Global Customer Data Science Company

The Aim

Strengthen data governance, foster innovation, and enhance security and quality whilst maintaining agility. The aim was to transform governance from a bottleneck into an enabler, improving data discoverability and collaboration whilst building robust controls that could support ambitious growth plans and accelerated product development.

Challenges

The company knew there was significant value in its data assets, but governance fragmentation and process gaps were limiting agility and creating risk. The assessment identified critical areas where improvements were needed:

• Lack of visibility and fragmented responsibilities: The company executives, security teams and data SMEs lacked a clear, unified overview of data assets, their location, and lineage, making it difficult to ensure compliance and hindering strategic decision-making; governance duties were distributed across multiple teams, leading to confusion, inefficiencies, and a lack of clear ownership and accountability.
• Poor data discoverability: The metadata catalogue was incomplete and outdated, forcing analysts, data scientists and anyone looking to build new data products to rely on personal networks to find the data they need, slowing down pace of growth, reliability and preventing change at scale.
• Innovation bottlenecks and siloed teams: Local data science teams and global R&D teams operated in silos, leading to duplicated efforts and slower innovation; the Data Governance Approval Process (DGAP) was seen as a bottleneck, not designed to support the iterative nature of data science projects, discouraging experimentation; in-market data scientists were often relegated to project management roles rather than leveraging their analytical skills.
• Data security and quality gaps: Data lineage was minimal or theoretical, creating uncertainty about data flows and making it difficult to assess the impact of changes and ensure quality; data quality dashboards were not widely used or understood outside of the data engineering team, leading to poor decision-making based on unreliable data; unstructured data, such as emails, was not subject to the same level of governance as structured data, creating significant security and compliance risks.

These combined challenges limited innovation velocity and created operational risk, signalling the need for a comprehensive, modern data governance framework focused on people, process, and technology.

Strategic Solutions

Analytics8 proposed a multi-faceted approach to transform data governance into a strategic enabler whilst strengthening controls and fostering collaboration.

• Centralised data catalogue. We recommended implementing a unified data catalogue to provide a comprehensive inventory of all data assets, including metadata, ownership, and access controls, improving data discoverability and lineage tracking across the organisation.
• Streamlined governance process. We proposed redesigning the DGAP, controls and risk classifications to emphasise agility and be pro-innovation, using automation and modern toolsets to ensure robust controls whilst fast-tracking low-risk projects and applying rigorous scrutiny to high-risk initiatives.
• Managing unstructured data. We recommended implementing tools to discover, classify, and manage unstructured data to mitigate security and compliance risks that weren’t being addressed by existing structured data governance.
• Enhanced data quality. We proposed improving the usability of data quality dashboards and scaling initiatives like Data Quality as a Service (DQaaS) to proactively identify and resolve data issues upstream, making quality metrics accessible beyond the data engineering team.
• Empowering and educating teams. We recommended investing in training and mentorship programmes to enhance data literacy and ensure that skilled professionals could contribute their expertise in more strategic roles, breaking down silos between local data science teams and global R&D.
• Collaboration platform. We proposed fostering a community to encourage the exchange of best practices and promote joint projects, enabling teams to work more effectively together and reducing duplicated efforts.
• Phased implementation roadmap. We developed a three-phase approach starting with a pilot programme in one domain and market, expanding to priority domains, then establishing ongoing optimisation with a data governance council and continuous improvement processes.

Results

The client established scalable data governance foundations that improved data visibility, reduced delivery friction, and enabled faster, more confident use of data across teams.

• Faster access to trusted data: A unified approach to metadata, lineage, and documentation improved data discoverability, enabling teams to find, understand, and trust data more quickly.
• Reduced approval fricition while strengthening control:
  • A tiered risk assessment model enabled low-risk use cases to progress through self-certification and automation, reducing manual reviews while maintaining strong governance for higher-risk scenarios.
  • Analysis showed that over 40% of historical requests had characteristics suitable for automated approval, representing a significant opportunity to reduce manual effort and improve turnaround times.
• Improved reuse and reduced duplication: Increased visibility of data assets, ownership, and definitions enabled teams to reuse existing data and pipelines, reducing duplicated effort across teams.
• Increased visibility of data footprint and optimisation opportunities: Discovery work identified significant volumes of data assets within individual client environments (e.g. ~75,000 objects in a single data scientist workspace), highlighting opportunities to reduce duplication, optimise storage, and improve lifecycle management.
• Enhanced collaboration across data, product, and governance teams: Shared standards and cross-organisational visibility improved alignment between Data Science, Engineering, Product, and Legal teams, supporting more efficient delivery.
• Foundations to scale AI and treat data as a strategic asset: Metadata, lineage, and governance were positioned as enabling layers, providing the structure required to safely scale advanced analytics, AI use cases, and data product innovation.
• Shift from governance as a bottleneck to an enabler of innovation: Streamlined processes, clearer ownership, and automation pathways enabled governance to support faster experimentation and delivery, rather than acting as a constraint.

From bottleneck to enabler: Modern governance framework transformed compliance exercise into strategic advantage, with streamlined processes, collaborative culture, and enhanced data quality and security accelerating product development lifecycle and improving data-driven decision-making whilst maintaining robust controls.