Process intelligence sits at the intersection of data science, automation, and organizational resilience. It uncovers game-changing visibility into how processes perform, fail, and can be improved – by tapping into the wealth of enterprise data.
This 2600+ word guide serves as a master resource on process intelligence capabilities, applications, and adoption roadmaps with insights tailored for analytics leaders.
The Rise of Process Intelligence
Businesses recognized process optimization as vital for competitiveness long before big data emerged. Methods like Lean Six Sigma built foundations for excellence.
However, gaps remained in understanding how processes operate, evolve, and affect customer outcomes. Limited data and static maps obscured why failures happen, how delays trigger downstream impacts, and where the greatest hidden waste lurks.
The explosion of process data over the last decade sparked new possibilities. Cheaper storage and sensor proliferation lead to exponentially growing event logs. Systems like ERPs and CRMs also contain vast troves of process execution signals.
Yet deriving meaning from these massive, messy datasets gets hindered by traditional analytics. This gave rise to process mining – leveraging algorithms to automatically discover processes as they unfold from system event logs.
Process mining revealed the tip of the iceberg. But its limitations around data scope, analytics sophistication, and optimization functionality sparked the next evolution – process intelligence.
Process intelligence builds on process mining with expanded data integration, advanced analytics, and automated enhancement
Let‘s examine what sets process intelligence apart and makes it invaluable for modern enterprises.
Expanding Data Horizons
Beyond Process Mining
While process mining can analyze structured event logs, process intelligence incorporates diverse, unstructured data streams:
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Sensor data from IoT devices across smart factories, connected products, equipment, etc.
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Task data on user activities across desktop apps, browsers, emails, and collaboration tools
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Enterprise data from systems like ERP, MES, PLM, SCM, and CRM
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External data including news, social media feeds, market trends, weather, traffic patterns, travel disruptions, etc.
This multi-source data integration provides unmatched context and reduction of blind spots.
Augmenting Intelligence
With Advanced Analytics
Process mining relies on algorithms for pattern discovery, anomaly detection, bottleneck identification, and conformance checking.
But process intelligence amplifies these capabilities using machine learning and AI:
- Predictive analytics for data-backed scenario planning and simulations
- Automated root cause analysis
- Natural Language Processing to ingest unstructured data
- Sentiment analysis decoding people‘s perceptions of processes
- Prescriptive guidance for operational decisions
This amplifies analytical prowess for unprecedented insight.
Operationalizing Insights
With Automation
While traditional process mining helps find improvement opportunities, acting on them requires manual efforts. Process intelligence closes the loop with embedded capabilities for:
- Robotic Process Automation to execute repetitive tasks
- Low-code workflow configuration
- Notification alerts and reporting dashboards
- Closed-loop handling of process failures and exceptions
Together, this drives rapid operationalization of analytics, eliminating reliance on slow, error-prone human execution.
The fusion of expanded data scope, upgraded analytics, and automated enhancement constitutes the process intelligence revolution. Let‘s examine adoption trends.
Industry Adoption of Process Intelligence
Process intelligence may seem nascent, but global tech spending data reveals astonishing growth.
Key stats:
- Worldwide process intelligence software revenue totaled $2.6B in 2021, registering 40% annual growth [1]
- By 2025, 60% of large organizations will have operationalized process intelligence, up from 20% in 2022 [2]
- 78% of business leaders cite process inefficiencies as slowing digital transformation
- Forbes designated process intelligence as one of the top five tech trends to accelerate business model changes [3]
This hockey stick growth gets fueled by organizations deploying process intelligence to drive major strategic priorities from cost optimization to customer experience enhancements:
| Use Case | Impact |
|---|---|
| Predictive Maintenance | ↓ maintenance costs by 20-40% |
| Order-to-Cash Process | ↑ on-time deliveries by 33% |
| Claims Management | ↓ cycle time by 62% |
| Invoice Processing | ↓ operating costs by 25-30% |
| Supply Chain Visibility | ↓ lost sales from stock-outs by 57% |
| Compliance Audits | ↑ audit pass rate from 60% to 99% |
Industry use cases showing process intelligence ROI
Forrester data shows finance departments gain the most value via Process Mining and intelligence adoption. Common pain points like invoice and payment reconciliations see 30-50% efficiency gains and 90% faster issue resolution [4].
