Digital Transformation 2.0 Approach

Building a scalable, integrated framework to align systems, foster innovation, and drive MOI's leadership in global investment through data and AI-driven solutions.

Design Methodology

The DQ DT2.0 framework, takes a structured approach to defining the design for the realisation of the DBP practice within the organisation.

  • Envision: Define the aspirations and positioning of the DBP practice within the organisation.

  • Target: Design of the target platform and operating model to support the DBP practice.

  • Baseline: Assess the current state of the practice against the target to identify the maturity gaps.

  • Initiative: Define the roadmap and blueprints required to realise the target state for the practice.

Deploy Methodology (Implement)

The DQ DT2.0 framework, takes a structured approach to executing the implementation for the activation of the practice within the organisation.

  • Formulate: Define the solution's scope through actionable epics and features.

  • Prepare: Develop detailed user stories and system architecture to guide the implementation.

  • Deliver: Configure and develop the system in an iterative manner with continuous releases.

  • Transition: Enable users adoption to onboard and activate the solution operations.

Deploy Methodology (Oversight)

The DQ DT2.0 framework, takes a structured approach to overseeing the realisation of the practice, against the defined design for the practice.

  • Sourcing: Ensure effective vendor identification, evaluation, and selection to realise the practice.

  • Vendor Management: Monitor performance and ensure deliverables meet quality standards.

  • Delivery: Oversee timelines, milestones, and adherence to quality benchmarks.

  • Architecture: Validate and align solution designs with scalable and compliant frameworks.

  • Change Management: Facilitate adoption, address gaps, and ensure seamless transition.

Blueprint for DT2.0 Approach

Empowering seamless integration, scalability, and AI-driven innovation for MoI’s data transformation