In an increasingly complex data landscape, companies need robust and enterprise-grade data architecture to drive competitive advantage. This article explains what data architecture entails, why it matters, how to develop it, emerging trends and recommended steps to get started.
The Data Deluge and Associated Challenges
Today every company is reckoning with explosive data growth from an ever expanding multitude of sources – IoT sensors, cloud SaaS apps, social media channels, customer engagement systems and more.
While data holds tremendous potential value, significant investment is needed to store, prepare, integrate, analyze and activate it. Without strong data architecture, organizational data ecosystems get complex and chaotic very quickly. This leads to common issues like:
- Data silos with no single source of truth
- Multiple versions of customer or product data
- Analytics limited to a small subset of available data
- Hard to onboard new data sources or implement new use cases
- Costly data management without commanding benefits
These end up hampering fact-based decision making, lowering operational efficiency and delaying responses to market changes.
What is Data Architecture and Why it Matters Now
Data architecture provides the critical foundation to avoid such data swamp scenarios and accelerate returns from data investments. It codifies structures, practices, technologies and capabilities needed to optimally acquire, organize, distribute and leverage data across the enterprise.
Well architected data landscapes allow easier analysis of integrated historical and newly emerging data. They prime organizations for advanced applications like AI/ML modeling, optimization and simulation. As per IDC, data-centric companies are 2X more likely to report better business outcomes.
Key Elements of a Modern Data Architecture
Though specifics vary for companies, robust data architectures have some common components:
Data Generation and Acquisition
Architectural guidelines and pipelines to ingest batch or streaming data from authorized sources consistently into landing zones.
Data Storage and Management
Foundational data repositories providing storage, organization, backup, access and integration – like data warehouses, lakes and marts.
Data Access and Movement
Standardized controlled mechanisms enabling discovery and movement of data to various applications via pipelines, service buses, catalogs etc.
Analytics and Activation
Enable data consumption for business insights across reporting, exploratory analytics, statistical modeling and machine learning use cases.
Data Governance
Consistent organizational mechanisms involving people, processes and tools to manage data as an enterprise asset.
Security and Compliance
Embed policies, access controls, auditing mechanisms and tools to ensure regulatory compliance and prevent unauthorized access.
Key Steps for Developing Enterprise Data Architecture
While data architecture efforts need to be tailored, following are best practice steps:
1. Set Data Strategy and Target State Objectives
Define key principles, policies, metrics and capabilities required in envisioned end-state architecture and roadmap.
2. Analyze As-Is Data Landscape
Document existing infrastructure, systems, sources, schema, owners, flows, interdependencies, issues and costs.
3. Design High Level Conceptual Architecture
Map out core components needed for future architecture in conceptual fashion with key data entities and relationships.
4. Create Logical Data Model
Provide abstracted definitions of critical data entities, attributes, interrelationships, flows, security tiers aligned to requirements.
5. Build Physical Data Model
Map logical architecture constructs to actual physical data stores, pipelines, movement processes, security and access mechanisms.
6. Prioritize and Implement Iteratively
Roll out architectural components iteratively based on business impact, starting with foundational elements.
7. Refine Architecture Continuously
Keep improving the architecture by monitoring efficacy, soliciting user feedback, introducing new capabilities.
Balancing Flexibility and Governance as Key Priority
Modern data architecture needs to counter two conflicting forces – the need for stability, consistency and governance on one hand and pressure to support flexibility, agility and democratization on the other.
They key is to put in place modular architecture with well controlled integration mechanisms between components like analytical data marts, warehouses and governance hubs. Such Layered Segmented Architectures balance reuse and decentralization – avoiding bottlenecks of central control but preventing uncontrolled proliferation.
Data Architecture is Continuous Journey, Not One-Time Project
It is important not to consider data architecture as a one-time canonical exercise but rather an evolving blueprint adapting to new innovations. For instance, incorporating newer storage approaches like data lakes and graphs, using more automated schema management with machine learning and building cloud-native serverless data platforms need to be part of the roadmap.
Equally important is skilling up, growing and empowering architecture teams for the deep specialization required in modern tech environments. STEM talent with a passion for translating business context into optimal data designs is key to maturing architecture capabilities.
For those starting their data architecture definition exercise, focusing on foundational elements that solve high impact pain points and deliver quick wins is the best path forward rather than boiling the ocean. The journey needs to start now because data waits for no one!