Data Modernisation Services for Enterprise

Data modernisation is the structured process of migrating an organisation's data assets, pipelines, and governance frameworks from legacy on-premises systems to cloud-native, scalable architectures. It encompasses retiring outdated mainframe data stores, replacing batch ETL pipelines with real-time ELT workflows, and adopting lakehouse or data mesh patterns that enable self-service analytics. The goal is not simply to lift data into the cloud but to restructure how data is owned, governed, and consumed across the enterprise. A successful modernisation programme delivers measurable improvements in data freshness, query performance, compliance posture, and total cost of ownership.

Our mainframe extraction methodology uses change data capture connectors and IBM MQ or VSAM file listeners to stream data off the mainframe continuously without locking production tables or interrupting batch windows. We operate a parallel-run phase during which the legacy system and modern platform remain in sync, allowing validation teams to reconcile record counts, checksums, and business rules before any cutover. All extraction agents are deployed with configurable throughput throttles to prevent I/O contention on DASD or tape subsystems. Cutover itself is executed during a pre-negotiated maintenance window with automated rollback scripts tested in staging beforehand.

ETL (Extract, Transform, Load) performs all data shaping in an intermediate processing tier before data lands in the target store, which was necessary when storage was expensive and query engines were weak. ELT (Extract, Load, Transform) loads raw data directly into the cloud data warehouse or lakehouse and leverages the platform's elastic compute to run transformations in-place using SQL or Spark. For modern cloud platforms such as Snowflake, BigQuery, or Databricks, ELT is significantly more cost-efficient because storage is cheap and massively parallel query engines can process transformations at scale without a separate ETL server fleet. ELT also improves data lineage because the raw layer is always preserved, enabling reprocessing and audit without re-ingestion.

The timeline depends heavily on schema complexity, the volume of PL/SQL stored procedures, the number of dependent applications, and the target platform chosen (Aurora PostgreSQL, Azure SQL Managed Instance, AlloyDB, or a columnar warehouse). For a mid-sized Oracle estate of 500 to 2,000 tables with moderate procedural logic, our structured programme typically runs 16 to 24 weeks from discovery to production cutover. Estates with heavy Oracle-specific features such as partitioned global indexes, Advanced Queuing, or Spatial require additional refactoring sprints. We use automated schema conversion tools (AWS SCT, Striim, Qlik) to accelerate the mechanical translation and focus human effort on the complex edge cases that automation cannot resolve.

A data warehouse imposes a predefined schema on structured data and excels at fast SQL analytics but struggles with semi-structured or unstructured content. A data lake stores everything in raw format on cheap object storage but historically lacked transactional consistency and performant SQL access, making it unreliable for BI workloads. A data lakehouse combines both paradigms: open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi add ACID transactions, schema evolution, and time-travel on top of object storage, while integrated query engines deliver sub-second interactive SQL. The result is a single platform that serves data science, real-time streaming, and traditional BI without the cost and complexity of maintaining separate systems.

Data mesh is a decentralised data architecture that assigns ownership of data products to the business domains that produce them rather than centralising everything in a monolithic data platform team. Each domain publishes its data as a discoverable, governed product through a self-serve infrastructure platform, enabling consumers across the organisation to find and use data without filing tickets with a central team. Data mesh is best suited to large organisations with multiple autonomous business units, significant data producer-to-consumer ratios, and a mature product engineering culture. Smaller organisations or those still building foundational data quality practices may find that a well-governed centralised lakehouse delivers more value with less organisational overhead in the near term.

Master data management (MDM) is often the highest-risk component of a modernisation programme because inconsistent entity definitions - customer, product, supplier, location - propagate errors into every downstream system. Our approach begins with a data profiling phase that identifies all golden-record candidates across source systems and scores them by completeness, uniqueness, and conformance. We then implement a hub-and-spoke MDM architecture using platforms such as Informatica Intelligent Data Management Cloud, Profisee, or a custom Medallion-layer identity resolution pipeline, depending on the client's tooling landscape. Stewardship workflows, conflict resolution rules, and survivorship logic are codified and reviewed with business data owners before the MDM hub goes live.

We begin with a full inventory of the HDFS namespace using Apache Atlas or custom metadata scanners to catalogue datasets, ownership, retention policies, and downstream consumers. Data is migrated in reverse chronological priority - hot partitions first via distcp-to-object-storage pipelines, cold archives via staged transfer with integrity checksums - so that production queries can switch to the cloud target while historical backfills continue in parallel. Hive Metastore is migrated to AWS Glue Data Catalog, Unity Catalog, or the equivalent managed service, preserving table definitions and partition metadata. Spark jobs are re-platformed to Databricks, EMR Serverless, or Dataproc with automated dependency scanning to flag HDFS path references, Kerberos dependencies, and on-premises JDBC connections that require remediation.

