The idea of integrating data gathered from multiple sources is hardly new, but interest in digital transformation and AI is pushing enterprises to tackle it again.
Data hyperaggregation collects, integrates, and processes large volumes of data from multiple sources. This method can pull data from cloud platforms and on-premises systems to create a unified and comprehensive data set. The goal is to enhance data visibility, thus improving decision-making, analytics, and operational efficiency.
As organizations push the boundaries of digital transformation, understanding and leveraging data hyperaggregation could be the key to unlocking unprecedented business value. Data hyperaggregation strives to create a seamless data ecosystem that works harmoniously with the organization’s objectives. It is emerging as a pivotal trend because data plus data visibility equals better decisions, and better decisions equal business value.
Who woulda thunk? Pretty much anyone working in enterprise IT.
Using abstraction
The backbone of effective data hyperaggregation lies in the infrastructure and technologies supporting it, better known as the middleware. Cloud platforms are tailored to manage and process immense data volumes. I’ve spoken about this in my rants about cloud complexity and cloud complexity management. Abstraction is one of the major ways to overcome cloud and data complexity. Businesses can streamline operations, security, governance, and configuration management by deploying an abstraction layer across cloud platforms. This abstraction layer not only enables better data integration but also mitigates multicloud complexities such as data silos and interoperability.
A common interface allows you to deal with very complex and poorly designed data structures, no matter where they exist. This is not a new idea. Indeed, I started following it in the 1980s. It’s also called data virtualization.
Automation in cloud provisioning and management has revolutionized how organizations can handle data. Automated provisioning allows businesses to access computational resources on demand, scaling their operations vertically or horizontally without requiring upfront infrastructure investments. This agility is crucial for companies looking to use the full potential of data hyperaggregation by quickly gaining insights into the data or aggregated data.
AI is the driver
If the concept of abstraction has been around for decades, albeit called different things, why is there renewed interest now? A significant factor driving data hyperaggregation is the growing focus on AI and machine learning (ML). As companies increasingly integrate AI and ML into their workflows, the need for consolidated and high-quality data becomes imperative. Cloud platforms, by virtue of their comprehensive service offerings, provide an ideal environment for AI-driven applications that require large-scale data processing and analysis. With hyperaggregation, AI models can access diverse, accurate data sets and improve the robustness and accuracy of their predictions.
In the context of economic viability, data hyperaggregation has a compelling sales pitch. Migrating to cloud platforms can involve costs, but the benefits derived from enhanced data analytics, reduced operational inefficiencies, and faster time to market often outweigh these expenses. Organizations are empowered to reallocate their financial resources more effectively, directing them toward innovation and strategic initiatives rather than hardware and infrastructure maintenance.
The push toward ubiquitous computing aligns perfectly with the principles of data hyperaggregation. By adopting a model where computing infrastructure spans edge locations, central data centers, and multiple cloud environments, businesses ensure that data is processed and consumed where it is most efficient and valuable. This approach optimizes costs and bolsters performance and resilience against potential disruptions.
Why should you care?
Data hyperaggregation is not simply a technological advancement. It’s a strategic initiative that aligns with the broader trend of digital transformation. Its ability to provide a unified view of disparate data sources empowers organizations to harness their data effectively, driving innovation and creating competitive advantages in the digital landscape. As the field continues to evolve, the fusion of data hyperaggregation with cutting-edge technologies will undoubtedly shape the future of cloud computing and enterprise data strategies.
The problems and solutions related to enterprise data aggregation are familiar. Indeed, I wrote books about it in the 1990s. In 2024, we still can’t get it right. The problems have actually gotten much worse with the addition of cloud providers and the unwillingness to break down data silos within enterprises. Things didn’t get simpler, they got more complex.
Now, AI needs access to most data sources that enterprises maintain. Because universal access methodologies still don’t exist, we invented a new buzzword, “data hyperaggregation.” If this iteration of data gathering catches on, we get to solve the disparate data problem for more reasons than just AI. I hold out hope. Am I naive? We’ll see.
