?

Even with the right tools and processes, true transformation hinges on that nuanced element of business: team culture. Adoption challenges included:

• Lack of data literacy: Employees across the business may not understand how to use data effectively.
• Unclear ownership: IT teams are often expected to “fix” data problems, but the true owners are the business units, leading to questions on knowledge ownership.
• Resistance to change: Legacy mindsets can view data management as an IT function rather than a business enabler.

Forward-thinking organisations are embedding data stewardship roles, where designated team members across departments ensure data accuracy and compliance. Additionally, some are leveraging gamification strategies to drive engagement—encouraging teams or departments to compete in improving data quality metrics.

Closing thoughts

For C-suite leaders, data transformation is not just a technical challenge—it is a business imperative. The road to building an agile business data strategy requires:

1. Define clear data ownership: Empower departments to take responsibility for their own data quality.
2. Invest in integration: Reduce friction between legacy and modern systems to enable real-time insights.
3. Prioritise quality over quantity: Ensure data is reliable before rushing into AI or analytics initiatives.
4. Foster a data-driven culture: Encourage upskilling, transparency, and cross-functional collaboration.
5. Adopt a value-first mindset: Link data initiatives directly to business outcomes to secure executive buy-in.

With most major digital transformations behind the industry, and AI on the horizon, businesses that fail to address their data challenges risk running before they can walk when it comes to the nascent technology. Leaders who prioritise a holistic, integrated and culturally aligned data strategy will be the ones driving sustained success in the years ahead.

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