Setting Business Objectives for Orchestration
Defining Strategic & Operational Imperatives
The first step to tying network orchestration to business value is aligning orchestration activities with the organization’s broader strategic goals. For network orchestration to drive tangible business value, it must serve both the strategic objectives of the business and the day-to-day operational needs.
Strategic Imperatives: These could include objectives such as cost reduction, operational agility, faster time-to-market for new services, or improving customer satisfaction.
Operational Imperatives: At the operational level, orchestration should help reduce manual intervention, lower error rates, and improve the consistency of network operations.
Examples:
- If your company is focusing on cost optimization, the primary objective might be reducing manual labor costs associated with repetitive network operations tasks.
- If the goal is agility and innovation, orchestration can enable rapid deployment of new network services, allowing your company to be more competitive.
Network orchestration should not be a technology-driven initiative alone; it must drive business value. When planning the transition, it’s essential to quantify the expected return on investment (ROI) of orchestration. Whether it’s through direct cost savings, revenue acceleration, or improved operational resilience, the impact must be measurable and aligned with executive-level business objectives.
Understanding the Orchestration Maturity Journey
Itential Automation & Orchestration Maturity Model
The journey from manual operations to full orchestration can be challenging. The Itential Automation & Orchestration Maturity Model provides a framework for understanding where an organization stands today and how to evolve through increasing levels of automation maturity. Understanding your organization’s automation maturity is important, as the level of business value that can be gained through automation and orchestration will be limited by where the organization is – for example, an organization where network personnel only engage in task automation will struggle to deliver value around reducing end-to-end time to complete multi-domain processes.
Level 1: Limited to No Automation
- Characteristics: Network operations are largely manual, relying on human intervention for routine tasks like configuration changes, provisioning, and monitoring. This leads to inconsistencies, high error rates, and delays.
- Challenges: Limited scalability, increased operational costs, risk of human error, and slow service delivery.
Level 2: Task Automation
- Characteristics: At this level, some tasks are automated (e.g., device configuration or ticketing), but these automations are siloed — performing activities across domains requires manual coordination.
- Challenges: While automation improves efficiency for isolated tasks, it does not enable end-to-end process orchestration or provide significant cost reduction for the business.
Level 3: Process Orchestration
- Characteristics: Multiple tasks are now integrated into end-to-end processes. For example, network provisioning might be fully automated from request to deployment, reducing human intervention.
- Challenges: Requires sophisticated workflow design and integration between tools and systems to account for standards, requirements, validation, etc.
Level 4: Self-Service Networking
- Characteristics: At the highest level of maturity, orchestration enables self-service capabilities for end users or teams. Stakeholders can request and receive network services on-demand without IT intervention, similar to the public cloud model.
- Challenges: Requires extensive automation infrastructure, governance, and alignment across teams.
Organizational Requirements
As companies progress through the maturity levels, they must address changes in their workforce, tools, and processes. For example, at higher maturity levels, organizations need more specialized roles like orchestration architects and automation engineers who can design, implement, and manage complex workflows. Additionally, team members must develop new skills in areas like low-code workflow design and API integrations.
Defining Use Cases for Network Orchestration
Choosing High-Value Use Cases
When starting the journey to automation, selecting the right use cases is critical. High-value use cases are those that:
- Provide immediate business impact: Such as reducing time-to-market for services, improving network availability, or enhancing customer satisfaction.
- Address pain points in manual processes: For example, repetitive configuration tasks or time-consuming compliance audits.
Example Use Case 1: Automated Device Provisioning
- Problem: Manual provisioning of network devices is time-consuming and error prone.
- Solution: Orchestrating workflows to execute device provisioning processes end-to-end can drastically reduce the time needed to deploy new devices and ensure consistency across the network.
- Business Impact: Faster service rollout, improved compliance with configuration standards, reduced operational costs.
Example Use Case 2: Network Health Monitoring and Remediation
- Problem: Network issues are detected too late, and manual intervention takes too long, leading to downtime and service degradation.
- Solution: Automated monitoring systems that can detect anomalies in real-time and trigger automated remediation workflows.
- Business Impact: Improved uptime, faster mean time to recovery (MTTR), increased customer satisfaction.
Prioritization by Business Impact
To maximize the business impact of network orchestration, prioritize use cases that align with your organization’s most pressing needs. A prioritization framework can help evaluate:
- Cost impact: How much will automating this process reduce operational costs?
