The data landscape is broken. Here's what's holding organizations back from realizing their potential.
Organizations struggle with data silos that prevent real-time insights, leading to delayed decision-making and missed opportunities. Custom scripts become maintenance nightmares as teams scale.
Average enterprise loses 20-30% of productivity due to data integration issues
The promise of data mesh architecture is compelling, but implementation requires complex domain modeling, governance frameworks, and cross-team coordination that most organizations can't achieve.
90% of data mesh initiatives fail due to implementation complexity
While GenAI tools are powerful, they require high-quality, well-governed data to deliver reliable results. Most enterprises lack the data infrastructure to support production AI applications.
80% of GenAI projects fail to reach production due to data quality issues
Cloud costs are growing 3x faster than revenue for most enterprises. Teams spin up their own tools and infrastructure, leading to massive cost overruns and security risks.
Average enterprise overspends 40% on cloud infrastructure due to poor governance
Most enterprises have 20+ years of legacy systems that need to be integrated with modern data platforms. This requires extensive custom coding and creates fragile dependencies.
Integration projects take 6-12 months longer than planned and cost 3x the original estimate
GDPR, CCPA, and industry-specific regulations require sophisticated data governance. Manual processes can't scale, leading to compliance risks and potential fines.
Average enterprise faces $2.5M in potential fines due to data governance gaps
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Code-heavy pipelines
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Tool sprawl
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GenAI as an afterthought
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Manual data governance
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