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Datacoves

Datacoves

Data Infrastructure and Analytics

Thousand Oaks, California 4,243 followers

Streamline your dbt data management platform

About us

Datacoves is an enterprise DataOps platform with managed VSCode for dbt development and Airflow. We enable you to implement data management best practices without compromising data security.

Website
datacoves.com
Industry
Data Infrastructure and Analytics
Company size
11-50 employees
Headquarters
Thousand Oaks, California
Type
Privately Held
Founded
2021

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  • View organization page for DATANOSCO

    2,504 followers

    The real bottleneck to data reliability is not technological, but human. It is the lack of alignment between business and technical teams. According to research cited from Anthropic, an AI deprived of business context achieves only about 21% analytical accuracy. When shared context is integrated, that figure jumps to over 95%. The current trap lies in what Noel Gomez calls 'confident wrong numbers.' Roughly 80% of organizational knowledge is implicit, buried in employees' heads or lost the moment a meeting wraps up. When a leader asks, 'How many active vendors do we have?', the AI serves up an instant calculation. But what does 'active' actually mean? A status flag checked in the ERP, or an invoice paid in the last six months? To bridge this gap, Datacoves is developing Atlas and introducing a new approach: the Agreement Layer. Positioned upstream of any line of SQL code or data contracts, this tier first captures the 'why' before technical teams tackle the 'how.' Rather than risking a guesswork answer, the tool initiates a conversation to resolve ambiguities. It favors a transparent 'we are not sure yet' over an invisible wrong answer. Once the definition is clarified with the relevant stakeholders, it becomes a versioned, approved and reusable 'Definition of Record' accessible across the organization, notably via the Model Context Protocol. Data governance should no longer be a documentation chore reserved for technical specialists, but a natural, collaborative reflex for the entire organization.

    Atlas, the Agreement Layer, by Datacoves

    Atlas, the Agreement Layer, by Datacoves

    www.linkedin.com

  • Big news from the Data & Analytics Summit Chicago. Our co-founder Noel Gomez is taking the stage to dig into a question every CPG data team is wrestling with right now: what has to be true before you put AI in front of business users, without losing control of the numbers. No hype. Just the real groundwork: governance, trust, and guardrails that let AI actually earn its seat at the table. Catch the session if you're in Chicago.

  • Today we are launching Atlas. Atlas is the agreement layer. It helps teams and their AI agents settle what data means before anyone answers a question, and turns that agreement into a governed Definition of Record that is versioned, attributed, and owned by you. It works with the warehouse, dbt, and tools you already have, over the open Model Context Protocol. No migration. No lock-in. Run it as a managed service, or entirely inside your own network. https://datacoves.ai

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  • Datacoves reposted this

    We are doing two big things this week. 1️⃣ We are a Silver Sponsor at dbt Summit 2️⃣ We are launching a new product I talk a lot about our philosophy. Years ago, we took a small investment from TinySeed for mentorship, not the growth-at-all-costs VC path. We do what we tell our customers to do. Build a solid foundation, go slow to move fast, and focus on the long term. Along the way, we gained the trust of customers like Johnson & Johnson, The Guitar Center Company, Orrum Clinical Analytics, and Sevita. Different industries, different problems, same reason they stay: we did not cut corners, and they don't worry about infrastructure. We also accelerated their data stack maturity and set the foundation needed for AI. It's been a rewarding journey. See you at booth 208 this coming week. #dbtsummit

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  • Datacoves reposted this

    Anthropic ran a self-service analytics experiment. Without a structured way for Claude to reliably find and use the right definitions, its accuracy on internal analytics questions did not exceed 21%. Add a layer that routes it to the correct governed source before it ever writes a query, and that number goes above 95%, regularly near 99% in some domains. Same model both times. They also tried an automated shortcut: have an LLM draft the underlying business definitions, pulled from raw tables and old queries. It backfired. The AI-written definitions just encoded the same ambiguity everyone already disagreed on, dressed up to look decided. Their fix was not more automation. It was a person owning each definition, with AI helping document it, not decide it. Everyone is focusing on how technology will "fix" the deficiencies in the org. Almost nobody is fixing the thing that determines whether the answer is right. Come to Datacoves booth no 208 and see how the agreement layer fixes this. Hint: it's not more work for the tech team. dbt Labs #dbtSummit

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  • When you are early in building your data platform, you do not know what you do not know. You talk to a warehouse vendor, an ingestion vendor, an orchestration vendor, a BI vendor, a migration vendor. Each one is honest about their part. None of them can describe the whole elephant, because none of them is responsible for the whole elephant, and none of them lives with the consequences if your elephant turns out to be a whale. This is not a knock on any of them. No one selling you the trunk is lying about the trunk. They just cannot tell you what the tail needs, because that was never their job. We do not show up and deploy dbt and Airflow and call it a day. We tell you there is real work: understanding your pain points and goals, figuring out what is holding you back, architecting the foundation that holds up for the next ten years, not just through the migration. We know that moving fast means moving a little slow at the start. Your org owns the agility, user experience, and reliability of your platform. You need to drive the migration because tools alone won't truly transform your org. The job to be done is not to "stand up new tools." It's to see the whole elephant at once so the platform holds together instead of having stakeholders optimize only their slice. We have done this enough times that we can show a team the whole elephant early. Our customers are loyal because we don't sugarcoat the truth; we help them chart the map to get to a mature process as quickly as possible. Because we know what true transformation involves.

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