Data architecture is the foundation of a data strategy that supports your organization's goals and
priorities. Agile data architecture does so in a collaborative and evolutionary (iterative and incremental)
manner. The goal of this article is to explore what agile data architecture is and why it is important. Other
articles in this series present heuristics for successful data architecture.
This article explores the following topics:
- What is agile data architecture?
- Why agile data architecture?
- Related resources
1. What is Data Architecture?
This is an important question because it depends on context. In Agile Data, as we show in Figure 1, we recognize that data architecture refers to three interrelated
concepts:
- The actualization of the data aspects of an organization or solution. This includes data assets
such as databases and files, data transport, and data technologies. It is important to note that data
architecture addresses a portion of your overall architecture. For example, the data architecture of an
organization is a subset of your overall enterprise architecture whereas the data architecture of a
solution (a system or application) is a subset of its overall solution architecture. With an agile
approach, your way of working (WoW) must be tailored to be fit-for-purpose for the situation that you
face - agilists adjust to their context. See Agile Data
Architecture in Context
for greater details.
- The description of the implementation. Data architecture is described in logical (what and why),
physical (how and where), and organizational (who, when, and value) terms. Data architectures can be
described by models, documents, data conventions, executable tests, and ideally reference architectures
in the form of working examples. These artifacts will address a range of architectural concerns such as
storage, security, flow, and more. Architectural descriptions may address the current architecture,
proposed or desired future architecture, and even your past architecture (often captured for regulatory
reasons). See
potential data architecture artifacts. With an agile approach these artifacts are concise,
fit-for-purpose, sufficient, and ideally executable. To do so we adopt strategies from the
Agile Modeling (AM) method.
- The activities around data architecture. This includes exploring, describing, building,
supporting and evolving the implementation and description. As you would imagine, the Agile Data (AD)
method describes many effective strategies for doing exactly these things in a collaborative and
evolutionary manner. See the description of the Agile data architect
role for details.
Figure 1. What is data architecture?
2. Why Agile Data Architecture?
Agile data architecture enables:
- A roadmap for effectively working with data. Data architecture creates a better understanding of
how your organization's systems ingest, process, and produce data. With an agile approach, this is done
so collaboratively via a fit-for-purpose approach.
- Data to become a shared asset.
Clean data architecture
eliminates departmental data silos to provide a complete view of your organization's data. This provides
an opportunity for a wider range of people to work with your existing data and hopefully not create yet
another copy of it.
- People to make data-driven decisions. For people to make effective decisions, they need ready
access to current data that pertains to that decisions. Clean data architectures provide interfaces that
make it easy for people to consume data using tools fit for their jobs. This includes data warehouse
(DW) and business intelligence (BI) platforms that deliver useful information and insights.
- System evolution. Clean data architectures are extensible and easily evolved to meet new business
needs. They do this by indicating where data is stored and how it flows through your enterprise, and by
providing consistent guidance for doing so.
- Data security. Data security is one aspect, in many cases the most critical aspect, of your
organization's overall security strategy. Clean data architecture addresses data security issues such as
access, encryption, integrity monitoring, and more.
- Technical quality. Clean data architecture helps improve data quality by reducing the number of
places source data is stored, streamlining data integration, and streamlining data transport.
- Agile data management. A clean data architecture is a crucial part of the data management
process, the overall goal of which is to ensure that data is used properly and meets business needs for
information.
- Lean data governance. Data governance guides and monitors the collection, storage, arrangement,
integration, and use of data in organizations, the goal of which is to ensure that your data is
accurate, consistent, and used appropriately.
3. Other Articles in This Series
This article is the first in a series. The other installments in this series are:
- Critical
success factors in agile data architecture
- Agile data
architecture in context
- The agile data
architecture process
- Clean data
architecture
- Potential data
architecture artifacts
- The agile data architect
4. Related Resources