- Time savings: How much faster will this process be completed — both for the consumer, and in terms of saving time for the network team?
- Scalability: Can this automation scale to handle growing network demands?
Business impact and business value can be characterized by the following categories: Productivity/Efficiency, Compliance, Speed, Quality, Innovation, and automation impact/coverage
How to Identify the Strategic Imperatives of the Business
To align automation efforts with business value, its critical to identify what is most valuable to the business. There are many ways to identify strategic imperatives for the business. Teams can follow the following steps as a guideline:
- Review Financial Statements
- Analyze the Annual Business Plan
- Understand Organizational KPIs and MBOs (Management by Objectives)
- Hold Discussions with Executive Leadership
- Engage with Business Unit Leaders
- Study Competitor Analysis Reports
- Examine Customer Feedback and Market Research
- Assess Internal Audit and Risk Reports
- Review Technology and Innovation Roadmaps
- Conduct SWOT Analysis
- Align with ESG Goals (Environmental, Social, and Governance)
Mapping Business Initiatives to Automation & Orchestration Flows
Mapping Business Goals to Automation
Once business goals are defined, the next step is aligning the business goals to automation and orchestration use cases. For example:
- If the business goal is to reduce operational costs by 20%, then automating repetitive network tasks like provisioning and patch management would be a high priority.
- If the goal is to improve customer satisfaction, automating network monitoring and resolution workflows might be more critical.
One of the critical success factors for achieving on the business goals is to define the appropriate metrics that will be used to measure progress against those goals. Metrics should be considered carefully to ensure that the data can be captured, calculated, and analyzed through automated processes whenever possible, so that executive leadership can receive regular updates on progress.
Metrics fall into different categories based on how they will impact the business, in areas such as improvements in productivity, improvements in delivery speed, or improvements in security or compliance management.
Defining Metrics for Success
Key Metrics
Measuring the impact of network orchestration and automation is crucial to ensure that the transformation aligns with business objectives and delivers operational improvements. The following key metrics have been defined to capture the business value, technical efficiency, and overall success of automation initiatives:
Productivity, Cost, & Efficiency Metrics
Productivity per Engineer
Definition: Measures the amount of work an engineer can complete with automation.
Business Impact: Helps quantify improvements in productivity due to automation.
Quantification: Productivity = Output per engineer / Number of engineers
Data Sources: Workflow logs, HR data.
Managed Service Cost Reduction in $$
Definition: The reduction in operational costs achieved by using network orchestration to automate tasks previously performed manually or by third-party managed service providers.
Business Impact: This directly reduces operational expenses by minimizing the need for external managed services or lowering the resource costs associated with manual processes.
Quantification: Managed Service Cost Reduction equals the Cost of pre-orchestration services minus the Cost of post-orchestration services.
Data Sources: Financial records (e.g., contracts with service providers), internal budget reports, cost-benefit analysis of automation versus third-party services.
Deployment Cost Reduction & Program Acceleration
Definition: The reduction in costs and time required to deploy new network services or updates through automation and orchestration compared to manual deployments.
Business Impact: Faster time to market for new services and updates, resulting in improved customer satisfaction, competitive advantage, and reduced operational costs.
Quantification: Cost Reduction equals Cost of manual deployment minus Cost of orchestrated deployment.
Time Savings equals (Manual deployment time minus Automated deployment time) divided by Manual deployment time, then multiplied by 100.
Data Sources: Project management systems, deployment records, financial reports, timelines of manual vs. automated deployments.
Cost per Instance of Process Execution
Definition: Measures the cost to execute one automated process instance.
Business Impact: Helps quantify cost savings directly attributable to automation.
Quantification: Cost per instance = Total Cost of automated process / Number of process executions
Data Sources: System logs, financial reports on automation tools and infrastructure.
Cost per Unit of Scalability
Definition: Measures the cost associated with scaling network tasks executions. This metric tracks how efficiently the organization can scale its network infrastructure or services in terms of costs relative to the increased capacity provided through orchestration.
Business Impact: A lower cost per unit of scalability means that the organization can scale more efficiently without significantly increasing headcount or operational expenses. This is critical for ensuring profitability as demand grows and for maintaining competitive pricing for services.
Quantification: Cost per Unit of Scalability equals Total Cost of scaling divided by the Increase in capacity of tasks performed.
