Areas of practice
The domains in which I have carried accountability, and the judgement I bring to each.
How I lead
Trusted data becomes valuable when people can use it responsibly in a decision. I connect ownership, quality and analytical capability to business purpose, with explicit limitations and human accountability for consequential uses.
At scale
1
Trusted source of truth
Replacing 37 conflicting reports
₹410 Cr
Value from analytics
Verified by finance, year three
220
Models governed
Under one responsible-AI framework
9 days → 4 hrs
Time to decision data
For the executive committee
In practice
Case 01
Business functions were maintaining different versions of a critical performance measure. I worked with the accountable owners to define the measure, document its source and agree how exceptions would be handled. The programme treated data ownership as an operating responsibility and gave users a visible path for resolving quality issues.
Case 02
An analytics initiative had produced technically sound outputs that were not part of the management routine. We redesigned the work around a specific decision, involved the decision owners in evaluation and made limitations visible in the way findings were presented. Adoption became part of the delivery definition.
Case 03
Teams wanted to move AI experiments into business use without a consistent review process. I convened product, data, risk and operating leaders to agree evaluation criteria, human accountability and monitoring responsibilities. The approach allowed use cases to be assessed on their actual purpose and exposure rather than on enthusiasm for the technology.