Transcript SITES MONITORED BY CDM (STB) Server & Tools Business (CDM) Cloud & Datacenter Management microsoft.com – MSDN - TechNet WindowsUpdate - Windows Intune Tier 1

SITES MONITORED BY CDM
(STB) Server & Tools Business
(CDM) Cloud & Datacenter Management
microsoft.com – MSDN - TechNet
WindowsUpdate - Windows Intune
Tier 1 & Monitoring Team supporting over 10,000 Servers
CUBE
Call Centers in Redmond & Hyderabad
Two Help Desks:
Infrastructure Requests/Team (IR)
Service Requests/Applications Team (SR)
TIER 1 24x7 Helpdesk
TIER 2 & 3 Engineering Team
Key takeaways:
• Service Level Agreement
commitments being met?
• Area’s for improvement?
• Problem Management
• Engineering Staffing Level’s
• Fewer Alerts/Small DB’s
• Escalations cost time/money
SLA
EMPLOYEE
COMPLIANCE PERFORMANCE
COST
SAVINGS
FORECAST &
CAPACITY
PLANNING
• Who are the Top Performers?
• What is the cost by Analyst,
Shift or Location?
• Who is driving down (or up)
your SLA Commitments?
• What is the cost of each
System or Server?
• What is the growth rate?
• Cost by Business Group?
INFRASTRUCTURE HELPDESK (IR)
APPLICATION HELPDESK (SR)
(IR) Infrastructure Request:
IR Driver: Operations Manager Alerts
(SR) Service Requests:
SR Driver: Email & Phone Calls
TOTAL IR/SR
(TTE IN/SLA)
(AVG TTE IN/SLA)
(AVG TTC IN/SLA)
(Worked/Billed)
(ESC OUT/SLA)
(AVG TTE OUT/SLA)
(AVG TTC OUT/SLA)
(Not Worked or Billed)
FIRST TOUCH RESOLUTION (FTR)
(% FTR In SLA)
(% FTR Out/SLA)
Building on the Core Metrics
 Adding “Assigned To” shows individual
 Add additional SM Queue’s to measure Tier
1Tier 3 SLA impact?
 Volumes worked, In/Out of SLA FTR (First
 Add Cost to Tier 2 & 3 shows the financial
impact for staffing.
metrics
Touch Resolution) & TTE (Time to Escalate)
Supported Business Units
How SM data shows the cost of their team:
Problem Management
 Reports to T2/T3 have significantly reduced total Alert Volumes from SCOM
 SCOM Management Pack clean underway, Engineers have a better sense of their systems
Adding OM MP data makes for easy fine-tuning;
 What MP’s (Management Pack) are noisy, which one’s to
address first
Pulling in the OM data aligned with SM IR’s;
 No more hunting for an MP monitor to adjust.
MPSD_DataMart Database
dbo Schema
DWDataMart Database
Views
Views
Service Manager DB
Tables
Tables
Audit Schema
Tables
Udf’s
Stage Schema
Operations Manager DB
Tables
Tables
Jobs
 Benefited usability of resulting cube
Service Manager
 Eliminates the need to move data
 Assumes SCSM DW is accurate
 Allows for SCSM to change the tables without impacting cube processing
Setup
Auditing
Retrieve
Server
List
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Stage_Alert
Stage_BaseManagedEntiity
Stage_ManagedType
Stage_ManagementPack
Stage_Monitor
Stage_Rules
incidentdim
DIMENSIONS
Incidents
Current Tier Queue
Current Classification
MEASURES
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
Incident Alert
Total Time
in Minutes
AVE TTE
Incident
Count
TTC
TTE
Alert Count
Source
Incident
Request
Status
Product
Tier 1 Analyst
Escalation State
TOTAL IR/SR
ESCALATED IN SLA (TTE IN/SLA)
ESCALATED OUT of SLA (ESC OUT/SLA)
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)
ACTIONABLE (Worked/Billed)
NON-ACTIONABLE (Not Worked or Billed)
FIRST TOUCH RESOLUTION (FTR)
Closed In SLA (% FTR In SLA)
Closed Out SLA (% FTR Out/SLA)
 No dependencies between work item types
 Better allocation of resources
 SR job finishes in under a minute runs every hour and provide near real time data
from CUBE
TOTAL IR’S
IN - SLA
WORKED IR’S
COST
60000
52124
50000
48215
46024
Goal 80%
47774
42004
41087
68%
38042
40000
34024
29870
66%
30000
58%
46%
20000
45%
45%
46%
49%
28705
6307
5740
5194
5786
6771
15425 14862
4567
5479
DEC
JAN
6393
5192
5601
13492
14087
14798
7950
7875
7783
SEPT
OCT
NOV
81%
76%
21957
33%
10000
86%
30316
54%
54%
60%
78%
87%
84%
6601
7042
6300
7406
12123
6972
0
JULY
AUG
SEPT
OCT
NOV
FEB
MAR
APR
CUBE Version 1
MAY
JUN
JULY
AUG
CUBE Version 2
DEC
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.
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Service Level Agreements
Employee Performance
Cost Analysis by Employee
Problem Management
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Top MP IR Generators
Cost of the OM System
Alerts by OM System
Forecast & Planning
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HW Specific Reports
Cost of Physicals vs. VM’s
What HW costs to run
Forecast & Planning