Unstructured data potential hindered by expertise & tool gaps
New research conducted by Qlik, a global leader in data integration, analytics, and artificial intelligence (AI), reveals that while enterprises are aware of the substantial potential of unstructured data to improve operational efficiency and yield significant insights, they are encountering substantial challenges in effectively utilising this resource. The survey indicates that a lack of expertise and inadequate tools are substantial roadblocks. Notably, only a minor fraction of enterprises allocate more than a quarter of their AI budget to unstructured data initiatives.
Brendan Grady, General Manager of Qlik’s Analytics Business Unit, commented, “With many sources citing that unstructured data makes up to 80% of the world’s data, it is no surprise that enterprise leaders want more real value from this untapped source.” He further noted, “Our survey highlights that nearly 70% agree their organisation is not well equipped to understand how Generative AI can be leveraged on their unstructured data.”
The survey, administered by Enterprise Technology Research in April 2024, tapped into the perspectives of 200 enterprise technology decision-makers across multiple industries. The findings detail several points of concern and priorities for organisations:
Data privacy and compliance emerge as paramount concerns, with 59% of respondents expressing significant apprehension about data privacy and 47% about regulatory compliance. These concerns markedly outweigh worries regarding return on investment (ROI), which stands at 19%.
When evaluating vendors, enterprise leaders prioritise system integration (55%), cost (50%), and governance features (49%). Interestingly, vendor reputation was a low priority for only 16% of respondents. Financial gains from unstructured data are expected to be modest, with 45% anticipating a 10%-20% improvement in either their top or bottom lines.
Despite a high interest in Generative AI (GenAI) for unstructured data, significant investment remains scarce. Two-thirds of respondents indicated plans to invest in a GenAI tool for unstructured data. However, just 22% of all respondents are making “significant” investments in AI technologies. A majority, at 62%, see unstructured data as a means to boost operational efficiency, yet only 31% see it as a driver for innovation. Roughly 45% described use-cases involving enhanced search and query tools to delve into internal documents.
There is strong agreement among respondents that traditional enterprise search tools are inadequate for maximising the value of extensive document libraries. A mere 16% have already acquired tools specifically designed to extract insights from unstructured data, with most initiatives still in nascent or pilot stages.
Erik Bradley, Chief Strategist and Director of Research at Enterprise Technology Research, stated, “The findings from our survey underscore a critical challenge facing enterprises today: the gap in expertise needed to harness the full potential of generative AI for unstructured data.” He added, “While the appetite for leveraging unstructured data is high, the lack of specialised skills and appropriate tools is a significant barrier. To truly capitalise on the opportunities presented by GenAI, organisations must invest in bridging this knowledge gap and integrating advanced AI capabilities seamlessly into their existing analytics frameworks.”
The full survey results provide a comprehensive look at how enterprise leaders are navigating the opportunities and challenges presented by unstructured data and generative AI. As the demand for actionable insights from vast reservoirs of unstructured data continues to grow, enterprises are at a critical juncture to develop the necessary expertise and invest in effective tools to make the most of this promising resource.
