Architecture

Building a Digital Business Platform with Microsoft Tech

| Charlie Ranalli

Key Concepts

  • Microsoft’s digital business platform vision has been developed over time, combining modular business applications, flexible infrastructure, and structurally embedded AI.
  • Dynamics 365 provides a composable architecture at multiple levels: business domain modularity, application modularity, and separation of data, logic, and user interfaces.
  • Dataverse and Microsoft Purview deliver a unified operational data layer and governance framework for data quality, consistency, and compliance.
  • Microsoft Fabric integrates real-time analytics, operational reporting, and AI-driven insights into a unified environment, enabling continuous intelligence.
  • Power Platform enables low/no-code development, extending business applications and workflows.
  • Azure platform services – hosting, identity governance with Microsoft Entra, CD/CI with Azure DevOps and monitoring with Azure Monitor.
  • Multi-vendor platform with systems like SAP S/4HANA can be integrated through API-first models, identity federation, DevOps pipelines, and unified monitoring.

This article follows on from Digital Business Platform: A Reference Architecture to Accelerate Digital Metabolism, moving from architectural principles into practical implementation and outlines how Microsoft’s technology ecosystem –Dynamics 365, Azure, and Microsoft Fabric – can be engineered to realize a composable, scalable digital business platform.

Microsoft’s vision has developed over time, shaped by three complementary dimensions that reflect a long-term platform strategy under the leadership of Satya Nadella: business applications as a platform, elastic cloud infrastructure as an enabler, and Data and AI as a ubiquitous, embedded capability across the stack.

Microsoft continues to develop its technologies with a platform-oriented mindset – supporting open standards, architectural extensibility, and modular design. This guiding principle is reflected in recent developments, like the announced support for Model Context Protocol (MCP) and Open Agent2Agent (A2A) standards originally created by Anthropic and Google respectively.

A Platform Approach to Business Applications

In the mid 2000s, Microsoft Business Solutions (now Microsoft Dynamics) was formed with a clear architectural ambition: business applications needed to be modular, extensible, and designed as a platform. One of the most consequential moves was the acquisition of Navision, whose Axapta product – later Dynamics AX and eventually Dynamics 365 – was built with distinct data, business logic, and UI layers. This architecture predates composability principles but laid the foundation for a suite of modular applications spanning finance, operations, sales, marketing, and service.

Azure: Cloud Infrastructure

In parallel, Microsoft built Azure as a global infrastructure-as-a-service (IaaS) platform to support this modular vision. More than a hosting environment, Azure was designed to be open and interoperable, capable of supporting Microsoft-native, open-source, and third-party technologies. It includes all foundational elements of a digital business platform – identity, security, integration, observability, and operations – and acts as the connective tissue that links business applications, analytics engines, and operational systems into a cohesive whole.

Azure also enables modern software engineering practices. Team Foundation Server (TFS), launched in 2005, introduced early CI/CD capabilities. It eventually evolved into Azure DevOps, which today supports high-velocity, modular, distributed delivery pipelines at scale.

AI: Intelligence Embedded Across the Platform

The third dimension – artificial intelligence – has increasingly become an integral layer within Microsoft’s platform strategy. Far from being treated as a separate system, AI is embedded into applications, development tools, and infrastructure services. Azure AI and Microsoft Copilot technologies exemplify this convergence: enabling natural language interaction, automating complex workflows, enriching analytics, and accelerating software development itself. AI is not simply layered on top of the platform – it is woven through it, transforming how capabilities are delivered, consumed, and scaled.

Implementation Blueprint

A digital business platform is not a single product or solution, but an architectural model for assembling modular technologies into a unified, scalable foundation to manage the infrastructure. It integrates core business applications, shared data services, analytics and AI engines, low-code tools, and modern infrastructure under a common governance model. The goal is to create a composable environment where capabilities can be deployed, extended, and reconfigured dynamically in response to business needs.

This blueprint outlines how Microsoft’s ecosystem – Dynamics 365, Azure, Microsoft Fabric, and Power Platform – can be engineered to implement a full-scale digital business platform. While based on Microsoft technologies, the architecture is vendor-neutral by design, enabling the structural adoption of systems such as SAP as first-class components. The document is structured around five layers: business applications, data services, analytics and AI, low-code, and platform management, and an extension exploring multi-vendor architecture.

1) Composable Business Architecture

True composability is achieved when business capabilities are modularized at different layers of the application and data stack. Microsoft’s modern business application ecosystem realizes this principle through three nested forms of modularity:

Macro Modularity: Independent Applications by Business Domain

At the highest level, Dynamics 365 offers a set of independent but interoperable applications organized around major business domains – commerce, finance, supply chain management, customer service, etc.

Credit: Microsoft Dynamics 365 Commerce Component Overview, © Microsoft Corporation

Each application is built to be deployable individually or in combination with others, enabling organizations to adopt and expand their platform gradually, based on business needs. This modular application approach allows organizations to compose and recompose their digital operations as market conditions and strategies evolve.

