The MyDataWork blog.

Most data-lineage tools are strongest where the work is governed, centralized, and platform-visible: warehouse tables, SQL transformations, dbt models, semantic layers, and dashboards connected to known sources. Inside that world, they can be genuinely useful. But a lot of analytical work still happens outside that clean boundary. It happens in Excel, in Alteryx, in local Python notebooks, in exported files, in one-off models, and in workflows that move between desktop tools, cloud warehouses, and BI

Open any analyst’s working folder and you will find the same thing: spreadsheets, SQL scripts, Alteryx workflows, dashboards, notebooks, a model or two, and a handful of exports. Each file is the product of real effort and real judgment. And not one of them can tell you why it exists. A file named dc_starting_inventory.xlsx does not record that it supports a weekly inventory decision worth $30,000 a year. A network-optimization workflow does not note who

How MyDataWork Applies AI to Real Data Work Most software that advertises AI starts with a chat box and leaves the rest to you. MyDataWork has an AI help assistant too, because users should be able to get guidance wherever they are in the app. But the assistant is not the main AI story. It is the help layer. The deeper AI story is what happens when AI is built directly into the workflow itself. That

Introducing Agent Access in MyDataWork: a way to let an AI agent read the context of your data work — what you’re working on, how it connects, and what it’s for — without ever exposing your data. The hardest part of putting an AI agent to work on real analytical problems isn’t the model. It’s context. An agent can be capable and still useless if it doesn’t understand the work it’s stepping into — which

Alteryx workflows have a way of becoming infrastructure. A workflow starts as a practical solution: blend the Excel assumptions file, clean up the partner CSV, join it to staged SQL, produce the forecast, push the output downstream. Then people begin to depend on it. Finance builds a dashboard from the output. Supply chain uses it in planning. A manager starts asking for the number every Monday. Months later, the workflow is no longer just an

Dataiku gives you a lot. The Flow is one of the most complete analytical surfaces in the market — a visual DAG of datasets, recipes, models, and outputs that captures an end-to-end pipeline from raw input to deployed model. You get real lineage out of the box: column-level dependencies, a data catalog across projects, and visibility into how data transforms through every recipe step. Add Dataiku Govern and you get governance workflows, sign-offs, metrics, attachments,