Everyone is talking about AI right now and for good reason. The wealth of opportunity and the potential impact on business value is vast and truly world-changing: from harnessing predictive AI for risk mitigation in supply chains or critical infrastructure, using digital twins to improve operational resilience in financial services or using ML models to decrease inefficiencies and accelerate the transition to renewables for the energy sector.
But organisations looking to avoid the hype and ensure AI delivers strong value and ROI need to focus on a data strategy first. Any AI application is only worth the data it’s built on. Without that, it’s difficult to get business buy-in to make ambitions a reality, the investment into AI could be wasted and any expectations are unrealistic.
A data strategy establishes a clear connection between business outcomes, value and data, informing what an organisation needs to prioritise and invest in. It’s inextricably linked to business objectives, which form the north star to which the what, how and why of data must look. The holy grail of data strategy is to be able to use data to determine the best course of action, creating a virtuous cycle of data strategy. A data strategy can take on multiple approaches:
A Data Mesh model to align business and technology around meaningful business domains, that produce and consume data, and would ensure scalability through a federated, product-based data operating model.
Introduce Data Products at scale in an interoperabile way. They provide a natural point to define and control quality attributes, implement data governance controls and abstract away systems of record and operational data.
Define Domains aligned with main business outcomes and create data products with end-to-end accountability within these business domains.
Evolve the Cloud Data Platform to support data democratisation at scale. A flexible, multi-tenant platform with shared services and a core enablement team will reduce the complexity and the cost of building new data products.
Introduce Product Thinking for data products to transition the business away from the view of data as a technical asset to a focus on value for the user. This approach can be used for AI products also, to ensure the best possible ROI and user engagement.
Establish a Data Democratisation Roadmap which prioritises data products and capabilities to support valuable use cases and to drive maturity across the organisation. This represents an opportunity to improve data quality and democratise access across the business.
It's clear that the cutting-edge capabilities from data & AI hold tremendous promise. However, bridging the gap between ambition and realisation requires enterprise organisations to change how they work with data and solve existing challenges.
They might have issues such as how data is structured, the quality, transparency and accessibility of data. However they may also face people challenges, such as fostering a more data-driven culture, increasing data literacy among the workforce and achieving buy-in across the business for better data practices.
There are a number of approaches that enterprises can take when building a data strategy to tackle such issues. By adopting data mesh for example, enterprises can address accessibility challenges. Data mesh decentralises and democratises data in a federated model so it can be accessed by anyone who needs to in the organisation, as and when they need it.
Treating data as a product in this way is a fundamental shift away from the view of data as a technical asset or problem, for example needing to buy tech or upgrade data platforms. It opens the door to widespread innovation and advanced AI use cases across the organisation that drive business outcomes, such as harnessing data approaches to knowledge graphs and extracting insights. Only then, can organisations truly unleash the full potential of AI.
It’s our mission to make sure that these larger enterprises don’t get left behind. The way they operate needs to change so we work with our customers to get them AI ready, reimagining how they operate to make their data and use of AI their competitive advantage.
Enterprise level firms also struggle to define a strategic link between AI use cases and business outcomes. Our conversations consistently revolve around achieving tangible results such as revenue growth, cost savings and risk reduction. Simply embracing AI for the sake of innovation is not enough; we must focus on how AI can become a powerful driver for sustainable success.
Without this strategic link to demonstrate value to the business, it becomes difficult to achieve buy-in and justify the investment in AI. At best, it becomes a siloed element of your tech stack instead of a cross organisation capability.
To understand an organisation’s data and AI readiness levels, we create a baseline based on strengths, areas of improvements and long-term strategic plans. An organisation-wide analysis of teams assesses the capabilities of different domains and product teams within a business, providing a granular understanding of where to dedicate resources and opportunities for knowledge sharing and collaboration. A peer analysis provides a comparative insight against industry trends and benchmarks your organisation against competitors.
We do all this through DARA, our Data and AI Readiness Application. It’s free-to-consume and you can understand your existing data and AI capabilities at speed.
An AI-mature organisation can repeatedly take ideas from conception through implementation and to generating business value with minimal friction. By developing your AI culture, ideation, delivery, trust and impact you can start to prove the value of AI to the business and deliver real business outcomes in a way that is iterative and scalable.
And underneath it all is a data strategy designed to drive business value, based on a well governed, high quality, democratised data estate.
.png)
.png)