Application Modularity: The Case of Dynamics 365 Commerce

Zooming in, each Dynamics 365 application itself is designed with modularity in mind. Dynamics 365 Commerce, for example, structures its internal architecture to support composability across retail channels, back-office operations, call centers, and e-commerce sites. Core subsystems – such as the Retail Server, Channel Database, and Commerce Scale Unit – are modular components that can be configured, scaled and extended independently, depending on channel-specific needs.

Credit: Microsoft Dynamics 365 Commerce Headless Architecture, © Microsoft Corporation

This modular design allows for greater operational flexibility and agility, enabling rapid rollout of new experiences across physical and digital storefronts without massive reengineering of backend systems.

Data, Logic, and UI Separation: Headless Engineering

The separation of data, business logic, and user interface is a fundamental modularity principle implemented in Dynamics 365 Commerce’s headless commerce model. In this design:

  • Commerce APIs expose business logic and data independently of any specific user interface.
  • Retail Server serves as an orchestration layer between front-end applications and core business services.
  • Channel Databases and Commerce Runtime (CRT) services maintain data and transactional integrity across retail stores and e-commerce channels.

This separation enables unique customer experiences across channels and devices while relying on consistent, reusable business processes underneath. Custom front-end applications (for example embedded in a branded mobile app), third-party platforms, and Microsoft Power Platform connectors can all consume these APIs to build tailored solutions, enabling a true omnichannel strategy.

API-First Integration: Building for Composability

An API-first integration approach guides all layers of modularity. Dynamics 365 Commerce APIs, delivered via OData protocols and Web API standards, offer standardized, discoverable interfaces that support both first-party (Microsoft) and third-party application development.

This model-driven, API-centric design:

  • Decouples service consumers from service implementations
  • Enables external systems to access business services cleanly and securely
  • Facilitates agility and scalability in integrating new applications, devices, or platforms

Support for industry standards like OData (an OASIS Open Standard) ensures that integration approaches are interoperable across ecosystems, whether consuming APIs directly from Dynamics 365 applications or via connectors.

2) Data Services

The digital business platform requires a unified data service capable of supporting operational transactions, integrating across applications, and providing governance across environments. Microsoft’s Dataverse, combined with Microsoft Purview and Azure integration services, addresses these needs within a modular and standardized framework.

Dataverse: Operational Data Layer

Dataverse provides a structured and secure environment to manage operational data models across business applications. Key capabilities include:

  • A standardized schema for common business entities (customers, products, transactions)
  • Support for role-based access control and security policies
  • Metadata services for defining validations, relationships, and business rules
  • Native integration points with Dynamics 365, Microsoft 365, Azure services, and the Power Platform ecosystem

Dataverse abstracts storage complexity, allowing applications to interact with business data through a consistent and scalable service layer.

Data Governance: Microsoft Purview Integration

Microsoft Purview extends governance capabilities across Dataverse and connected data sources:

  • Register and scan environments to catalog assets and track data lineage
  • Define and enforce data policies, classifications, and retention strategies
  • Manage compliance and data protection across multi-cloud, on-premises, and SaaS environments

Purview standardizes governance processes, supporting auditability and operational compliance without duplicating management efforts.

Data Integration and Interoperability

Dataverse supports integration through an OData-compliant API surface, providing a standardized REST-based protocol for external and internal service communication:

  • Direct exposure of entity data models for programmatic access
  • Compatibility with OData-enabled platforms like SAP Gateway
  • Integration with external services via standardized connectors, such as Informatica’s OData Connector

This architecture supports loose coupling between systems, enabling dynamic integration patterns across operational and analytical workflows without requiring bespoke adapters or point-to-point configurations.

3) Analytics, AI, and Data-Driven Workstreams

A digital business platform must integrate operational and analytical layers to enable data-driven processes across the enterprise. Microsoft Fabric provides an end-to-end environment for integrating, managing, analyzing, and visualizing data, connecting directly with operational sources such as Dataverse and Dynamics 365.

Fabric as the End-to-End Analytics Platform

Microsoft Fabric consolidates data integration, engineering, data science, real-time analytics, and business intelligence into a unified SaaS-based architecture with:

  • Centralized data storage and processing across data lakes, warehouses, and real-time engines
  • Native integration with Power BI for operational and strategic reporting
  • Role-based workspaces supporting both IT-driven and business-user-driven analytics

Fabric’s architecture aligns with composability principles by allowing businesses to manage diverse analytics workloads through modular, interconnected services, reducing redundancy and complexity.

Real-Time Operational Analytics: Azure Synapse Link for Dataverse

Azure Synapse Link enables near real-time replication of Dataverse data into Fabric environments without requiring complex ETL.

  • Changes in Dataverse are automatically synchronized to Fabric, enabling up-to-date analytics without impacting transactional systems.
  • Data remains available for advanced modeling, dashboarding, and real-time insight generation through Power BI and other Fabric services.
  • Insights can be operationalized by surfacing recommendations within Dynamics 365 workflows, enabling closed-loop decisioning between data and action.

This architecture supports continuous, data-driven operations while minimizing the latency and complexity traditionally associated with analytical data flows.

Data Integration for Analytics Workflows

Fabric extends its operational analytics capabilities through Data Factory pipelines, supporting over 200 prebuilt connectors to integrate data from internal systems, cloud platforms, and external providers.

