Data
2025
The Year of Data Clarity.
2025 marked a turning point for data maturity.
Organisations across every sector worked to modernise their foundations, rebuild trust in their data, and accelerate their path to insights. Throughout the year, it|venture helped firms confront these challenges head-on, streamlining processes, strengthening architecture, and guiding teams toward more scalable, efficient and resilient data practices.
Below, we recap the biggest trends we saw, the types of projects clients invested in, and the challenges we expect to shape 2026.
The major trends that shaped 2025
Full ownership of data became essential
More organisations moved toward full data ownership to reduce fragmentation and gain clearer, more reliable insight. By centralising data, firms can consolidate data, strengthen governance, and support scalable analytics and AI. Owning the full data lifecycle became a strategic priority for improving confidence, speed, and decision-making.
Ongoing bottlenecks impact decision making
Reporting latency remained a major constraint, with legacy infrastructure, manual handling, and inconsistent processes slowing insight and pushing teams toward reactive decision-making. This reinforced the need for automated, resilient workflows and a more consistent approach to reporting.
Workflows change as technology enhances
As new systems were implemented and data capability increased, organisational workflows were reshaped end-to-end. Routine tasks that previously relied on heavy cross-team communication were reduced through better-integrated systems. This shift pushed organisations to redesign processes, ensuring technology didn’t just support operations but actively improved how work moved across the business, enabling faster execution and clearer ownership.
Power BI surfacing insights
Power BI became the go-to visualisation platform across both operational teams and the C-suite, enabling a shared view of performance through a common set of trusted metrics. Its flexibility allowed users to create, adapt, and share insights quickly, helping teams spot patterns, prioritise issues, and act with greater confidence. In most cases, this reduced the need for separate reporting streams, with tailored executive outputs produced only where a more customised narrative was required.
Manual processes remained a significant drag on performance
Despite rising investment in digital transformation, manual workflows continued to slow organisations down. From ad-hoc data manipulation to repetitive reporting tasks, many teams remained reliant on people rather than automation, introducing risk, inconsistency, and delay. This reinforced the growing demand for process automation, data systemisation, and scalable data architecture to improve efficiency, reduce operational friction, and free teams to focus on higher-value work.
2025 projects
Where we made an impact.
Clarifying the data journey
A large proportion of engagements began with advisory work, as organisations sought clarity on priorities before investing in technology. it|venture helped clients assess their maturity, align stakeholders, and build roadmaps that integrated strategy, governance, technology, people, and processes, giving teams clearer direction and confidence before moving into delivery. Across these advisory projects, clients gained sharper direction, stronger decision-making and a solid foundation for scalable transformation.
Reducing reliance on manual processes
Manual workflows remained one of the most common and costly challenges. In one engagement, we identified 15 manual processes repeated each month and reduced this to just five through a combination of Connect automations, improved data workflows, and modern data warehousing.
Across all projects, we focused on streamlining day-to-day operations, strengthening core systems, and improving data architecture, with a longer-term ambition of eliminating manual intervention entirely. These initiatives laid the foundation for more efficient, scalable operations, where manual processes become the exception rather than the norm.
Rebuilding trust in data
Limited trust in data often stemmed from fragmented datasets, manual interventions and the absence of a centralised repository. We helped organisations implement data warehouses to provide a single source of truth, deploy accessible dashboards, and strengthen governance practices. These efforts fostered a stronger data culture, reduced reliance on ad-hoc information requests, and enabled faster, more confident decision-making across the business.
Improving speed to insight
Slow, reactive insights were typically caused by manual processes and scattered systems. Across numerous engagements, our work focused on centralising data, creating resilient pipelines, and deploying dashboards to create a single source of truth that brings the right data to the right people at the right time. The result was faster decisions, reduced dependency on individuals, and more confidence in day-to-day reporting.
Enabling scalable growth
A significant number of organisations struggled to scale efficiently, often relying on expanding headcount rather than improving processes. Throughout our projects, we focused on reducing operational friction, automating repetitive tasks, and strengthening data flows. This allowed teams to focus on delivering value rather than administrative tasks, enabling organisations to scale sustainably without proportional cost increases.
Looking ahead
The big challenges for 2026.
Having completed the discovery stage we then produce a detailed recommendation report tailored to your specific needs and requirements, outlining both the target state model we recommend and how to achieve it. The focus of our recommendations is always pragmatic, with clients typically seeking solutions that allow the on-going demonstration of value and business impact throughout the project, addressing the needs and requirements in a measured, iterative fashion.
Full data ownership will become a non-negotiable
In 2026, organisations will face increased pressure to fully own their data, from ingestion to governance to analytics. As regulatory expectations tighten and AI models demand higher-quality inputs, relying on fragmented systems or data locked inside proprietary environments will become increasingly risky. The challenge won’t just be building a data repository, but maintaining data accuracy, lineage, and accountability at scale.
Becoming truly agile with AI
AI adoption will only continue to accelerate, but the real challenge will be using an organisation’s own data to drive meaningful outcomes. To enable this, a strong data foundation, often underpinned by data warehousing, is essential. With reliable, well-structured data in place, teams can rapidly trial and test AI use cases, run proofs of concept, and “fail fast” to identify what truly delivers value.
The organisations that gain a competitive edge will be those able to operationalise AI quickly, embed it seamlessly into workflows, and ensure each iteration is aligned with measurable business impact while maintaining appropriate governance around risk, privacy, and model performance.
Semantic Layers will become critical
As datasets grow more complex, semantic layers will become essential. Organisations will need metadata-rich structures that create shared definitions, trusted metrics, and consistent context across teams. For AI, semantic layers will act as the connective tissue, helping models interpret organisational data accurately, improving explainability, and reducing misalignment between business logic and machine outputs.
The rise of the data lakehouse
In 2026, the convergence of data lakes and data warehouses will accelerate as organisations adopt lakehouse architectures to handle both structured and unstructured data at scale. The lakehouse model will become increasingly attractive for firms needing flexible storage, high-performance analytics, and AI-ready data pipelines, without the complexity and cost of stitching together multiple platforms.
This unified approach will allow teams to ingest raw data, govern it centrally, and expose it to analytics and machine learning workloads from a single environment. As AI and real-time reporting demands grow, the lakehouse will emerge not just as a technical preference but as a strategic enabler for speed, scalability, and innovation.
Final thoughts.
2025 demonstrated how critical data maturity has become to operational resilience, efficiency and strategic growth. As organisations prepare for 2026, the priorities are clear; modernise core systems, reduce friction, strengthen governance, and build the foundation needed for AI-enabled decision-making.
To explore how our Data Strategy team can support your next phase of data evolution, get in touch.