FEATURE
Fikile Sibiya, CIO at e4
Durandt Eksteen, Chief Information Technology and Data Protection Officer at NEC XON
Too often, data initiatives become technical exercises, new tools, and new dashboards, with no clear link to business objectives.
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 datadriven decision-making becomes second nature.
In summary, organisations most often struggle with four interconnected barriers:
1. Fragmented, poor-quality data 2. Misaligned or unclear data strategy 3. Cultural resistance to data-driven decision-making 4. 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 •.
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