I've been following the Web3 and decentralized data space for a while now, and one pattern keeps coming up in conversations companies rush to build data lakes without a clear strategy, then wonder why the ROI never shows up.
The promise is real: centralize your raw data from ERP, CRM, APIs, and cloud sources into one scalable layer, then build analytics, ML pipelines, or BI dashboards on top. But somewhere between "we need a data lake" and actually running it in production, things go sideways.
The most common mistake I see? Treating a data lake as just "cheap storage." Teams dump data in, skip proper schema governance, and end up with what engineers call a "data swamp" data is in there somewhere, but nobody can reliably use it. Another killer is ignoring the ETL side of things. Moving data in isn't the challenge; moving it in a structured, versioned, and auditable way is. Especially when you're pulling from legacy ERP systems or older databases that weren't designed with modern analytics in mind.
The second thing that tanks data lake projects is lack of ownership. Without someone defining what data quality means for your organization, the lake grows but never matures. Governance is boring but it's the difference between a lake that becomes a core business asset and one that gets quietly abandoned two years later.
If you're at the stage where you're evaluating whether a data lake makes sense for your company, this overview of data lake consulting services - https://cobit-solutions.com/en/services/data-lake-consulting-services/ - does a solid job explaining what a proper implementation actually involves, beyond just picking a cloud provider.
The blockchain and Web3 space deals with similar challenges around distributed data integrity, which is why I think this topic resonates here. Decentralized platforms live or die by data architecture decisions made early on.
Posted by Waivio guest: @waivio_mark-rikhter
