Data management: the grand challenge of the AI revolution?

Investing in modern data is critical to successfully scaling artificial intelligence, but half of businesses face cost barriers, according to a study. Businesses that can invest in data management now will become AI leaders in the long term

69% of respondents are involved in at least one ongoing AI project, including 28% projects have reached enterprise scale. While businesses and research institutions are accelerating the adoption of AI as they seek to create new value propositions, the study shows that data infrastructure and AI sustainability challenges act as barriers to successful implementation of AI at scale. The report highlights that the development of generative AI within enterprises will increase rapidly through 2023, further exacerbating these challenges

Will data management become the biggest challenge of the AI revolution?

The use of artificial intelligence continues to increase, but enterprise scaling remains a challenge. WEKA and Standard & Poor's jointly conducted a survey of 1,500 global artificial intelligence decision-makers and published these results. The survey identifies the opportunities and barriers companies encounter on their AI journeys, as well as the unique drivers of AI adoption across industries around the world. The survey also provides insights into what steps businesses need to take to successfully use AI in the future

32% of respondents cited data management as a technical barrier to AI/ML deployment. In addition, 26% of respondents cited security issues and 20% cited computing performance issues as current major challenges, indicating that many enterprises’ existing data architectures are unable to support the AI revolution

According to the survey, 77% of respondents believe legacy architecture and data infrastructure have an impact on their sustainability performance, while 74% say moving workloads to the public cloud is important to achieving sustainability Or Key Driver

68% of respondents said they are concerned about the impact of AI/ML on their business’s energy use and carbon footprint

As AI initiatives grow Going further, a hybrid approach and multiple deployment locations will be needed to support workload needs. Legacy data infrastructure has a direct negative impact on its ability to use AI efficiently and sustainably at scale because they were not developed with modern performance-intensive workloads or hybrid cloud and edge models in mind

Just like we wouldn’t expect to use battery technology developed in the 1990s to power state-of-the-art electric vehicles like Tesla, we can’t expect data management methods designed for the data challenges of the last century to support Next-generation applications like generative AI

Enterprises that build modern data stacks designed to support the needs of AI workloads that span seamlessly from edge to core to cloud will be the leaders and disruptors of the future. .

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