Governance is embedded throughout the programme rather than bolted on at the end. During migration we implement column-level lineage tracking using dbt, OpenLineage, or Marquez so that every transformation is traceable from source to consumption. Data quality rules are encoded as automated checks in the pipeline using Great Expectations, Monte Carlo, or Soda, with alerting integrated into the operations runbook. Post-migration, we configure role-based access control, row-level security policies, and dynamic data masking in the target platform to ensure that sensitive PII, financial, or health data is accessible only to authorised consumers. Data catalogues (Collibra, Atlan, or DataHub) are populated and linked to the lineage graph so that stewards can manage ownership and certification on an ongoing basis.

We deliver modernisation programmes across all major cloud providers: AWS (Redshift, S3 Data Lake, Glue, Lake Formation, MSK, EMR Serverless), Microsoft Azure (Fabric, Synapse Analytics, ADLS Gen2, Event Hubs, Purview), and Google Cloud (BigQuery, Dataplex, Dataflow, Pub/Sub, Looker). We are cloud-agnostic in our architecture recommendations and will recommend the platform - or multi-cloud topology - that best aligns with the client's existing vendor relationships, licensing position, and skill base. For clients already invested in Snowflake or Databricks, we integrate those platforms as the lakehouse layer regardless of the underlying cloud provider, maximising the value of existing contracts.

Data quality degradation during migration typically originates from three sources: encoding and character set mismatches, precision loss in numeric type conversions, and silent null-handling differences between source and target platforms. Our migration framework includes a pre-migration profiling report that documents baseline quality metrics - null rates, cardinality, value distributions, referential integrity ratios - for every migrated table. During migration, automated reconciliation jobs run after each batch or incremental load and compare row counts, column-level statistics, and a configurable sample of business-critical fields between source and target. Any divergence above the agreed tolerance threshold triggers an alert and pauses the pipeline for investigation, ensuring that no data quality regressions reach the production-ready state without sign-off.

A full modernisation programme requires a cross-functional team spanning data engineering, data architecture, cloud infrastructure, security, and business analysis. Our delivery model embeds a programme architect who owns the technical vision and platform decisions, senior data engineers who build and test migration pipelines, a data governance lead who works with business data stewards on MDM and cataloguing, a cloud infrastructure engineer who provisions and secures the target environment, and a QA lead who owns reconciliation, regression, and performance testing. We also assign a dedicated delivery manager who coordinates sprint ceremonies, stakeholder reporting, and dependency tracking across the client's IT and business teams. Business analysts from the client side are essential partners for validating that migrated data meets the semantic requirements of downstream applications.

Compliance requirements are mapped at the outset of the programme during a data classification workshop where we identify all data elements subject to regulatory obligations and tag them in the source catalogue. Sensitive fields are pseudonymised or encrypted at rest and in transit before any data leaves the on-premises boundary, and transfer mechanisms are validated against the client's information security policies and, where applicable, standard contractual clauses for cross-border transfers. In the target platform, we implement encryption key management (AWS KMS, Azure Key Vault, Google CMEK) with client-managed keys, audit logging for all data access, and automated data retention and deletion pipelines that enforce regulatory retention schedules. A post-migration compliance attestation report is produced for review by the client's data protection officer or compliance team.

Data modernisation engagements are typically structured as a fixed-scope discovery and design phase followed by a time-and-materials or milestone-based delivery phase. The discovery phase - covering data estate inventory, architecture design, risk assessment, and roadmap - usually runs four to six weeks and represents roughly 15 to 20 percent of the total programme investment. Delivery costs vary based on estate complexity, the degree of automated migration tooling applicable, and the target platform, but mid-market enterprise programmes commonly range from six to eighteen months of engagement. Cloud infrastructure costs are separate and depend on data volumes and compute requirements; we provide detailed TCO modelling during discovery so clients can compare the cost of the modernisation investment against the ongoing savings from retiring legacy infrastructure licences and hardware maintenance contracts.

Yes - incremental modernisation, sometimes called the strangler fig approach applied to data, is often the lowest-risk path for organisations that cannot tolerate a hard cutover. In this model, new data products and analytical workloads are built on the modern platform from the outset, while legacy systems continue to serve existing consumers. A synchronisation layer keeps both environments consistent during the transition period, and workloads are migrated one domain or product at a time with independent validation and go-live gates. This approach extends the overall timeline compared to a big-bang migration but reduces change risk, distributes organisational learning across a longer period, and allows the business to realise value from modernised domains before the entire estate is migrated.

Success metrics are agreed with the client during the programme definition phase and typically span four dimensions: technical performance, cost, data quality, and business value enablement. Technical metrics include query response time improvements, pipeline latency reductions, system availability, and the elimination of manual reconciliation processes. Cost metrics track the reduction in legacy licence fees, hardware maintenance costs, and the total cloud spend relative to the pre-migration baseline. Data quality metrics include improvement in completeness, accuracy, and timeliness scores for critical datasets as measured by the automated quality monitoring layer. Business value metrics - such as time-to-insight for analytics teams, new data products launched post-modernisation, or revenue attribution from data-driven decisions - are tracked against the business case established at programme inception.