Data Sources: Financial records, capacity planning tools, network utilization reports, cost analysis for infrastructure or cloud services, orchestration platform logs.
Staff Replacement/Augmentation/Reduction
Definition: The reduction or realignment of staff due to automation and orchestration, enabling more efficient resource allocation or a reduction in personnel costs.
Business Impact: Reduces labor costs by either lowering the number of employees needed for routine tasks or freeing up staff for higher-value activities, improving productivity.
Quantification: Staff Impact equals (Number of manual positions pre-orchestration minus Number of positions post-orchestration) divided by Total manual positions pre-orchestration, then multiplied by 100.
Data Sources: HR systems, labor cost reports, job descriptions, staff efficiency reports.
Compliance & Security Metrics
Compliance Automation Rate
Definition: The percentage of compliance-driven changes automatically implemented.
Business Impact: Reduces compliance risks and ensures regulatory adherence.
Quantification: Compliance Rate = (Automated compliance actions / Total compliance actions) * 100
Data Sources: Compliance systems, audit reports.
Time to Complete Security Patch Against X% of Devices, Compliance Requirements, PSIRT Advisories
Definition: The time required to apply a security patch across a given percentage (X%) of devices, ensuring compliance with security requirements such as PSIRT advisories.
Business Impact: Reduces security risks and ensures timely compliance with regulations, protecting the organization from potential breaches and fines.
Quantification: Patch Time equals Total time to apply patches across X% of devices, divided by X.
Data Sources: Patch management systems, compliance reports, PSIRT advisory compliance logs, device inventory.
Time to React to Resolve Critical Vulnerabilities (Config or Firmware)
Definition: The time taken to identify, address, and resolve critical network vulnerabilities, whether in configuration or firmware, through automation.
Business Impact: Minimizes exposure to security threats, reduces the likelihood of breaches, and ensures faster remediation of critical vulnerabilities.
Quantification: Time to Resolve equals Total time from vulnerability detection to remediation divided by Number of vulnerabilities.
Data Sources: Vulnerability management systems, network monitoring tools, incident response logs.
Time to React to Security Incident Policy Change/Blocking
Definition: The time taken to respond to a security incident by implementing policy changes or blocking malicious activity.
Business Impact: Improves the organization’s ability to mitigate threats quickly, ensuring minimal impact on network operations and reducing the risk of data loss or breach.
Quantification: Incident Response Time equals Time from incident detection to policy change or blocking action divided by Number of incidents.
Data Sources: Security information and event management (SIEM) systems, incident response logs, network security tools.
Time, Performance, & Speed Metrics
Average Time for End-to-End Process Execution
Definition: The time taken from initiation to completion of a process.
Business Impact: Identifies efficiency gains from faster process execution.
Quantification: Avg. Process Time = Total process time / Number of processes
Data Sources: System logs, workflow execution data.
End-User Request Fulfillment Time
Definition: The average time it takes to complete end-user requests (e.g., provisioning, configuration) using orchestration.
Business Value: Reduces friction for internal and external customers, improves satisfaction, and accelerates revenue realization.
Quantification: Measure the time between when a request is received and when it is fulfilled, before and after implementing automation.
Customer Activation Time
Definition: The time taken from a customer’s request or subscription to the point at which their network service is fully activated.
Business Impact: Faster activation leads to improved customer satisfaction and quicker revenue realization, as well as reduced operational overhead.
Quantification: Activation Time equals Time from customer request to service activation.
Data Sources: CRM systems, service activation logs, customer request systems.
Time to First Response vs. Time to Completion
Definition: Measures the time it takes to initiate and complete a workflow after receiving a request.
Business Impact: Provides insights into responsiveness and process duration.
Quantification: Track the time from request to first response and total time to completion.
Data Sources: Workflow execution logs.
Mean Time to Recovery (MTTR)
Definition: The average time to recover normal operations after a failure.
Business Impact: Reducing MTTR minimizes downtime and improves service continuity.
Quantification: MTTR = Total recovery time / Number of incidents
Data Sources: Incident management systems, system logs.
Variation in Execution Time
Definition: Measures the inconsistency in the time it takes to complete a particular automated workflow or process. This metric tracks the variability between the fastest and slowest executions, indicating how stable and reliable the automation process is over time.
Business Impact: A lower variation in execution time ensures predictability and consistency in service delivery, which improves operational efficiency and enhances customer satisfaction. High variation may indicate bottlenecks, inefficiencies, or issues in the automated process that need attention.
