Navigating Infinite Change

Digital Asset #1 - A Unified digital platform

Now and Wow is the new normal for today’s buyers. To thrive, organizations must go beyond customer focus to become customer-obsessed, meeting customers’ needs quickly and conveniently at all turns.

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This means embracing new operating models that are customer-obsessed, insights-driven, fast and connected. This is the only way to win a customer’s business, trust and loyalty in their moment of need, where all business processes must come together to create an exceptional experience.

We’re not competitor obsessed, we’re customer obsessed. We start with the customer and then we work backwards.

Jeff Bezos, Amazon

The problem? Large companies typically operate in silos, each with their own systems, data definitions and business processes. Creating a common view of customers can be difficult in these organizations, making it impossible to implement advanced approaches to customer engagement or process optimization.

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As a result, one of the main reasons leading companies are suddenly outcompeted by ‘born-digital’ startups is that the latter have a unified data and process from the get-go. This enables newer, faster movers to gain customer advantage through analytics and personalization. Traditional companies often need to invest (heavily) in integrating data and processes across their organization – with firms that have already implemented ERP and CRM systems usually a step ahead of others.

Platforms that integrate data and processes are therefore the fundamental requirement for any organization to transform itself into a digital business.

Target Technologies

The principles underlying cloud have long been accepted as sound governance for IT infrastructure management. Standardization, virtualization, automation, self-service and hardware optimization effectively reduce ownership costs, by running servers at higher utilization rates and increasing infrastructural agility.

Service-oriented architecture and integration strategies that rely on standardized APIs add another layer of agility at the software level, where it becomes significantly easier to leverage off-the-shelve functionality as a service.

Today, the move to cloud is entering its endgame, with enterprises having realized that, while increased efficiency was what brought them to the public cloud, accelerated innovation is what keeps them there. New and advanced technologies can easily be accessed and integrated through a simple API call. Even organizations stubbornly pursuing on-premises strategies are starting to wonder if faster innovation is possible without leveraging advanced technologies through the cloud, especially when they come under pressure from competitors with new business models that are based on integrations with systems beyond their organizational boundaries. It’s cloud architecture that underpins the platform-based models that transform industries and evolve product stovepipes companies into customer-obsessed service companies.

Cloud is becoming a no-brainer; not because it’s technologically the right thing to do, but because it’s the only thing to do for a company to survive consecutive, disruptive waves of innovation. This is why the foundation of any Unified Digital Platform is a cloud environment – whereby multi-cloud, hybrid and edge capabilities address the need for ubiquitous and decentralized computing power.


Key components of unified digital platforms include:

  • Multi-cloud management, whereby workloads are updated and managed on different environments with the same security and policy settings. Microsoft’s Azure Arc is an example of a software solution that enables protection of on-premise and multi-cloud resources, such as virtual or physical servers and Kubernetes clusters. Resources are managed as if they’re all running in Azure, with a single view of an organization’s entire data estate.
  • Edge cloud integration, whereby public cloud providers offer services like Azure Stack Hub, Anthos, Tanzu and OutPost that make it possible to run workloads in a multi-cloud management-format, regardless of the underlying cloud platform. Workloads are updated and managed centrally, with the same security and policy settings. These edge environments are becoming smaller and less dependent on the individual cloud provider, making them useful in many more scenarios than previously.
  • DevSecOps ensures fully integrated security. With cloud security having assumed paramount importance, organizations have started to consider security at an earlier stage in their system development cycle – a concept known as ‘shift left’. As a result, there is now much interest in the concept of Site Reliability Engineering (SRE) that originated from the engineers at Google. SRE helps teams to find a balance between releasing new features fast and making sure they are reliable for users. The cloud principles of standardization and automation are important elements of SRE, improving system reliability today and over time. As such, SRE fits well with the cloud-native approach, making it possible to find vulnerabilities faster, move from batched validation to continuous validation and remedy cloud platform issues automatically.
  • FinOps is used to optimize cloud usage. Auto-scaling enables resources to be scaled up and down, started, stopped, created and deleted based on real-time usage insights. Many cloud platforms now include built-in sustainability management and reporting options. Cloud resource templates can also be used to accelerate development and to implement industry-specific regulatory and compliance functionality.
  • API management, serverless systems, Logic Apps and Eventbus have made integrating new systems easier, while the complexity of systems has increased.


  • Data organization is essential for businesses to drive value from their data – and demands new data models. Traditional relational databases do not scale to every cloud scenario, so that many organizations are considering NoSQL, graphs and other ways to store data for their new event-driven, microservices-based systems.
  • Data Lakehouses make it possible to access and create value from huge amounts of data in all types of format. They combine the security and governance capabilities of data warehouses with the ability to support AI operations and ingest all kinds of data, while reducing the complexity of data ingestion.
  • Data mesh connects the operational and analytical data planes to derive maximum value from analytical data.
  • Logical Architecture for AI engineering supports AI development activities, by validating and maintaining AI models for normal version control.
  • DataOps combines best practices in data management, communication and automation to reduce cycle times for delivering business value from data analytics.
  • Through Privacy Enhancing Computation (PEC) techniques, sensitive data becomes available for analysis without exposing personal data or risking regulatory problems, by protecting privacy at the data-, software- or hardware-level.
  • Purpose-built data platforms provide highly specialized services that can be quickly deployed. Customer data platform (CDP) solutions, for example, are used to build 360 views of customers and segments, to deploy AI models to drive marketing campaigns.
  • Finally, NoOps autonomous, self-healing and self-maintaining systems offer ever-expanding automation capabilities. At some point, human teams will no longer be required to management cloud. Until then, we will see a continual decline in the level of human intervention needed to operate and maintain cloud systems.

Testing & Validation

Unified digital platforms orchestrate and link everything vital to an organization's business objectives, from customer interaction to business operations and innovation. Platforms must therefore be available 24 hours a day, seven days a week, function effectively and adhere to highest standards of security, accessibility and ease of use.

Platform quality has a direct impact on business performance, image and long-term client retention. In recent years, we have witnessed a growing awareness of system quality among senior management teams, and a clear shift towards business value assurance rather than merely software defect prevention. Our global quality surveys have revealed that security, digital happiness, client satisfaction and brand protection all are now among the primary quality objectives of leadership teams.

Extending, adapting and upgrading a unified digital platform takes far more than simply testing software applications during development.

Quality validation is expanded beyond software testing to include continuous quality monitoring in production. Automatic and self-running validations, together with predictive analytics based on user pattern analysis, can assist in identifying probable quality flaws prior to their occurrence. Chaos engineering is used to continuously monitor platform resilience and self-healing capabilities in the event of component failure. Additionally, interoperability and robustness are critical for securing the platform's communication among all end-user devices, end-user applications, enterprise systems and smart products. Full platform validation will be spread across many levels of testing across multiple components.


In future, we will see an increased focus on device testing, enterprise system business assurance, IoT and product validation, infrastructure testing and continuous Quality Engineering (QE) across all components. Future QE approaches are bound to require the automation of mundane tasks by leveraging AI to cope with increased quality requirements, offer increased agility and free up human creativity.

This shift from software application testing to continuous QE requires organizations to identify and define the core quality metrics for their unified digital platform. These quality metrics should be made actionable by implementing an enterprise-wide quality dashboard that combines source data from production monitoring and incident management systems, with operational software development data. Quality dashboards should be powered by smart analytic engines that aggregate source data to produce these quality indicators, and must include distinct views for senior management, technology area leaders, product owners and operational teams.