By Andrew Carr, Managing Director, Camwood
Artificial intelligence (AI) is enveloped in a pervasive hype. It’s the fastest-adopted business technology in history, offering endless opportunities for growth. From automating mundane tasks and enhancing predictive maintenance to powering medical diagnostics and streamlining customer service, the real-world use cases are wide, and their pool keeps on growing. In the UK, approximately one-in-six organisations have already embraced at least one AI technology.
However, implementing AI isn’t a matter of purchasing an off-the-shelf solution, switching it on and instantly seeing it pay off. AI isn’t a universal remedy for all business pain-points and inefficiencies. The accuracy and effectiveness of AI systems hinges directly on the quality of their underlying data. After all, AI algorithms learn patterns and make predictions based on the data they are trained on. High-quality data is essential to minimise biases and inconsistencies and create reliable AI models.
Getting AI-ready
According to CIO’s State of the CIO 2023 report, 26% of IT leaders believe that AI and machine learning will dominate their investments. However, ensuring AI-readiness is crucial to prevent these investments from not delivering the expected return. Businesses must extract actionable insights from their data environments rather than merely glimpsing snapshots of them.
Yet, many organisations face a serious problem as they have essentially locked their data down so that no member of staff can access it. This might work in averting data breaches, but it’s essentially preventing people from opening a treasure chest of valuable information. Building AI solutions on fragmented, inaccessible data can lead to significant and costly issues. Incomplete datasets and duplicates may result in biased or inaccurate conclusions, as exemplified by Amazon‘s 2018 experience when an AI recruitment tool, trained on predominantly male data submissions over a decade, inadvertently favoured male candidates.
Moreover, improperly labelled data prevents algorithms from effectively learning and automating tasks that humans may lack the time or expertise to perform. To harness the full potential of AI, businesses must liberate their data from constraints, enabling them to understand its full scope and derive maximum value from it.
Achieving data fitness
To capitalise on the full spectrum of opportunities AI presents, organisations first need to achieve a sufficient level of data fitness. This journey begins with a comprehensive data assessment that uncovers the whereabouts of every piece of data within the technology ecosystem. This audit also determines the storage type for each dataset, its retention period, and when it was last accessed by staff.
Accurate and clean, tagged data is essential for AI solutions to function effectively. Fragmented data, currently obscured, can be relocated to more suitable storage locations. Priority datasets required for AI applications can be moved to accessible storage, while less critical data can be shifted to cost-effective storage solutions. Eliminating unnecessary duplicate files ensures that learning algorithms have precise information to leverage and apply to automated processes.
Implementing an effective governance framework with established rules ensures a reliable flow of valuable data, vital for significant AI investments. This strategy empowers staff with a self-service approach to organisational data, fostering connectivity between datasets rather than segregating them into silos. Metadata-based labelling of internal and external data enriches context, enabling users to extract insights efficiently from AI-driven tools.
The value of adopting a data fitness strategy lies in enabling organisations to reduce reliance on expensive data scientists through enhanced self-service capabilities. By prioritising data fitness, businesses can streamline operations and reduce costs associated with specialised professionals.
Achieving data fitness is crucial in the evolving landscape of AI. It promises transformative outcomes, empowering individuals with faster access to information for informed decision-making. As AI transitions from novelty to ubiquity, mastering data management becomes invaluable. Just as PCs became ubiquitous in offices worldwide, AI is poised to pervade every facet of business. However, success hinges on adept data management, as organisations failing to prioritise it within the next 2-5 years risk falling behind competitively.
Maximising AI’s potential
There’s no denying that AI as a technology is the most exciting and promising one of the decade. But realising the true potential of AI demands robust data management practices. As AI integration becomes more and more mainstream and essential to business functions, firms must ensure their data is accessible, well-governed, complete and clean. A robust level of data fitness not only mitigates errors in AI deployments but also empowers businesses to make agile, informed decisions to propel their growth. Companies investing in comprehensive data management today position themselves competitively in the AI-driven future. Conversely, those neglecting this imperative may struggle to keep pace. Which company do you want to be?