Quantification: Variation in Execution Time equals the Standard deviation of execution times across multiple workflow instances. Alternatively variation can be expressed as the difference between the maximum and minimum execution times (e.g., Max execution time – Min execution time).
Data Sources: Workflow logs, orchestration platform performance metrics, execution time tracking tools.
Innovation, Revenue Expansion, & Customer Impact Metrics
Orchestration Innovation Index
Definition: Measures how many new services or products are delivered through automation.
Business Impact: Drives innovation and business growth by enabling faster service rollouts.
Quantification: Innovation Index = (New services launched / Total services launched) * 100
Data Sources: Service catalogs, orchestration platform data.
Time to New Service Introduction
Definition: The time taken to introduce a new service using orchestration.
Business Impact: Faster time to market accelerates revenue and competitive advantage.
Quantification: Track time from service conception to customer availability.
Data Sources: Product management systems, service catalogs.
Incremental Revenue
Definition: Revenue generated or accelerated due to orchestration.
Business Impact: Directly ties orchestration to revenue growth.
Quantification: Incremental Revenue = New Revenue – Pre-Automation Revenue
Data Sources: Financial records, sales reports.
Revenue Acceleration Metric
Definition: Measures the reduction in time it takes to generate revenue from a new service, product, or customer due to the implementation of orchestration and automation. This metric tracks how quickly the organization can realize revenue after launching a service or acquiring a new customer, compared to pre-automation.
Business Impact: Accelerating revenue generation helps improve cash flow, shorten the time to profitability, and increase competitive advantage by enabling faster service rollouts or onboarding new customers more efficiently.
Quantification: Revenue Acceleration equals (Time to Revenue pre-orchestration minus Time to Revenue post-orchestration) divided by Time to Revenue pre-orchestration, then multiplied by 100.
Data Sources: Financial records, CRM systems, revenue recognition reports, service deployment logs, customer onboarding systems.
Customer Activation Time
Definition: The time it takes to fully activate a customer after onboarding.
Business Impact: Faster activation increases customer satisfaction and accelerates revenue.
Quantification: Activation Time = Time from customer request to service activation
Data Sources: CRM systems, activation workflows.
Quality Metrics
Error Rate Reduction
Definition: Measures the decrease in the number of errors that occur during network processes or workflows after implementing orchestration and automation. This metric tracks the improvements in accuracy and the reduction of manual errors due to the automation of repetitive and complex tasks.
Business Impact: Reducing error rates improves operational efficiency, minimizes the need for rework, enhances service quality, and decreases the risk of service outages or disruptions. It also leads to cost savings by reducing the resources needed to troubleshoot and fix errors.
Quantification: Error Rate Reduction equals (Error rate pre-orchestration minus Error rate post-orchestration) divided by Error rate pre-orchestration, then multiplied by 100.
Data Sources: Incident management systems, workflow logs, network monitoring systems, service performance reports, troubleshooting logs.
Ticket Resolution Acceleration (Through Orchestration & Reduction in Ticket Rejection Due to Insufficient Detail)
Definition: The reduction in the time taken to resolve support tickets by automating workflows and improving the accuracy and completeness of ticket details.
Business Impact: Faster ticket resolution increases customer satisfaction, reduces operational backlog, and improves overall service levels.
Quantification:
Average Ticket Resolution Time equals Total Time to resolve tickets divided by Number of tickets.
Rejection Reduction equals (Rejected Tickets pre-orchestration minus Rejected Tickets post-orchestration) divided by Rejected Tickets pre-orchestration, then multiplied by 100.
Data Sources: IT service management (ITSM) systems, help desk reports, ticket resolution times, ticket rejection records.
Reduction in Network Downtime/Improvements in Availability
Definition: The reduction in the total amount of network downtime, leading to improved overall network availability due to the implementation of orchestration.
Business Impact: Higher availability improves business continuity, reduces revenue losses from downtime, and increases customer satisfaction.
Quantification:
Downtime Reduction equals (Downtime pre-orchestration minus Downtime post-orchestration) divided by Downtime pre-orchestration, then multiplied by 100.
Availability equals Uptime divided by Total time, then multiplied by 100.
Data Sources: Network monitoring tools, uptime/downtime logs, incident reports
Automation Impact & Coverage Metrics
Process Automation Coverage
Definition: The percentage of processes that are fully automated.
