Data and Automation Diagnostic
Map every data source—emails, MS Power BI, Google Analytics, machines, POS systems. Understand formats, volumes, and quality.
Advisory Methodology
How Zorinthia Evaluates Data Strategy
and Automation Decisions
Independent guidance on data and automation diagnostic, strategy evaluation, and governance assessment—helping you make informed decisions about your data investments.
Most automation projects fail because they don't start with the data. Zorinthia helps you evaluate your data sources, assess governance gaps, identify quality risks, and make informed decisions about automation investments. The focus is on guidance and risk management—not building systems. Learn more about the advisory approach and advisory services.
The Problem with Generic Automation
Why Evaluation Starts with Data
Off-the-shelf software assumes your data is clean, structured, and standardized. But real data isn't like that. Invoices arrive in different formats. Statements come from multiple sources. Machine data uses proprietary protocols. Google Analytics, Sage, email attachments, POS systems—every data source has its own structure, quality issues, and integration requirements.
That's why evaluation starts with the data, not the software.
Advisory Evaluation Framework
Structured Guidance for Data Strategy Decisions
Zorinthia provides independent guidance using a structured evaluation framework—from data and automation diagnostic to strategy assessment, governance review, and quality evaluation. The focus is on using existing data within the company to improve on revenue, time savings or customer service using proven data product methodology. Before committing to advanced analytics or AI initiatives, leaders should understand what AI readiness means at an executive level—ensuring that organisational foundations support analytics investments rather than undermine them.
PHASE 1: SOURCE
Understanding Your Data
Understanding Your Data
Before Making Decisions
You can't evaluate automation options without understanding your data. Zorinthia helps you audit your data sources, assess quality risks, and evaluate strategy options that work with your existing systems—not against them.
Data Strategy
Evaluate data architecture options, assess integration requirements, review transformation needs, and evaluate governance frameworks.
Data Governance
Establish data ownership, access controls,
compliance tracking (POPIA, Basel), and
complete data lineage for audit trails.
Phase 1 Deep Dive
The Data and Automation Diagnostic: Where Every Project Starts
Before evaluating automation options, the process includes a comprehensive data and automation diagnostic. It maps every data source in your workflow—invoices arriving via email, statements from banks and suppliers, transactions from Sage or QuickBooks, machine data from PLCs, analytics from Google Analytics, POS system data, payroll files, and any other sources feeding your business processes.
For each source, the process assesses: data format (PDF, CSV, API, industrial protocol), volume (transactions per day/month), quality (completeness, accuracy, consistency), and integration complexity (APIs available, authentication requirements, rate limits). This diagnostic reveals the gaps, inconsistencies, and quality issues that would break generic automation.
Example: For invoice automation, the process doesn't just count invoices. It analyzes invoice formats (scanned PDFs vs structured invoices), sender patterns (how vendors send invoices), validation requirements (PO matching, GL code rules), approval complexity (multi-level workflows, amount thresholds), and ERP integration constraints (API capabilities, field mappings, batch processing limits).
PHASE 2: INTEGRATION
Evaluating Integration Requirements
Evaluating Integration Requirements
and Data Quality Needs
Once the process understands your data sources, Zorinthia helps you evaluate integration requirements, assess data quality risks, and identify what capabilities are needed to handle your data reality.
Data Ingestion
Evaluate email monitoring capabilities, API integration options, ERP connectivity requirements, and data source compatibility.
Data Quality
Assess duplicate detection capabilities, validation rule requirements, data enrichment needs, and anomaly detection requirements.
Data Lineage
Evaluate lineage tracking capabilities, transformation documentation requirements, and compliance trail needs (POPIA, Basel, IFRS).
Phase 2 Deep Dive
Evaluating Integration and Quality Requirements
Data ingestion isn't just about downloading files. It requires resilient pipelines that handle variability, errors, and scale. The process helps you evaluate integration capabilities: monitoring email inboxes (capturing invoices, statements, and documents as they arrive), connecting to APIs (Sage, QuickBooks, MS Power BI, Google Analytics, banking systems), processing files (PDFs, CSVs, Excel, XML), and interfacing with machines (PLC data via OPC UA, Modbus, or custom protocols).
The process assesses required capabilities: duplicate detection using content hashing (prevents reprocessing the same invoice or statement), format validation (ensures data matches expected schema), error handling (logs failures, retries with backoff, alerts on persistent issues), and data lineage tracking (records where data came from, when, and what transformations were applied).
