See How Your Processes Actually Work — Not How You Think They Work
Business processes documented in manuals and flowcharts rarely match reality. Process mining analyzes actual event logs from your systems to reconstruct real workflows — revealing bottlenecks, rework loops, compliance deviations, and automation opportunities invisible to observation. Companies implementing process mining discover 30% more efficiency gains than traditional analysis, identify automation opportunities worth $1M+ in savings, and reduce process cycle times by 25-40%. The process mining market reached $2.1 billion in 2025 (according to Gartner).
You Can't Optimize What You Can't See
Process improvement traditionally starts with workshops where stakeholders draw flowcharts of how they think work flows. But the actual process — with its exceptions, workarounds, rework loops, and bottlenecks — is far more complex than anyone remembers.
A purchase order process has 3 steps in the manual but 47 variants in practice. An insurance claim takes 5 days on the happy path but averages 23 days because of rework and approvals nobody documented. Customer onboarding has 8 handoffs between 4 teams, each adding 2-3 days of delay.
Without seeing the real process, you automate the wrong steps, optimize the wrong bottlenecks, and invest in improvements that don't impact the actual pain points.
Data-Driven Process Discovery and Optimization
We implement process mining that turns your system logs into actionable process intelligence.
Process discovery reconstructs actual process flows from event log data — every variant, every path, every exception. The resulting process map shows how work actually moves through your organization, not how it's supposed to.
Bottleneck analysis identifies where processes stall: which activities take longest, where queues form, which handoffs create delays, and which resources are overloaded. Heat maps highlight time-consuming steps and waiting times.
Conformance checking compares actual processes against intended processes, identifying deviations that cause delays, errors, or compliance risks. You see exactly where and how often people deviate from standard procedures.
Automation opportunity scoring ranks process steps by automation potential: repetitive tasks with clear rules score high, judgment-intensive tasks with many exceptions score low. This prioritization ensures you automate the highest-ROI steps first.
Continuous monitoring tracks process performance over time, detecting when cycle times increase, when new bottlenecks form, or when process changes have the intended effect.
Process Mining Implementation Process
Data Source Identification(1-2 weeks)
We identify which systems contain relevant event logs (ERP, CRM, ITSM, BPM), assess data quality, and define the process scope. Most modern systems already generate the logs needed.
Data Extraction & Preparation(2-3 weeks)
We extract event logs, clean and standardize data, create case IDs where needed, and prepare the dataset for mining. This step handles data quality issues that affect analysis accuracy.
Process Discovery & Analysis(2-3 weeks)
We run process mining algorithms to discover actual workflows, identify variants, analyze bottlenecks, check conformance, and score automation opportunities.
Insights & Action Plan(1-2 weeks)
We present findings with interactive process maps, bottleneck visualizations, and a prioritized action plan for process improvement and automation initiatives.
Process Mining Technology Stack
Ready to Automate?
No commitments. Tell us what you need and we'll tell you how we'd solve it.
