Industry executives and experts share their predictions for 2024. Read them in this 16th annual VMblog.com series exclusive.
2024: Data, AI, and Accountability
By Manish Sood, Chief Executive Officer, Founder and Chairman at Reltio
If the past year has been a whirlwind of discussions around artificial intelligence, machine learning, and Generative AI (AI, ML and GenAI), the coming 12 months will witness increased demands for accountability around projects and solutions involving those technologies. Expect to see more scrutiny around the value of AI projects, growing concerns around data security and privacy, and escalating demand for high-quality and unified data for AI.
Greater Scrutiny of Data
As we move into 2024, scrutiny of the underlying data for AI projects will intensify. As corporate boards and members of the C-suite want assurances that AI efforts will pay off, that spotlight will magnify the imperative to resolve underlying data issues.
For the coming 12 months, my experience tells me more CEOs and boardrooms will increasingly realize that data is the linchpin for AI’s success. I am witnessing a seismic shift in the executive mindset; for the first time in years, CEOs, inspired by the potential of GenAI, are actively seeking to increase their technology spending as they see great promise to realize digital transformation.
It is not just that CEOs are intrigued by AI’s potential, though. They are also captivated by the promise of GenAI to redefine the very fabric of how they conduct business-from revolutionizing customer experiences to optimizing supply chains and improving risk management.
The allure of AI is undeniable; it holds the key to unlocking new markets, potentially generating significant cost savings, opening new revenue possibilities, and catapulting companies into a league of their own. However, the sobering truth every CIO understands is that AI is not a plug-and-play miracle. The Achilles’ heel lies within the company’s data-the most valuable yet underperforming asset due to its fragmented nature.
Investments in AI are futile without unifying and managing data to ensure it is clean, connected, and trustworthy. The path to AI’s promise is paved with data unification. It’s about transforming data into a singular, interoperable product that can catalyze digital transformation and harness AI’s transformative power.
Protecting, Governing and Leveraging Information
The age of AI/ML is also heightening the triple threat of data sovereignty, privacy, and security, and it is rapidly transforming how we protect, govern, and leverage information. In an era where the digital frontier continually expands, the specter of cyber threats looms larger than ever.
While there is always a pressing imperative for enterprises to fortify their defenses against the relentless tide of risks, breaches, and ever-tightening global regulations, the rise of AI has magnified these concerns. For 2024, it will further necessitate a robust shield for our most valuable asset: data.
Leveraging data unification and management solutions, which are inherently flexible and scalable, is not just a strategic move-it is a cornerstone for ensuring compliance and securing the bastions of our digital identities. These tools are the vanguards that can help us navigate the complex labyrinth of security risks and regulatory demands, ensuring that our data remains inviolable and sovereign.
Focus on Resolving Underlying Data Issues
Similarly, the need for reusable or “interoperable” data will drive the adoption of data management and unification tools integrated with AI/ML capabilities in the coming months.
We are currently on the cusp of a data renaissance where sophisticated data management and unification tools, seamlessly integrated with AI and ML capabilities, enhance and revolutionize how we automate and deliver data products. This is about crafting certified, effortlessly consumable, and eminently reusable data assets tailored to many business use cases.
So, rather than making data work smarter, it is also important that we architect a future in which data becomes the lifeblood of decision-making and operations, driving unprecedented efficiency and innovation across industries.
GenAI and Data Quality
Finally, as GenAI propels us into an era of conversational user experience fueled by AI, the success of those applications will be unequivocally tied to the quality of data. The user experience will be conversational, which means they will hinge on their ability to access, interpret, and learn from clean, trusted, and continuously enriched datasets.
As we see this shift take place, the most transformative applications will be those that are not just data-driven but data-intelligent-capable of refining their outputs through continuous data assimilation, ensuring ever-increasing accuracy and relevance in a dynamically changing world.
Accountability
While AI will continue to dominate the tech conversation in 2024, there must be accountability around the quality of data. Projects that start with the question, “Is the data fueling this project trustworthy, reliable, and clean?” have the best foundation for future success.
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ABOUT THE AUTHOR
Manish Sood, CEO, Chairman, and Founder at Reltio
Manish Sood is the CEO, founder, and chairman of Reltio, the first cloud-native, software-as-a-service (SaaS) data platform. He currently oversees Reltio‘s Office of the CEO, M&A strategy, business development, long-term product strategy, and innovation. An entrepreneur with a vision of the big-picture ways data can drive business and industry transformations, Manish founded Reltio in 2011 to help organizations accelerate the value of their data. Since its inception, Manish has led Reltio‘s evolution from the concept stage to a company recently valued at $1.7 billion and with nearly $100 million in annual revenue. Today, Reltio unifies multi-source, complex data into a single source of trusted information for about 125 enterprise customers in more than 140 countries globally. Manish previously led product strategy and management for the Master Data Management (MDM) platform at Informatica and Siperian. During his career, Manish has architected some of the largest and most widely used data management solutions used by Fortune 100 companies today.
