What Telecom Billing Taught Me About Data Quality
Lessons from implementing telecom OSS/BSS systems for MTNL on mediation, reconciliation and data quality, and why they matter for AI and analytics.
Technology
Software, enterprise systems and digital transformation
AI sits on top of software, data and processes. When those are weak, AI makes the weakness faster. Most of Rohit's career has been spent on the foundations.
Quick answer
Quick answer
What does Rohit mean by getting the foundations right?
It means modelling data so it matches how the business actually works, designing for the exceptions that generate support calls, integrating systems deliberately rather than later, keeping an audit trail wherever money or records are involved, and writing software the next person can change. These decisions outlive every screen and every framework.
Software engineering and architecture
Rohit's engineering background runs from Oracle schema design and PL/SQL through Unix automation to application architecture for multi-location businesses. Since 2011 his teams have built software for distributors, clinics, hotels and restaurants, schools and an international franchise network. Each domain looked different on the surface and turned out to share the same underlying problems: identity, inventory, money, schedules and permissions.
- Data models that match how the business actually works
- Integration planned from the start, not bolted on
- Clear audit trails for anything involving money or records
- Performance at the moment of use: the counter, the front desk, the ward
- Documentation and handover that survive staff changes
Enterprise systems, at two very different scales
Three years implementing operations and business support systems for MTNL at Xalted in Bengaluru covered process mediation, interconnect billing, fraud management and reconciliation. Those systems handle enormous volumes of records, and the discipline they demand, reconciling, validating and tracing every figure back to its source, applies to any organisation that depends on its numbers being right.
For a smaller organisation the same idea is humbler and just as important: one reliable source for customers, stock and accounts, and reports that people trust enough to act on.
Transformation, done in the right order
Transformation projects fail less often because of software and more often because the process was never redesigned. Automating a confused process produces faster confusion. The order that works is to draw the current process honestly, including the workarounds, agree the target process with the people who do the work, and only then choose tools.
Map the real process
Including informal steps, spreadsheets and phone calls.
Find the friction
Delays, rework, errors and waiting.
Redesign with the team
Remove steps before automating any.
Choose and build
Simple tools first, AI where it clearly helps.
Measure and adjust
Compare against the baseline, not the plan.
Insights
Related essays
Before You Automate, Draw the Process on Paper
A practical method for digital transformation: map the real process, find the friction, redesign with the team, then choose automation or AI.
Fifteen Years of Building Software for Everyday Businesses
Lessons from building business software since 2011 for distributors, clinics, restaurants, hotels and schools: customers, simplicity, support and staying power.
Questions
Questions about software and digital transformation
Should a small business build custom software or buy a product?
Buy wherever the process is ordinary, and build only where the way the organisation works is genuinely different and that difference earns money. A good consulting conversation often ends with a smaller build than the one first imagined.
Why does data quality matter so much before AI?
Every automation and every model inherits the flaws in the data it reads. Reconciliation, validation and traceability are unglamorous and they decide whether the output can be trusted.
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- Software and digital transformation