0
Reason #114 For Learning Math:
Using Analytics to Improve Service Assurance

Follow Us: #ITSMSummit!
Andrew White

Cloud and Smarter Infrastructure Solution Specialist
IBM Corporation
Mr. White has fifteen years of experienc...
http://weheartit.com/entry/12433848!
Ground rules for this
session…
•  If you can’t tell if I am trying to be funny…
–  
 GO AHEAD AND LAUGH!

•  Feel free to ...
I am here today to share some of what I have learned about
CIO’s turn to innovative technologies to
deliver better outcomes
Big Data Analytics
§  Analyze an enormous variety of inf...
Why is problem solving hard?
Non-transparency (lack of
clarity of the situation)

Polytely (multiple goals)

Complexity (l...
Problem Cycle
Evaluation	
  

Control

Recognition

Validation

Observation

Solution
Follow Us: #ITSMSummit!

Analysis
Predictive Modeling Timeline

Past Behavior
• The observation period
used to feed the
forecasting models

Follow Us: #ITSM...
Predictive models
harness the information
lost in past data so you
can identify discretely
identify situations and
react t...
Analytics 1.0
In the early days, we were just
happy to know if the network
was up or down.

We suffered from event floods
a...
Analytics 2.0
Eventually the technology
allowed us to correlate based
on topology and filter
unnecessary events.

Dashboard...
Evolution of Analytics

Value

What	
  Will	
  
Happen?	
  
Why	
  Did	
  It	
  
Happen?	
  
What	
  
Happened?	
  

How	
...
First…

… we need
to talk a little
bit about
your brain
The Triune Brain
Cognitive Brain
(neocortex)

Mammalian Brain
(limbic system)

Reptilian Brain
(basal ganglia)

Follow Us:...
Our Thought Process
Most primitive, seat of unconscious

Cognition
Stimulus

Perception

Limbic Center

(via the senses)**...
Short Term Memory
Short-term memory is
where the real work of
sense-making takes place

Short-term memory
has a limited
am...
Quantity

Information the brain can consume

Follow Us: #ITSMSummit!

Time
Information is cheap.
Understanding is expensive.
-Karl Fast, Professor of UX Design, Kent State University
From Data to Wisdom
Wisdom

Communication
Repetition

• Accountability
• Foresight
• Synthesis

Intelligence

Context

• D...
Data

Knowledge
yi = α 0 + αi xi + ε i

Information
Follow Us: #ITSMSummit!

y

x
Why Knowledge?
Future

Past
Tangible

Data

Abstract

Information

Knowledge

Intelligence

Knowledge is the point of tran...
All You Need

Love
Models of Reasoning
Theory	
  Development	
  

Theory	
  

Interpreta@on	
  

Hypothesis	
  

Data	
  

Hypothesis	
  Tes@...
Two Types of Decision Making
Programmed Decisions
– 
– 
– 
– 

Routine
Repetitive
Well-Structured
Predetermined Decision
R...
How To Improve Decision Making
•  Programmed Decision
Making
– 
– 
– 
– 

Collect evidence
Identify the problem
Select a s...
Four Sources of Bad
Decisions
• 
• 
• 
• 


Failure to frame the problem correctly
Poor use of evidence
Faulty decision ma...
Common Logical Fallacies
• 
• 
• 
• 
• 
• 
• 
• 
• 

Appeals to Authority – where you rely on an expert source to form the...
The problem is not that
there are no silver bullets…
the problem is that there are
no werewolves.
- Jim Tussing, CTO, Nati...
Global Warming and Inflation
Global warming

Inflation

Follow Us: #ITSMSummit!
Hidden Factors
Hidden
Factor

Smoking

Follow Us: #ITSMSummit!

Lung
Cancer
Follow Us: #ITSMSummit!
Boyd’s Loop
Observe

Orient

Decide

Act

Implicit Guidance & Control
Unfolding
Circumstances

Cultural
Norms

Observation...
Where the Breakdown
Occurs
Systemic Influences!

