Today, new technologies create new commercial and operational opportunities and have the potential to fundamentally change businesses. In many case traditional strategies will not work when trying to infuse AI across the whole enterprises. So this is why we need to take a different approach to industrializing AI to deliver at scale, while importantly keeping data centricity at the core.
Our clients from various industries are currently looking for ways to address data explosion and enabling new routes to market, improve operational efficiency, and innovate without technology constraints.
Our Next-Gen Enterprise Data & Artificial Intelligence Platform reference architecture enables our clients to meet their business vision and objectives as part of their digital transformation initiatives underpinned by a strong data strategy. This further helps reduce the complexity, cost, and risk of platform solution implementation and provides a blueprint for data-centric transformation, agnostic of architecture patterns, namely data lake, a data hub, and logical data warehouse.
Our next-gen reference architecture can be understood in terms of five main layers, all underpinned by secure data:
- Platform foundation – hybrid cloud platform implementation, cloud strategy, and end-to-end provisioning, and platform infrastructure management
- Data trust – accelerators, frameworks, and services to define and implement data lifecycle management
- Data-centricity foundation – capabilities for data preparation, transformation, and storage
- AI and analytics foundation – capabilities to design and deploy AI/ML services supported by the data-centricity foundation
- AI and analytics execution – capabilities to deploy and execute custom AI and BI applications in production at scale.
Typical use cases for adopting a modern data and AI platform
It is essential to have a clearly defined use case for the platform to support the business aims, such as
- Innovation and research – flexible and cost-efficient processes and platform tooling to support data science experiments
- Unified data management – harmonize data ingestion, transformation, and storage processes for on-premises, cloud-native, and SaaS data sources integrated with data trust capabilities
- Departmental analytics – build and run analytical models with scale and acceptable performance
- Non-functional – meet enterprise policies and guidelines for data security and privacy, resilience, cost efficiency, performance and scale, operations, and audit.
How do we approach creating architecture artifacts for our clients using our reference architecture?
It’s important to ensure that an organization understands the steps necessary, such as:
- Describe the foundation building blocks of an end-to-end architecture for a target platform solution
- Take an industry sector-agnostic approach, supporting all solution focus areas
- Use a common language to represent and describe architecture building blocks
- Develop a common framework for platform project scope identification, roadmap definition, risk assessment, and capability gap assessment
- Use a common architectural framework to conceptualize the problem statement
- Leverage platform accelerators, reference implementations, and partner ecosystem as being part of a well-thought-out, integrated architecture.
Design to value
We work as a partner on our client’s data-centric journey, helping our clients realize business value through “data-centric digital transformation” by analyzing, advising, and “walking the talk”. We generally find that these imperatives are driving the adoption of this approach:
- Revenue growth and market capture through AI-led data landscape transformation
- Efficiency and optimization by infusing digitalized and AI capabilities into business and operational processes, backed by a solid data foundation and data trust
- Maximize value from existing IT investments and operations while modernizing data and AI infrastructure and applications.
We advise taking a pragmatic approach that is consistent with both a company’s short- and long-term vision.
Authored by: Shuvadeep Dutta