Repair data production.
Correct failed scheduled work, configuration and path problems, and unsuitable execution resources.
Back to selected work
BAIDU · 2022–2023 IMPLEMENTED IMPROVEMENTS
How do you make daily reporting dependable?
I repaired the reporting foundations so business analysis could rely on complete, consistent, and timely numbers.
- My role
- Data diagnosis & implementation
- Tools used
- SQL · BI · Scheduled workflows
- Deliverable
- Implemented improvements
The problem
Missing days, conflicting totals, and late outputs forced analysts to investigate the reports before they could investigate the business.
What I did
- Repaired scheduled processing and shared metric logic beneath the dashboards.
- Improved recurring processing and added retries, monitoring, and a route for resolving failures.
Repairing the foundations improved many reports at once.
Standardize the metrics.
Reconcile DAU, revenue, and retention definitions, then update conflicting reporting around shared logic.
Reduce repeated work.
Improve SQL and reuse intermediate results so core reporting can be delivered earlier.
Build checks and recovery into daily reporting.
The operating approach connected metric definitions, reusable processing, deadlines, output checks, retries, and communication when something failed.
From data production to a usable business signal.
More dependable reporting. Less recurring firefighting.
- More reliable execution: repaired production tasks and fewer recurring problems.
- Earlier availability: core reporting moved into the morning business cadence.
- Less reactive support: a smaller share of time spent on repeated investigation and repair.
These qualitative improvements are recorded in my historical work review. They describe the product’s reporting workflow, not all Baidu infrastructure.
Scope, evidence & reporting definitions
The work is documented in my historical review rather than an independent audit. Exact internal measurements are omitted.
- Availability: the required reporting output is complete and usable.
- Consistency: core metrics share the intended population, period, and calculation.
- Timeliness: output is available by the business decision deadline.
- Recovery: missing, unexpected, or late results have a detection and resolution path.
Metric agreement alone is not correctness; shared logic can still carry a shared error. The work did not establish a revenue uplift or the implementation of the separate growth recommendations.
The workflow diagram is conceptual. Employer systems, scheduler screenshots, SQL, schemas, and infrastructure topology remain private.