• System Capability!
• Interface Design!
• Stress & Workload!
• Complexity...
Sometimes We Miss What
is Going On
Say… what’s a mountain
goat doing all the way up
here in these clouds?

Follow Us: #ITS...
Rare Events
“one chance in a million” will undoubtedly occur,
with no less and no more than it’s appropriate
frequency, ho...
The Gaussian Bell Curve
Mean	
  

-1σ

+1σ

-2σ
-3σ

+2σ
67%
95%

Follow Us: #ITSMSummit!

99.5%

+3σ
The trick is not to spend our
time trying to get better at
predicting this world, or
making it more predictable, for
both ...
Normative Decision
Making Model
•  Limited Information Collection
–  7 +/- 2
–  Tendency to acquire manageable rather than...
The Analytics Focus…
In addition to handling monitoring and performance alerts, it
helps drive improved availability.
The ...
Most Common Modeling Tasks
• 
• 
• 
• 
• 
• 
• 

Classification: predicting an item class, “decision tree”
Clustering: findi...
Types of Analytical Algorithms
Algorithm

Description

Decision Tree

Calculating the odds of an outcome

Association Rule...
Questions Answered by Analytics
Business Question
What is the best that can happen?

Optimization

What will happen next?
...
Understanding what is
already known but has
not been shown.
Detection Time

Response Time

Repair Time

Recovery Time

Down Time
Observe

Follow Us: #ITSMSummit!

Orient

Decide

Res...
Anatomy of an Outage
!2!
!

5:45-ish pm: CICS ABENDS
start flooding the console but
not high enough to ticket!

6:00-ish pm...
Why did this happen?!

hKp://www.ithakabound.com/wp-­‐content/uploads/2010/02/DC-­‐Snow-­‐men-­‐pushing-­‐car.jpg	
  
Foll...
The Problem
Why aren’t operations teams preventative today?
§ Too much data to analyze manually
§ Existing analytic tech...
Processing Streams
Real-Time
Event Streams

Situational
Awareness
Engine

Patterns from
Historical Data

Follow Us: #ITSMS...
Complex Event Processing
Event Queries

A
Data Events
Control Event
Other Events

B
C

Event Filter
Time Window

Feedback ...
One Integrated Environment
CMDB
Paging

Presentation Framework

Service Desk

Knowledge

3rd Party Providers

Asset Mgmt

...
Integrate Your Processes
Audit Information and Suspicious Activity

Automated
Discovery

Status Indications

Trend-Related...
Service Provider
Managed Monitoring
System!

Vendor Managed
Monitoring System!

Automated Action!

KM
Entries!

Triage!

A...
Optimized
Performance

Track,	
  Op3mize,	
  and	
  Predict	
  
capacity	
  and	
  performance	
  needs	
  
over	
  3me	
 ...
Let’s keep the
conversation going…
APWhite@us.ibm.com!
Andrew.P.White@Gmail.com!
@SystemsMgmtZen!
SystemsManagementZen.Wor...
Brighttalk   reason 114 for learning math - final
Brighttalk   reason 114 for learning math - final
Brighttalk   reason 114 for learning math - final
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Transcript of "Brighttalk reason 114 for learning math - final"

