Build reusable data marts that standardize your logic (or / and your SQL) and power dashboards in Sheets, Looker Studio, Excel, or enable automated AI Insights delivered to MS Teams, Slack, Google Chat or simply by Email.
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Decide where do you want your data to be stored - any data warehouse
Choose your app, give necessary permissions to account.
Select the fields your want to import. You can get the fields available in the API.
You're ready to go. Run connectors & set update frequency.
You don't want single-source reports, right? Collect more data!
Build a collection of data marts: SQL, tables, views or connectors.
Select a data destination: Sheets, Looker Studio, Email or Chat tools.
Get focused on data insights, set delivery and forget about routine.
Ad-hoc SQL and manual csv files don’t scale – especially when data analysts need to support growing business teams with reliable answers to their questions.
Build a library of reusable reporting-ready data marts. With OWOX you can build data marts using:
(1) SQL queries;
(2) Source tables, views or patterns from your data warehouse(s);
(3) Connectors to 3-rd party APIs (like Google or Meta Ads)
Monitor, control versions, add descriptions & field aliases, publish changes from a single UI.
Describe fields with aliases, metadata, and documentation – like an internal API for data analytics.
Teams duplicate the same logic across Sheets, dashboards, and workflows – and it breaks constantly... And you have to rewrite the same queries. Again and again...
Create reusable data marts that power every downstream tool:
(1) Define once, feed into Google Sheets or Looker Studio;
(2) Organize by project, area, domain, or client;
(3) Version, update, delete and monitor data delivery.
But that's not everything. Set prompts to analyze your data on a schedule a deliver trusted insights (the once you'd provide) into Slack, Teams, Google Chat or by Email. WIth or without AI.
When field names are cryptic and logic is undocumented, trust disappears fast. Plus, you get constant pings from teams figuring out what this or that field means.
Treat data marts like internal products, complete with structure, descriptions and context:
(1) Add field-level descriptions, aliases, and expected outputs;
(2) Document your logic for teammates and future-you;
(3) Ensure clarity and trust in every downstream report.
This not only ads clarity to your work, this enables your data for AI analysis.
Business teams want self-service. Analysts want control. Most companies get neither.
With well-structured data marts, you get both:
(1) Business users explore trusted data through filters, reports, dashboards, or with AI Insights that YOU prompt & schedule.
(2) Data teams own the underlying logic, access permissions, the governance, as well as prompts, and schedules.
As a result - everyone works faster – without breaking things.
