Data mesh, data fabrics, and data weaves are designed to map, connect, and guide data across an enterprise, ensuring such data moves efficiently, remains accessible, and fosters innovation
Since 2022, the unprecedented explosion of intelligent applications — spanning AI-driven customer insights, machine learning (ML) models for risk mitigation, and retrieval-augmented generation tools — has presented industry leaders with a double-edged sword of boundless opportunity and mounting angst.
While the promise of increased revenues, streamlined efficiencies, and customer-centric solutions remains enticing, many organizations are grappling with the harsh realities of legacy technological debt and an acute talent gap.
Consider a financial services firm that invests millions of dollars in an AI pilot program to detect fraud in real-time, only to see it falter at scale — unable to adapt to new data streams or integrate itself with legacy infrastructure. Within regulatory compliance, a groundbreaking ML application designed to predict regulatory outcomes might crumble under scrutiny, exposing blind spots in data lineage and governance. Across the boardroom, leaders begin to ask themselves: “Why did this fail when it seemed so promising?”
What went wrong? Was it the technology itself? Or, was it the processes, the people, or even a more traditional issue, the data? And, most critically, who within the organization will emerge as the leaders capable of navigating these challenges and ultimately turn intelligent applications into enduring competitive advantages?
To navigate this bumpy road, organizations must look beyond surface outcomes and instead, to the root causes. Like the unseen forces of the ocean, there are powerful hidden data flows deep within an enterprise that drive ecosystems, influence outcomes, and connect multiple domains via their systems-of-record. Just as understanding and harnessing deep sea currents can transform navigation, organizations must grasp and control their data flows to unlock the true potential of intelligent applications.
Indeed, for intelligent AI-driven solutions to reach their full potential, leadership must gain control of their organizations’ data — whether that data is structured, unstructured, semi-structured, metadata, or anything else. They must also identify next-generation data methods, architectures, and technologies that move beyond individual toolsets and then work to create the processes needed to logically and physically segment data for rapid ingestion, (pre)processing, and traceability.
The 3-pillar solution
This is where advanced solutions of data mesh, data fabrics, and data weaves come into play. Like advanced oceanographic systems, these pillar architectures are designed to map, connect, and guide data across the enterprise, ensuring that such data moves efficiently, remains accessible, and fosters innovation. But what exactly do these frameworks do? And how can they address the bottlenecks that many organizations face today regardless of discrete functions, auditability, regulatory compliance, or legal requirements?
Let’s start with a conceptual representation of how each of these pillars connects within the systems of the banking, financial services & insurance (BFSI) industry. When taken holistically, rather than their traditional separate architectures, these pillars form a cohesive web of capabilities that not just initially support changing AI systems but also adapt to the future needs of the organization.
For rising intelligent system capabilities, the data strategy of orchestrating a three-pillar design becomes critical in addressing the evolving requirements of AI. Each pillar can be expanded independently and simultaneously be included in multimodal integrations that crosses domains, unlocks value, and continuously includes event-driven data from their originating sources (for example, from captive, third-party, cloud, or partner sources).
While the three-pillar architecture seems esoteric without precise tools mentioned, these tools increasingly represent the technological debt which hinders intelligent system adoption and continuous adaptation. To move beyond toolset or vendor software axioms within IT departments, internal AI leaders — such as an organization’s Chief AI Officer — are utilizing new data architectures with data stacks to break requirements into manageable, measurable projects that are interconnected and deterministic.
What are the distinct characteristics of each pillar — and how do they compare and contrast? When interconnected, how can the pillar-provide results be greater than the sum of the parts? And how can they, individual or collectively, deliver emerging, customer-centric use cases when overlaid with rapid cycle AI technologies?
Addressing the transformation
As the pace of AI and data-driven innovation accelerates, industry leaders must address the comprehensive transformation taking place — the movement from system ideations to data ideations. As highlighted in my previous articles, AI in all its forms is simply a data-driven solution set.
The continued expansion across intelligent systems and its capabilities are immense, yet the challenges of harnessing these next generation toolsets to drive tangible, lasting value remain daunting. Success across AI implementations hinges on a willingness by leaders to look beneath surface-level metrics, and into the intricate undercurrents across the three pillars of enterprise data.
Leaders also must recognize that no single tool or platform holds the key. Instead, only a symphony of advanced data architecture — that included data meshes, data fabrics, and data weaves — can achieve the scalability, interoperability, and adaptability that is currently eluding more than 85% of teams. These data frameworks can empower organizations to bridge silos, unify disparate data streams, and fuel the kind of real-time insights that can redefine industries.
As we stand at the crossroads of technological possibility and operational complexity, the question is no longer, “Can we adopt intelligent systems?” but rather, “How will we adapt in order to lead?” By mastering the data undercurrents that shape enterprise ecosystems, organizations can turn challenges into opportunities — and opportunities into transformative success.
For a single AI solution that’s now using trillions of parameters to tune and deploy, the time is now to actively address the untapped undercurrents ignored in the previous enterprise technical debt: data.
You can find more about the digital transformation going on in many organizations here.