Enterprise Data Services
Turn enterprise data into trusted operational evidence.
Design, integrate, govern and support the data pipelines, databases, warehouses and reporting models that important decisions depend on.
Discuss enterprise data services →Senior expertise focused on what the organisation needs to protect and improve.
Design, integrate, govern and support the data pipelines, databases, warehouses and reporting models that important decisions depend on. Every engagement begins with the operating context, evidence and a clearly defined outcome.
A practical, accountable service.
Data foundations
Build controlled pipelines with validation, monitoring, error handling and clear ownership.
Integration & migration
Connect platforms and move information with reconciliation, auditability and business confidence.
Analytics foundations
Create governed models and trusted definitions for reporting, dashboards and analysis.
Clear steps. Visible decisions.
Where data work usually goes wrong
Three failures account for most of what we are asked to fix, and none of them are about tooling.
The reporting layer was built before the data was fit to report on. A dashboard sitting on inconsistent source data produces numbers people argue about, and once a report has been publicly wrong twice nobody uses it again. Confidence is difficult to rebuild.
Nobody owns the definitions
Three departments each have a number for revenue, all three are calculated differently and all three are defensible. That is a governance problem presenting as a technical one, and no amount of engineering fixes it.
The pipeline has no owner
An integration was built for a project, it works, and when it fails silently three months later the data quietly goes stale. Reports keep rendering. Nobody notices for weeks.
What follows from that
The sequence is definitions, then quality, then integration, then warehouse, then reporting. Building in the reverse order is common and it is why so many analytics projects are abandoned.
The data services we deliver
Data engineering
Pipeline design and build, orchestration, scheduling, error handling and monitoring. The plumbing that moves data reliably and tells you when it has not. Covered on data engineering.
Data integration
Connecting source systems: ERP, finance, warehouse management, CRM, operational platforms and files. Batch, incremental and near-real-time where the requirement justifies it. Covered on data integration.
Data warehousing
Modelling, storage design and the structures that make analytical queries fast without punishing the source systems. Covered on data warehousing.
Data analytics and business intelligence
Semantic models, reporting and dashboards on Power BI and equivalent platforms, built on a governed model rather than on direct connections to production. Covered on data analytics and BI.
Database services
Administration, performance, capacity and maintenance for the databases underneath both operational and analytical workloads. Covered on database services.
Data quality
Profiling, rules, monitoring and remediation, so that problems are found in the pipeline rather than in a board pack. Covered on data quality.
Data migration
Moving data between systems during a platform change, with reconciliation that proves what arrived matches what left. Covered on data migration.
Managed data services
Ongoing operation of the pipelines, warehouse and reporting layer, with monitoring and support. Covered on managed data services.
What makes our data work different
We also run the infrastructure
Most data consultancies hand a performance problem to your infrastructure provider and wait. We look at the query plan and the storage latency in the same conversation, because both practices sit in the same firm. Covered on server and database infrastructure.
We also run an EPM practice
Financial consolidation and planning systems are among the most demanding data consumers in any organisation, and the mapping, hierarchy and reconciliation discipline that Oracle Hyperion, FCCS and OneStream work demands transfers directly to warehouse design. Covered on Oracle EPM and Hyperion services.
Senior-led
The person who designs the model is accountable for it afterwards.
We are direct about scope
Data work has a habit of expanding. We would rather deliver one governed subject area that people trust than a broad platform nobody uses.
Platforms
Databases
Microsoft SQL Server, Oracle Database, PostgreSQL and MySQL.
Integration and orchestration
SQL Server Integration Services, Azure Data Factory, Oracle Data Integrator, and scripted pipelines where a lighter approach fits better than a platform.
Warehouse
On-premises SQL Server or Oracle, Azure SQL and Synapse, or AWS-hosted equivalents. Chosen against the volume and the query pattern rather than by preference.
Analytics
Power BI primarily, given how widely Microsoft licensing is already held in this market, and others where an organisation has an existing investment.
We will use what you already own where it is capable. Most mid-market organisations hold Power BI licensing through Microsoft 365 and a SQL Server instance with capacity. That is frequently enough for a first governed subject area, and starting there is a considerably better test of value than a platform purchase.
Common questions about Enterprise Data Services
What are data engineering services?
Data engineering services build and operate the pipelines that move data from source systems into a structure fit for reporting and analysis: extraction, transformation, loading, orchestration, error handling and monitoring.
It is distinct from analytics, which is what happens once the data is there. The distinction matters because most disappointing analytics projects have an engineering problem underneath them: the data arriving is inconsistent, incomplete or silently stale, and no amount of dashboard work fixes that.
Where should we start?
With definitions, then quality, then integration, then the warehouse, then reporting. Building in the reverse order is the most common sequence and it is why analytics projects get abandoned.
Practically, start with one subject area that matters and take it end to end: agreed definitions, verified quality, a reliable pipeline and a report people trust. One governed subject area that gets used is worth considerably more than a broad platform nobody believes.
Can you work with the data platform we already have?
Almost always, and it is usually the right first move. Most mid-market organisations already hold Power BI licensing through Microsoft 365 and have a SQL Server instance with spare capacity.
That combination is frequently enough for a first governed subject area. Starting there tests whether the organisation will actually use the output, which is a better basis for a platform decision than a business case written before anyone has seen a working report.
Why do our reports disagree with each other?
Almost always because the definitions differ rather than because the data is wrong. Three departments each calculating revenue in a defensible but different way produces three defensible but different numbers.
That is a governance problem presenting as a technical one. It gets fixed by agreeing definitions, writing them down, implementing them once in a shared model and having everything report from that model. Rebuilding the reports without agreeing the definitions reproduces the problem in new software.
Do we need a data warehouse?
Not always. Where reporting needs span several source systems, or where analytical queries are slowing your operational systems, yes. Where you have one system and its native reporting is adequate, no.
The intermediate answer that suits many mid-market organisations is a modest warehouse covering two or three subject areas rather than an enterprise-wide model. It costs a fraction, it delivers most of the value and it can be extended when the case is proven. Covered on data warehousing.
How do you handle POPIA in data work?
Personal information in a warehouse or a reporting layer carries the same obligations it does anywhere else: a lawful basis, purpose limitation, retention and access control.
Two practical points. Data warehouses concentrate personal information from several systems into one place, which raises the consequence of an access failure. And copying production data into development or test environments, which is routine, moves personal information into environments with weaker controls. Masking or synthetic data for non-production is the standard answer and it is frequently not done.
How long does a first data project take?
Six to twelve weeks for one subject area taken end to end, including definitions, integration, model and a first set of reports.
The variable is agreement on definitions rather than technical effort, and that depends on business availability. Projects that resource the definition work properly finish faster than ones that treat it as a formality and rework the model afterwards.
When do we not need data services?
When one operational system holds the data and its native reporting answers the questions people actually ask. Adding a warehouse and a BI layer to that produces cost and complexity with no gain.
The case gets real when reporting spans multiple systems, when analytical queries are affecting operational performance, when people are exporting to spreadsheets and reconciling by hand, or when nobody can agree on a number. If none of those apply, spend the money elsewhere and we will say so.