Process Mining Use Cases
Order-to-Cash
Challenge: Average order-to-cash cycle was 38 days but nobody could explain why — the documented process showed 12 days as the target
Solution: Process mining across ERP, CRM, and billing systems revealed 23 process variants, with credit check loops and manual approval bottlenecks adding 20+ days to 35% of orders
Result: Identified 3 bottlenecks accounting for 85% of delays; automated credit check reduced cycle by 8 days; standardized approval routing saved 6 more days; average cycle dropped to 18 days
IT Service Management
Challenge: Average incident resolution was 14 hours against a 4-hour SLA — ITSM team believed the problem was staffing, requesting 3 additional headcount
Solution: Process mining of ServiceNow logs showed that 45% of incidents bounced between 3+ teams before reaching the right resolver — the problem was routing, not capacity
Result: Smart routing rules eliminated 70% of bounces; resolution time dropped to 5.2 hours; no additional headcount needed; saved $250K annual staffing cost
Insurance Claims
Challenge: Claims processing averaged 23 days — customer complaints increased, and management assumed the bottleneck was adjuster capacity
Solution: Mining claims system logs revealed that 40% of claims entered rework loops due to missing documentation that could be caught upfront, and 25% waited 5+ days for a single approval step
Result: Front-end documentation validation reduced rework by 65%; parallel approval routing eliminated 5-day bottleneck; average claims time dropped to 11 days
Procurement
Challenge: Procurement cycle averaged 45 days with high variance (15-120 days) — CFO wanted to understand why identical purchases took vastly different times
Solution: Process mining across procurement, approvals, and vendor management systems revealed that 60% of variance came from 3 approval bottlenecks and inconsistent vendor onboarding paths
Result: Standardized approval routing reduced variance by 70%; automated vendor checks cut onboarding from 15 days to 3; average procurement cycle dropped to 22 days
Why idataweb for Process Mining
Modern Production Stack
Data systems built on Next.js 16 + PostgreSQL with pgvector for embeddings and similarity search. No external vector database fees. Payload CMS 3 manages data sources and pipeline configuration through an admin panel your team controls directly.
AI-Native Team
We use Claude, GPT-4o, Deepgram, and ElevenLabs in production daily — for coding, content generation, voice automation, and customer interactions. We're not consultants who read about AI; we're practitioners who ship AI systems every week.
Self-Hosted Infrastructure
Your data stays on your infrastructure. PostgreSQL with pgvector handles embeddings locally — no external vector database sending your proprietary information to third-party servers. Self-hosted means GDPR-compliant by architecture.
End-to-End Delivery
Strategy, architecture, development, deployment, and ongoing support — all from one team. No handoffs between consultants, designers, and developers. The engineers who build your system are the same ones who maintain it.
Automation-First Operations
Our own operations are automated end-to-end: CI/CD pipelines, infrastructure monitoring with Telegram alerts, daily database backups, automated content publishing, and AI-assisted development workflows. We build automation for clients because automation is how we run our own business.
Transparent Fixed Pricing
Fixed-price projects with clear milestones and deliverables. You approve each phase before we proceed to the next. No open-ended hourly billing, no scope creep surprises. Ongoing support is a separate, transparent monthly agreement.
Frequently Asked Questions
What data does process mining need?
Event logs from your business systems containing three elements per event: a case identifier (order number, ticket ID, claim number), an activity name (created, approved, shipped), and a timestamp. Most modern systems — ERP (SAP, Oracle), CRM (Salesforce), ITSM (ServiceNow, Jira), and BPM tools — already generate these logs. We extract, transform, and prepare this data for mining. If your systems lack proper event logging, we help configure it.
How is process mining different from business process mapping?
Traditional BPM relies on interviews, workshops, and documentation — capturing how people think processes work. This misses variants, workarounds, exceptions, and actual timing. Process mining analyzes real event data from systems, showing how processes actually execute — including all 47 variants of your 'standard' process, the rework loops nobody mentioned, and the exact minutes/hours/days spent at each step. It's evidence-based vs opinion-based.
How long does a process mining project take?
A focused analysis of one process (order-to-cash, incident management, claims processing) takes 6-10 weeks from data extraction to actionable insights. Broader multi-process analysis takes 3-4 months. The timeline depends primarily on data quality — clean, well-structured event logs accelerate analysis; fragmented data across multiple systems requires more preparation time.
Can process mining run continuously?
Yes. After the initial analysis, we set up continuous process monitoring that tracks KPIs in real-time: cycle times, bottleneck utilization, conformance rates, and variant distribution. Alerts trigger when processes deviate from targets. This converts process mining from a one-time project into an ongoing operational intelligence capability that detects issues before they become problems.
Which Processes Take Longer Than They Should — And You Can't Explain Why?
Tell us about the business processes you want to understand and optimize. We'll assess your data readiness and estimate the cycle time reductions process mining would reveal.
Free data readiness assessment · 30% efficiency gains · Evidence-based optimization