The Importance Of Converting Data Into Insight
India is witnessing a surge in enterprise data creation, with total data generated from the country expected to reach 11.2 zetabytes by the end of this year. This data explosion has fundamentally changed how Indian enterprises operate today:
- Being Data Driven: 74% of Indian firms now consider data a core asset for scaling operations & outperforming rivals. It wouldn’t be erroneous to state that data is the new gold in today’s landscape.
- Converting Data To Insights: Indian enterprises investing in analytics report 2-3x faster revenue growth compared to those that don’t. Additionally, 80% of BFSI firms and 70% of retail businesses are doubling down on AI-powered analytics – using real-time data to enhance risk detection, inventory optimization & customer experience.
- The Discoverability Problem: Enterprise agility suffers when teams spend too much time finding, validating and describing datasets – globally, 70% of analytics work is spent just on data prep & lookup. This trend also translates specifically to India – nearly 60% of organizations in the country don’t have a centralized catalog or a consistent tagging strategy, leading to redundant data, poor insights and compliance blind spots.
As organizations continue to get bombarded with more & more data, the importance of truly harnessing it becomes critical for an organization’s well-being. That’s where metadata comes in.
What Is Metadata?
If data is the new gold, then metadata is what mines and refines it. Metadata is data about your data – it describes your data’s context, characteristics & structure. In essence, it acts as the labels, blueprints & instruction manuals for every dataset in your organization. Here are some examples of metadata organizations possess:
Why Is Strong Metadata Management Crucial For Your Business?
Therefore, in today’s insight-driven era, managing your metadata becomes as important as managing your actual data. In fact, strong metadata management ends up streamlining your actual data in myriad ways:
What Is The Metadata Discovery Challenge?
However, the data explosion currently seen in the Indian landscape creates a ‘discovery challenge’ with various elements that serve as hurdles to achieving strong metadata management:
- Data Volumes & Velocity: As your data exponentially grows, so does your metadata at the very same pace. Unfortunately, manual metadata cataloging or static discovery processes simply can’t keep pace.
- Fragmentation from Hybrid & Multicloud Proliferation: Nowadays, data is spread across various platforms like AWS, Azure, GCP, private cloud and on-prem systems. Each environment has its own metadata formats and tools, making visibility fragmented.
- SaaS & Shadow IT Explosion: Today’s departments adopt SaaS tools (Salesforce, Zoho, Workday, Slack) outside IT’s control. These applications generate shadow metadata (undocumented datasets, hidden flows) that don’t appear in central catalogs.
- Complex Data Lineage: Data passes through ETL pipelines, APIs, analytics engines and BI dashboards. Each transformation creates new layers of metadata, which becomes scattered without any effective lineage mapping.
- Unstructured & Semi-Structured Data Growth: Beyond structured databases, metadata now needs to cover documents, PDFs, images, IoT logs, sensor data and even AI models. Traditional metadata tools struggle to capture this diversity.
That means metadata becomes a double-edged sword. If managed well, it unlocks the true value of your data – if mismanaged, it creates chaos, compliance gaps and cyber vulnerabilities. Therefore, to crack the discovery challenge, your metadata management should be based on these 3 key pillars:
Centralisation
This breaks down silos by consolidating metadata across all your environments into a single unified catalog – providing a single source of truth for your teams.
Automation
Manual tagging & lineage tracking can’t keep up with exponentially growing metadata volumes. AI/ML-powered harvesting, enrichment and lineage mapping ensures metadata is always current & accurate.
Contextualization
Metadata is only useful when paired with business meaning and lineage. Contextualization ensures that every dataset carries all the relevant details – like who owns it, its lineage, its usage and how it maps to compliance frameworks.
Cloudera + Octopai: Experience Comprehensive, Cutting-Edge Metadata Management
Cloudera, powered by Octopai, delivers an enterprise-grade metadata management solution that unifies, automates and contextualizes metadata across all your IT environments. Unlike traditional tools, it doesn’t just catalog metadata – it turns metadata into a business accelerator through these industry-leading features:
- Automated Harvesting At Scale: Cloudera ingests metadata from hundreds of sources (ETL, BI, SaaS, cloud, legacy systems) with zero manual effort.
- Deep, Cross-System Lineage: Our solution tracks data flows across every stage – from raw ingestion to BI dashboards – ensuring full traceability.
- AI/ML Enrichment: Our AI-driven systems automatically classify, tag and map metadata to business terms, compliance categories and ownership.
- Unified Metadata Catalog: We provide a central hub that brings fragmented metadata into a single, searchable interface.
- Compliance-Ready Governance: Our solution has out-of-the-box support for all relevant regulatory frameworks (DPDPA, SEBI, RBI, GDPR) your organization has to comply with.
- Business-User Friendly: Our Google-like search and intuitive lineage maps allow self-service access without IT dependency.
Solve The Metadata Discovery Challenge, with Cloudera + Octopai
Cloudera + Octopai has everything in its arsenal to solve the discovery challenge and help you achieve strong metadata management at scale:
