.png){kind=logo}
Most enterprises today have a hybrid and multi-cloud environment. In a 2024 study conducted by Virtana, a cloud observability platform, 83% of CIOs surveyed said that they have more than one cloud service provider and 54% of them were running more than eight public cloud instances.
Multi-cloud architecture provides numerous advantages like flexibility, freedom and rapid scalability. It also poses several challenges, with Gartner predicting that 50% of enterprises should not expect to see results from such implementations till 2029. These challenges arise from complexities within multiple environments, cost-control issues, lack of global visibility and optimization.
Despite complications, the future of enterprise infrastructure is a hybrid of private and public multi-cloud, combined with on-premises solutions. Like other applications, analytics and business intelligence (BI) also needs to adapt and efficiently work with this trend. Workloads would continue to move to this multi-cloud setting, and hence, it is critical that tools develop capabilities to operate within the new landscape.
As a new fluid data environment becomes the norm, analysts and IT teams are required to rethink their architectural choices. Enterprises cannot be forced into complex ETL processes or migrating every workload to fit the requirements of analytics tools. Instead, platforms must adapt and meet the data where it lives.
Volumes and a variety of data is being generated across cloud environments, regions and applications, creating a fragmented landscape that is continually expanding. Disjointed insights, conflicting metrics and effort duplication across teams are inevitable. Tools sprawl, caused by each business unit using its own analytics application, building its own data pipe and computing logic, is causing a loss in consistency. As teams negotiate and repeatedly spend effort to align to a common view, trust in insights erodes and crucial time to action is lost.
The only sustainable way to extract value from this chaos is to build a unified analytical layer, that overlays and abstracts the underlying data complexity and sprawl. It should be able to integrate structured and unstructured data to deliver seamless performance with real-time as well as historical data. The unified data layer must provide a consistent and governed view of the data across all environments, whether multi-cloud or in a data center.
In this new paradigm, analytics platforms need to be separate from the data layer, with a semantically intelligent layer in between. This layer not only supports fluid, federated access to a multi-could storage layer, but also provides governance, security, scalability and flexibility to work with any type of data.
As business leaders base critical decisions on data insights, trust on analytics is a prerequisite. Even with data sprawled across multiple systems, each having its own format, definition and access rules, they expect that analytics platform harmonize across them and present a single, unified view of truth.
Though technically challenging, it is non-negotiable for data-driven decisions. Also, duplicating and moving data to a central warehouse is not an efficient solution. A semantic layer that sits atop distributed storage and provides analytics-ready data in a uniform, context-aware manner is required for the job.
A semantic layer reconciles across multiple data formats and standardizes business definitions, so that “revenue,” “active user” or “MRR” mean the same thing, even when used by multiple visualization and BI tools.
The erstwhile BI approach that relied on centralizing data into a single warehouse or data lake fails to scale and does not remain cost effective when data is scattered across geographies, providers and applications. To thrive in this new reality, modern analytics must embrace an elastic and federated architecture.
Elasticity allows compute and storage to scale independently, in response to demand changes. Whether running queries over a month of sales data or drawing insights from changes in consumer behavior over a three-year period, compute should flex instantly and automatically.
Federation, at the same time, breaks away from dependency on centralized data that is duplicated from various stores. Instead, modern BI platforms are designed to query data wherever it resides. It operates across several sources simultaneously, presenting a single virtual view.
With scattered data, integration becomes vital to analytics success. Manual integration or using a library of connectors is not enough as it slows time-to-insight. Integration for modern workloads needs to be intelligent, seamless and adaptive.
It should support low-code or no-code ETL/ELT and maintain continuous data flow, working with both streaming data and batch processing. The ability to orchestrate complex workflows while managing dependencies, handling exceptions and adapting to changing data conditions in real time is essential.
Deep analytics capabilities on historical data are needed along with real-time ingestion and enrichment with operational use cases, ensuring that insights are both timely and context rich.
Security is non-negotiable even with a multi-cloud architecture. It needs to be pervasive, consistent and scalable across every layer of the data and analytics stack. Data may be scattered, but fragmented security models do not work. It can lead to policy drift, inconsistent enforcement and increased risk exposure.
Unified security that operates seamlessly across environments and scales with the business is critical for success. Role-based access, row and column-level security and fine-grained controls must be defined and enforced uniformly across all users and data, not for each individual system or environment.
End-to-end encryption, both at rest and in-transit should be built into the platform by design. Comprehensive auditability is required, with every access, transformation or view tracked and logged for compliance as well as accountability and governance.
All of this should happen without slowing down analytics and insight workflows. Security should be designed to automatically adapt and scale, with the users shielded from its enforcement over a multi-tenant data structure.
In modern distributed, multi-cloud environments, data is constantly in motion across regions, from on-prem to cloud and between business units. Once data moves beyond its original domain, lineage is usually lost, metadata is dropped and access policies break.
Whether a dataset moves from an EU cloud region to a U.S. data warehouse, or from staging to production, its governance cannot be compromised. Policies to manage sensitive customer information, data masking, anonymization and classification should thus be data specific and not storage- or location-specific. Data lineage or the full trace of where it originated, how it was transformed and who transacted with it, must be maintained.
It is a strategic imperative today that an analytics environment is designed to operate wherever data resides, in the cloud, data center, warehouse, lake or even at the edge. In a world where data-backed, real-time and context-rich business intelligence is a critical competitive advantage, the data-to-insights pipeline should be frictionless, regardless of the infrastructure. The future of analytics is cloud-agnostic, semantically intelligent and architecturally elastic.
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]
