SPARCs with SAS: Part 1—Data Management. Navigating "Any Data Anywhere" for AI Success
Originally published on ARCweb.com by Colin Masson of ARC Advisory Group Inc. on January 9th 2025.
Welcome to the first of our three-part SPARC (Short Podcasts by ARC) series with SAS, where we'll be unpacking the complexities of adopting artificial intelligence (AI) in the industrial sector. As an industry analyst at ARC Advisory Group Inc. , I've had the opportunity to speak with numerous organizations, and one thing is clear: while the potential of AI is immense, so are the challenges. In this series, I'll be exploring industrial AI challenges with Bryan Saunders, Global Director and Head of IoT Industry Consulting at SAS to share some key insights. In part 1, we're diving deep into the foundational issue: data management.
Watch or listen to Part 1.
Stay tuned for Parts 2 and 3, and future SPARCs.
SPARC SUMMARY
The AI Buzz and the Reality Check
ARC Advisory Groups recent annual survey of over 500 industrial organizations reveals that AI is seen as the most impactful technology for the next three years. The buzz around Generative AI has only intensified this, leading to increased investment. However, our research also uncovered a significant hurdle: data quality. This isn't a new problem, but it's one that's particularly acute when it comes to AI. The industrial sector is not short on data. In fact, it is quite the opposite. The challenge is how to make that data useful.
The Three Pillars of AI and Analytics
Bryan Saunders from 赛仕软件 frames the discussion around an AI and analytics lifecycle, which is comprised of three main areas: data management, model development,?and deployment of insights. It's important to note that everything starts with the data. The foundation of any successful AI initiative is, without a doubt, high-quality data. And this is where many organizations are struggling.
The "Any Data Anywhere" Challenge
In the industrial landscape, data comes in many forms: sensor readings, text documents, video, and acoustic data, to name a few. This data also exists in different states: at rest in traditional databases, and in motion from real-time operations. Add to that the distributed nature of industrial environments—with data generated at the edge, in the cloud, and everywhere in between—and you have a complex data management challenge. As Bryan puts it, the key is to manage "any data, anywhere".
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The Importance of Data Governance
The management of data is not just about access, but also about preparation and governance. Data has a lifecycle and a lineage. Organizations need to be able to trace the source of the data and understand what operations were performed on the data in order to be able to use it in AI-driven decisions, especially in regulated or controlled environments. This traceability is not just best practice; it’s essential for responsible AI adoption.
The Pitfalls of the "Utopian Data Fabric"
Many organizations believe that they need to move all their data into a standardized, normalized, and cataloged system—a kind of "utopian data fabric." However, as Bryan points out, this approach is often not feasible and can become a significant obstacle. It requires considerable effort and resources, and more importantly, it takes time, which can be the enemy of any industrial operation. Organizations become paralyzed by the goal of a perfect data fabric, ultimately delaying or derailing their AI initiatives.
Key Takeaways from Part 1
Looking Ahead
In Part 2 of this series, we will delve into more of this conversation with Bryan and discuss how industrial organizations can adopt a more pragmatic approach to data management and AI deployment. We will discover that starting small and focusing on value are key to success.
Learn More About ARC and SAS Views on the AI and Analytics Lifecycle
Interest SPARCed?
Thanks for tuning in to this SPARC! Share these episodes with others who are passionate about applying technology the right way to address skills gaps, create more intelligent processes, while building a more profitable?and?sustainable future. For those looking to go deeper, ARC Advisory Group offers longer format?Digital Transformation and Sustainability Podcasts,?and unparalleled guidance in developing digital transformation and sustainability strategies for industrial organizations. Reach out to us for expert insights and support that can help you turn these ideas into action and lead your organization toward a more profitable and sustainable path.
To contribute to SPARCs, or the Digital Transformation and Sustainability Podcasts, contact Colin Masson or Jim Frazer at ARC Advisory Group through your client manager.
For ARC Advisory Group recommendations for?closing the digital divide by embracing Industrial AI, and governing and guiding major decisions about enterprise, cloud, industrial edge and AI software, please contact?Colin Masson?at?[email protected].