Data and Analytics

Build a resilient and scalable data backbone for your organisation.

• Data strategy: Comprehensive roadmap that defines how data collects, manages, secures, utilises, and preserves to achieve business goals.
  
  
• Data quality: Implement rules for accuracy, completeness, consistency, and timeliness.
  
  
• Cloud migration: Migrate legacy systems to cloud-native environments.
  
  
• Data deletion and purging: Ensure secure and compliant disposal of obsolete data.

Build a foundation of trust that fuels innovation.

• Trust fabric and data democratisation: Building a secure, transparent ecosystem that empowers users with equitable access to trusted data.
  
  
• Lineage, quality and integrity: Ensuring data reliability through traceable origins, consistent standards, and ethical stewardship.
  
  
• Governance-driven innovation and optimisation: Leveraging strong governance to unlock smarter, scalable, and compliant innovation.
  
  
• Cloud-first, hybrid-ready: Prioritising cloud agility while seamlessly integrating on-premises and hybrid environments.
  
  
• Stewardship, accountability and compliance: Promoting responsible data practices aligned with regulatory and ethical standards.
  
  
• Lifecycle intelligence and metadata mastery: Harnessing metadata to optimise data value across its entire lifecycle.

• Data storytelling and exploration: Transform complex datasets into compelling visual narratives with interactive dashboards, drilldowns, and filters—making insights accessible, engaging, and actionable for all stakeholders.
  
  
• Real-time and predictive insights: Monitor metrics and trends as they happen, and anticipate future outcomes using predictive analytics and machine learning—enabling proactive, informed decision-making.
  
  
• Customisable visualisations and automated reporting: Tailor charts, layouts, and dashboards to specific needs, while automating recurring reports for scheduling, distribution, and updates—saving time and reducing manual errors.
  
  
• KPI tracking and embedded analytics: Track performance against goals with intuitive metrics dashboards, and embed insights directly into business applications so decisions can be made without switching platforms.
  
  
• AI-powered insights and natural language querying: Surface hidden patterns, anomalies, and recommendations automatically, and enable users to ask questions in plain language for instant, visual answers—making analytics intuitive for everyone.
  
  
• User-centric, secure and compliant design: Deliver intuitive, responsive visual experiences optimised for any device, while enforcing role-based access, maintaining compliance, and safeguarding sensitive data.

• Customer analytics, segmentation and forecasting: Build scalable models for customer profiling, segmentation, and forecasting using advanced statistical and machine learning techniques.
  
  
• Predictive modelling and machine learning: Develop supervised and unsupervised models for classification, regression, clustering, and anomaly detection across diverse business domains.
  
  
• Descriptive and unstructured analytics: Analyse data, identify patterns, visualise trends and extract insights from structured and unstructured data (text, images, video) using advanced statistical techniques, NLP and computer vision.
  
  
• Prescriptive and causal analytics: Optimise decisions and simulate outcomes using causal inference and scenario modelling to drive strategic interventions.
  
  
• AI and generative AI algorithms: Deploy GenAI for intelligent automation, NLP for text analytics, and deep learning for pattern recognition and decision support.

Beyond traditional applications, our data and analytics expertise also support a range of business functions resulting in enhanced assurance, sustainability, and data quality.

• Internal audit data and analytics: Automate testing, improve risk coverage, and provide continuous assurance with audit-focused analytics.
  
  
• ESG data management and analytics: Leverage data to monitor ESG performance, manage regulatory risks, and achieve sustainability targets.
  
  
• Data governance and data quality management: Establish enterprise-wide trust in data through quality monitoring, stewardship, and remediation frameworks.