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Multimodal AI Demands Multimodal Data Pipelines

Innovation is the driving force behind human progress and we believe in the power of technology to enable humans to push beyond the boundaries of what’s possible. In this rapidly evolving landscape, staying ahead of the curve is what sets good organizations apart from the great ones. Over the years, the realm of possibility has expanded in both incremental steps and monumental leaps. Let’s take a closer look at the transformative journey from “Big Data” to Generative AI.

In the early 2010s, the advent of “Big Data” technology ushered in a new era of possibilities. Organizations across the globe began dreaming of the potential that lay across their vast and disparate data and documents. Those who effectively harnessed Big Data witnessed unprecedented innovation and, consequently, market cap gains. They explored innovative avenues previously deemed unattainable and set themselves apart from their competitors. Soon on the scene was the Cloud Computing wave which allowed for unprecedented access to infrastructure and dramatic cost and performance scale and efficiencies for properly rearchitected cloud native workloads. Fast forwarding to today, the landscape has evolved once again with the emergence of Generative AI and the foundational technology of Large Language Models.

The Big Data and Cloud Computing waves have ultimately democratized data, analytics and machine learning on a massive scale. However, the promise of unlocking all data remains elusive. Traditional analytics and AI/ML rely heavily on structured data and require specialized knowledge to access and interpret this data, especially in the case of machine learning. Surprisingly, according to IDC about 90% of the world’s data remains unstructured and largely untapped. Enter Generative AI, a game-changer that opens up access to ALL data, making it accessible to anyone who can speak in human language. The possibilities are endless. Where are organizations getting stuck on their journey to harnessing the power of GenAI and LLMs?

The key challenge lies in the absence of systems, and the skill sets, capable of handling unstructured data effectively. Without these systems, insights remain confined to the 10% of data that is already structured. To address this gap, early innovators have resorted to assembling armies of engineers to write custom, point-to-point data pipelines. While this approach can succeed with highly skilled developers and a “build-first” mentality, it exacerbates existing issues. Most organizations lack a clear understanding of how data flows within their systems, let alone the ability to monitor and control that flow. Furthermore, adding new data sources or altering existing models necessitates revisiting custom code—a cumbersome and inefficient process.

Fortunately, a solution exists. NiFi was purpose-built from its inception at the NSA to manage the orchestration and execution of unstructured data pipelines. Over the past nine years, the open-source community has dedicated considerable effort to enhancing structured data capabilities. Today, NiFi is trusted by thousands of the world’s largest and most secure organizations. Datavolo leverages this technology to deliver a containerized, cloud-native managed service that empowers our customers to swiftly develop and operationalize secure multimodal data pipelines for their AI models.

Innovation is the catalyst that propels organizations to greatness, and data is the fuel that powers this journey. Datavolo’s mission is simple yet deeply passionate: to make our customers wildly successful by providing their AI systems with all the data they need, wherever they need it. Let’s innovate together and ride the AI wave to previously unimagined heights.

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