Do I Need a Data Strategy or an AI Strategy – or Both?

by Faye Murray (EMBA) , Chief Data Officer, Emrys

A question I’m frequently asked with the advent of AI is, ‘what is the difference between a data and AI strategy?’, followed by, ‘which do I need?’. In this article I provide a high-level breakdown of the similarities and differences between the two – and the opportunities for synergy with an organisation.

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What is a Data Strategy?

A data strategy is a plan which defines how an organisation will manage and utilise data to achieve its strategic goals. It entails data storage and management (e.g. a data lake and lakehouse), data governance, data technologies, data analytics and insights, plus data science. Critically, it’s about engendering a data culture at an organisation, promoting data literacy – and beyond this, data fluency – across the workforce.

What is an AI Strategy?

An AI strategy is a plan which defines how an organisation can use appropriate AI technologies to achieve its strategic goals. It entails identifying potential applications and opportunities to use AI to add value before selecting the appropriate AI tools and technologies and creating a roadmap for the development (and training) and deployment of AI models.

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Overlap/similarities between a Data Strategy and an AI Strategy

1. Both should be aligned with organisational objectives and developed with input from key stakeholders.
2. Both aim to leverage data assets to derive maximum organisational value.
3. Both play central roles in driving innovation, making a company agile and fostering competitive advantage. Through a strategic approach to data, companies can identify emerging trends, anticipate customer needs, and identify untapped market opportunities. Through the adoption of AI organisations can unlock new opportunities for automation, optimisation and personalisation.
4. Linked points 1. and 2. both, therefore, support data-driven, evidence-based decision-making within organisations.
5. Both should incorporate ethical considerations whilst remaining compliant with regulations.
6. Data collection – which generally falls under data management, an IT function – should be guided by both data and AI strategy.

Differences between a Data Strategy and an AI Strategy

1. Data strategy comprises a broader spectrum of technical and non-technical activities linked to data lifecycle management whereas AI is primarily concerned with the development and deployment of AI only, through the utilisation of advanced statistical and computational techniques (like machine learning and natural language processing).
2. As noted above, both are concerned with deriving maximum value through the utilisation of data assets, however their focus is different. Whilst data strategy focuses on optimising data management practices, particularly through data governance programmes and the use of data lakes/lakehouses, AI strategy effectively ‘piggy backs’ on this work, leveraging these optimised data pipelines (and ideally structured data) to train AI models and drive insights.

So, what do I need?

As discussed in this article, data strategy – and in particular data governance – are critical for your organisation to be able to successfully deliver and scale AI because the success of any AI model is contingent on the quality and integrity of a dataset. Therefore, to ensure your AI strategy has the greatest chance of success, a detailed data strategy aligned with your organisation’s values, strategic goals, budget and timeframe, should already be in place. This data strategy also needs to evolve and flex over time because naturally business strategies advance and change, as do AI tools and approaches. Moving straight to an AI strategy without a data strategy can mean you miss opportunities for the ethical and legal adoption of AI, whilst also struggling with an increase in data errors, incorrect insights and models – and ultimately a lot of wasted effort and resource.

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