But applications now span functions from supply chain to healthcare. Let‘s glimpse key application trends by sector.
Process intelligence unlocks structural cost savings, resilient operations, and strategic agility – making it integral to both recovery and growth. This promise prompts technology leaders to prioritize quick wins as well as long-range roadmaps.
Quick Win Use Cases
Most organizations discover immediate high-ROI applications in:
- Order-to-Cash: Accelerating delivery and cash application to optimize working capital
- Record-to-Report: Minimizing financial period close timelines with automated reconciliations
- Issue-to-Resolution: Defusing customer escalations before they erupt into crises
Strategic Roadmaps
Over longer timeframes, analytics leaders roadmap process intelligence to transform:
- New Product Development: Simulating pipeline scenarios for agile Stage Gate decisions
- Margin Assurance: Prescriptive price modeling balancing profitability vs demand elasticity
- Risk Scoring: Custom predictive models that adjust operational safeguards and inspections to address emerging threats before incidents strike
Now let’s open the hood to peek into process intelligence architectures.
Inside Process Intelligence Platforms: A Technical Overview
While outcomes focus on operational metrics, process intelligence rests on technical foundations in data and analytics. For technology leaders, navigating provider capabilities requires understanding key components:
Process intelligence platforms integrate inputs, analytics engines, and operational modules
Inputs: Data Acquisition & Transformation
Most process data gets generated from digitized systems, but connecting new sources poses challenges:
- Legacy authentication protocols → APIs and connectors for access
- Proprietary data formats → Mapping and normalization
- Protocol differences → Translation layer for consolidation
- Referential integrity → Master data management
With data ingestion barriers overcome, the extraction module securely pulls relevant datasets from permitted sources in aligned formats. This extraction may execute at regular intervals or use change data capture for constant streaming.
Analytics Engine: Process Discovery, Monitoring & Enhancement
At the analytics core, powerful machine learning algorithms drive key functions:
Process Discovery
- Automated process model generation from raw event logs
- Conformance checking between actual vs designed flows
- Variant analysis identifying distinct process paths
Analytics
- Root cause identification in process failures
- Prediction of future outcomes via simulations
- Conformance checking between actual vs designed flows
- Variant analysis identifying distinct process paths
Monitoring & Enhancement
- Dashboards visualizing KPIs
- Notification triggers for defined alert states
- Workflow automation for streamlining processes
- Decision models codifying analytical insights for reuse
Increasingly, solutions also integrate digital twin technology for dynamic modeling, scenario testing, and predictive guidance.
Output: Operationalization Modules
Analytical outputs mean little without institutionalization. Process intelligence aims beyond isolated process improvement projects towards continuous optimization. This requires operational modules for:
- End user portals to access reporting dashboards and trigger actions
- Automation bots executing prescribed steps per algorithms
- Integration with helpdesk platforms for managed issue resolution
- Workflow configuration instead of complex programming
Let‘s explore how leading technology vendors deliver across this architectural stack.
Notable Process Intelligence Platforms
Over 100 process intelligence tools exist, leveraging rapid innovation in analytics, automation and low-code application development. Here we profile leaders recognized for technical sophistication and enterprise scalability.
| Platform | Key Differentiators | Industry Applications |
|---|---|---|
| Celonis | Pioneer in process mining + ML, frictionless UX | Banking, Insurance, Manufacturing |
| myInvenio | Deep process analytics + simulation | Energy, Mining |
| QPR ProcessAnalyzer | Real-time dashboards + prescriptive actions | Telcos, Business Services |
| ABBYY Timeline | Powerful process discovery from unstructured data | BPOs, Shared Services |
| UI Path | Tight RPA integration | All sectors |
| Signavio | Strong process modeling capabilities | Healthcare, Public Sector |
Leading process intelligence platform capabilities
Evaluate process mining vendor comparisons for detailed technical and capability analysis.
Beyond software, implementation success requires strategic foundations. Leading practices for process intelligence adoption follow.
Launching Process Intelligence: Proven Approaches
Technology promises aside, half of process improvement programs fail to meet objectives or sustain outcomes [5]. Avoiding pitfalls requires strategic governance and change leadership.
