Our approach focuses on business drivers, so you’re not constrained by individual team requirements. You get templates and frameworks that are flexible and scalable, so you avoid early irreversible decisions and your data platform can adapt to evolving technologies and business requirements.
Data & AI Strategy.
Understand your data’s actual value – and discover the best way to maximise it. Because if you want to leverage all the cool models and technologies out there, it’s best to start with the right action plan.
Get in Touch Is your data estate suffering from any of these symptoms?
- Data silos have developed naturally over time – meaning the estate is littered with inefficiencies and shadow data copies. It all works, but there’s no standardised data management, and the organisation is exposed to data-related risks
- Generative AI doesn’t have a cohesive approach – there’s lots of ad-hoc playing around but no strategic or scalable POCs
- The organisation lacks enterprise data connectivity and a centralised operating model – which restricts the pace of transformation and stymies your ability to industrialise AI and machine learning
- You’re implementing purely technical answers to what are actually business problems – and you’re having difficulty scaling technical pinpoint POCs into corporate-level data services
- You find yourself with vendor, technical and/or resource locks – or that previously implemented designs are irreversible
If any of those sound familiar, it’s time to rethink your data and AI strategy. Given the current pace of innovation, engagement with data and popularity of generative AI, you need an approach that gives stakeholders guidance and guardrails while enabling them to gain insights and develop services. And you need to do this in a way that’s both scalable and future-proof.
Our approach is designed to give you a strategy that focuses on business value, with realistic targets and a roadmap to reach them (without reinventing the wheel).
OpenAI: Don’t just play, make it pay.
If you’re sick of gen AI 101 theory/basics and looking for practical ideas, this is the webinar for you. Watch this panel discussion where Nordcloud and Microsoft Azure experts discuss how to go beyond the OpenAI hype and dabbling.
Watch it here
Why Nordcloud?
Reduced costs and risks
Embedded data governance
Data governance factors are built in from the outset. You get holistic governance and discoverability, but with a federated approach where teams can still own and manage their data. This gives you a foundation for industrialising POCs, understanding value networks and collaborating with vendors.
Hyperscaler best practices + cloud-native experience
Our frameworks are based on hyperscaler reference architectures, but with added value from our broad experience of technologies, documentation, skills and partners available in the market. You therefore get more effective shared models and flexible support for moving, producing, publishing and protecting data assets across teams.
Data Architecture Review.
Get a report that guides your data journey – in just 3 weeks
Our Data Architecture Review uses Nordcloud’s experience-driven methodology to help you avoid common pitfalls and unlock more value from your data assets over the short and long term.
In just 3 weeks, you get a report rooted in key business drivers that identifies:
- Goals, target user groups, scopes and restrictions
- Target architecture
- How to translate business requirements into technical design across different customer teams and business units
- Skills and role gaps, with proposed improvement actions
- Pilot scope selected from the identified business drivers (smallest effort, biggest business impact)
- Plan to build a scalable Data Foundation pilot accounting for customer-specific technologies
- Roadmap covering how to incorporate all data assets and products under one Data Foundation governance framework
Data Foundation.
Clear the path to data enablement
Our Data Foundation reduces the time, effort and risk associated with data-driven transformation. Leveraging hyperscaler reference architectures, it gives you:
- Scalable, repeatable framework – that adapts to your organisation’s unique drivers, enabling you to create a modern, secure, scalable and intelligent data platform
- Customisable, ready-made artefacts and operating model guidance – so you don’t have to start from scratch, and teams can focus on creating value from data rather than on architecting and deploying cloud infrastructure.
- Embedded data governance – all within the design
- Guidance on data mesh, data fabric and data hub – and how to strike the right balance
AI Blueprint & Accelerator.
Making AI work and scale
This is about having fun with traditional and generative AI in a way that generates value. This strategy exercise gives you a blueprint for AI and machine learning product development and delivery, so you can:
- Avoid common misunderstandings – and align infra, data and AI/ML teams to coordinate effort, reduce complexity and develop production-ready POCs
- Ensure vendor and resource neutrality – leveraging available models where appropriate and making sure all documentation is centralised and accessible
- Industrialise POCs – coordinating value networks within the organisation, sharing tools and processes, and condensing the test-plan process to a few week
AI Everywhere: Navigating the AI curve from novelty to necessity.
Read it here
Get in Touch.
Let’s discuss how we can help with your data and AI requirements. We can also arrange a free 1-hour briefing, where you can learn more about common problem scenarios with data platforms (data lakes, data warehouses, etc) and AI – and how our methodology and process help you avoid them.
Risto Jäntti
Global Data GtM Lead
