Executive summary
- Digital transformation programmes, which take a shallow approach, do not modernise core platforms and do not yield optimum benefit.
- Modernising and building a strong digital core platform can yield the maximum business benefit.
- Cloud, data, AI, security, automation, and modern architecture are the fundamental building blocks of a digital core platform.
- We have identified seven key practices to successfully establish a strong digital core platform.
Challenges in digital transformation programmes
In today's world of rapidly changing business priorities and macroeconomic pressures, organisations are often adopting a lower-impact digital transformation by focusing on customer experience and introducing new technology. While this approach offers some benefits, it falls short in:
- Increasing top-line revenue
- Reducing costs
- Improving operational efficiency
- Improving customer experience
The key challenges forcing organisations to adopt lower-impact digital transformation are:
- Lack of a strong IT and infrastructure foundation
- Technology debt across infrastructure, applications, data, and legacy technology stacks
Security and compliance challenges
- Unavailability of enterprise-wide data, hindering the full utilisation of AI
- Complicated integration stack and challenges in integrating cloud and on-premises systems
- Sporadic use of automation, lack of an enterprise-wide automation strategy
- Lack of focus on business processes and operations
Digital core platform (DCP) to the rescue
A DCP helps expand surface-oriented digital transformation to the core. A DCP is the lifeline of modern intelligent enterprises. At the heart of the platform is the enterprise core, comprising ERP, HCM, and other core systems. Increasingly, these systems are running on the cloud. The DCP is powered by cloud, data, and AI. Organisations are using a mix of hybrid cloud, multi-cloud, sovereign cloud, and edge computing. Having a strong data foundation helps maximise the advantages of advanced analytics, generative AI, and AI/ML. Utilising and prototyping with both generative AI, predictive AI, and AI-led intelligent automation is key to gaining a competitive advantage with the DCP.
The DCP often co-exists with a legacy core comprising of Mainframes, AS/400, iSeries, Power Systems, Tandem, and other legacy systems. Many industries, such as banking and telecom, need to maintain these systems due to challenges like regulation, complexity of modernisation, etc. The integration and data interchange between the new DCP and legacy code should be seamless. The industry-accepted practice is to ‘shrink the core’ piece by piece, ensuring that there is no downtime, etc. The strangler/strangler fig pattern is typically utilised for this.
Elements of a DCP
- Enterprise core: It comprises core solutions essential for an organisation’s operations. It includes ERP, HCM, and other industry-specific solutions. For banking, this would include core banking systems, KYC/AML, payment solutions, fraud detection solutions, trading systems, treasury and capital market (TCM) solutions, etc. For retailers, it would include inventory management systems, supply chain management, logistics management, etc.
- Cloud: Cloud is more than just a hosting location or deployment choice. It opens up vast possibilities, from utilising next-gen solutions (infrastructure, compute, storage, and data) to leveraging generative AI, AR/VR/MR, Metaverse, and quantum computing.
- Security: It is a crucial component of the DCP. Security should be embedded throughout the stack, and organisations should adopt a ‘Security by design’ principle. The DCP consists of modernised cloud-hosted/SaaS security solutions, including Identity and Access Management (IAM), SIEM/SOAR, firewall and DDOS protection solutions, SecOps solutions, cloud and hybrid threat protection solutions, policy management solutions, data security solutions, and key vault solutions, among others.
- Data: As companies adopt generative AI, a strong data foundation becomes essential. Cloud offers numerous data solutions, including OLTP (both SQL and NoSQL), analytics (data warehouse, data lake, and lakehouse), ETL/ELT, and data engineering. These modern data solutions complement modern cloud-based data architecture.
- Generative AI, AI/ML: Generative AI and predictive AI play pivotal roles in modernising and reimagining the core.
- Next-gen applications: The DCP is being reimagined with next-gen applications that leverage cloud, data, low-code/no-code, intelligent automation, and generative AI to enhance the applications. Next-gen applications are intelligent (aka intelligent applications) that significantly improve customer experience.