Data Quality Controls: The process evaluates validation rule requirements: checking for required fields, reasonable value ranges, referential integrity (do vendor codes exist in master data?), and business logic (does invoice date fall within fiscal period?). Quality issues should be flagged immediately, not discovered weeks later during month-end close.
PHASE 3: EVALUATION
Evaluating Automation Capabilities
Evaluating Automation Capabilities
and Compliance Requirements
With integration and quality requirements understood, Zorinthia helps you evaluate AI-powered automation capabilities, assess KPI tracking options, review monitoring dashboards, and ensure compliance requirements are met.
AI & Automation
Evaluate AI model capabilities for invoice reading, receipt classification, and transaction categorization—assess whether solutions can handle your data and formats.
KPIs & Monitoring
Assess KPI tracking capabilities, evaluate monitoring dashboard options, and review real-time visibility into data quality and automation performance.
Compliance Ready
Complete audit trails, data lineage, access controls, and compliance tracking for POPIA, Basel, IFRS, and other regulations.
REAL-WORLD EXAMPLE
Finance Automation:
Finance Automation:
An Evaluation Framework Example
Here's how the advisory evaluation framework works in practice with the data product lifecycle, using accounting automation as the example.
Phase 1: Source — Data and Automation Diagnostic & Strategy
Data and Automation Diagnostic
The process maps all financial data sources: invoices arriving via email from 50+ vendors (formats: PDF scans, structured PDFs, Excel), bank statements from 3 banks (downloaded PDFs, API feeds), supplier statements (email PDFs, supplier portals), Sage ERP data (API integration), credit card transactions (CSV exports), and Google Analytics (to track vendor portal usage later).
The process assesses data quality: 30% of invoices are poor-quality scans requiring advanced OCR, 15% lack purchase order numbers (requiring manual approval routing), vendor names are inconsistent (same vendor, multiple name variations), and Sage GL codes don't always align with invoice line items (requiring mapping rules).
Data Strategy
Based on the audit, the process evaluates data architecture options: centralized invoice inbox (email monitoring + supplier portal), OCR pipeline requirements (custom models trained on client's invoice formats), validation layer needs (PO matching, GL code rules, vendor master data), approval workflow requirements (routing based on amount thresholds and department rules), and ERP integration requirements (syncing approved invoices to Sage via API).
Data Governance
The process assesses governance framework requirements: data ownership (finance owns invoice data, IT owns integration infrastructure), access controls (who can approve invoices, who can modify workflows), data retention policies (invoices retained for 7 years per IFRS requirements), and compliance tracking (POPIA for vendor data privacy, audit trail requirements for external auditors).
Phase 2: Integration — Evaluating Integration Requirements
Data Ingestion Pipelines
The process evaluates ingestion pipeline requirements: monitoring email inboxes every 5 minutes (capturing invoices as they arrive), calculating file hashes to detect duplicates (prevents reprocessing the same invoice), extracting attachments and metadata (sender, subject line, timestamp), and queueing files for OCR processing.
For structured data sources (Sage, bank feeds), the process assesses API integration capabilities: proper authentication, error handling, and rate limiting. For Google Analytics, it evaluates API connectivity options to pull traffic and conversion data (used later for supplier portal optimization).
Data Quality & Validation
The process assesses quality check requirements: OCR confidence scoring (low-confidence fields flagged for manual review), vendor name normalization (maps "ABC Ltd" and "ABC Limited" to same vendor), PO matching validation (checks if PO exists, matches amount, not already fully invoiced), GL code validation (ensures codes exist in chart of accounts), and duplicate invoice detection (checks invoice number + vendor combinations).
The process evaluates data quality dashboard requirements: daily invoice volume, OCR accuracy rates, validation failure rates by type, duplicate detection statistics, and processing time metrics. Finance teams need to see data quality in real-time, not weeks later.
Data Lineage & Compliance
The process assesses lineage tracking capabilities: source (email, sender, timestamp), transformations (OCR applied, validation rules executed, enrichment performed), approvals (who approved, when, approval reason), and destination (Sage invoice number, posting date, GL accounts affected).
This lineage must support compliance requirements: POPIA (vendor data privacy, consent tracking, data retention), Basel II/III (for financial institutions requiring complete transaction audit trails), IFRS (invoice retention and audit requirements), and internal audit (demonstrating control effectiveness and segregation of duties).
Phase 3: Evaluation — Assessing Automation Capabilities
AI Model Capabilities
Generic OCR doesn't work well on real invoices—poor scans, handwritten notes, non-standard layouts. The process evaluates custom AI model requirements: vendor-specific templates (learns the layout of invoices from your top vendors), line item extraction (identifies and extracts itemized charges, quantities, unit prices), field extraction with context (distinguishes invoice total from subtotal, tax amounts, discounts), and confidence scoring (flags uncertain extractions for manual review).