  • Ingestion pipelines enable hybrid analytics scenarios combining Dataverse operational data, ERP historical data, and external market or partner data.
  • Data transformations and enrichment processes can be managed within Fabric workspaces, supporting advanced analytics, machine learning, and AI-driven models.
  • Open-standard integration protocols and connectors ensure interoperability with enterprise platforms such as SAP, Salesforce, and others.

Standardizing ingestion and transformation enables the creation of scalable, reliable analytics environments tightly coupled with operational systems.

A Connected Analytics and Operations Loop

The combination of Dynamics 365, Dataverse, Azure Synapse Link, and Fabric enables a fully connected operational and analytical architecture. Data is captured through business applications, replicated and analyzed in near real time, and operationalized back into workflows and decision points. This model supports not only reporting and dashboarding, but also the development of AI-augmented processes, predictive insights, and autonomous operations, consistent with modern digital business platform principles.

4) AI integration + Low-Code/No-Code

Extending the digital business platform requires a structured approach to building new capabilities with minimal overhead, maintaining alignment with core operational systems, and avoiding complex and risky customizations that increase technical debt.

Microsoft’s Power Platform is the unified low-code/no-code environment designed to accelerate solution development, automate processes, and broaden participation in digital innovation.

Power Platform: Unified Low-Code/No-Code Environment

The Power Platform suite combines tools for application development, process automation, data analysis, and AI. Core components include:

  • Power Apps for custom business application development
  • Power Automate for workflow automation
  • Power Pages for secure external-facing portals
  • Copilot Studio for building conversational AI agents

Native integration with Dynamics 365 and Dataverse ensures that applications and workflows built within Power Platform are compatible with existing security models, operational datasets, and compliance frameworks.

Extending Line-of-Business Systems: Power Apps

Power Apps enables the development of custom applications that connect directly to Dataverse and Dynamics 365 data models. Applications can be designed by both professional developers and citizen developers, supporting a range of use cases from simple data capture forms to complex business process extensions. Prebuilt templates, reusable components, and connectors accelerate the development of solutions that extend ERP, CRM, and operational processes, enabling rapid adaptation without compromising system integrity.

Process Automation: Power Automate

Power Automate facilitates the automation of business processes by connecting applications and services through configurable workflows.

  • Supports event-driven, scheduled, and manually triggered workflows
  • Integrates with a broad range of Microsoft and third-party services through standard connectors
  • Enables automation of approvals, notifications, data synchronization, and task management based on business events

Workflows developed in Power Automate can interact with both operational systems (e.g., Dynamics 365) and external services, improving process efficiency and reducing manual effort.

AI-Enabled Agents: Copilot Studio

Copilot Studio allows the creation of conversational agents and automated assistants that interact with users across multiple channels, including web, mobile, and messaging platforms.

  • Agents can connect to Dataverse, APIs, and external services to retrieve and process information
  • Supports guided conversations, knowledge base retrieval, and action execution
  • Enables organizations to augment traditional applications with natural language interaction and AI-driven automation

By structurally and natively embedding AI agents into business workflows, organizations can streamline support processes, enhance customer engagement, and introduce new operational models.

Extending Composability Through Citizen Development

Power Platform extends the composable architecture of the digital business platform by enabling faster development cycles, broader participation in innovation initiatives, and the embedding of automation and AI into business processes. Structured governance, standardized connectors, and integration with Dataverse ensure that low-code solutions maintain alignment with enterprise operational and compliance standards.

5) Centralized Management, Provisioning, and Delivery

Hosting, identity governance, integration orchestration, software delivery, and monitoring must all align with the same modularity principles to ensure a structured, scalable approach to operational management and agile delivery. Azure provides a comprehensive set of services to standardize and streamline management, provisioning, and delivery across the platform architecture.

Hybrid Hosting and Platform Services: Azure Core Services

Azure supports flexible hosting models, including public cloud, private cloud, and hybrid deployments. Core services for compute, storage, networking, containers, and Kubernetes orchestration enable IT teams to optimize workloads based on performance, scalability, and security requirements. Modern provisioning patterns such as Infrastructure-as-Code can elastically scale resources, maintain consistent configurations, and enforce operational policies across environments.

Identity and Access Management: Microsoft Entra

Microsoft Entra provides a comprehensive suite of services for identity governance, authentication, conditional access, and policy enforcement across cloud and hybrid environments. Through Entra, organizations can implement Zero Trust principles by ensuring that every user, device, and service is authenticated, authorized, and continuously validated. Identity federation and role-based access control further simplify managing access across Dynamics 365, Power Platform, Azure services, and third-party applications, ensuring security and compliance at scale.

Software Delivery and ITOps: Azure DevOps

Efficient software delivery is essential to sustaining a modular platform. Azure DevOps delivers an integrated toolchain for source control, build automation, continuous integration, continuous delivery, and release management. Standardizing deployment pipelines across application modules and infrastructure components ensures consistent, repeatable, and auditable delivery processes. Integration with Microsoft services, open-source tools, and third-party systems enables end-to-end automation, supporting agile development methodologies and DevOps practices across the digital business platform.

