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It may feel like artificial intelligence (AI) has become a topic of conversation overnight, but its adoption has been of interest to UK businesses for years. Last year, the UK Government discovered that 15% of British businesses, around 500,000 firms, had implemented at least one form of AI technology already.
Driven by a need for operational efficiencies, firms are leaning on AI to reach their productivity potential. What’s more, the combination of challenging economic headwinds, rising costs, and the growing battle for talent has made it more crucial than ever for businesses to maximise their technology investments.
As businesses increasingly turn to AI, it has become clear that data challenges constantly threaten successful adoption. Organisations require confidence in their data strategies to ensure they can unlock AI’s full potential, both in the near future and long term. But, how can a considered data management strategy truly help businesses with AI adoption?
Organised, clean data
Information is crucial for AI algorithms. This is how they learn. But businesses face a variety of challenges when it comes to data preparation, and unmanaged data can restrict the potential of AI. Disorganised data can hamper the productivity potential for organisations, limiting the usable data their AI can access. Take the UK as an example. Data management and analysis solutions are the most popular AI solutions in Britain, but where is all of that data? As Forrester found, it is often siloed and scattered, hindering the success of solutions.
Data comes from multiple systems and sources making it tricky to connect before it can be processed. When firms truly understand their data landscape, they can then strategically plan how much preparatory work is required to identify the right data, collect it, and organise it. That organised and up-to-date data is key - it is crucial for actionable, and valuable insights.
It’s important to note that businesses can’t underestimate the challenges that come with managing vast quantities of data. They must take time to prepare in advance. Firms can’t solely rely on having data, they must control the way they gather, prepare, and store the information to fully utilise solutions, particularly those with emerging technologies such as AI. With a considered data management strategy, firms can prioritise their data pipeline - critical to the success of the technologies they adopt and to their business operations. Setting up an effective AI strategy with scalability and performance integrated from the start will undoubtedly put businesses on the right track and help set them for success.
A performance first approach
To achieve a smooth data pipeline, performance and scalability must be built in at every stage. This is imperative to unlocking AI’s long-term potential. When scalability is factored into a data pipeline, businesses can prepare to incorporate newer technologies and the latest capabilities. By considering the importance of flexibility, firms can also establish a data pipeline that has the capacity to grow alongside their business interests.
Highly capable, high performance elements are key. Organisations can fuel data pipelines and, once the foundations are in place, GPU’s (Graphics Processing Unit) can be leveraged to speed up data exploration. GPU’s offer a basis for inference model training, processing many pieces of data simultaneously and thus supporting AI learning. Without high performance data storage and powerful data, those expensive GPUs will sit idle waiting for data instead of running as efficiently as possible. In addition, neglecting performance when planning a data strategy will limit the value of those investments, and restrict the insights and learnings businesses can benefit from.
Alongside performance, scalability is an essential aspect for the long-term success of AI, particularly when it comes to expansion. AI capabilities can adapt to new markets, and help firms create competitive advantages. And crucially, businesses can scale without having to pause, and rearrange operations, wasting time, money, and momentum.
Finding the right key to unlock your AI door
Whether using AI enabled data analysis for predictive maintenance or customer insights, successful AI integration is dependent on an intelligent data management strategy. Mismanaged data must be removed to ensure improved data flows. This will provide a foundation for an effective data pipeline. With performance, scalability, and quality data, organisations will be well-equipped from the very start of their AI journeys.
Those that neglect the importance of organised data risk falling behind with mismanaged AI solutions that lag effectiveness. For this reason, data strategies that are aware of common challenges are unrivalled. Well prepared organisations will understand the impact of high-powered performance. By intelligently adopting AI, these leaders will benefit from highly valuable insights that will support further success.