For invoice and receipt classification, the process assesses model requirements: categorizing documents by type (invoice vs receipt vs statement vs purchase order), vendor (automatically identifies and tags vendor from document content), GL code prediction (suggests correct GL codes based on line item descriptions and historical patterns), and priority classification (flags urgent invoices, high-value transactions, or regulatory-sensitive items).
These models should be trained on your custom data—not generic datasets. They need to learn your vendor patterns, your GL code structure, and your business rules. As they process more invoices, accuracy should improve continuously.
KPIs & Performance Tracking
Zorinthia helps you define KPIs that measure automation performance: Data Quality KPIs — OCR accuracy rate, validation failure rate, duplicate detection rate, missing field percentage. Automation KPIs — Invoices processed automatically (vs manually), average processing time per invoice, exception rate (invoices requiring manual intervention), approval cycle time. Business Impact KPIs — Time saved per month, cost per invoice processed, month-end close time reduction, error rate reduction.
The process evaluates real-time dashboard requirements: current processing queue status, data quality trends over time, automation performance vs manual baseline, exception volumes by category, and cost savings realized. Finance leadership needs to see the impact immediately, not in quarterly reports.
Ongoing Support & Optimization
Automation isn't "deploy and forget." The process assesses ongoing support requirements: retraining AI models as new invoice formats appear, tuning validation rules based on exception patterns, optimizing ingestion pipelines for performance, and expanding to additional data sources (new vendors, additional bank accounts, new Sage modules).
The automation should evolve with your business. As you add vendors, change processes, or expand to new entities, the automation should adapt—because it's built on a flexible data architecture, not rigid software.
EVALUATION FRAMEWORK
Why Data Architecture Matters
Data Ingestion: Handling Multiple Sources
Robust data ingestion handles the reality of enterprise data: invoices arrive via email (from Gmail, Outlook, vendor portals), files come in multiple formats (PDF, Excel, CSV, XML, images), data sources use different protocols (REST APIs for Sage, MS Power BI, OPC UA for machines, SMTP for email, OAuth for Google Analytics), and volumes vary (100 invoices one day, 500 the next).
The process evaluates ingestion pipeline requirements: content-based deduplication using SHA-256 hashing (prevents processing the same file twice even if renamed), retry logic with exponential backoff (handles temporary failures gracefully), monitoring and alerting (notifies when ingestion fails or performance degrades), and scalable architecture (handles volume spikes without performance degradation).
Data Quality: Building Trust in Automation
Automation is only as good as the data feeding it. The process assesses multi-layer data quality control requirements: schema validation (ensures data matches expected structure), business rule validation (checks domain-specific requirements like "invoice date cannot be future-dated"), referential integrity checks (validates foreign keys like vendor codes, GL codes, cost centers), statistical outlier detection (flags unusual amounts, unexpected vendors, anomalous patterns), and completeness checks (ensures required fields are populated).
The process evaluates quality metric tracking requirements: percentage of records passing validation, most common validation failures, data completeness scores by source, and quality trends over time. Poor data quality should trigger alerts before it impacts month-end close.
Data Lineage: Compliance & Auditability
Every data transformation is tracked: source system and timestamp (where did this data come from and when?), transformations applied (OCR extraction, validation rules, enrichment logic), data quality results (did validation pass? what issues were flagged?), approvals and decisions (who approved this invoice? when? why?), and destination (where did this data end up in the ERP?).
This lineage supports regulatory compliance: POPIA requires demonstrating lawful data processing and retention, Basel II/III requires complete audit trails for financial transactions, IFRS requires documented invoice processing and retention, and internal audit requires segregation of duties and control effectiveness evidence.
AI-Powered Processing: Invoice Reading & Classification
The process evaluates custom AI model requirements for invoice reading: document classification models that distinguish invoices from receipts, statements, and other documents, OCR models fine-tuned on client's specific vendor invoice formats, field extraction models that identify and extract invoice number, date, vendor, amounts, line items, and tax, and validation models that flag suspect data (unlikely amounts, missing fields, format inconsistencies).
For invoice classification, the process assesses model requirements: predicting correct GL codes based on line item descriptions and historical patterns, identifying cost center allocations based on invoice content and business rules, flagging exceptions requiring manual review (new vendors, PO mismatches, amount thresholds), and routing to appropriate approvers based on learned approval patterns.
These models should be trained on your custom data—your invoices, your GL codes, your approval patterns. They should learn what "normal" looks like for your business and flag deviations automatically.
