In the rapidly evolving landscape of technology, the ability to harness data and leverage artificial intelligence (AI) has become a cornerstone for innovation and efficiency. At the heart of this transformation lies a paradox that challenges IT professionals: the demand for more data versus the struggle to convert that data into actionable insights. So, what are the critical considerations to help organizations overcome this looming paradox? Read on for a recap from four of the industry’s most prominent thought leaders from Deloitte, ServiceNow and Dell Technologies as they share insights and learnings on tackling the data dilemma, the opportunities and challenges presented by GenAI and the importance of quick insights for driving innovation at speed.
Key Takeaways
- Quality data is essential for effective AI outcomes.
- Focus on business outcomes to drive value from AI initiatives.
- Invest strategically in infrastructure to manage and protect data.
- Quick insights are critical for rapid innovation.
The Data Paradox: More Data, Less Insight?
Moderated by Mindy Cancila, VP of Corporate Strategy at Dell Technologies, the panel began asking each leader—Baris Sarer, AI Practice Leader and Consulting Principal for Telecom, Media & Entertainment, and Technology Industries at Deloitte, Doug Bowers, SVP of Global Cloud Services at ServiceNow and Jeff Boudreau, Chief AI Officer at Dell Technologies—about the importance of data in driving their AI strategies. They explored the data paradox where organizations are inundated with data yet struggle to turn it into insights. “It’s the nucleus of everything. And if you’re not able to use your data to drive insights, you’re not going to be able to advance your innovation efforts,” says Cancila. Boudreau emphasized the importance of quality data, stating, “If you have bad data, you’re going to have bad AI. If you have no data, you’re going to have no AI.” A robust data strategy, including management practices and governance, is essential for ensuring data quality and, consequently, accurate insights. He also highlighted three critical areas for organizations to tackle: data, talent and infrastructure.
GenAI: A World of Opportunities and Challenges
The discussion then shifted to the opportunities and challenges presented by GenAI. Bowers shared insights from ServiceNow’s perspective, focusing on the importance of knowledge and action derived from data. He pointed out that generative AI is a significant driver for both, enabling better customer and employee experiences by surfacing relevant information for informed decision-making.
Sarer brought attention to the challenges of managing the exponential growth of unstructured data, which consists of 80% of all available data. He stressed the need for reimagining data governance and security strategies in the context of this data explosion. Moreover, he discussed the necessity of improving infrastructure to manage the increasing variety and volume of data efficiently. “…with this increase in the number of data in a variety of data and the cost and demand for compute, now we’re going to have to do slightly more optimization in terms of bringing AI to data, right sizing it and from a compute perspective, right locating it, whether it’s public cloud, private cloud, on-prem, all the way to end-user devices.”
Quick Insights: The Fast Track to Innovation
Quick insights are crucial for organizations to innovate at speed. The panelists agreed that focusing on business outcomes and aligning AI initiatives with business strategies is vital for driving value. Boudreau advised organizations to be specific about the outcomes they aim to achieve and to align their data and architecture accordingly, “It’s about the art of the possible. And how do I drive new growth? It’s new offers, new markets.”
In conclusion, the panelists concurred that the key to leveraging data and AI effectively lies in a focused approach to business value, strategic infrastructure investment and a robust data strategy. For IT professionals, the message is clear: embrace the technology, be curious and prioritize data quality to unlock the full potential of AI for innovation.
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To watch the full panel conversation, click the video below.