Monitoring and Observability: Azure Monitor

Azure Monitor provides centralized telemetry collection, log analytics, metrics tracking, and alerting across applications, infrastructure, and services. Azure Monitor supports real-time health dashboards, implement predictive analytics for operational tuning, and automate incident response workflows. Unified observability accelerates troubleshooting, enhances performance optimization, and supports a proactive operations model that aligns with modern platform management practices.

Multi-Vendor Platform: SAP Technologies as an Example

While this blueprint is coiceived using Microsoft technologies, the architectural model is intentionally vendor-neutral. A digital business platform must be designed to support the structural adoption of modules from different enterprise vendors – not simply integrate with them.

Microsoft’s open ecosystem philosophy, exemplified by its support for Linux, Kubernetes, and multi-cloud patterns, extends to core enterprise platforms such as SAP. In this context, SAP is not treated as an external system to be interfaced, but as a foundational participant in the composable architecture – capable of occupying critical roles in finance, supply chain, and operations within a unified platform model.

S/4HANA workloads on Azure

Microsoft Azure offers certified reference architectures for deploying SAP S/4HANA, accommodating various deployment options, including public cloud, private environments, and hybrid models, providing flexibility in adopting or modernizing SAP’s core modules.

Implementation strategies include traditional lift-and-shift migrations of ECC or S/4HANA instances, as well as fully re-architected, cloud-native implementations aligned with SAP’s clean core principles. Azure supports these approaches with elastic scalability, high availability, and integrated disaster recovery capabilities, utilizing SAP-certified virtual machines, storage configurations, and networking patterns.

In a composable platform architecture, SAP workloads can be integrated as interoperable modules within the broader system. Running S/4HANA on Azure facilitates participation in shared identity frameworks, such as Azure Entra (formerly know as Active Directory), observability services like Azure Monitor, DevOps pipelines through Azure DevOps, and data integration layers alongside Microsoft-native components. This integration ensures that SAP functions cohesively within a unified digital business platform.

Integration Patterns: API-First and Data Exchange

SAP Gateway enables exposure of SAP business logic and data models as OData-compliant services, facilitating API-based integration with Microsoft services and external applications.
Azure Integration Services, combined with Service Bus, provides a middleware layer for:

  • Event-driven integration between SAP and Dynamics 365 applications
  • Data ingestion and transformation pipelines into Microsoft Fabric for unified analytics
  • Federation of SAP operational data with Dataverse and Power Platform applications

These integration patterns allow SAP systems to participate in hybrid operational workflows and contribute data into broader data-driven processes across the digital business platform.

Identity and Access Governance Across Systems

Microsoft Entra extends centralized identity governance to SAP systems, enabling:

  • Synchronization of SAP user roles and permissions with Azure Active Directory
  • Unified authentication and conditional access across SAP and Microsoft environments
  • Consistent policy enforcement and compliance reporting

Support of Zero Trust security principles simplifies access management across heterogeneous platforms.

Deployment and DevOps Integration

SAP environments can be deployed and managed on Azure using the SAP Deployment Automation Framework, which automates infrastructure provisioning, installation, and configuration processes. DevOps practices can also be applied to SAP application development, particularly in SAPUI5 and Fiori projects. Azure DevOps can orchestrate source control, continuous integration, and release management for SAP artifacts.

Monitoring and Observability: Azure Monitor for SAP Solutions

Azure Monitor provides dedicated services for SAP observability, including telemetry collection across:

  • SAP infrastructure (compute, storage, network)
  • Databases (HANA, AnyDB)
  • SAP application layers

Unified monitoring enables proactive incident detection, performance optimization, and integrated reporting across SAP modules and broader platform components.

Conclusion

Implementing a digital business platform requires a methodical approach to modularity across applications, data services, analytics, low-code development, operational management, and integration frameworks. Microsoft’s ecosystem – Dynamics 365, Azure, Microsoft Fabric, and Power Platform – provides a comprehensive and interoperable foundation aligned with these principles, enabling organizations to modernize systematically while managing operational complexity.

The platform architecture is inherently extensible. It supports not only Microsoft-native applications but also the integration of third-party and custom-built solutions. SAP was presented as a reference model to illustrate coexistence, interoperability, and operational governance; however, the integration patterns apply broadly to other enterprise platforms and ecosystems.

References

Composable business architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/commerce-architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/retail-server-architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/crt-services
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/define-retail-channel-communications-cdx
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/consume-retail-server-api

API/Model Driven App
https://learn.microsoft.com/en-us/power-apps/developer/data-platform/webapi/overview
https://learn.microsoft.com/en-us/power-apps/developer/data-platform/overview
https://www.oasis-open.org/standards/

Data services
https://www.microsoft.com/en-us/power-platform/dataverse
https://www.microsoft.com/en-us/security/business/microsoft-purview
https://www.microsoft.com/en-us/security/business/risk-management/microsoft-purview-data-governance
https://learn.microsoft.com/en-us/purview/register-scan-dataverse
https://www.microsoft.com/en-us/power-platform/blog/power-apps/govern-your-business-applications-data-with-microsoft-purview/

Data integration
https://www.odata.org/ecosystem/
https://marketplace.informatica.com/listings/cloud/connectors/odata_connector.html
https://pages.community.sap.com/topics/gateway

