Organisations continue to invest heavily in data platforms and capabilities, yet many still struggle to convert insight into meaningful business outcomes. Fikile Sibiya, CIO at e4 and Durandt Eksteen, Chief Information Technology and Data Protection Officer at NEC XON, explore these issues in depth, offering a clear view of the barriers organisations face and the steps required to overcome them.
Data has long been positioned as one of the most valuable assets an organisation can possess.
From boardrooms to IT departments, the narrative is consistent: those that can harness their data effectively will outperform competitors, innovate faster and make better decisions. Yet, despite years of investment in platforms, tools and infrastructure, many organisations still struggle to translate data into tangible business value.
This disconnect raises an important question. If the technology is largely in place, why does the promise of data-driven decision-making remain so elusive?
Over the past decade, organisations have poured significant resources into building modern data environments. Cloud adoption has accelerated, data lakes have proliferated and advanced analytics and Artificial Intelligence capabilities have become more accessible than ever. On paper, the foundations for data-driven success have never been stronger.
However, the reality inside many organisations tells a different story. Data is often abundant but underutilised. Insights are generated but not always acted upon. Dashboards are created but not consistently trusted. In many cases, decision-makers continue to rely on instinct and experience rather than the data at their disposal.
This gap between potential and reality highlights a critical shift in the data conversation. The challenge is no longer simply about collecting or storing data, it is about making it usable, trusted and aligned with business outcomes.
A growing number of industry leaders are recognising that the barriers to data value are less about technology and more about organisational complexity. Data rarely exists in a single, unified environment. Instead, it is distributed across departments, systems and formats, often reflecting the historical evolution of the business rather than a deliberate, strategic design. This fragmentation creates significant challenges for organisations attempting to build a single, reliable view of their operations.
At the same time, the human element cannot be overlooked. Even the most sophisticated data platforms are ineffective if the people using them do not trust the outputs or understand how to apply them. Data literacy, cultural alignment and clear ownership all play a crucial role in determining whether data becomes a strategic asset or remains an underused resource.
Another common challenge lies in the alignment between data initiatives and business objectives. Too often, data projects are driven by technology teams in isolation, resulting in solutions that are technically impressive but disconnected from the decisions that matter most. Without a clear link to outcomes such as revenue growth, operational efficiency or customer experience, it becomes difficult to measure impact or justify continued investment.
Trust is also emerging as a defining factor. Inconsistent metrics, conflicting reports and unclear data lineage can quickly erode confidence among decision-makers. When trust breaks down, adoption follows. Leaders are unlikely to rely on insights they do not fully understand or believe in, regardless of how advanced the underlying technology may be.
These challenges are compounded by organisational structures that fragment responsibility for data. In many businesses, ownership is spread across IT, analytics teams and business units, creating gaps in accountability and making it harder to establish consistent standards and practices. As data moves between teams, its context and integrity can be diluted, further reducing its value.
What is becoming increasingly clear is that unlocking the true value of data requires a more holistic approach. Technology remains an important enabler, but it must be supported by strong governance, clear ownership models, aligned strategy and a culture that embraces data-driven decision-making. Organisations must also invest in developing the skills needed to interpret and act on data, ensuring that insights lead to meaningful outcomes rather than remaining theoretical.
In this context, the perspectives of industry leaders provide valuable insight into where organisations are going wrong and, more importantly, how they can move forward. While approaches may differ, there is a growing consensus that the path to data value lies in addressing structural, cultural and strategic challenges in tandem.
The following insights from Fikile Sibiya, CIO at e4, and Durandt Eksteen, Chief Information Technology and Data Protection Officer at NEC XON, explore these issues in depth, offering a clear view of the barriers organisations face and the steps required to overcome them.
Fikile Sibiya, CIO at e4
The biggest barrier is not technology, it is fragmentation. In most organisations, data lives in silos, scattered across business units, duplicated in legacy systems, trapped in inconsistent formats, or owned informally by whoever created it. This fragmentation makes enterprise-wide insights impossible. Leaders end up making decisions on partial, conflicting or inaccessible information.
Solving this begins with modern data architecture. Organisations need platforms that integrate and consolidate data across departments, removing structural barriers that keep information isolated. But technology alone is insufficient. Sustained value comes from building cross-functional data ownership models that encourage collaboration, shared responsibility, and accountability for data outcomes.
Strong data governance is the backbone of this. Clear standards, consistent definitions, quality controls and well-defined stewardship roles ensure that data is trusted and usable. No amount of advanced analytics can compensate for poor-quality data.
Equally important is strategic alignment. Too often, data initiatives become technical exercises, new tools, and new dashboards, with no clear link to business objectives. A data strategy must be anchored in the organisation’s core outcomes: revenue growth, operational efficiency, customer experience or market share expansion. When data efforts directly advance these goals, value becomes measurable, and momentum grows.
Finally, data literacy remains an underrated barrier. Even with clean, integrated data, organisations struggle to leverage it if teams lack the skills to interpret and apply insights. Ongoing education, upskilling and change management are essential in creating a culture where data-driven decision-making becomes second nature.
In summary, organisations most often struggle with four interconnected barriers:
- Fragmented, poor-quality data
- Misaligned or unclear data strategy
- Cultural resistance to data-driven decision-making
- Skills shortages and low data literacy
Addressing these holistically, not in isolation, is what ultimately unlocks the true value of data.
Durandt Eksteen, Chief Information,Technology and Data Protection Officer at NEC XON
The biggest barrier to organisations realising value from their data is not technology. It’s whether decision-makers trust, understand, and feel confident using it.
Most organisations have already invested heavily in data infrastructure (cloud platforms, BI tools, data lakes, even AI). Yet, adoption at the decision-making level still lags. What I often see is a trust gap, often driven by contradictory dashboards or metrics that aren’t clearly understood. When that happens, leaders revert to instinct rather than data.
There is also a persistent translation problem. Data teams tend to communicate in technical terms, while the business operates in outcomes. Without a strong bridge between the two, insights are produced but not acted on. Closely linked to this is fragmented ownership (IT manages pipelines, analytics teams produce reporting, and the business makes decisions). With each handoff of data, accountability and ultimately value are diluted.
Addressing this requires structural and cultural change. Embedding data professionals within business teams has proven far more effective than centralised models, as it closes the translation gap and builds trust through proximity. At the same time, improving data literacy among decision-makers (not at a technical level, but in terms of asking better questions and interpreting outputs) is critical.
Clear ownership of data quality also plays a key role in rebuilding trust. Finally, organisations need to align their data strategy to decisions, starting with “what do we need to decide better?” rather than “what data do we have?”
In practice, the constraint is rarely data availability. It’s the organisation’s ability to convert that data into confident, informed action.