- DevOps and next-gen engineering: The enterprise core should utilise DevOps practices, such as Continuous Integration (CI), Continuous Delivery & Deployment (CD) in order to accelerate feature delivery to customers and gather continuous feedback. This often involves blue/green and canary deployments, A/B testing. Essentially, it is about adopting agile methodology and making progress in small iterative cycles rather than large, big-bang releases. Next-gen engineering techniques, such as generative AI-driven software development and platform engineering, help developers focus on core tasks and release software faster.
- Modern architecture and integration: Modern architecture and integration unify these elements. The DCP integrates inward with the legacy core and outward with customer, vendor/partner, and employee experience applications. These integrations are becoming real-time. Architecture patterns, such as microservices architecture, event-driven architecture, serverless and containerisation, are being employed.
Apart from the aforementioned technical building blocks, a DCP also requires new ways of thinking and operating, including:
- Business process reimagination
- Business IT alignment
- Digital operations
- Change management
Seven key practices to establish a DCP
- Establish a strong cloud foundation: Building a secure, enterprise-grade cloud landing zone is foundational for successful cloud migration and transformation. Key building blocks include organisation and account structure, networking, IAM, billing, and automation. A strong cloud foundation can also be established by:
- Incorporating security standards and utilising cloud policies.
- Employing infrastructure as code and cloud automation to automate infrastructure provisioning and configuration.
- Creating a cloud operating model encompassing people, processes, technology, and tools.
- Establishing operational processes, service management, monitoring, and logging standards.
- Migrate and modernise your enterprise data to the cloud: For large enterprises, data is often siloed. Discovering, cleaning, and categorising data are initial steps. Cloud data migration involves securing data, developing a data migration strategy, identifying cloud services, such as Database as a Service (DBaaS) and data and analytics PaaS platforms, cloud data lake, data warehouse, and data lake house. Data modernisation includes using next-generation data architectures, such as data fabric, data mesh, and data hub. Cultivate a ‘Data as a product’ mentality and implement proper governance. Finally, use data observability and data ops for smoother and efficient operations.
- Utilise modern architecture: Employ modern architectures, such as microservices, domain-driven design, 12-factor app, composable architecture, and event-driven architecture (EDA). Modernise and containerise applications. Leverage serverless and other cloud PaaS services.
- Build next-gen applications: Modernise applications and data. Upgrade custom applications using the latest frameworks and libraries. Redesign the UI layer and utilise modern JavaScript libraries like React, Angular, or Next.js. Employ patterns such as Single Page Application (SPA) and Micro UI. Also consider modernising monolithic apps to microservices.
- Simplify and modernise the integration layer: Streamline the integration and API layer. Over time, enterprise integration layers have become complex, using various integration protocols and technologies, such as SOAP, REST, WebSocket, Message Queues, and ESBs. Newer integration technologies like APIs, GraphQL, iPaaS (Integration Platform as a Service), Kafka, and Service Mesh have been added. Simplifying and modernising this complex integration layer is crucial for building a successful DCP.
- Utilise DevOps and next-gen engineering: Leverage DevOps, Agile, and next-gen engineering to reimagine the core.
- Experiment and adopt generative AI: Experiment and adopt generative AI across the stack. Generative AI and AI should not be limited to data scientists and ML engineers. Enhance every application with generative AI-powered intelligence. Convert applications into intelligent applications using generative AI and AI.
Conclusion
Reimagining the DCP is essential for organisations seeking successful digital transformation. By addressing inherent challenges and focusing on core elements, such as cloud, data, AI, security, and automation, organisations can build a robust DCP that enhances operational efficiency, customer experience, and drives sustainable growth. Implementing the outlined seven key practices will establish a strong foundation for the DCP, ensuring competitiveness in the evolving digital landscape. Embracing this holistic approach to digital transformation empowers organisations to thrive amidst changing business priorities and economic pressures.