  1. 1. Reason #114 For Learning Math: Using Analytics to Improve Service Assurance Follow Us: #ITSMSummit!
  2. 2. Andrew White Cloud and Smarter Infrastructure Solution Specialist IBM Corporation Mr. White has fifteen years of experience designing and managing the deployment of Systems Monitoring and Event Management software. Prior to joining IBM, Mr. White held various positions including the leader of the Monitoring and Event Management organization of a Fortune 100 company and developing solutions as a consultant for a wide variety of organizations, including the Mexican Secretaría de Hacienda y Crédito Público, Telmex, Wal-Mart of Mexico, JP Morgan Chase, Nationwide Insurance and the US Navy Facilities and Engineering Command. Follow Us: #ITSMSummit!
  3. 3. http://weheartit.com/entry/12433848!
  4. 4. Ground rules for this session… •  If you can’t tell if I am trying to be funny… –  GO AHEAD AND LAUGH! •  Feel free to text, tweet, yammer, or whatever to share with the rest of the attendees •  If you have a question, no need to wait until the end. Just interrupt me. Seriously… I don’t mind. Follow Us: #ITSMSummit!
  5. 5. I am here today to share some of what I have learned about
  6. 6. CIO’s turn to innovative technologies to deliver better outcomes Big Data Analytics §  Analyze an enormous variety of information sources §  Real-time insights & actions on streaming data Security Intelligence Mobile Enterprise §  Hybrid mobile " app development §  Multi-channel integration §  Device management §  Workloads on the move Follow Us: #ITSMSummit! Cloud & Optimized Workloads §  Agile provisioning §  Elastic compute power §  Scalable storage resources §  Intelligent services §  People & identity §  Data & information §  Application security §  Security analytics IBM  CIO  Study  (2012)    
  7. 7. Why is problem solving hard? Non-transparency (lack of clarity of the situation) Polytely (multiple goals) Complexity (large numbers of items, interrelations, and decisions) Dynamics (time considerations) Follow Us: #ITSMSummit! •  commencement opacity •  continuation opacity •  inexpressiveness •  opposition •  transience •  enumerability •  connectivity (hierarchy relation, communication relation, allocation relation) •  heterogeneity •  temporal constraints •  temporal sensitivity •  phase effects •  dynamic unpredictability
  8. 8. Problem Cycle Evaluation   Control Recognition Validation Observation Solution Follow Us: #ITSMSummit! Analysis
  9. 9. Predictive Modeling Timeline Past Behavior • The observation period used to feed the forecasting models Follow Us: #ITSMSummit! Point of Observation Future Behavior • The performance period the model is trying to predict
  10. 10. Predictive models harness the information lost in past data so you can identify discretely identify situations and react to them quickly.
  11. 11. Analytics 1.0 In the early days, we were just happy to know if the network was up or down. We suffered from event floods and the perpetually red event console. Follow Us: #ITSMSummit!
  12. 12. Analytics 2.0 Eventually the technology allowed us to correlate based on topology and filter unnecessary events. Dashboards were all the rage and were measured in data per square inch. Follow Us: #ITSMSummit!
  13. 13. Evolution of Analytics Value What  Will   Happen?   Why  Did  It   Happen?   What   Happened?   How  Do   We  Make  It   Happen?   Prescriptive Analytics Predictive Analytics Diagnostic Analytics Descriptive Analytics Follow Us: #ITSMSummit! Difficulty Adapted from Gartner
  14. 14. First… … we need to talk a little bit about your brain
  15. 15. The Triune Brain Cognitive Brain (neocortex) Mammalian Brain (limbic system) Reptilian Brain (basal ganglia) Follow Us: #ITSMSummit!
  16. 16. Our Thought Process Most primitive, seat of unconscious Cognition Stimulus Perception Limbic Center (via the senses)*** (hypocampus and amygdala) Conscious Choice Pre-Frontal Cortex (via motor centers) Conscious, meaning, choice Long-term memory Follow Us: #ITSMSummit! (hypocampus and amygdala) Cortex (hypocampus and amygdala) *** not very reliable
  17. 17. Short Term Memory Short-term memory is where the real work of sense-making takes place Short-term memory has a limited amount of space (The estimate is 7 ± 2) Follow Us: #ITSMSummit! Working Memory Understanding Judgement Relationship Your Brain
