Process excellence is indisputably pivotal for operational and organizational success in increasingly uncertain markets. Yet traditional methods of understanding processes through tribal knowledge, static documentation and assumptions fail to reflect ground realities. This inability to capture fluid processes leads to chronic inefficiencies, costs and risks – impeding growth, agility and customer experience.
The solution lies in evolving from intuition to intelligence – where real-time data and algorithms dynamically map processes. This field of "process intelligence" is gaining mainstream relevance as the digital analytics foundation enabling process improvement and transformation.
What is Process Intelligence?
Process intelligence refers to the technological capability to automatically discover, analyze, monitor, predict and optimize end-to-end business processes using data and algorithms rather than solely human hypotheses.
Instead of one-time analysis providing just the tip of the iceberg view, process intelligence offers continuous insights revealing bottlenecks, waste, risks and solutions previously remaining unseen.
Key functionalities under the process intelligence umbrella include:
Process Mining – Extracts data from IT systems to visualize actual end-to-end processes, variants, metrics and user actions
Task Mining – Specifically traces user interactions across apps/websites used within processes providing micro-level visibility
Conformance Checking – Compares actual versus ideal process flows to quantify deviations for targeted improvement
Automated Process Discovery – AI/ML algorithms auto-document processes, identify enhancements and enable automation
Simulation – Creates "digital twin" process models to simulate scenarios and mitigate optimization risks
Predictive Analytics – Forecasts process outcomes and risk factors enabling data-driven planning
Such intelligent features enable fact-based understanding, monitoring and enhancement of operations. Let‘s examine why process intelligence is invaluable.
Critical Benefits of Process Intelligence
1. Transforming Visibility
Process intelligence eliminates blind spots by seamlessly capturing end-to-end processes, variants and metrics – aspects manual assessments simply cannot provide.
This unprecedented transparency enables fact-based targeting, rather than assuming, of root causes behind bottlenecks, failures or delays.
2. Boosting Efficiency
Pinpointing improvement areas allows streamlining processes and removing waste through initiatives like automation. Typical efficiency gains range from 20-50% as per research.
For example, automated invoice processing workflows shortened approval times from over 10 days to under an hour – fast-tracking downstream functions. Such exponential increases in productivity are realized across functions like order-to-cash, claims management and beyond.
3. Informing Decisions
Analytics-based recommendations guide informed decisions on process resource allocation, gap remediation and digitization roadmaps based on statistical evidence rather than gut feel.
4. Driving Cost Savings
Gartner estimates companies waste 20-30% of overall budgets on process inefficiencies annually. Eliminating manual rework, enhancing SLAs and right-sizing roles leveraging process intelligence provides over 20% in cost savings.
5. Mitigating Risk
Identifying process risks pre-emptively before failures or compliance issues occur is invaluable in regulated sectors. This allows avoiding negative business impacts through controls and alerts.
6. Delighting Customers
Smoother workflows and optimal user experiences, unlocked through task mining and journey analysis, directly boost customer satisfaction levels as per Forbes.
The collective impact compounds competitive advantage. But with myriad fast-evolving options, navigating the complex process intelligence software market remains challenging.
Choosing The Right Process Intelligence Software
Over 80 vendors now offer overlapping process intelligence functionalities spanning focus areas like discovery, mining, automation and analytics. Selecting solutions truly matching environment-specific requirements is vital yet tricky.
Figure 1: High-level vendor capability analysis by key criteria
Let‘s examine five aspects crucial for software evaluation:
1. Analytics Capabilities
Sophisticated algorithms separating leaders from laggards drive actionability of insights surfaced. Look beyond just data visualization offerings.
2. Flexible Customization
Each organization and process landscape differs. Seek highly customizable platforms providing tailored analysis instead of one-size-fits-all.
3. Intuitive User Interface
Poor interfaces hamper user adoption and accuracy of insights. Contextual guidance enables all employees to seamlessly leverage process data.
4. Scalable Infrastructure
As process complexity and data volumes spike, ensure the underlying architecture scales effectively without limiting functionality or inducing costs.
5. Data Security
Meeting privacy and compliance regulations is non-negotiable when managing sensitive customer data. Audit security infrastructure before purchase.
Figure 2: Key criteria for selecting optimal process intelligence software
Now let‘s open the hood to understand what happens behind the scenes powering process intelligence.
Anatomy of Process Intelligence Software
While techniques differ slightly across vendors, platforms commonly follow three core phases:
1. Data Ingestion
First, relevant structured and unstructured data is extracted from sources like ERPs, emails, chat logs and legacy systems via APIs or custom connectors.
2. Process Discovery
Proprietary algorithms analyze aggregated data to reveal actual process flows, variants, metrics, decisions and user actions.
Advanced systems creatively leverage emerging techniques like artificial intelligence, robotic process automation and natural language processing to gather contextual data points and auto-identify enhancement areas.
3. Analysis and Modeling
Mined processes are imported into interactive process maps and pre-defined/custom reports providing visibility into performance, compliance and trouble spots. Users can slice and dice various dimensions to diagnose root causes and hypotheticals can be modeled to simulate future state processes.
Ongoing monitoring measured against benchmarks tracks improvement over time – enabling continuous refinement aligned to process KPIs.
Figure 3: Common high-level process intelligence methodology
Underpinning the value realization are cutting-edge technologies taking analytics to the next level:
Predictive Engines – Machine learning algorithms examining historical data forecast scenarios surrounding market changes, new product launches, planned process optimizations and model likely process impact. This empowers leaders to proactively tweak strategies.
Natural Language Processing – With NLP decoding unstructured data like text, chat, emails and documents, the variety of data sources tapped into grows multifold, improving discovery.
Smart Simulation – Digital twin process modeling allows "what-if" analysis to simulate modifications, introduce technologies like AI and estimate outcomes in a risk-free parallel world before costly real world application.
As exponential data growth intersects with analytics advancements, process intelligence is proving fundamental for gaining a competitive edge through innovation rather than reactive catch up.
Process Intelligence Software Vendor Landscape
The expanding market has diversified into specialized categories catering to complementary needs:
Pure Play Process Mining Tools
Vendors like Celonis, UiPath Process Mining and QPR ProcessAnalyzer focus purely on providing actionable analytics rather than adjacent process automation capabilities.
Task Mining Specialists
Platforms like Netguru explicitly trace and enhance user interactions with systems across account opening, inventory lookups etc. steps.
Intelligent Process Automation Suited
IPA tools from leading robotic automation vendors like Microsoft Power Automate Desktop and IBM Watson blend automated process discovery, mining and remediation under unified platforms.
General Enterprise Software
ERP majors like SAP are augmenting existing solutions with process intelligence modules to connect operational data with business insights.
Niche Operational Analytics
Startups like Prodly enable process mapping/simulation and task analytics targeted for product managers rather than centralized process teams.
Identifying tools balancing functionalities like analytical depth, ease of integration with existing landscapes and data protection is vital based on specific environments and use cases.
Realizing Intelligent Operations
On average, organizations still utilize only 12% of available process data according to Deloitte. Tapping into rich locked-away insights needs the breakthrough fuel of process intelligence to boost speed, quality and competitiveness.
While historically siloed across tools, integrated analytics suites now offer the most potent out-of-the-box capabilities. But culture, organization structures and KPIs require realignment to fully benefit.
When complemented by broader change management, process intelligence rapidly optimizes at scale generating over $2 million in annual savings and over 20% efficiency gains as per leading research.
The collective financial impact and process excellence unlocked provide indisputable competitive differentiation; making adoption imperative.
