By Anurag Sanghai, Principal Solutions Architect at Intellicus Technologies
The New Reality of Multi-Cloud
A multi-cloud environment is sometimes an outcome, instead of being a choice. A merger or acquisition may result in multiple cloud footprints. It may be a deliberate strategy for others, adopted to mitigate vendor lock-in, or leverage best-of-breed services and technological options. Whether accidental or by design, what is certain is that a multi-cloud environment is now resident in over 81% of organisations, according to a Gartner survey.
This new data landscape is dynamic and complex. AWS may be the go-to for storage agility, Snowflake on Azure may power analytical workloads and Google Cloud may be the destination for advanced AI. While it brings advantages with the best of technological options, agility and innovation, it also results in a data sprawl and extremely complicated management.
Security blind spots emerge, along with increased governance challenges. Consider an example of a global retailer who uses AWS for storage, Azure for analytics and Google Cloud for AI-driven personalisation. A customer’s personally identifiable information (PII) might be masked in AWS, but left exposed when replicated into Snowflake on Azure. Marketing analysts running AI models in Google Cloud may unknowingly access sensitive attributes that should have been restricted.
Without unified governance, each platform enforces its own policies. This creates gaps that hackers exploit, or has the potential to be accidentally misused internally. Another challenge in a multi-cloud environment is inconsistency. Different teams may define the same metric in different ways, enforce different data quality standards or interpret access policies differently.
Unified Governance with a Semantic Layer
A semantic layer solves the governance challenge by acting as a single, authoritative source of definitions and rules across different BI tools. Whether one uses Tableau, PowerBI or Looker – the semantic layer centralises the business logic and access controls.
Metric definitions, security classifications and access permissions are enforced uniformly. Serving as a common reference point, the semantic layer also creates a clear audit trail. This is critical for transparency and regulatory compliance.
It also removes the risks of shadow IT and policy drift. Without a semantic layer, teams often duplicate datasets and write their own metrics definition to make their reports. This shadow IT is eliminated with governed, consistent and trusted definitions, uniformly accessed by all teams and BI tools from the semantic layer.
Self-Service Analytics within Governance Framework
Business teams want to quickly explore data and generate insights without waiting on IT approvals. However, Multi-cloud BI must balance self-service with governance. Conversational analytics takes this a step forward, with natural language queries and AI driven insights. A business leader may simply ask “What were Q2 sales in the EMEA region?” and receive a certified and compliant answer.
It’s essential, therefore, that governance guardrails are embedded within the self-service experience. Building a central, searchable hub of certified metrics, curated datasets and pre-aggregated, approved reports and making them accessible is the best policy to mitigate trust and compliance issues. Users can then explore and analyse data on their own, but always from trusted, policy-compliant assets. Agility is preserved, while risks are contained.
An Implementation Blueprint
The first phase is about discovering the data. All data assets across all cloud providers and on-prem environments should be catalogued with metadata such as ownership, validation date, certification status and usage context. Critical business metrics, sensitive information and compliance obligations are to be identified.
Next, architecting a semantic layer is crucial to ensure consistency of definitions, rules and access policies. The layer must integrate smoothly with both cloud platforms (data layer) and BI tools (consumption layer). Security and compliance policies are coded into the layer, implementing access controls to row-levels and masking PII.
Modern implementations take this a step further by coding governance rules in declarative formats such as YAML. Policies are written as machine-readable instructions, like a YAML snippet that specifies that customer email is always masked. Being decoupled from specific tooling, these are applied universally, being portable across the multi-cloud stack.
The semantic layer is also the repository for the metrics catalog. Reports may be pre-populated with high demand query results. Reports that are transparent, auditable and complaint build trust in relying on data insights for critical business decisions. This helps in adoption of the BI platform throughout the organisation.
The semantic layer can also be extended to become a “data marketplace”, essentially a central hub of certified data assets, with open access and backed up with information on lineage, along with user guidance. This accelerates self-service analytics, while making sure that all teams work with the same governed, policy-compliant datasets, without any duplications.
As new data sources get added, a universal semantic layer helps in scaling up the governance coverage across multi-cloud with ease. Modern environments use AI and automation to monitor data usage patterns and provide proactive recommendations to optimise policies.
For example, AIOps (Artificial Intelligence for IT Operations) can be used to alert security teams, when a sudden shift in access is detected for a sensitive dataset. It can also be configured to automatically trigger stricter access controls through the semantic layer, if needed.
Unused datasets can be flagged for retirement, policies may be fine-tuned and resources may be optimised to balance cost and performance. It would classify new assets automatically, while enforcing policies and surfacing insights without manual intervention. Thus, by embedding AI, organisations develop a self-managing, self-healing, intelligent governance framework that evolves alongside their multi-cloud environment.
Governance Drives Trust in Analytics
The advantages of a multi-cloud strategy could be locked away due to data governance challenges. Business users expect faster answers than ever before, even as data volumes multiply. Patched up governance approach fails to deliver and inconsistency chips away the trust to rely on insights for crucial decisions.
A foundational approach with a semantic layer and embedded governance practices, ensures that even with several providers, platforms and BI tools, the metrics and rules remain consistent, and regulatory compliance is adhered.