  18. 18. Quantity Information the brain can consume Follow Us: #ITSMSummit! Time
  19. 19. Information is cheap. Understanding is expensive. -Karl Fast, Professor of UX Design, Kent State University
  20. 20. From Data to Wisdom Wisdom Communication Repetition • Accountability • Foresight • Synthesis Intelligence Context • Decisions • Skill • Adaptation Knowledge • Trends • Generalizations • Beliefs Information • Patterns • Comparisons • Organization • Symbols • Metrics • Facts Data Follow Us: #ITSMSummit! Application Analysis Correlation Complexity Understanding
  21. 21. Data Knowledge yi = α 0 + αi xi + ε i Information Follow Us: #ITSMSummit! y x
  22. 22. Why Knowledge? Future Past Tangible Data Abstract Information Knowledge Intelligence Knowledge is the point of transition Follow Us: #ITSMSummit! Wisdom
  23. 23. All You Need Love
  24. 24. Models of Reasoning Theory  Development   Theory   Interpreta@on   Hypothesis   Data   Hypothesis  Tes@ng   •  Inductive –  Starts with Data Available –  Concludes with Possible Hypotheses –  Bottom Up “Data Driven Approach” Follow Us: #ITSMSummit! •  Deductive –  Starts with Theoretical Framework –  Concludes with Logical Deductions –  Theory Driven Approach
  25. 25. Two Types of Decision Making Programmed Decisions –  –  –  –  Routine Repetitive Well-Structured Predetermined Decision Rules Follow Us: #ITSMSummit! Non-Programmed Decisions –  –  –  –  Unique Presence of Risk Presence of Uncertainty Black Swans
  26. 26. How To Improve Decision Making •  Programmed Decision Making –  –  –  –  Collect evidence Identify the problem Select a solution Implement and evaluate the outcome Follow Us: #ITSMSummit! •  Non-Programmed Decision Making –  Narrow evidence down to the ideal level –  Apply heuristics to limit the impact of cognitive bias –  Present options to a human for a decision
  27. 27. Four Sources of Bad Decisions •  •  •  •  Failure to frame the problem correctly Poor use of evidence Faulty decision making process No feedback for improvement Follow Us: #ITSMSummit!
  28. 28. Common Logical Fallacies •  •  •  •  •  •  •  •  •  Appeals to Authority – where you rely on an expert source to form the basis of your argument False Inductions – where you infer a causal relationship where none is evident Reification – when you rely on taking a hypothesis or potential theory and present it as a known truth The Slippery Slope – when you base an argument on the thinking that once one action is taken, it will trigger a sequence of events that will result in the direst of consequences The Band Wagon – when you present an argument as true on the basis of its popularity The False Dichotomy – when you provide only two options and force a choice to be made The Straw Man – when you create a false argument and refute it implying that the counter argument is true Observational Selection – when you draw attention to the positive aspects of an idea and ignore the negatives Statistics of Small Numbers – when you take one (or a very small sample) and use it to draw a general conclusion Follow Us: #ITSMSummit!
  29. 29. The problem is not that there are no silver bullets… the problem is that there are no werewolves. - Jim Tussing, CTO, Nationwide Insurance
  30. 30. Global Warming and Inflation Global warming Inflation Follow Us: #ITSMSummit!
  31. 31. Hidden Factors Hidden Factor Smoking Follow Us: #ITSMSummit! Lung Cancer
  32. 32. Follow Us: #ITSMSummit!
  33. 33. Boyd’s Loop Observe Orient Decide Act Implicit Guidance & Control Unfolding Circumstances Cultural Norms Observation Feed Forward Knowledge Life Cycle New Information Outside Information Cognitive Abilities Feed Forward Decision (Hypothesis) Feed Forward Action (Test) Prior Wisdom Feedback Feedback Unfolding Interaction With Environment •  Note how observation shapes orientation, shapes decision, shapes action, and in turn is shaped by the feedback and other phenomena coming into our sensing or observing window. •  Also note how the entire “loop” (not just orientation) is an ongoing many-sided implicit cross-referencing process of projection, empathy, correlation, and rejection. Follow Us: #ITSMSummit! From “The Essence of Winning and Losing,” John R. Boyd, January 1996.
