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Artificial intelligence. Machine learning. Quantum computing. Technologies that were once the stuff of science fiction are now shaping a new era of intelligent innovation, evolving faster than anyone could have imagined.
Data is the lifeblood of these digital transformation engines. To run optimally and efficiently, the GPUs (powerful chips used for performance-intensive computing applications) that fuel them require enormous amounts of data moving at scale at near-impossible speeds. As access to GPUs and AI algorithms continue to be commoditized, data quality and the ability to effectively train AI and ML models are rapidly becoming competitive differentiators.
If it sounds daunting, that’s because it is. Many organizations are struggling to fuel their GPUs with enough quality data to keep them running efficiently and reach their full potential. Devoid of data to process, GPUs run idle, burning enormous amounts of energy and emitting needless carbon while AI business initiatives and research projects stall.
One key reason is that the legacy data architectures and data management approaches that previously governed enterprise technology stacks were conceived and constructed long before the emergence of the cloud and AI era. In the wake of accelerated compute and a new breed of performance-intensive workloads, the approaches we’ve relied on for decades are rapidly devolving.
A recent global survey of more than 1,500 AI practitioners and decision-makers conducted by S&P Global Market Intelligence found that data management is the most frequently cited technical inhibitor to AI initiatives—outweighing even data security and compute performance challenges. This underscores the fact that many organizations are still using legacy data architectures that aren’t fit for purpose in the AI revolution. A new approach is needed.
Data platforms have emerged as an effective tool to help organizations adapt to our new AI-driven, digital world by enabling data to flow more readily to resources that need it, like GPUs.
Key Differentiators Of Data Platforms
Data platforms stand out from traditional data management and storage methods in two key ways: First, they empower the creation of dynamic data pipelines for seamless data movement across distributed data environments, and second, they facilitate data liquidity.
Data liquidity incorporates the efficiencies and scalability of cloud infrastructure directly into the data, serving as a hassle-free alternative to traditional storage technologies that tend to generate problematic silos, latency and performance bottlenecks.
Instead of aggregating data and metadata into large, static datasets confined within storage silos, data liquidity breaks down information into countless tiny shards. These shards can then be efficiently distributed across computing cores and storage resources, facilitating the unrestricted flow of data to the precise resources that require it. This allows it to become a simple-to-consume infrastructure utility that frees data administrators from the mechanics of moving and storing data so they can address more strategic tasks focused on value creation.
To understand why these elements are crucial to support modern data architectures, we should first revisit how we got here.
The Evolution Of Data Infrastructure
In the early 2000s, compute virtualization revolutionized IT by dividing a single server into multiple operating systems (or virtual machines) that were completely firewalled from one another. This led to drastically higher compute utilization rates, allowing for numerous IT efficiencies while driving down associated energy consumption and costs.
More recently, the convergence of a number of disruptive technologies—including containers, NVMe and open networking (which could now propel data at hundreds of gigabytes per second)—as well as the steady growth of cloud adoption, began forcing organizations to take a hard look at their traditional data infrastructure.
Fast forward to 2023, when generative AI exploded on the scene. Traditional data architectures and data storage approaches can no longer accommodate the scale, velocity and data flow requirements of AI workloads, necessitating a new data framework for the future. A data platform approach has never been more critical.
A Multifaceted Approach To Data Platform Success
Although data platforms provide a strong foundation to help solve scalability, performance and data accessibility challenges, they can give way to other issues. Finding the right data within a giant pool can be challenging, necessitating the evolution of systems to enhance data retrieval efficiency through improved context, structure and labeling.
Likewise, as data platforms centralize data on a single platform, security and privacy become increasingly important, and advanced protection and threat detection are needed. Finally, data processing can consume copious amounts of energy, so measures to improve data platforms’ efficiency and sustainability cannot be bolted on as an afterthought—it must be purpose-built and mindfully baked in from the start.
Overcoming these challenges will be critical to harnessing the full potential of data platforms in the era of data-driven decision-making. Addressing the data explosion while fostering efficiency, security and sustainability within the data environment demands a multifaceted strategy. This approach should blend technological innovation, robust security practices and a commitment to environmental responsibility.
I've only scratched the surface in this exploration of enterprise data stack evolution and the rise of the data platform. Deciding to migrate to a data platform architecture is only the first step in overcoming the data management challenges for AI initiatives. Other key considerations are whether to build or buy a data platform and how to make the jump from legacy data architectures, which I plan to explore in subsequent articles.
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