Services
Data hygiene is the state of your records: product masters that agree across systems, transactions that reconcile, fields that mean what they say. Process hygiene is what keeps them that way: the handoffs, EDI habits, item-setup routines, and weekly checks that produce the data in the first place. Fix the first without the second and you’re clean for a quarter.
Every engagement starts the same way: I look at the data, figure out what’s wrong, quantify the downstream impact, and deliver something your team can act on. The specifics depend on the problem.
The work runs in three lanes: CPG retail & deduction data, campaign & web analytics, and rebate & incentive payouts.
The Data Health Snapshot: one dataset, seven business days
The typical first engagement, sized for a first-time buyer. You send one dataset: a product master export, up to twelve months of remittances, a transaction extract, a campaign or analytics export, or a rebate or incentive payout ledger. Seven business days later you have written findings: every defect class found, its annualized cost, and a top-10 fix list your team can start on immediately.
Fixed fee: $950. Credited in full toward a scoped audit within 60 days.
Start with the Snapshot →Data Hygiene & Validation
Data quality problems usually surface as money lost somewhere else. In CPG retail it is a chargeback: an automatic deduction triggered by a mismatched case dimension, a missing GTIN-14, or a serving size that conflicts with the nutritional panel. In campaign analytics it is spend on clicks your analytics cannot trace to the campaign that paid for them. In an incentive program it is a payout the program’s own rules never earned. By then, the cost has been compounding for months.
My audit works backward from the financial impact. I trace every chargeback, reconciliation failure, untraceable click, and wrong payout to the specific field that caused it, across whatever systems and data sources are involved. The deliverable is a written report with the annualized cost of each defect and a prioritized remediation plan your team can execute without my involvement.
See it proved: Product Data Health Audit · Retail Readiness & Launch · Campaign Math · Payout Math
Decision Frameworks & Analytics
I build tools that answer a specific question for a specific person: the CEO who needs to know which SKUs to cut, the CFO who needs to see where trade spend is leaking, the growth lead who needs to know which campaign earned its budget under every attribution rule and which only wins under one.
In CPG retail that means velocity analysis, distribution strategy, SKU rationalization, launch trajectory diagnostics, pricing power, and promo ROI. In campaign analytics it means attribution computed under every transparent rule, so a ranking that flips with the setting shows up as one. Each tool is built for the decision it serves: an interactive app for the analyst, a one-page tearsheet for the board meeting, a triage workbook for the person fixing the data.
See it proved: SKU Portfolio Audit · Channel Profitability & Capital Allocation · Campaign Math
For ongoing competitive visibility, see Competitive Shelf Intelligence →
Data Quality Engineering
Validation that runs before the data enters your system, not after it breaks something downstream. I build audit scripts and automated quality scoring that catches mixed formats, phantom duplicates, placeholder floods, and the numbers a spreadsheet silently turns into text.
In CPG retail, that includes GS1 and GTIN validation with format rules for Walmart, Costco, and Whole Foods; distributors UNFI and KeHE; and 1WorldSync GDSN syndication. FSMA Rule 204 compliance analysis, GS1 Sunrise 2027 readiness checks, and custom exception-handling protocols that give your team a clear path from defect to resolution.
For campaign data, it is a check for the defects that break attribution: missing UTMs, payment-gateway self-referrals, bot traffic. For an incentive program, it is a validator that re-derives every payout from the program’s written rules and shows each exception with the rule it breaks and its dollars.
See it proved: Product Data Health Audit · Payout Math · Data Hygiene Auditor · datascope
See the engagement: Validation Pipeline Build →
Systems & Infrastructure
The audit pipeline that runs every week without someone remembering to trigger it. The validation layer that catches defects before they enter the ERP or the payout run. The reporting infrastructure that rebuilds itself when the source data updates. I build the tooling (Python CLI tools, automated scoring scripts, reproducible R pipelines, orchestrated workflows) so that data quality becomes a system property, not a manual process.
This is the work that makes everything else sustainable. An audit is a one-time finding. Infrastructure is the reason the finding stays fixed.
See it proved: Trade Spend & Deduction Recovery · Fulfillment & OTIF Diagnostic
Solution Architecture
Translating ambiguous business needs into clear technical specifications your team can implement: requirements, one spec the owner of each system agrees to, and a feasibility check, grounded in two decades of building and operating the systems, not just specifying them.
I’ve been on both sides: designing the system and operating it when something breaks at 10 p.m. on a Friday. Both jobs shape every specification I write.
What You Get
Every engagement starts with a written intake, moves to a written scope of work, and ends with a custom solution built for your specific problem. The deliverable is yours: a written report, an interactive tool, a triage workbook, a validation pipeline, whatever the scope calls for. No hourly billing, no timesheets.
I use Claude Code as a pair programmer for audit scripts and data quality pipelines, which is part of why flat-fee pricing works for both sides.
Monthly retainers include ongoing operational support: competitive shelf monitoring (pricing, promo activity, and out-of-stock tracking across your category), recurring data quality sweeps, and decision-framework maintenance as your retailers, campaigns, or programs change. Ongoing work is an option, not an expectation. Shelf monitoring is its own defined engagement: see Competitive Shelf Intelligence.
Common Questions
What types of data does Lailara LLC audit?
Transaction and purchase data, incentive and rebate payment data, product master records (UPC/GTIN, GDSN, 1WorldSync), and operational and fulfillment data. I trace every downstream failure back to the specific field that caused it.
How long does a typical data audit take?
A scoped audit typically takes 4-6 weeks. It includes a written deliverable with findings, quantified financial impact, and a prioritized remediation plan your team can execute independently.
What engagement models are available?
Three, and they work as a sequence rather than a menu. Recover: a scoped audit (4-6 weeks, fixed fee: $3,500 for one or two data sources, $6,500 for multi-source reconciliation) finds the problems and prices them. Prevent: a project SOW with defined scope and timeline (from $9,500) builds the validation layer so they stop recurring. Preserve: a monthly retainer (deliverables-based, no hourly billing; from $1,500 per month) keeps the data and the process clean as your retailers, campaigns, or programs change. Recover is the first rung. Where you stop is up to you. All engagements are outcomes-based, and every rung credits in full toward the next.
What industries does Lailara LLC serve?
Brands and manufacturers, in three lanes. CPG retail and deduction data: specialty food and CPG brands from roughly $3M to $50M in revenue, scaling into national retail. Campaign and web analytics: marketing and growth leads at small and mid-sized brands who run paid campaigns. Rebate and incentive payouts: operations and finance leads at manufacturers that run consumer rebate or channel incentive programs. Every stage gets a different engagement: emerging brands are building their first data discipline, and established ones are untangling scale.
Start with the data problem.
Tell me what’s broken in the form. I’ll reply in writing with where the cost shows up. No call.