EVALUATION PROCESS
How to Evaluate Automation Software Without Vendor Bias
A structured approach reduces risk and improves outcomes. Follow these steps before committing.
1
Measure Current State
Track how much time current processes take. Count invoice processing hours. Count reconciliation hours. Measure error rates. Document exception handling time. Without accurate baseline data, ROI claims cannot be validated.
2
Document Requirements
Write down what the software must do. Not what features it should have. Focus on outcomes. "Reduce invoice processing time by 15 hours per week" is a requirement. "AI-powered OCR" is a feature. Requirements drive selection. Features drive marketing.
3
Test with Real Data
Vendor demos use clean sample data. Real invoices are messier. Supplier names vary. Invoice formats differ. Line items are inconsistent. Upload 50 actual invoices during evaluation. Check OCR accuracy. Review exception handling. Measure manual correction needed.
4
Calculate Total Cost
Software price is only part of total cost. Add setup fees. Add training time. Add IT support needs. Add ongoing maintenance. Add staff time for exception handling. Model costs at current volume. Model costs at 50% growth. Model costs at 100% growth.
5
Plan for Data Governance
Automation needs ongoing attention. Exception queues need daily checks. Approval workflows need periodic review. OCR accuracy drops when supplier formats change. Integration breaks when accounting systems upgrade. Assign ownership before launch. Not after problems appear.
BEYOND FINANCE
The Same Methodology,
The Same Methodology,
Different Data Sources
The evaluation framework Zorinthia uses for finance automation applies to any data source. Manufacturing companies use it for PLC-to-ERP integration. Retail businesses use it for POS-to-inventory automation. Marketing teams use it for multi-channel analytics consolidation. See an example scenario for manufacturing.
The evaluation process is the same: audit data sources, assess integration architecture requirements, evaluate quality control needs, review governance requirements, evaluate AI-powered automation capabilities, assess KPI tracking options, and review compliance-ready lineage needs.
Zorinthia starts with accounting automation because finance teams feel the pain most acutely—read why finance teams are moving beyond manual workflows. But the evaluation framework applies to marketing automation (Google Analytics, ad platform data, CRM integration), operations automation (machine data, supply chain visibility, inventory optimization), and cross-functional data products (combining finance, marketing, operations, and supply chain data for executive dashboards and decision support).
WHY THIS APPROACH WORKS
Data Products vs. Software Products
Software Products Assume Clean Data
Off-the-shelf software assumes your data arrives structured, complete, and consistent. When it doesn't—and real data never does—the software breaks, requires manual workarounds, or forces you to change your processes to fit the software's limitations.
Data Products Are Built for Real Data
Data products start with data and automation diagnostic and quality assessment. They're designed around your actual data sources—messy PDFs, inconsistent vendor formats, multiple ERPs, fragmented systems. The automation is built to handle your data reality, not an idealized version of it.
The Result: Making Informed Decisions
By starting with data—not software—you can identify solutions that handle your vendor invoice variations, work with your Sage customizations, respect your approval workflows, integrate with your existing systems (not replace them), and adapt as your data sources and business requirements change. This approach ensures organisational readiness for advanced analytics—the governance, ownership clarity, and decision frameworks that make automation investments sustainable.
This is why the evaluation framework helps you identify solutions that deliver measurable results: 70-90% time reduction in invoice processing and reconciliation, 95%+ automation rates with low exception volumes, month-end close time reduced by 3-5 days, and complete compliance-ready audit trails.
Getting Started: The 3-Phase Evaluation Framework
Every engagement follows the same proven evaluation framework—whether evaluating finance automation or cross-functional data strategy decisions.
01
Data and Automation Diagnostic
Map your data sources, assess quality risks, identify integration requirements, and evaluate data strategy options. Typical duration: 2-3 weeks.
02
Solution Evaluation
Evaluate solution capabilities, assess integration requirements, review quality control options, and compare automation features against your needs.
03
Decision Support
Get clear recommendations, risk assessment, implementation guidance, and ongoing support for solution selection and rollout planning.
Data & Automation Diagnostic
A short, onsite diagnostic to understand how data and automation are actually working today — and where the real risks and opportunities sit.
Typically completed within 2–3 weeks, depending on organisational size, access to stakeholders, and scope.
For larger or more complex environments, the diagnostic may be staged while remaining tightly bounded.
Entails data strategy and capabilities assessment.
Outcome: a clear, written view of current-state reality, key risks, and practical options for what to address next — without committing to vendors, platforms, or delivery programmes.