Enterprise data architecture, analytics, and governance — building scalable data lakes, pipelines, and analytics platforms that turn raw data into business intelligence.
Most organisations are data-rich and insight-poor. Siloed systems, inconsistent data quality, and disconnected tools mean that even with years of accumulated data, generating reliable business intelligence remains slow, expensive, and error-prone. IPGlobal's Data Solutions practice solves this — building the scalable foundations that make your data genuinely useful.
From designing petabyte-scale data lakes and real-time streaming pipelines to implementing data governance frameworks and delivering BI platforms your teams actually use, we take a vendor-neutral approach that fits your existing stack and scales with your ambition.
Design and build petabyte-scale data lake architectures on AWS S3, Azure Data Lake, and Google Cloud Storage — with medallion architecture (bronze, silver, gold), storage optimisation, and seamless integration with your downstream analytics platforms.
Robust, testable ETL and ELT pipeline development using dbt, Apache Spark, AWS Glue, and Azure Data Factory. We build pipelines with automated data quality checks, lineage tracking, and alerting so your data flows are reliable and observable from source to destination.
End-to-end streaming architecture and implementation using Apache Kafka, AWS Kinesis, and Azure Event Hubs — enabling real-time fraud detection, live operational dashboards, IoT data processing, and event-driven application architectures that respond to business events as they happen.
Data governance frameworks that make your data trustworthy — covering data cataloguing, lineage tracking, quality rules, ownership and stewardship, classification and sensitivity labelling. Implemented using Apache Atlas, Microsoft Purview, or AWS Glue Data Catalog, integrated into your data pipelines.
End-to-end BI platform delivery using Tableau, Power BI, and Looker — including semantic layer design, governed metric definitions, executive dashboards, and self-service analytics environments that give every business user access to reliable, consistent data insights.
Complex data migration projects from legacy on-premise warehouses, databases, and file systems to modern cloud data platforms — with automated validation, reconciliation reporting, rollback procedures, and zero-disruption cutover planning to protect your business continuity throughout the migration.
Data source inventory, maturity scoring, quality profiling, and identification of highest-value analytics use cases to prioritise.
Target-state data architecture designed — lake, lakehouse, or warehouse — with platform selection, storage strategy, and integration patterns agreed.
ETL / ELT pipelines built and tested, streaming ingestion configured, and data quality checks embedded throughout the data flow.
Data catalogue populated, lineage established, quality rules activated, and data ownership and stewardship model agreed and documented.
BI platform deployed, dashboards built, user training delivered, and ongoing pipeline monitoring and optimisation handed to your operations team.
We are not aligned to any single cloud or data platform vendor. Our recommendations are driven by your requirements — data volumes, latency needs, existing investments, and team capability — not by partner incentives or preferred tools.
Our data engineers have designed and operated data platforms handling petabytes of data across financial services, retail, and media organisations. We know where the architectural decisions that affect scale are made, and we get them right from the start.
Not every data team has experience building production-grade real-time streaming systems. IPGlobal's engineers have designed and operated Kafka and Kinesis pipelines at scale — and know the operational differences between a proof of concept and a reliable production system.
Data governance is embedded in every pipeline and platform we build — not added as a separate workstream at the end. This means your data is catalogued, lineaged, and quality-checked from the moment it enters your platform, not retrospectively.
Our BI specialists don't just build dashboards — they design semantic layers, govern metric definitions, and create self-service analytics environments that empower your business users to answer their own questions without depending on the data team for every report.
Data projects for global organisations often span multiple time zones, regulatory jurisdictions, and data residency requirements. IPGlobal's global delivery capability and data sovereignty expertise ensures your data platform meets localisation requirements wherever your business operates.
A data lake is a centralised repository that stores structured, semi-structured, and unstructured data at any scale — typically built on cloud object storage such as AWS S3, Azure Data Lake, or Google Cloud Storage. Unlike a traditional data warehouse, a data lake retains raw data in its native format until it's needed for analysis. If your organisation needs to consolidate data from multiple sources, retain historical data cost-effectively, or support diverse analytical workloads including machine learning, a data lake is almost certainly the right foundation.
ETL (Extract, Transform, Load) processes transform data before loading it into the destination system — the traditional approach suited to structured data warehouses with defined schemas. ELT (Extract, Load, Transform) loads raw data first and transforms it within the destination platform using its own compute power — the modern approach favoured by cloud data platforms like Snowflake, BigQuery, and Databricks where compute is elastic and schema-on-read is possible. IPGlobal designs pipelines using the appropriate pattern for your data volumes, latency requirements, and target platform.
Real-time data streaming is the continuous processing of data events as they occur, rather than collecting data and processing it in batches. Technologies like Apache Kafka, AWS Kinesis, and Azure Event Hubs enable this capability. You need real-time streaming when your business requires immediate insights — fraud detection, live inventory updates, customer behaviour personalisation, IoT sensor monitoring, or operational dashboards that reflect the current state of your business rather than yesterday's snapshot.
Data governance is the framework of policies, standards, and controls that ensure your data is accurate, consistent, discoverable, secure, and compliant with regulatory requirements. In practice, this includes data cataloguing and lineage tracking, data quality rules and monitoring, ownership and stewardship assignments, classification and sensitivity labelling, and retention and deletion policies. IPGlobal implements data governance using tools like Apache Atlas, AWS Glue Data Catalog, and Microsoft Purview — embedding governance into your data pipelines rather than applying it as an afterthought.
IPGlobal is vendor-neutral across the BI landscape. We work with Tableau, Microsoft Power BI, Looker (Google Cloud), and Apache Superset — selecting the right tool based on your existing technology stack, user base, and reporting requirements. We also design semantic layers and data models that ensure consistent, governed metrics across all BI tools, preventing the "multiple versions of the truth" problem that plagues organisations with ungoverned analytics environments.
Data migration complexity depends on data volumes, source system diversity, data quality issues, and business continuity requirements. Simple migrations from a single source to a cloud data warehouse can complete in 4–8 weeks. Complex enterprise migrations involving legacy on-premise data warehouses, multiple source systems, and regulatory requirements typically take 3–9 months with phased delivery. IPGlobal uses proven migration frameworks with automated data validation, reconciliation reporting, and rollback procedures to minimise risk throughout the process.
Talk to an IPGlobal data specialist today. We'll assess your data maturity and provide a tailored architecture recommendation within 24 hours.
