Solution Summary:

With the rapid growth of data volumes from diverse sources — including mobile apps, web platforms, and IoT devices — organizations face an urgent need to build, modify, and manage data pipelines efficiently. While hyperscale cloud providers offer managed services, these often function as “black boxes,” limiting visibility and control.
To address this, enterprises require automated yet vigilant DataOps processes in Azure — leveraging services like Azure Data Factory, Azure Synapse Analytics, and Azure Monitor — to continuously optimize pipeline performance and costs.
At the same time, increasingly complex data governance requirements — covering policies, processes, and access controls — demand a robust compliance framework built on Microsoft Purview to ensure accuracy, lineage, and regulatory adherence.
Although firms invest heavily in data projects, many overlook establishing resilient Azure DataOps capabilities. Without this foundation, organizations remain vulnerable to fluctuations in data sources, upstream systems, and consumption demands — ultimately risking adoption and long-term business value.

Orchestrated Ops:

Democratization of data with data catalog that helps understand, view, subscribe and consume data for business use

Understanding of Data platform
SLA, dependent tables defined and with clear objectives of monitoring define
Mostly repeatable manual processes
Manual monitoring of all jobs

Automated Ops:

Bots to process ad hoc data requests and automatically create and assign tickets in JIRA based on events or via Slack discussion

Seamless workflow integration and orchestration
Automated processes for alerts, auditing for jobs and source data checks
Ability to monitor and respond.
Ability to identify and address recurring issues

Intelligent Ops:

AI integrated approach that delivers Suggestions, recommendations, preventions and RCAs with proper information

Intelligent systems capable of auto remediation
Ability to predict and prevent
AI/ML driven continuous improvement.
ML driven log analysis to identify root cause and provide possible resolution options

Solution:

Prebuilt Azure DevOps-enabled utilities and Azure automations with rapid-deployment templates for accelerated cloud onboarding.
Ready-to-use automation runbooks in Azure Automation that can be deployed or customized for day-to-day operational management.
End-to-end automation for provisioning enterprise-grade Azure data infrastructure, including networking, compute, storage, and security components.
Accelerates the time-to-production for Azure workloads through reusable deployment blueprints.
Azure Monitor and Log Analytics for comprehensive telemetry collection, analysis, and real-time alerting from all Azure services.
Application Insights and platform monitoring to maximize uptime, performance, and user experience of Azure-hosted applications.
Azure Cost Management, Azure DevSecOps and Microsoft FinOps practices for deep cost analysis, forecasting, security and optimization recommendations.
Helping clients reduce cloud overspend and optimize Azure resource allocation for maximum ROI

Solution highlights :

Multiple in-house accelerators that provide a comprehensive solution for managing the full data lifecycle (Visibility across ingestion, processing, management & consumption)
Platform agnostic DataOps and governance capabilities i.e., on-premise, Azure cloud, hybrid Cloud, compatibility with existing tech stack
Multiple case studies on end to end Azure Data Governance and ops with cost out model
Data Governance (usually bundled with Azure DataOps or platform modernization projects with data quality, discovery)
Intuitive & reusable Azure data governance solutions that capture details such as data sources, usage, metadata, data lineage, etc.
Help organizations streamline data management processes and have greater visibility to data pipelines (especially failures and breakdowns)
Ensure Azure governance and compliance with standards, such as GDPR, HIPAA, and PCI DSS.
Conversational interface for deeper analysis & understanding.

Highlights:

H1: 5-25% potential savings in DataOps spend through the integration of automated processes and balanced utilization of resources

H2: 5-10% reduction in time to market for analytical models using improved data governance (e.g. automated scripts for encryption and audit for satisfying compliance such as GDPR)

H3: Enhanced data interoperability due to implementation of common standards for data ingestion, cleansing, quality etc.

H4: TCO: 40% Savings on annual TCO

ADAM – AI led DataOps

Brillio

Tiedot