Data, AI and platformBI, AI and ML for the business
Data from business systems, checked before use
Files, databases and online services connect from one catalogue, and each becomes a table with no schema to design. Before the data reaches a report, written rules keep the bad rows out and set them aside in quarantine with the reason.
Illustrative scenario: it describes a typical case, not a customer project.
ERP data arrives dirty. Better to know before the report does.
An order with a negative quantity, a customer with no region, a date in the future: every ERP has rows like these. Usually whoever reads the report finds them, when a total does not add up and nobody can say which row moved it.
Connecting a system is not enough. You need written rules that check every row on the way in, keep the bad ones out without losing them, and tell whoever has to fix them what is wrong.
- Who it's for
- IT and data teams who connect the systems; management accounting, sales and anyone who uses the reports; master data owners who fix things at the source.
In Muvia, step by step
Real product screens, recorded on a project with sample data.
The video · Files, databases and online services connect from one catalogue, and each becomes a table with no schema to design. Before the data reaches a report, written rules keep the bad rows out and set them aside in quarantine with the reason.
What you get
- Errors stop at the door
- A bad row never reaches the report: it stays in quarantine, visible, with its reason.
- You know what to fix
- Master data owners see the row and the broken rule, and fix it at the source instead of in a spreadsheet.
- Quality is a number
- The share of valid rows travels with the query: a sudden drop is noticed before a total stops adding up.
- No integration project
- A new source connects with no tables to design and nothing to touch in the system it comes from.
For the technical team
How it is built in Muvia
- 1
Pick from the catalogue
Add a source and choose among files, databases and online services. Each source becomes a table and a query, with no schema to design and nothing to change in the source system, which Muvia only reads.
- 2
Schedule the copies
The ERP database is copied on a schedule, incrementally and over an encrypted connection, without querying it live. New columns are added automatically.
- 3
Write the rules
On the orders query, declare the Data Checks: positive quantity, known customer, region present, no future dates, amount in a plausible range. Each rule says whether a row is excluded.
- 4
Manage the quarantine
Excluded rows go to quarantine with the rule they broke. The share of valid rows sits on the query, and whoever manages the data gets an in-app notice.
- 5
Build on checked data
Dashboards, notebooks and Athena read only the rows that passed the checks. Once the source is fixed, the row becomes valid on the next load.
- The data it needs
- Orders and order lines from the ERP on SQL Server, copied every night incrementally
- Customers and contacts from Salesforce
- Store and item master data, from Excel files
- Daily store sales, from the files in a SharePoint folder
More cases in this line
All use cases- Data science on company dataYour data scientist trains wherever they like, in their own Jupyter with their own tools. Muvia gives them governed data to start from, runs the model on a schedule next to the data, and puts the scores into dashboards, alarms and reports.
- Athena: from a question to a query and a notebookOne question in plain language and the work is done: Athena saves the query in the Lab, writes the notebook with the ranking and the conclusion, and explains from the data what happened. Everything stays in the project, can be checked, and is one keystroke away.
- Tables that update themselvesEvery hour a file of orders arrives from the website, the app and the marketplace. Muvia loads it, cleans it, checks it and updates the business-ready tables in cascade: when someone opens the dashboard, the latest file is already in.
Bring us a question you can't answer today.
We start from a real question your business has and walk the path from source to dashboard with your systems, not a demo dataset.