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HomeIT LeadershipWhat is data architecture? A framework for managing data
by Thor Olavsrud
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WHAT IS DATA ARCHITECTURE? A FRAMEWORK FOR MANAGING DATA

Feature
Jan 24, 2022
AnalyticsData ManagementData Mining
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DATA ARCHITECTURE TRANSLATES BUSINESS NEEDS INTO DATA AND SYSTEM REQUIREMENTS
AND SEEKS TO MANAGE DATA AND ITS FLOW THROUGH THE ENTERPRISE.



Credit: Brandon Mowinkel


DATA ARCHITECTURE DEFINITION

Data architecture describes the structure of an organization’s logical and
physical data assets and data management resources, according to The Open Group
Architecture Framework (TOGAF). It is an offshoot of enterprise architecture
that comprises the models, policies, rules, and standards that govern the
collection, storage, arrangement, integration, and use of data in organizations.
An organization’s data architecture is the purview of data architects.


DATA ARCHITECTURE GOALS

The goal of data architecture is to translate business needs into data and
system requirements and to manage data and its flow through the enterprise. Many
organizations today are looking to modernize their data architecture as a
foundation to fully leverage AI and enable digital transformation. Consulting
firm McKinsey Digital notes that many organizations fall short of their digital
and AI transformation goals due to process complexity rather than technical
complexity.


DATA ARCHITECTURE PRINCIPLES

According to Joshua Klahr, vice president of product management, core products,
at Splunk, and former vice president of product management at AtScale, six
principles form the foundation of modern data architecture:


 1. Data is a shared asset. A modern data architecture needs to eliminate
    departmental data silos and give all stakeholders a complete view of the
    company.
 2. Users require adequate access to data. Beyond breaking down silos, modern
    data architectures need to provide interfaces that make it easy for users to
    consume data using tools fit for their jobs.
 3. Security is essential. Modern data architectures must be designed for
    security and they must support data policies and access controls directly on
    the raw data.
 4. Common vocabularies ensure common understanding. Shared data assets, such as
    product catalogs, fiscal calendar dimensions, and KPI definitions, require a
    common vocabulary to help avoid disputes during analysis.
 5. Data should be curated. Invest in core functions that perform data curation
    (modeling important relationships, cleansing raw data, and curating key
    dimensions and measures).
 6. Data flows should be optimized for agility. Reduce the number of times data
    must be moved to reduce cost, increase data freshness, and optimize
    enterprise agility.


DATA ARCHITECTURE COMPONENTS

A modern data architecture consists of the following components, according to IT
consulting firm BMC:

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 * Data pipelines. A data pipeline is the process in which data is collected,
   moved, and refined. It includes data collection, refinement, storage,
   analysis, and delivery.
 * Cloud storage. Not all data architectures leverage cloud storage, but many
   modern data architectures use public, private, or hybrid clouds to provide
   agility.
 * Cloud computing. In addition to using cloud for storage, many modern data
   architectures make use of cloud computing to analyze and manage data.
 * Modern data architectures use APIs to make it easy to expose and share data.
 * AI and ML models. AI and ML are used to automate systems for tasks such as
   data collection, labeling, etc. At the same time, modern data architectures
   can help organizations unlock the ability to leverage AI and ML at scale.
 * Data streaming. Data streaming is flowing data continuously from a source to
   a destination for processing and analysis in real-time or near real-time.
 * Container orchestration. A container orchestration system such as open-source
   Kubernetes is often used to automate software deployment, scaling, and
   management.
 * Real-time analytics. The goal of many modern data architectures is to deliver
   real-time analytics, the ability to perform analytics on new data as it
   arrives in the environment.


DATA ARCHITECTURE VS. DATA MODELING

According to Data Management Book of Knowledge (DMBOK 2), data architecture
defines the blueprint for managing data assets by aligning with organizational
strategy to establish strategic data requirements and designs to meet those
requirements. On the other hand, DMBOK 2 defines data modeling as, “the process
of discovering, analyzing, representing, and communicating data requirements in
a precise form called the data model.”

While both data architecture and data modeling seek to bridge the gap between
business goals and technology, data architecture is about the macro view that
seeks to understand and support the relationships between an organization’s
functions, technology, and data types. Data modeling takes a more focused view
of specific systems or business cases.




DATA ARCHITECTURE FRAMEWORKS

There are several enterprise architecture frameworks that commonly serve as the
foundation for building an organization’s data architecture framework.

 * DAMA-DMBOK 2. DAMA International’s Data Management Body of Knowledge is a
   framework specifically for data management. It provides standard definitions
   for data management functions, deliverables, roles, and other terminology,
   and presents guiding principles for data management.
 * Zachman Framework for Enterprise Architecture. The Zachman Framework is an
   enterprise ontology created by John Zachman at IBM in the 1980s. The “data”
   column of the Zachman Framework comprises multiple layers, including
   architectural standards important to the business, a semantic model or
   conceptual/enterprise data model, an enterprise/logical data model, a
   physical data model, and actual databases.
 * The Open Group Architecture Framework (TOGAF). TOGAF is an enterprise
   architecture methodology that offers a high-level framework for enterprise
   software development. Phase C of TOGAF covers developing a data architecture
   and building a data architecture roadmap.


MODERN DATA ARCHITECTURE BEST PRACTICES

Modern data architectures must be designed to take advantage of emerging
technologies such as artificial intelligence (AI), automation, internet of
things (IoT), and blockchain. Dan Sutherland, senior director, technology
consulting, Protiviti, says modern data architectures should adhere to the
following best practices:

 * Cloud-native. Modern data architectures should be designed to support elastic
   scaling, high availability, end-to-end security for data in motion and data
   at rest, and cost and performance scalability.
 * Scalable data pipelines. To take advantage of emerging technologies, data
   architectures should support real-time data streaming and micro-batch data
   bursts.
 * Seamless data integration. Data architectures should integrate with legacy
   applications using standard API interfaces. They should also be optimized for
   sharing data across systems, geographies, and organizations.
 * Real-time data enablement. Modern data architectures should support the
   ability to deploy automated and active data validation, classification,
   management, and governance.
 * Decoupled and extensible. Modern data architectures should be designed to be
   loosely coupled, enabling services to perform minimal tasks independent of
   other services.


DATA ARCHITECTURE ROLES

Here are some of the most popular job titles related to data architecture and
the average salary for each position, according to data from PayScale:


 * Data architect: $79K-$160K
 * Project manager: $58K-$129K
 * Solutions architect: $76K-$163K
 * Data engineer: $66K-$132K
 * Data analyst: $45K-$87K
 * Data scientist: $68K-$136K


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