Process Intelligence Center of Excellence
The most successful adopters centralize process analytics expertise into a dedicated enablement group like a Center of Excellence (CoE). This core team commanders responsibilities like:
- Data engineering: Ensuring consistent data collection, reliability, and access
- Analytics engineering: Industrializing algorithms, modeling, monitoring and insights delivery
- Evangelizing: Encouraging process documentation, data capture across the organization
- Mentoring: Coaching process owners on analytical techniques to self-serve
- Program management: Overseeing portfolio of improvement projects, sharing best practices
Beyond technical duties, the CoE spearheads ignition and incubation of process transformation initiatives before transitioning ownership to operational leaders.
Staffing the CoE
Blend business and technical competencies by recruiting across functions:
- Data/Analytics leads: Data scientists, BI experts
- Technical teams: Engineering, Architecture, Infrastructure
- Business partners: Ops management, Process owners
This cross-functional squad can augment core capabilities like data science with external partnerships or managed analytics services.
Maturing CoEs
As process intelligence usage grows, CoEs evolve from a single centralized group into a network model with satellite teams embedded inside business units. However, coordination and oversight responsibilities remain consolidated to ensure consistency.
The Path to Scaled Deployment
Process intelligence demands immersive, persistent initiatives not isolated projects. The roadmap below outlines steps for instituting sustainable capabilities:
Securing quick wins builds momentum while broader change management ensures adoption.
Through this phased progression, process intelligence transforms into a cornerstone of strategic decision-making, not just periodic optimizations.
Risks and Considerations for Process Intelligence
For all its promise, process intelligence also introduces new risks and implementation pitfalls. Keep these factors on the radar when architecting solutions:
Data Privacy: Centralizing sensitive customer information for analysis raises fresh data governance considerations around access controls, transparency, and regulatory compliance.
Unconscious Bias: AI algorithms can inherit and amplify biases. Rigorously inspect models for discrimination against protected classes.
Overadjustment: Metrics naturally fluctuate. Reactively tuning operations in response to every minor shift overcorrects systems. Focus just on statistically significant swings.
Alert Fatigue: Change data capture and real-time monitoring provide constant status streams. But excessive alerts overwhelm users and get ignored, disabling the system.
Misleading Metrics: Distilling enterprise complexities into metrics oversimplifies. Guard against improving the metric but degrading the customer experience.
Wrong Conclusions: Correlation ≠ Causation. Question assumptions and remain vigilant for coincidental patterns that get misinterpreted as insightful relationships.
While ripe with opportunities, process intelligence also introduces new failure vectors. Your opportunity assessment must weigh benefits against these hazards lying in wait along the road to modernization.
Industry Leader Perspectives
Process intelligence progressions demand bold vision and execution dexterity. We conclude by tapping insights from those spearheading adoption.
“Every broken process directly equates to increased cost or lost revenue. In fierce global competition, solving process problems provides the fuel for customer experience innovation and profitable growth."
Ram Singampalli, CIO and Head of Operations, Volvo Financial Services
"First generation process mining solved the problem of shining light into processes that were previously blind spots. The next generation we call ‘connected intelligence‘ goes beyond visibility to drive action."
Matt Richie, Global Head of Process Excellence, Zurich Insurance
"Process intelligence gives us a proving ground for innovations. We can accurately predict how disruptive technologies like blockchain may impact operations before full-scale technology transformation.”
Joann O’Brien, Director Business Management, Blue Cross Blue Shield
These sentiments from leaders on the digital frontier confirm process intelligence‘s expansive impact potential. Separating winners from laggards in leveraging its possibilities comes down to change proficiency as much as technical savvy.
Next Steps Along Your Process Intelligence Journey
Process data integration, advanced analytics and automation constitute the next wave of competitive advantage. Leaders are riding this wave to boost efficiency, resilience and innovation capacity.
However, unlocking process intelligence depends on selecting solutions aligned to your needs and capabilities. Our comprehensive guide on top process mining vendors details the leading platforms along with an evaluation framework for finding your match.
To get custom advice on launching your first process intelligence initiatives or building an enterprise roadmap, connect with our experts here.
