Generative AI is set to transform businesses across all sectors. As the popularity of applications such as ChatGPT has demonstrated, the technology potential for businesses is huge, enabling them to reinvent ways of working for greater efficiency, effectiveness, and productivity.
But, as organisations everywhere move to exploit generative AI’s extraordinary possibilities, new evidence suggests that most aren’t yet ready to unlock its true value. Generative AI is only as good as the data it’s trained on – and herein lies the problem. According to a recent survey by Alteryx, two in five IT leaders (41%) don’t have a centralised data or analytics function and almost half (48%) report data silos within their organisations. With issues such as these posing key barriers for IT teams around the globe, companies must prepare for the mass rollout of new technologies like generative AI by first addressing flaws in their data stacks and cultures.
Trust in data
Firms of all types have become increasingly reliant on data-driven systems to run nearly every aspect of their business. But, as technologies continue to advance – not least with the advent of generative AI – teams must be confident that their business is set up to make the most of the data they hold right now.
The increasing complexity and volume of data make it much more challenging for enterprises to maximise its value. Refining data so it can be used in a meaningful way is a process that is often slow and ineffective, leaving an untapped goldmine of usable data untouched. Companies looking to take advantage of Gen AI must therefore accelerate the data journey to ensure that all parts of the business can thrive in this ‘era of intelligence.’
High-quality data is a sine qua non for successful AI projects: readers need not be reminded of that old adage among machine learning developers that feeding models garbage training data is bound to result in transparently garbage outputs. As such, those companies that wish to capitalise on AI’s potential must ensure their data is of impeccable quality and ready to be used on a massive scale. The report is encouraging in that respect, revealing that three-quarters of businesses (76%) trust their data, while more than half (54%) rate their data maturity as ‘good’ or ‘advanced.’ Meanwhile, a fifth of businesses in the study highlighted challenges around areas including data bias (22%) and data quality (20%.)
Leaders must also recognise the inherent risk of using imperfect or non-compliant data, potentially heavy with age-old biases and carefully consider strategies for mitigating data ethics, governance, security, and privacy concerns. And, while three-quarters of respondents (76%) said they relied on technology to clean and pre-process data, with more than two-thirds (69%) using technology to combat bias, analytics remains the perfect domain to practice responsible AI.
Domain expert analysts who understand the shape of an organisation’s data and are always present to interrogate the resulting output empower non-technical users to access and analyse data. Their presence also fosters an environment where other groups within the organisation can utilise AI. Furthermore, with the right guardrails in place to include practical checks on data quality, privacy, and governance, companies can truly harness the potential of generative AI to create new heights in value from data.
Importance of investing in your data stack
As more leaders foster environments where AI-derived, data-driven insights enhance workers’ decision-making, it’s crucial to modernise data management and empower all to extract value from data at the speed and scale by employing the right data tech stack. However, only one in ten surveyed stated they have what can be classified as a modern data stack. Indeed, one in five respondents (19%) said limitations in their data stack may cause them – or have already caused them – to rely more on outdated or legacy technology. Clearly, then, the foundations of an organisation’s current data stack can be a limiting factor in its successful rollout of generative AI.
It’s not all doom and gloom, however. Many businesses appear to be aware of the need to improve the effectiveness of their data stacks. Almost half (47%) said they were actively working to modernise their current systems in a bid to improve data outcomes. Indeed, the report indicated a recognition of the importance of investing in new technology to overcome the data-related challenges hindering the adoption of generative AI, with global IT leaders citing the improvement of data quality as the most desired outcome.
Given the speed at which generative AI is evolving, business leaders and IT teams need to realise that data is the one differentiating element that can either make or break a company. To succeed in this era of automated, AI-powered, data-driven intelligence, a modern data stack is needed to accelerate the data journey across the business.
Read more: Becoming ‘data ready’ for generative AI
