4 Strategic Mistakes to Avoid When Defining Service Level Management Processes

Strategic errors made when defining service levels can have a detrimental, cascading effect on operational performance – leading to additional costs and service delays. Affected areas of managed services include:

• Vendor efficiency and effectiveness
• Organization recovery and remediation capabilities
• The achievement of service targets and business outcomes

Such operational-level issues cannot be resolved without identifying and addressing the strategic mistakes that caused them. Here are 4 strategic errors to avoid when defining service levels and instituting Service Level Management (SLM) governance.

Service levels are only as effective as they are fair to service needs. Both under- and over-aggressive requirements can prevent a Service Level Agreement (SLA) from achieving its business objectives:

• Under-aggressive, lax service level terms damage value by reducing vendor incentives to perform. They may also provide room to excuse subpar service performances and avoid trigger actions.
• Over-aggressive, punitive service level requirements discourage vendors from participating at all. Unnecessarily granular terms also introduce complexity that increases the likelihood of errors, confusion, and inefficiency.

Organizations can strike a balance by aligning service levels to business needs as closely as possible and keeping the definition process lasered in on outcomes. Methodology and target metrics need to be fair to both you and the vendor’s tools and capabilities.

It isn’t enough for vendor services to meet target performance levels – they must continue to do so even as business circumstances change. Failing to adjust service levels can disrupt vendor effectiveness and your operational performance. In a worst-case scenario, providers would be free to fulfill outdated contractual obligations without meeting evolved business needs.

Organizations can formulate contingencies to allow service level targets to evolve over time with the following best practices:

• Continuous, collaborative service level definition between organizations and vendors to improve the accuracy of resource and investment estimations
• Regular check-ins to identify negative performance trends early and clarify updated expectations and penalties
• Integrate investment cost projections and timeline adjustment contingencies to vendor requirements as early as the proposal contract development stage

Remember that service providers are not obliged to assess changing situations, adjust solutions, or challenge client thought processes. It is imperative for organizations to introduce contingencies that address a lack of vendor engagement or responsiveness to change.

Granular requirements do not translate to service effectiveness. And volume-based data capture cannot replace a precise understanding of processes, business needs, and capabilities. Telltale signs of over-engineered service levels include:

• Overly complex, minute metrics collecting data unaligned to operational or business needs
• Collecting data that is hard to capture and collate, intangible, or prone to ambiguity
• Focusing on data volume over quality obscures valuable findings, overburdens analytics capabilities, and slows down adjustments
• Needlessly complex, interdependent technical algorithms and formulas that are difficult to maintain and modify
• Labyrinthine governance and monitoring architectures that inhibit easy navigation for end-users, vendors, and dedicated service level teams

Design your service level data and decision-making architectures for reliability, precision, and ease of use. Focus capture efforts on data that clarifies vendor performance via established metrics. Data that does not immediately contribute to service level monitoring and optimization is extraneous and should be ignored.

Using service levels exclusively as monitoring tools loses out on their full value. Besides punitive actions against vendors in breach of service level targets, effective SLM processes enable proactive capabilities to calibrate ongoing services, such as:

• Risk mitigation. Trend analysis can identify and evaluate service issues and emergent risks before they become major inefficiencies.
• Areas for operational improvement. Use monitoring data to assess whether service levels remain relevant to business goals over time. Subsequent evaluations provide data to:
  • Optimize the service level to suit current conditions
  • Present a business case for further investment

The most efficient way to maximize service level effectiveness is to minimize the emergence of operational issues from the start. Consult an expert for guidance on identifying and avoiding strategic mistakes in your organization’s unique service level definition process.