Analytics + AI
https://www.microsoft.com/en-us/microsoft-fabric
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric
https://learn.microsoft.com/en-us/%20fabric/data-factory/connector-overview

Data driven
https://www.microsoft.com/en-us/power-platform
https://www.microsoft.com/en-us/power-platform/products/power-apps
https://www.microsoft.com/en-us/power-platform/solutions/extend-lob-systems
https://www.microsoft.com/en-us/power-platform/products/power-automate
https://www.microsoft.com/en-us/copilot/microsoft-copilot-studio Agents

Platform services
https://azure.microsoft.com/en-us/products

Digital Business Platform

Digital business platform
| Charlie Ranalli

A Reference Architecture to Accelerate Digital Metabolism

Key Concepts

  • Why it matters: overcome technical rigidity to accelerate innovation and growth
  • Architecture + infrastructure: combines design and management for flexibility and agility
  • Composable business architecture: modular capabilities enable adaptable and flexible operations
  • Data services: unified, high-performance data store for master and operational data
  • Analytics and AI integration: real-time insights “loops” for data-driven operational workflows
  • Low/no-code platform: tools for rapid innovation through citizen-developed apps
  • Centralized management and DevOps: standardized provisioning and delivery across the platform
  • Technology selection: balance best-of-breed solutions with seamless interoperability for agility
  • Simplicity as a mindset: streamlined, standardized design enhances efficiency

Breaking Free from Legacy Architectures

All the organizations we’ve worked with over the past few years to develop or accelerate their digital strategies faced technical debt in their tech stack, the product of legacy architectures, amorphous cloud migrations and complex operations practices. This debt hampered their “digital metabolism”—the speed and agility with which they could innovate, adapt, and transform digitally.

Some signs of this debt are easy to spot: outdated legacy applications, siloed business processes, point-to-point integrations, and inaccessible or poor-quality data. Others are more subtle and require a more ambitious aim, like modern data governance, DevOps, and release management practices—capabilities that digital leaders have mastered. (As a side note: coming from the technology industry certainly helps some of us identify modern engineering practices that can be adapted and applied broadly.)

Technical debt slows down new projects, drives up costs, limits data accessibility and usability, and often results in makeshift attempts at digitization and automation. These fragmented approaches compound the “digital spaghetti” problem: a tangled clutter of overlapping processes, apps, technologies, and data flows. The result? Slower innovation, higher risks, and in some cases, stalled digital transformations.

Technical debt is expensive: according to McKinsey & Co., “Some 30 percent of CIOs we surveyed believe that more than 20 percent of their technical budget ostensibly dedicated to new products is diverted to resolving issues related to tech debt.”

For years, business and technical thought leaders have been sounding the alarm and proposing solutions, including composable architectures, planned cloud migration, application refactoring, and data modernization. Until recently, these ideas seemed futuristic or, at best, targeted efforts to address isolated problems.

The good news is that advances in off-the-shelf applications, cloud platforms, integration technologies, engineering practices and industry standards have made holistic solutions more achievable, with surprisingly lower costs and risks than before.

This article introduces the concept of a digital business platform, bridging the gap between a reference architecture and operations framework to help organizations approach the management of their technology ecosystem holistically to break free from innovation-limiting legacy infrastructures and address technical debt. These design and management principles –curated from real-world cases– provide a structured, methodical approach to building an agile, scalable infrastructure.

Key Components of a Digital Business Platform

1) Composable Business Architecture

At the core of the digital business platform is a modular grid of business capabilities (marketing, sales, production, logistics and the like) implemented in applications –or, in some cases, custom-built software– and wired together in macro-processes to support the desired business models, operating processes, customer journeys and employee experiences.

Gartner’s concept of composability is a robust framework for designing this architecture. Just a few years ago, implementing this approach was difficult for most organizations due to the limited modularity and flexibility of off-the-shelf solutions. Today, with the latest omnichannel, customer relationship management (CRM), supply chain, digital banking, and financial management solutions, composability is much more attainable and affordable.

To maximize flexibility, reusability, and long-term value, discrete business capabilities and connected end-to-end scenarios should be managed as digital products—designed for continuous evolution, adaptation and reuse across projects, businesses and geographies.

2) Data Services

In the client-server era, data architecture was relatively straightforward: data resided in centralized databases, neatly structured and managed to support business applications, maybe with a universal integration bus orchestrating flows and transactions. Analytics platforms accessed, processed and organized this data for visualization tools, advanced models for decision making and, in some cases, feed augmented insights into the centralized stores.

SaaS platforms changed all this, offering elasticity, lower costs of ownership, faster implementation, and reduced risk, but obscuring data management. Each platform usually comes with its own proprietary data structures.

This challenge can be addressed deploying a Data Service that meets both data and integration architecture needs. Data services can be assembled using various underlying technologies, and is designed to provide standardized data models, centralized governance and security, reusable interfaces, and robust data management – ensuring that data is handled with high performance and reliability.

Unlike data lakes or data warehouses, which are typically associated with analytics-focused architectures, a Data Service handles day-to-day business transactions and real-time integrations. In contrast to an integration bus that merely replicates data across systems, the Data Service maintains the reference version of each dataset as the authoritative source.