Business Impact: Provides visibility into automation progress and labor savings.
Quantification: Coverage = (Number of automated processes / Total processes) * 100
Data Sources: Workflow documentation, orchestration platform logs.
Automation Resilience
Definition: The ability of the system to recover from failures without human intervention.
Business Impact: Reduces downtime and manual troubleshooting efforts.
Quantification: Resilience = (Automatically recovered incidents / Total incidents) * 100
Data Sources: Incident reports, orchestration system logs.
Orchestration Capacity Utilization
Definition: Measures the usage of available orchestration resources.
Business Impact: Helps optimize resource allocation and identify underutilization.
Quantification: Utilization = (Used orchestration resources / Total available resources) * 100
Data Sources: Resource management dashboards, system logs.
Environmental Impact (Green Orchestration)
Definition: The reduction in energy consumption or carbon footprint through optimized orchestration processes.
Business Value: Reduces operational costs while aligning with sustainability goals.
Quantification: Calculate the difference in energy use (or other environmental factors) between manual and automated network operations.
Using Metrics to Drive Improvement
Each of the metrics listed above provides a foundation for continuous improvement. Once the orchestration system is live, these metrics should be tracked regularly, and their insights should be used to fine-tune processes. Metrics like MTTR and automation resilience offer valuable feedback on the system’s reliability, while business-focused metrics like cost per instance and incremental revenue ensure that the orchestration aligns with strategic business goals.
Reporting Results to Leadership
Aligning Metrics with Executive Priorities
For network orchestration to gain the support of leadership, it is essential to present metrics that resonate with the business’s strategic priorities. Metrics such as cost savings, operational efficiency, and revenue acceleration should be highlighted and tied to business outcomes that matter to executives.
For example, reducing the average time to provision a network service directly impacts time-to-market, which executives often associate with revenue acceleration. Similarly, improving compliance automation reduces regulatory risks, a critical consideration for organizations in regulated industries.
Balanced Scorecard Approach
The Balanced Scorecard framework is a widely accepted tool for communicating results to executive leadership. It integrates financial, operational, customer, and innovation perspectives, providing a holistic view of orchestration’s impact.
- Financial: Highlight metrics like cost per process execution and incremental revenue.
- Operational: Emphasize productivity improvements, MTTR, and automation coverage.
- Customer: Showcase improvements in customer service (e.g., faster provisioning times).
- Innovation: Demonstrate how orchestration has enabled faster service rollouts and supported new initiatives.
Automated Reporting Tools
An orchestration platform can generate real-time dashboards and reports that track key metrics and visualize trends over time. These reports can be automatically shared with leadership, keeping them informed of the orchestration’s business impact. By integrating tools like Itential with business intelligence (BI) platforms, such as Power BI or Tableau, you can create tailored executive-level dashboards that are updated continuously.
Building for Continuous Improvement
Iterating & Scaling Orchestration
Network orchestration is not a one-time implementation; it requires ongoing iteration and scaling. As the needs of the business evolve and as new technologies emerge, orchestration processes should be continuously refined.
- Evaluating New Use Cases: Regularly review additional network processes that could benefit from automation as the organization grows. For example, as more services move to the cloud, orchestration can extend to cloud networking and multi-cloud operations.
- Scaling Across Domains: Once core processes are orchestrated, expand the system to integrate with other areas of the business, such as IT operations, cloud management, and security.
- Tracking and Refining Metrics: Metrics such as coverage, MTTR, and productivity should be continuously monitored, and new metrics can be introduced as the system evolves.
Building a Culture of Automation
The long-term success of network orchestration depends on fostering a culture of automation. This involves:
- Training and Development: Ensure that all relevant teams are continuously trained on automation tools and processes. Building expertise in orchestration within your organization will empower teams to innovate and further improve efficiency.
- Cross-Functional Collaboration: Encourage collaboration between network teams, IT teams, and business units. Cross-functional collaboration can unlock new use cases and align automation initiatives with business objectives.
- Automation Champions: Appoint internal “automation champions” who can drive initiatives forward, troubleshoot challenges, and encourage automation and orchestration adoption across the organization.
Conclusion
The journey from manual network operations to full orchestration and automation is transformative, offering significant benefits in terms of cost efficiency, productivity, scalability, and operational reliability. By carefully aligning orchestration with business objectives, selecting high-value use cases, and continuously measuring and refining processes, organizations can unlock the full potential of automation.
