In the midst of the UK AI Safety Summit, Kirsty Biddiscombe, UK Head for AI, ML & Analytics at NetApp, discusses the need for efficient data management and why it’s important not to over-regulate when it comes to the risks posed.
Artificial Intelligence is not entirely new for businesses across the UK. It has been on the digital agenda for years, long before the birth of ChatGPT. However, the excitement of AI and generative AI has certainly gone up another gear in the past year. In fact, AI has been chosen as the word of the year for 2023!
Today, 72% of enterprises are using generative AI in some manner in their operations. What’s more, AI is being effectively used to support businesses – noting AI’s long-considered potential – to save time and money and reduce the administrative burden of many tasks freeing up employees.
The UK AI Safety Summit has been hotly anticipated and hasn’t been far from the headlines since it was first announced, indicating that enterprises of all sizes are interested in the balance of risks and opportunities that AI offers.
However, risking over-regulation could result in reduced innovation. Instead, the answer to progress lies in rethinking AI implementation, starting from the beginning with effective data management. Focusing on the foundation of AI – good, clean data – could be the answer to effectively deploy the advanced technology organisations crave and the UK Summit attendees wish to better understand.
Data management and AI go hand in hand
With the anniversary of ChatGPT around the corner, this year marks an unprecedented whirlwind of interest in landmark AI technology. While interest in ‘big’ AI continues, the effective implementation of AI for everyday enterprise use can’t be overlooked. It takes time, investment of resources, and in many instances, considerable effort to access AI-enabled insights.
But it’s easy to forget that the data that enterprises collect and generate is key in accelerating AI. In fact, data is the fuel behind AI. The cleaner the fuel, the more effective AI solutions will be. Data can therefore power these tools to ensure they meet their full potential.
Regulation of AI is undoubtedly important, but the risk of over-regulation for everyday enterprise advanced technologies could lead to the loss of untapped potential for efficiencies and developments. As businesses rightfully consider their use cases of AI, a progressive approach lies in the data strategies at the heart of these solutions. Because for safer, responsible AI, there must be considered data management behind the solutions.
In fact, better handling of data can lead to more effective AI use. If a business handles its data improperly, impractically, and unclearly, AI solutions will be at the mercy of this poorly labelled and mishandled information. The result? Efficiency, accuracy, and in extreme cases, safety will pay the price.
Going back to the beginning
In truth, data is the beginning for all AI processes. Its management and governance are foundational for the effective development of AI.
The solution to bad data is, of course, better data management. And the best data is relevant, organised, and secure. But getting your data to this point takes a considerable amount of preparation, and constant cleaning. It refers to an intentional strategic data approach that knows the value of feeding AI high-quality information for those golden insights.
This means businesses must start at the source. With data being stored and generated from multiple systems, the data landscape for many enterprises is vast, complex, and siloed. Joining up scattered data into one system is crucial – it’s impossible to start your journey ahead when your vehicle parts are separate.
And even then, there are the actual types of data, videos, images, and the varying structured and unstructured sources that make it even more difficult to sort through. With such dynamic data, a thorough understanding of data can prove too complex to attempt or achieve. However, and rather uniquely, with the help of AI, data can be sorted to actually help further AI tools.
There is an opportunity for businesses to take advantage of the beneficial partnership between AI and data management strategies. AI can automate, clean, and organise datasets to get information to a state ready for advanced analytics. This cleaner, better data can then be used to train further AI tools with higher quality data, generating the most actionable insights and having a significant impact on business operations, processes, and decision-making.
Start as you mean to go on
When we talk about AI, it’s fair to say that data management unlocking innovative everyday AI won’t be the most attractive discussion topic. Starting from the beginning is never appealing when we’re all focused on the results, the potential, and the opportunities, but it is the most important aspect to get right. A strategic data approach will be key for the long-term continuation of AI success which businesses benefit from.
It’s up to enterprises to prioritise their data strategy for ongoing AI-enabled progress, allowing them to move beyond survival and into growth, away from administrative tasks and towards creative complex thinking.
We shouldn’t need to move mountains to reach the summit of AI success. Instead, we recommend having a planned path in place. Only by starting with intelligent data management can enterprises navigate and continue to climb to new heights of AI innovation.
