Today's organisations are inundated with more data than ever, with data insights enabling better business decisions and competitive advantages. But the likelihood that they are fully reaping the benefits and insights is small.
Today's organisations are inundated with more data than ever, with data insights enabling better business decisions and competitive advantages. But the likelihood that they are fully reaping the benefits and insights is small.
AI can enable organisations to get more from their data through automated data entry, predictive analytics, data mining, data modelling, and many more.
In short, AI's strengths lie with making sense of large quantities of data, freeing up employees to focus on other tasks, while also reducing the chance of human error.
In a recent survey of 146 IT leaders, Computing found that 72% of those that have implemented AI said it is currently being used for data analysis at their organisation.
However, AI and machine learning are only as good as the data used to fuel them, and poor data quality, be it incomplete, incorrect, or biased, may be holding back organisations' AI aspirations.
High quality training data directly impacts the reliability of AI models. When an AI model can access accurate and complete datasets, there is a higher chance that it will generate insights of real business value. However, outcomes cannot be relied upon if the data is poor or incomplete.
In fact, 20% of Computing survey respondents cited a lack of suitable data as a factor slowing down their AI implementation.
This was echoed by James Woods, public cloud director UK&I at Arrow:
"It's all about the data that's within your business. If you're plugging a ChatGPT or Copilot to look around your data and pull stuff from there. If it's not correct information it's going to be giving you the wrong information. So that data cleanse at the start is super key."
So how can organisations ensure they are data-ready and can fully realise the value of AI?
This relies in having a good-quality dataset backed up by a good data strategy, covering the types of data collected, stored in the right formats and systems and in the right quantity.
Organisations should take steps to assess data quality, perform a data cleanse if needed, make sure it internal and external data is classified correctly, correlate data across datasets, and address any data governance or security issues that may arise.
This strategy should be overarching, covering the whole organisation to ensure missing or disconnected data does not become a pitfall during AI implementation.
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This article is sponsored by Arrow
