Forum Discussion

codeautomation's avatar
codeautomation
Icon for Advocate I rankAdvocate I
1 day ago

How Can Teams Automate Data Workflows and Reduce Manual Operational Tasks?

As businesses handle increasing volumes of data, managing workflows manually can become time-consuming and difficult to scale.

I'm interested in understanding how teams are automating their data operations, particularly when multiple systems and business processes need to work together.

A few areas I'd like to explore:

  1. How do you automate repetitive data processing and validation tasks?
  2. What approaches do you use to handle failed pipeline executions and retry mechanisms?
  3. How do you coordinate notifications, approvals, and downstream business processes?
  4. Are you using Microsoft Fabric, Power Automate, Azure services, or custom automation solutions?

I'd appreciate hearing about practical implementations, tools, and lessons learned from your experience.

What has worked best for your team?

API for GraphQL
data engineering
data pipeline
Environment

1 Reply

  • kiara-mehra35's avatar
    kiara-mehra35
    Icon for Advocate II rankAdvocate II
    5 hours ago

    Hi codeautomation​,

    While working on a healthcare analytics project at DataToBiz, we came across a similar challenge where data was coming from multiple sources, including SQL Server, SharePoint, clinical systems, and IoT devices. Managing these workflows manually would have made data processing and reporting difficult to maintain as the volume grew.

    We used Microsoft Fabric to bring the data together and automate ingestion and transformation workflows. Power Automate also helped with notifications and governance-related processes, reducing the need for manual intervention.

    One thing I find particularly important in these implementations is how failures are handled. Automating the successful execution of a pipeline is only half the job. Having retry policies, error logging, alerts, and proper exception handling is equally important. Otherwise, automation can simply make problems harder to notice.

    I'd also keep business notifications separate from pipeline execution wherever possible, so technical failures can be addressed by the data team while business approvals reach the relevant stakeholders.