DATA & BI
Self-service analytics: business users build their own data flows
Self-service analytics with TIMi lets business users, often called citizen developers, build their own data flows in Anatella with boxes on a canvas, without SQL or programming, while sources, access and servers stay under IT control.
No SQL required
Build data flows with boxes on a canvas.
Code when needed
Complex steps can use JavaScript, R or Python.
Standard server
No large infrastructure needed.
HOW IT WORKS
How self-service analytics works.
Three steps, in one engine, on a standard server with no cluster.
1. Connect the sources
IT sets up the connections once. Anatella reads from more than 80 sources.
2. Build the flow
The business user assembles boxes to join, filter, aggregate and derive. Most operators need no meta-data definition.
3. Share the result
Results go to Kibella dashboards, Office files, databases or predictive models.
AnatellaGETTING IT RIGHT
Choices that shape a self-service setup.
Self-service does not mean uncontrolled. The rules are set before the first flow.
Define governance first
Decide who can access which sources and who validates the flows that feed decisions. Sources and access remain managed centrally.
Start with a few champions
A first group of business users builds flows and helps colleagues. The scope can then widen.
Keep experts for the hard parts
Complex transformations stay with data specialists, who can add reusable steps in JavaScript, R or Python that others use as boxes.
TRANSPARENCY
Every step is visible.
A flow is a graph of boxes. A colleague, a manager or an auditor can follow how a figure was produced, from source to output, without reading code.
For reviewers
The graph shows each transformation in order.
For teams
A flow can be reused and adapted by another user.
For IT
Sources and access remain managed centrally.
DATA
What data self-service flows can reach.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Databases
Relational databases through ODBC and OLEDB connectors.
Files
CSV, Excel, SAS, SPSS and Stata files, including compressed text files.
Web services
REST APIs and other web sources.
Streams
IoT streams over MQTT.
PROOF
What a small team can do.
We do not have a published case study on a self-service program. The reference below is indirect: it shows what a small team achieved without a large data infrastructure.
Model built by 3 people on 1 PC in 8 weeks
Not a documented self-service program. A team of three built the model in eight weeks on a single PC, with 97.2% accuracy (2.78% margin).
FAQ
Common questions.
Do business users need to know SQL?
No. Data flows are built with boxes on a canvas, and most operators need no meta-data definition. Programming is optional for complex steps.
Who controls access to the data?
IT. Connections to sources are set up centrally, and access rules stay under your control.
What if a transformation is too complex for boxes?
A data specialist can write the step in JavaScript, R or Python. Users can then reuse it in their flows.
Does it replace the data team?
No. It moves routine data preparation to the people who know the business, and keeps the data team for complex and governed work.
RELATED USE CASES
Keep exploring.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
