Shadow analytics — analytics conducted outside of governed IT environments — remain one of the biggest blind spots for IT teams. Whether it’s called shadow IT or decentralized decision-making, the result is the same: duplicated data, scattered workflows, and a lack of oversight that puts compliance and security at risk.
This blog explores how Dataiku helps IT eliminate shadow analytics without slowing down innovation by offering a secure, governed, and scalable platform for analytics and AI. In a recent webinar, Cody Brooks, Solutions Engineer at Dataiku, demonstrated how this works in practice.
The Typical Customer Journey
The typical analytics journey often starts with good intentions. As Cody explained in the webinar, organizations looking to modernize their analytics typically begin by adopting desktop tools like Alteryx. These tools offer self-service capabilities for tasks like data wrangling and spreadsheet automation.
The appeal is understandable: fast onboarding, spreadsheet automation, and early value delivered by business users. But as adoption spreads, so do the challenges. Teams end up with isolated workflows on desktops, duplicated datasets, limited access to cloud, and minimal oversight. At this point, IT is left grappling with a fragmented ecosystem that lacks governance and scalability.
This is where Dataiku changes the game. Rather than stitching together isolated workflows, Dataiku provides a centralized, governed, and collaborative platform — ensuring IT and business teams can operate in sync and scale analytics responsibly.
The 4 Pillars for The Universal AI Platform™
Dataiku is designed to bring harmony between data users and IT through four key pillars:
- People: It’s not just for coders. Dataiku supports both code-first and no-code users, allowing business analysts, engineers, and data scientists to collaborate in a single workspace.
- Data: With Dataiku, you're not locked into any one environment. It connects to structured and unstructured data, on-prem or in the cloud, and runs across multiple compute engines — all while ensuring performance and scalability.
- Governance: As AI adoption grows, so do concerns around risk and compliance. Dataiku bakes governance into the entire data-to-AI lifecycle, giving IT visibility and control from data prep to deployment.
- Tech Optionality: No vendor lock-in. Dataiku lets you integrate with any cloud provider, data stack, open source tools, or large language models.
A Central Hub for Enterprise AI Workflows
Dataiku supports the full spectrum of analytics and AI workflows in one platform. Teams can experiment with AI using code or low-code tools, deploy with built-in CI/CD and monitoring, and integrate insights directly into business applications via APIs and BI tools. For IT, this means centralized deployment, orchestration, and integration — without operational chaos.
The platform’s modular architecture includes the Design Node for building pipelines and models, the Deployer for managing production workflows, the Automation Node for scheduled execution, and the API Node for real-time predictions. Critically, the Govern Node —your control tower, if you will — ties it all together, offering full traceability, auditability, and compliance across every stage of the AI lifecycle.
Governance That Moves as Fast as You Do
IT leaders don’t want AI projects stuck in limbo — or worse, heading to production without proper review. Dataiku’s governance capabilities are built into the platform, so oversight happens in real time, not retroactively.
The Govern Node, Dataiku’s centralized control tower for analytics and AI initiatives, gives IT leaders and executives the visibility they need to track progress, assess risk, and enforce compliance across projects. From design to deployment, governance is part of the workflow — not a disconnected spreadsheet.
Key features include:
- Prebuilt structures to help teams standardize analytics and AI projects and reduce inconsistencies.
- A value and risk qualification framework to assess impact before models are pushed to production.
- Structured sign-off workflows that prevent unapproved or underperforming models from being deployed.
- A centralized model registry to monitor performance and support responsible iteration.
Reusability and Collaboration by Design
Too often, teams rebuild workflows from scratch, leading to inefficiencies and duplicated effort. Dataiku promotes reuse by enabling teams to store, share, and scale existing use cases, templates, and best practices. Whether you're rolling out a similar initiative across departments or adapting an existing solution, teams can build on prior work without starting over.
Powering AI Through Seamless Cloud Access
Furthermore, Dataiku integrates natively with cloud infrastructure, ensuring scalability, performance, and governance in equal measure. IT and business teams can collaborate in real time on analytics and AI, with full access to cloud services for compute and data storage — while maintaining strict governance controls.
Demo: Diagnosing, Fixing, and Governing AI Workflows — All in One Place
Next, Cody walked through a live demo to illustrate how IT can detect, investigate, and resolve data pipeline errors in a governed, end-to-end workflow — all without switching tools or relying on manual interventions.
To skip straight to the walkthrough, start the video at 11:25.
The scenario began with a common IT challenge: a recurring data quality issue that was causing execution failures in production. Using the Dataiku Launchpad — the centralized hub where users can access all nodes in a single interface — Cody navigated to the Deployer to identify a project with an execution error. He inspected the scenario, traced the root cause using column lineage, and confirmed the failure stemmed from a data quality rule in the Design Node flagging missing IP values.
He then demonstrated how to use the Git-backed timeline to audit project changes and how the built-in discussion panel enables cross-functional collaboration, such as tagging a data engineer to fix the issue upstream. Once the dataset was corrected, he created a new bundle, submitted it for approval in the Govern Node, and showed how role-based review workflows prevent unauthorized deployments. Only after approval was the project redeployed via the Deployer and automation resumed.
The demo highlighted core Dataiku features that make this workflow possible, including end-to-end node integration, column lineage, auditability, built-in review gates, and centralized deployment — all from within The Universal AI Platform.™