A data service must include or be integrated with a data governance solution to ensure visibility, consistency, compliance and ultimately high-quality data across all applications, enabling trusted, well-managed data for both operational and analytical use.

3) Analytics, AI, and Data-Driven Workstreams

A well-designed common data service can feed master and transactional data into advanced analytics and AI platforms via efficient data pipes and extract, transform and load (ETL) processes. Output from advanced analytics models featuring leading-edge data science and AI then loop back into business applications to enable data-driven workstreams —fully-automated or human-in-the-middle processes that respond to real-time insights.

Depending on the use case, technology stack, and performance requirements, this “data-driven loop” can be implemented using various architectures, from event-driven systems to data streaming.

4) Low/No-Code Platform

To extend the functionality of out-of-the-box applications and accelerate innovation, the digital business platform should prescribe a single low-code/no-code platform. These tools empower “citizen developers” to create small applications and workflows without deep technical expertise.

For easy adoption and maximum impact, these platforms should be structurally integrated with business applications, the common data service, and analytics tools. This set-up allows business users to quickly roll-out new business processes and leverage data driven insights at speed and scale, while maintaining alignment with IT standards.

5) Centralized Management, Provisioning, and Delivery

Agile IT delivery requires more than just a well-designed architecture—it depends equally on robust infrastructure management. While architecture defines how applications, data, and processes interact, infrastructure management ensures that these components are deployed, provided, and maintained efficiently.

A flexible digital platform operates within a hybrid hosting model, conceived to seamlessly manage workloads across cloud, on-premises, and edge environments. This flexibility allows case-specific optimization: leveraging the cloud for performance-intensive tasks, on-premises infrastructure for high-availability requirements, and secure edge zones with high-speed connectivity to support sensors, industrial controllers, and other IoT devices.

A standardized hosting model serves as an enabler of the organization’s cybersecurity strategy. As distributed hybrid infrastructures become the norm, the zero-trust security model has emerged as the predominant approach, requiring strict verification for every user, device, and service attempting to access resources. To be effective, zero-trust principles must be tightly aligned with the integration architecture, DevOps pipelines, and change management processes, ensuring that security is embedded at every stage of the design, engineering and deployment to support a robust DevSecOps program, where security is not an afterthought but an integral part of the platform’s lifecycle.

Normalized identity and access management services and policies improve user experience, accelerate adoption, enhance security, and reduce total cost of ownership.

Meanwhile, a common DevOps platform, synchronized release schedules and centralized configuration management streamline the orchestrated delivery of new features and updates across applications and infrastructure, increasing speed, productivity and simplifying change management. These delivery mechanisms should be harmonized with relevant transformation and IT “ways-of-working” like project management and Scrum.

This platform-based approach transforms the IT operations and support teams into a highly efficient internal “service provider,” offering a scalable and flexible infrastructure. By standardizing processes, tools, and delivery mechanisms, IT can seamlessly provision resources on demand and elastically support a diverse range of “internal customers” from project teams to entire business units.

Technology Selection

Choosing the right technology to build a digital business platform depends on the organization’s business context, application portfolio and digital strategy. Organizations that rely primarily on off-the-shelf SaaS or IaaS solutions focus on hosting, integration, and management technologies that support and interoperate with these platforms.

In contrast, organizations with a sizable number of custom-developed applications can adopt dynamic provisioning patterns such as virtualization, containerization, and infrastructure-as-code, enabling flexible, scalable, cost-effective deployments.

Selecting technologies to build a digital business platform requires an exquisite compromise between best-of-breed solutions and seamless interoperability. While best-of-breed technologies may offer specialized capabilities and advanced features, their integration into a broader ecosystem can introduce complexity, increased costs, and operational inefficiencies.

Prioritizing technologies that support integrated management is desirable, as this enables greater flexibility, agility, and cost-efficiency. An integrated and gradually automated infrastructure simplifies deployment, maintenance, and updates while ensuring streamlined processes and data flows across layers of the architecture.

Beyond TOGAF: Architecture Design and Infrastructure Management

A keen observer would reason that these principles reflect the TOGAF architecture framework, which also emphasizes modularity, interoperability, and alignment between business and IT, and addressed the same “layers”: business, data, applications, technology and governance.

The TOGAF framework –widely applicable in this context– certainly provides a strong foundation for architecture design. However, it often falls short or is too complex in addressing modern operational needs, such as hybrid hosting, DevOps practices, and streamlined resource provisioning in cloud environments – areas that expert practitioners might reasonably place within the technology or governance layers.

By embedding these management elements with simplicity, the digital business platform seamlessly extends TOGAF, enabling organizations to not only design scalable architectures but also implement them with speed, efficiency, and agility.

Simplicity to Manage Complexity

While extremely powerful in streamlining IT delivery, the digital business platform is –and must be approached as– a very simple concept: a set of architecture patterns and technology standards methodically adopted to enhance flexibility, scalability, and operational efficiency. It ensures that business processes, data services, and applications are modular, interconnected, and easily adaptable to evolving business needs.

