Who are we? Cloud & Datacenter Management (STB) Server & Tools Business (CDM) Cloud & Datacenter Management Tier 1 & Monitoring Team supporting over 10,000 Servers Call Centers in Redmond & Hyderabad Two Help Desks: Infrastructure Requests/Team (IR) Service Requests/Applications Team (SR) microsoft.com – MSDN - TechNet WindowsUpdate - Windows Intune TIER 1 24x7 Helpdesk TIER 2 & 3 Engineering Team CUBE SITES MONITORED BY CDM

Session Objectives And Takeaways

Who are the Top Performers? What is the cost by Analyst, Shift or Location? Who is driving down (or up) your SLA Commitments? EMPLOYEE PERFORMANCE What is the cost of each System or Server? What is the growth rate? Cost by Business Group? FORECAST & CAPACITY PLANNING Problems to solve Service Level Agreement commitments being met? Area’s for improvement? Problem Management SLA COMPLIANCE Engineering Staffing Level’s Fewer Alerts/Small DB’s Escalations cost time/money COST SAVINGS

CUBE – Metrics Defined INFRASTRUCTURE HELPDESK (IR) (IR) Infrastructure Request: IR Driver: Operations Manager Alerts APPLICATION HELPDESK (SR) (SR) Service Requests: SR Driver: & Phone Calls TOTAL IR/SR FIRST TOUCH RESOLUTION (FTR)

Help Desk Tier 1 Building on the Core Metrics  Add additional SM Queue’s to measure Tier 1  Tier 3 SLA impact?  Add Cost to Tier 2 & 3 shows the financial impact for staffing.

Supported Business Units

Operations Manager: Fine-Tuning (MP-IR Report)

Technical Challenges

What We Built Tables Udf’s Jobs Operations Manager DB Tables DWDataMart Database Views Service Manager DB Tables ETL MPSD_DataMart Database dbo Schema ETL Select From Tables Stage Schema Views Tables Audit Schema

Designed for usability from the start

How it works?

Getting OpsMgr Alert data Setup Auditing Retrieve Server List Stage_Alert Stage_BaseManagedEntiity Stage_ManagedType Stage_ManagementPack Stage_Monitor Stage_Rules

Correlating Alert to incident

What we get in our cube Control the calculations  Split out by Support Tier  Assist in troubleshooting and maintenance Control the grain  Cube grain in Incident or Service request  Provide drill through into incident details TOTAL IR/SR ESCALATED IN SLA (TTE IN/SLA) ESCALATED OUT of SLA (ESC OUT/SLA) FIRST TOUCH RESOLUTION (FTR) Closed In SLA (% FTR In SLA) Closed Out SLA (% FTR Out/SLA) ACTIONABLE (Worked/Billed) NON-ACTIONABLE (Not Worked or Billed) AVG Time To ESC IN SLA (AVG TTE IN/SLA) AVG Time ESC Out of SLA (AVG TTE OUT/SLA) AVG Time To Closed IN SLA (AVG TTC IN/SLA) AVG Time Closed Out SLA (AVG TTC OUT/SLA) DIMENSIONS Incidents Current Tier Queue Current Classification Incident Alert Source Status Product Tier 1 Analyst Escalation State MEASURES Total Time in Minutes AVE TTE Incident Count TTC TTEAlert Count Incident Request

Lessons learned – Building the Cube

Help Desk Improvements Goal 80% CUBE Version 1 CUBE Version 2 WORKED IR’STOTAL IR’S IN - SLACOST

Forecast & Capacity Planning Building on our OM & SM CUBE, we will add Configuration Manager data:  What is the cost of an ‘old’ physical server vs. a new Azure VM?  What does the Monitoring footprint look like, where do we need new management servers? What we look at in the CUBE for Planning:  What hardware (property) is creating the most work in T1? And measure those alerts across all Tiers.  What does the growing data volumes tell us and what is the cost to manage these volumes?  Adding CM data will give us a complete picture of our Hosting & Engineering Cost & Performance.

Building on the Cube - VNext  Service Level Agreements  Employee Performance  Cost Analysis by Employee  Problem Management  Top MP IR Generators  Cost of the OM System  Alerts by OM System  Forecast & Planning  HW Specific Reports  Cost of Physicals vs. VM’s  What HW costs to run  Forecast & Planning

Key takeaways

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