Data Architecture is all about understanding and organising your data landscape: knowing what data is valuable to your business, what it means and where it lives.
Reduce duplication and fragmentation across your organisation
Standardise terminology and promote a common understanding
Make your data more simple and less costly to manage
Improve discoverability and insightful analysis of your data
Make it easier to comply with industry regulations
Core to Ortecha’s work, we define meaningful, coherent Data Architectures that unlock value and make your data landscape easier to understand and cheaper to maintain.
We’ve seen it all! Conceptual, logical, semantic & physical models
We are skilled at both Application & Enterprise level Architecture
We endeavour to use existing standards rather than building new
We’re recognised as experts that deliver value to our clients
Our consultants have been involved in Architecture throughout their career, some for over 20 years
If your data landscape is complex it’s helpful to define how you’ll tackle your Data Architecture. We can help you set your vision and adopt best practice standards so modelling activities work in harmony.
It’s difficult to derive value from data that is misunderstood, misused, duplicated or ambiguous. We can help you pin down definitions, meanings and relationships, so you can make the most of your data.
Complex ETL (Extract-Transform-Load) processes can be tricky to follow and almost impossible to explain. We can help you design your data flows and data transformations so they’re clear and efficient.
Wherever you are with Data Architecture we can support you. Whether you need high-level one-off advice or regular ongoing guidance, we can help you efficiently manage your data landscape.
A Global Investment Bank was struggling with the challenges of regulatory demands such as BCBS239, because they lacked a common definition of the data across their different business units.
With thousands of applications, databases and feeds distributed across the organisation, they needed to better understand what data they had, how it flowed and where it was used.
Built Logical and Physical Data Models across each risk domain
Developed data standards to provide common definitions for all
All of the bank’s data described and visualised in data models
Common data sourcing led to thousands of legacy feeds being decommissioned
Ability to join disparate datasets together for analytics and reporting purposes
Improved discoverability, enabling the bank to comply with risk regulations