How to turn old data into a competitive advantage

Most organizations treat old data like clutter—but it’s actually their most valuable asset. With CAEVES Deep Storage, archived data becomes searchable, AI-ready intelligence that drives compliance, forecasting, and innovation. Read the latest Storage Mythbusters article: “Old Data Has No Value? Think Again,” below. https://lnkd.in/eriCwSG8

“This is what enterprises want” — Holger Mueller on Domino Data Lab’s focus: autoscaling to meet demand, spot instances to rein in spend, discovery to boost reuse, and data planes for compliance + lower data movement. Read more via Mike Wheatley at SiliconANGLE & theCUBE: https://lnkd.in/g83tcHY8

😓 Most enterprises today deal with fragmented data systems: warehouses for structured analytics, lakes for big data, specialist ML platforms, separate governance tools. That spells complexity, slow time-to-insight and increased risk. The Lakehouse paradigm changes that. By unifying data types, analytics workloads, and governance under one roof, it reduces silos and accelerates innovation. Discover how we guide organizations through this transformation. https://lnkd.in/gHG-FxFT Sign up for a call with us today: https://lnkd.in/gDf2YTQN #TransformXperience #DigitalTransformation

You’d think more data means better answers… But in knowledge graphs, the opposite can happen. Your graph grows from thousands to millions of nodes customer data, product updates, internal docs, and suddenly, quality drops. Why? Because rapid ingestion without controls introduces: - Syntax errors - Misclassified relationships - Outdated facts - Blank nodes with no meaning You end up with a graph that’s bloated with noise. More results… fewer that matter. #KnowledgeGraphs #DataQuality #AIGovernance #GraphRAG #MLOps #InformationQuality #DataEngineering

The next big data problem won't be capacity. It will be curation at scale. The industry solved storage. It solved scale. It made it cheap to collect everything. And so…we did. Sensor data. App telemetry. Clickstream logs. Backup of the backup of the backup. Cold storage. Hot storage. Object storage. Snowball drives. We've got petabytes of "just in case." The problem isn't how much we store. It's that no one knows what matters. We don't need more pipelines. We need more judgment. Because raw data doesn't create value. Context does. And context is hard to automate. Every company is sitting on a landfill of data they're afraid to delete. But afraid to touch. Because if you can't trace its purpose, you can't decide what it's worth. This is the next big data problem: Curation. At scale. Not collection. Not compute. Not "data gravity." Just the quiet discipline of knowing: - What's useful - What's legacy - What's toxic - And what's actually driving decisions Because storage is cheap. Confusion is expensive. And the companies that win next? They won't be the ones who collected the most. They'll be the ones who understood the least…perfectly.

“I’ve noticed that the data movement principles of old are shifting to a new, integrated approach. Instead of parallel processes for different data types, organisations need to move structured and unstructured data in the same pipelines,” says Airbyte CEO Michel Tricot. “This starts at ingestion. Whether data arrives as database records or files, capture relationship metadata immediately. A customer email isn’t just text, it’s text connected to a customer ID, a timestamp, a sender and potentially a case number. These connections must travel with the data.” Read more on why proper data management and data integration are vital to products built with AI from Adrian Bridgwater for Techzine. https://bit.ly/3L2WRzG

“Data product” gets thrown around a lot — but without a shared definition, it’s hard to build anything that works in practice. In our latest blog, Thorben Louw, Data & Machine Learning Engineer, breaks down a concrete, implementable way to define data products — without needing to go “all in” on data mesh. It covers: • What a data product actually includes (code, schemas, contracts, access, governance) • How source data products enable reuse without duplication • Why public vs private interfaces matter for stability and security • The split between platform teams and product teams • How to get the benefits of data mesh without restructuring your org ➡️ Read the full post: https://lnkd.in/eREHt5vJ #DataProducts #DataEngineering #Governance #DataPlatforms

⚙️ 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬: 𝐁𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐌𝐨𝐯𝐞𝐦𝐞𝐧𝐭 As data platforms scale, the challenge is no longer just about moving data from one place to another. The real responsibility is ensuring that 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐫𝐫𝐢𝐯𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐩𝐨𝐢𝐧𝐭 𝐨𝐟 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐬 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐭𝐢𝐦𝐞𝐥𝐲, 𝐚𝐧𝐝 𝐭𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲. A modern data pipeline needs to enforce quality and structure at every stage — not just at the end. This is why well-architected pipelines follow a layered approach: ingest from source systems, validate and monitor data quality, transform with consistency, store in a reliable lakehouse layer, and enable analytics and machine learning from a single, governed source of truth. When each layer has clear purpose and accountability, 𝐝𝐚𝐭𝐚 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐟𝐫𝐚𝐠𝐢𝐥𝐞. The result is not just faster insights — it is  𝐝𝐚𝐭𝐚 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞. And in today’s environment, confidence in data is what truly drives decision-making and operational intelligence. #DataEngineering #DataPipelines #DataArchitecture #Lakehouse #Analytics #ML #DataQuality #CloudComputing #FutureOfData

Synthetic Data as the New Oil! 1. Address Issues related to Data Management( Quality, Volume, Veracity) 2. Address Issues related to Privacy 3. Provide faster and quicker development of systems 4. Simulate whatif scenarios better from synthetic data where Real Data availability is a challenge Has it started replacing dependencies on real data? Any thoughts? #Techforthought #Syntheticdata #Supplychain

This is one of the best OT posts I've seen made in quite some time. Absolutely spells out what data context means for machine data. Remember context is always human and is unique to each consumer and hand off in the data flow.