Questions worth asking

Business systems are the applications organisations use to run day-to-day operations. This includes ERP, CRM, line-of-business applications, operational systems, specialist software and legacy platforms.

No. We have deep experience with Microsoft Dynamics 365, Dynamics AX and Dataverse, but our approach is application agnostic. We work with the systems and data needed to solve the problem, regardless of vendor.

Yes. Legacy and bespoke systems often contain critical business processes and data. We can help understand their structures, integrate them with newer platforms and determine where they should be retained, improved or replaced.

We start by understanding the business process behind the data. Knowing how transactions, customers, products, finance and operations are represented in the source system allows us to model and use the data correctly downstream.

Yes. We identify where information originates, how it flows between systems and which source should be authoritative. This can expose duplicate records, inconsistent definitions and process problems as well as technical issues.

Yes. We can support data discovery, mapping, transformation, migration design, validation and reconciliation when moving between business systems or consolidating platforms.

Yes. We work with APIs, data pipelines, databases and integration platforms to connect business systems with operational applications, data platforms and reporting solutions.

We look at the business problem first. Replacement is not automatically the answer. Sometimes a process, integration or data issue can be resolved without introducing an entirely new platform.

Yes. We can work backwards from databases, APIs, configuration, reports and user processes to understand how a system operates and how its data is structured.

Because data without business context is easy to misinterpret. Understanding the processes and systems that create the data leads to better integrations, models, reporting and decisions.

A modern data platform brings together the storage, processing, integration, governance and consumption of organisational data. The right architecture depends on the scale, speed, variety and value of the data rather than a fixed technology template.

Not necessarily. Cloud services can offer significant advantages, but the right architecture depends on your existing estate, security requirements, cost, skills and business objectives. Hybrid solutions can be entirely appropriate.

Yes. We work across Azure data services including Azure SQL, Data Lake, Data Factory, Microsoft Fabric and related platform services to design and deliver data architectures suited to the organisation.

Integration focuses on moving and connecting information between systems. Data engineering goes further by transforming, structuring, validating and preparing that information so it can be reliably used for analytics, applications and decision-making.

We base the decision on business need. Real-time processing adds complexity and cost, so it should be used where the value of immediate information justifies it. Many workloads are better served by simpler scheduled processing.

Yes. We start by understanding what already works, where the bottlenecks are and which changes would create the most value. Focused improvement can be better than a wholesale rebuild.

We identify where quality problems originate, define appropriate rules and controls, and decide where they should be corrected. The goal is not simply to clean data downstream but to reduce the causes of unreliable information.

Governance should help people use data confidently rather than create bureaucracy. We focus on practical ownership, definitions, quality, security, lineage and controls appropriate to the organisation and its risks.

Yes. We use source control, infrastructure as code, CI/CD and DataOps practices to make data platforms easier to deploy, test, operate and change safely.

A good architecture supports the required outcomes without unnecessary complexity. It should be understandable, reliable, secure, maintainable, adaptable and proportionate to the value it creates.

Reporting usually explains what has happened or what is happening. Analytics goes further by exploring why it happened, what may happen next and what action could be taken.

Yes. We work with Power BI across data modelling, semantic models, DAX, reporting, security, deployment and wider enterprise reporting architecture.

Yes. We can review existing reports, models and data flows to identify performance, usability, consistency and governance problems before deciding whether redevelopment is necessary.

A good dashboard makes the important information obvious. It should support a specific decision or audience, use clear measures, minimise unnecessary visual noise and make exceptions or changes easy to identify.

Yes. We work with stakeholders to translate objectives into measurable indicators and ensure the underlying data and definitions support consistent interpretation.

Usually by agreeing common definitions, ownership and calculation logic, then implementing those consistently in shared data and semantic models. Technology alone cannot resolve disagreements about what a metric actually means.

AI can help identify patterns, summarise information, explore scenarios and accelerate analysis. We treat it as augmentation rather than a replacement for human judgement, particularly where decisions have significant consequences.

Yes. Depending on the problem, this can include forecasting, statistical analysis, geospatial analysis, segmentation, anomaly detection and other analytical techniques.

Trust comes from reliable source data, clear definitions, controlled transformations, appropriate testing and transparency about assumptions and limitations.

We connect analytics back to decisions and outcomes. If information is not being used, improving decisions or changing behaviour, producing more reports is unlikely to solve the problem.

We provide practical training across data platforms, analytics, business systems and related delivery practices. The emphasis is on helping people understand how the technology works, how it fits the wider architecture and how to use it confidently.

Both. We can adapt the level and emphasis for architects, engineers, analysts, developers, managers, product owners and business users.

Yes. Training can cover areas such as Azure data services, Microsoft Fabric, Power BI, data engineering, integration, architecture and related Microsoft technologies.

Yes. Where appropriate, we can use your architecture, terminology, processes and examples so the training relates directly to the environment people actually work in.

Yes. This can range from report development and data modelling through to DAX, semantic models, governance, deployment and enterprise Power BI practices.

Yes. Training can be built into delivery rather than treated as a separate activity. This helps teams understand the solution as it is developed and reduces dependency after handover.

Yes. We can help teams understand data architecture, modelling, integration patterns, platform design, decision-making and the trade-offs involved in choosing between different approaches.

Yes. Workshops are often more useful when the objective is to solve a specific problem, explore a technology or align a team around a common approach.

Yes. Training can be delivered remotely or in person depending on the audience, subject and location.

The goal is capability, not dependency. People should leave with a clearer understanding, greater confidence and the ability to apply what they have learned without needing continual external support.