By also envisioning and standardizing infrastructure management and delivery elements such as hosting, access control, and DevOps practices, it enables organizations to rapidly deploy solutions, optimize costs, and continuously innovate without being weighed down by technical rigidity.

Simplicity should be both a target and a mindset, guiding design and decision-making to create a streamlined, efficient ecosystem. When applied methodically and thoroughly, simplicity unlocks extraordinary long-term benefits. In most cases we’ve worked on, retroactive normalization and rationalization have resulted in less work for operations teams, not more.

Accelerating Digital Metabolism

The platform approach accelerates digital metabolism, enabling faster time-to-market for digitally enabled business innovations, enhanced data-driven decision-making, and continuous process improvements. Over time, it fosters a “digital-first” mindset by dispelling the myth of overwhelming technical complexity.

Implementing a modern digital business platform to manage technical debt is a strategic enabler of growth. As McKinsey & Co. notes, “Companies effectively managing technical debt experience revenue growth rates 20% higher than those with poor technical debt management.”

In the next article, we’ll present an example of a digital business platform implemented with actual technology components. In the third article, we’ll explore the organizational model necessary to support this architecture.

Further Reading

Tech debt: Reclaiming tech equity, McKinsey & Co.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-debt-reclaiming-tech-equity

Digital acceleration is just a dream without a new approach to tech, Boston Consulting Group
https://www.bcg.com/publications/2020/how-to-successfully-accelerate-digital-transformation

Strategic Roadmap For The Composable Future Of Applications, Gartner, Inc.
https://www.gartner.com/en/doc/433984-2021-strategic-roadmap-for-the-composable-future-of-applications

Why You — Yes, You — Need Enterprise Architecture, MIT Sloan
https://sloanreview.mit.edu/article/why-you-yes-you-need-enterprise-architecture

How to build a data architecture to drive innovation—today and tomorrow, McKinsey & Co.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/how-to-build-a-data-architecture-to-drive-innovation-today-and-tomorrow

8 Steps for a High-Impact Enterprise Architecture Program, Gartner, Inc.
https://www.gartner.com/smarterwithgartner/8-steps-for-a-high-impact-enterprise-architecture-program

Six Concepts That Will Shape Your Digital Strategy

Six Concepts That Will Shape Your Digital Strategy: Vision, People, Models, Architecture, Data and Execution
| Charlie Ranalli

We recently published an updated digital strategy framework to help organizations start – or restart – their digital strategies.

Drawing on years of experience and collaboration with visionary leaders, this article explores the six essential concepts at the heart of the framework. These concepts—Vision, People, Models, Architecture, Data, and Execution—offer a clear and actionable foundation for organizations to achieve transformative success, even amid the inherent complexity of the implementation.

Vision: The Catalyst for Change

At the heart of any digital strategy lies a bold and innovative vision. This vision sets the organization on a new path—or accelerates its existing trajectory—by leveraging technology as a transformative force. It is the driver that redefines what’s possible, enabling businesses to challenge and disrupt the status quo.

It’s easy to dismiss Netflix, Amazon and Uber as “digital natives”, but they exemplify how a compelling vision can revolutionize industries. By reimagining entertainment, retail, and transportation, they disrupted decades—and in some cases centuries—of established norms, leaving competitors struggling to catch up.

Netflix executed three transformative strategy pivots over three decades, each reshaping its industry. First, in the late 90s, it delighted customer eliminating late fees and decimated Blockbuster with a DVD delivery subscription model. Ten years later, it revolutionized television with on-demand streaming, replacing traditional viewing habits. Finally, Netflix disrupted Hollywood itself by producing its own content —and established binge-watching, starting with House of Cards— in the process becoming a powerhouse studio and redefining entertainment forever.

A powerful vision doesn’t merely guide technology adoption; it redefines the organization’s mission and purpose in a digital-first world. It inspires teams to think beyond incremental improvements, pushing them to explore entirely new business models and customer experiences. The vision must be ambitious yet achievable, offering a clear direction that unites stakeholders around a common goal.

People: The Driving Force Behind Transformation

While technology is an enabler, people are the true rainmakers of digital innovation. From executives to frontline employees, the success of a digital strategy depends on the creativity, expertise, and commitment of the organization’s workforce.

Digital transformation starts at the top, with leadership embracing their role as champions of change. The CEO and executive team must drive the vision, ensuring alignment across all levels of the organization. However, transformation cannot stop at the leadership level. A digital strategy requires a cultural shift that permeates every corner of the organization, encouraging innovation, collaboration, and a willingness to challenge the status quo.

Empowering teams with the skills, tools, and autonomy to experiment and iterate is critical. As technology becomes increasingly commoditized, it is the ingenuity and determination of people that will differentiate successful organizations from their competitors.

Models: Unlocking Innovation Through Disruption

Identifying opportunities for innovation should be first step of any digital strategy. Yet, even creative business leaders sometimes struggle to envision what to transform, especially in traditional industries where long-standing practices and structures dominate.

This is where the Models component of the framework comes into play. By disassembling the business model, customer lifecycle, and operating frameworks, organizations can uncover hidden opportunities for disruption and growth.

