Data Architecture
Data Architecture is the discipline of understanding and shaping a business’ data landscape to govern its lifecycle and utility. Data is an enabler; the value of data lies in how it enables a business to carry out activities that drive revenue and growth. Data therefore has to be understood in the context of the business. For this reason, Data Architecture is usually aligned closely to Business Architecture.
Why Would You Need This Service?
Having a bird’s eye view of the data landscape will allows your business to better conduct scenario tests and data impact analysis.
All data-related decisions, whether impacting people, process or technology, can be assessed comprehensively from an enterprise perspective and taken with confidence.
Enhanced information management, driven by Data Architecture, will enable your management team to take proactive steps, to see risks before they occur and spot opportunities that may potentially drive a competitive edge for your business.
How We Deliver This Service
We are often commissioned to produce data architecture artefacts as part of a broader enterprise architecture or data management capability development engagement. However, architecture models may also be commissioned to articulate enterprise context at different levels of abstraction, and it is perfectly normal to commission data architecture artefacts and models to understand and govern a particular domain or area of problem.
For example, the supply department may have a particular problem with a proliferation of duplicate supplier accounts which prevents the organisation from fully leveraging volume discounts. As a first step towards tackling this problem an as-is Supply data model may be commissioned to understand the size of the issue at hand followed by a to-be model to describe a aspired future estate.
Deliverables
Data Architecture Principles and Standards: a set of enduring guidelines and values to govern the approach to architecture design, management and decision-making.
Conceptual Data Model: a model showing the highest level of the data architecture hierarchy that groups together related data entities by subject area.
Logical Data Model: Catalogues and diagrams describing all the entities for each subject area and their attributes how they are related and the nature of their relationships at the detailed level including field lengths, data rules etc.
Physical Data Model: Catalogues and diagrams ready for utilisation within physical products and tools e.g. master data management.
Data Dictionary: Catalogues of the data entities and attributes, with the definitions, characteristics, business rules and standards that govern how they are stored and used.
CRUD Matrices: Matrix of data vs. business processes, user roles and other useful views of data.
Typical Outcomes
Data Architecture has the following benefits:
- Availability of a map that charts your business’ current data landscape that will aid in issue resolution, organisational impact assessment and scenario planning;
- Data standards to be used as a benchmark across the organisation thereby enabling standardisation;
- Availability of tools to trace the data footprint;
- Enables auditability of business change and its impact on the data landscape (and viceversa);
- Establishes data ownership within the business, leading to improved data quality;
- Creates a common language of communication between data and business professionals.
Case Studies
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