This process of creative deconstruction allows leaders to think like startups—challenging established paradigms and imagining new ways to deliver value. Whether it’s rethinking customer engagement, rewiring supply chains, or introducing new pricing models, the possibilities are endless. The goal is to create structural and lasting competitive advantages that set the organization apart in a crowded marketplace.

Architecture: Building the Foundation for Innovation

Once new business models and processes are identified, the next step is to design a Digital Business Platform that brings them to life. The Architecture component of the framework focuses on building a modular, flexible, and scalable infrastructure that enables rapid innovation and adaptation. This requires more than just adopting state-of-the-art technology—it demands a fundamental rethinking of how processes and models are reflected in technology.

Digital leaders are not afraid to start with a blank slate, rebuilding their IT tech stacks and engineering practices to enable composable designs that align with operational capabilities. These architectures must enable seamless integration across legacy systems, cloud platforms, and emerging technologies.

None of the organizations we worked with over the past decade had architecture functions configured to drive digital innovation effectively from the outset. But with the right approach, they were able to build teams to support agile, customer-centric operations and enable transformative growth.

Data: The Nervous System of the Digital Enterprise

Data is the central nervous system that connects every moving part of the organization. To succeed, businesses must skillfully capture, manage, and analyze data. The goal is to create a unified data repository that removes silos, enables efficient execution and becomes a single source of truth that informs decision-making, uncovers insights, and drives innovation. This often requires overhauling existing data models, architectures, and governance practices.

The importance of data-driven decision-making has grown exponentially with advancements in analytics and AI. These technologies offer organizations unprecedented opportunities to understand customers, optimize operations, and uncover new pockets of growth.

However, building a data-driven culture takes time and effort. It requires robust governance structures, seamless integration of data assets, and a commitment to turning raw data into actionable intelligence.

Execution: Connecting Strategy to Results

The final concept—Execution—is where ambition meets reality. Execution is about connecting all the pieces of the digital strategy with agility, precision, and a relentless focus on value.

It starts with a solid strategic execution discipline, ensuring that initiatives and investments are aligned with business priorities and measured against clear performance targets. Organizations with well-established strategic execution practices have a significant advantage, as they can integrate digital initiatives into existing management processes and drive results more effectively.

Success requires a remarkable commitment to managerial hygiene: plan, execute, measure, adjust, and repeat. While this may sound straightforward, the graveyard of failed digital transformations proves that it’s anything but. According to McKinsey & Company, approximately 70% of digital transformation initiatives fail to achieve their intended goals. Leaders must prioritize alignment, accountability, and continuous learning, ensuring that the organization remains focused on implementing change and delivering meaningful outcomes. Digital innovation is a marathon, not a sprint.

The six concepts—Vision, People, Models, Architecture, Data, and Execution—are the foundation of a successful digital strategy. They offer a structured yet flexible framework that empowers organizations to navigate the complexities of digital innovation with confidence.

Why You — Yes, You — Need Enterprise Architecture

| Charlie Ranalli

The authors define enterprise architecture as the holistic design of people, processes, and technology to execute digitally inspired strategic goals. They argue that every unpleasant customer interaction via a company app, website or telephone call exposes architectural inadequacies. Left unsolved, these issues will destroy formerly great organizations.

They suggest adopting three principles to tap the benefits of enterprise architecture: break processes and products into components, empower cross-functional teams, and allow business design to influence strategy.

Jeanne Ross and Cynthia Beath are coauthors of Designed for Digital: How to Architect Your Business for Sustained Success (MIT Press, 2019). Ross was principal research scientist for MIT’s Center for Information Systems Research for almost 27 years. Beath is professor emerita of information systems at the McCombs School of Business at the University of Texas at Austin.

Jeanne Ross and Cynthia Beath, “Why You — Yes, You — Need Enterprise Architecture”
MIT Sloan Management Review, August 2020

Organizing for Digital Acceleration: Making a Two-Speed IT Operating Model Work

| Charlie Ranalli

McKinsey has long argued that delivering an enriched customer experience requires a new digital architecture running alongside legacy systems.

Furthermore, by adopting a digital product management model, companies can get the most from their IT architectures and deliver innovative online customer experiences faster.

A digital product management approach requires that companies systematically incorporate customers’ needs and insights into their products and services. Teams are held accountable for implementing specific improvements to digital products or digital experiences. Other groups, such as marketing, logistics, and customer service, may be collaborators, but ultimately digital product managers are measured on outcomes associated with the revised product or experience

Oliver Bossert, Martin Harrysson and Roger Roberts
Organizing for digital acceleration: Making a two-speed IT operating model work
McKinsey Insights, October 2015

A Two-Speed IT Architecture for The Digital Enterprise

| Charlie Ranalli

Delivering an enriched customer experience requires a new digital architecture running alongside legacy systems.

Unlike enterprises that are born digital, traditional companies don’t have the luxury of starting with a clean slate; they must build an architecture designed for the digital enterprise on a legacy foundation.

A two-speed IT architecture will help companies develop their customer-facing capabilities at high speed while decoupling legacy systems for which release cycles of new functionality stay at a slower pace.

Oliver Bossert, Chris Ip, and Jürgen Laartz
A two-speed IT architecture for the digital enterprise
McKinsey Insights, December 2014