  34. 34. Where the Breakdown Occurs Systemic Influences! • System Capability! • Interface Design! • Stress & Workload! • Complexity! • Automation! Current State! Feedback! Situational Awareness! Perception of Elements in Current Situation! ! Level 1! Observe! Comprehension of Current Situation! ! Level 2! Projection of Future Status! ! ! Level 3! Orient! • Goals & Objectives! • Preconceptions! • Expectations! Decision! Decide! Individual Influences! Adapted from Endsley, M.R. (1995b). Toward a theory of situation awareness Follow Us: #ITSMSummit! in dynamic systems. Human Factors 37(1), 32–64.! Act! Cognitive Processes! Long Term Memory! • Abilities! • Experience! • Training! Performance of Actions! Automaticity!
  35. 35. Sometimes We Miss What is Going On Say… what’s a mountain goat doing all the way up here in these clouds? Follow Us: #ITSMSummit!
  36. 36. Rare Events “one chance in a million” will undoubtedly occur, with no less and no more than it’s appropriate frequency, however surprised we may be that it should occur to us. Sir Ronald A. Fisher Follow Us: #ITSMSummit! ©  Aquire  Inc.  2012  
  37. 37. The Gaussian Bell Curve Mean   -1σ +1σ -2σ -3σ +2σ 67% 95% Follow Us: #ITSMSummit! 99.5% +3σ
  38. 38. The trick is not to spend our time trying to get better at predicting this world, or making it more predictable, for both of these strategies are bound to fail. - Nassim Nicholas Taleb, Author and Philosopher
  39. 39. Normative Decision Making Model •  Limited Information Collection –  7 +/- 2 –  Tendency to acquire manageable rather than optimal amounts of information –  Difficulty identifying all possible options •  Judgmental Heuristics –  Judgmental heuristics - rules of thumb or shortcuts that people use to reduce information processing demands –  Availability heuristic - tendency to base decisions on information readily available in memory –  Representativeness heuristic - tendency to assess the likelihood of an event occurring based on impressions about similar occurrences •  Satisficing –  Choosing a solution that meets a minimum standard of acceptance Follow Us: #ITSMSummit!
  40. 40. The Analytics Focus… In addition to handling monitoring and performance alerts, it helps drive improved availability. The Formula: 1.  Continually collect, categorize, and analyze all events from as many sources as possible 2.  Correlate events and analyze them using previous outages as patterns to identify situations worth investigating 3.  Notify a support team so the situation can be mitigated before becoming an outage 4.  Automate responses that have well established situational fingerprints and proven resolution steps Follow Us: #ITSMSummit!
  41. 41. Most Common Modeling Tasks •  •  •  •  •  •  •  Classification: predicting an item class, “decision tree” Clustering: finding natural groups or clusters in data Association: finding things that occur together Deviation: finding changes or outliers Estimation: predicting values Linkage: finding relationships among actors Mining: extracting information from data Follow Us: #ITSMSummit!
  42. 42. Types of Analytical Algorithms Algorithm Description Decision Tree Calculating the odds of an outcome Association Rules Identifying the relationships between elements Naïve Bayes Clearly showing the differences in a particular variable Sequence Clustering Grouping data based on a sequence of events Time Series Analyze and forecast time-based data Neural Networks Seek to uncover non-intuitive relationships in data Text Mining Analyze unstructured text data looking for context and meaning Linear Regression Determine the relationship between columns to predict an outcome Logistic Regression Evaluate the relationship between columns in order to evaluate the probability that a column will contain a specific state Follow Us: #ITSMSummit!
  43. 43. Questions Answered by Analytics Business Question What is the best that can happen? Optimization What will happen next? Predictive What if this trend continues? Predictive/Forecasting Why is this happening? Variance analysis/Root Cause Is some action needed? Alerts Where is the problem? Query/Drill Down How many, how often, when? Value Method Ad hoc reports What happened? Standard reports Follow Us: #ITSMSummit!
  44. 44. Understanding what is already known but has not been shown.
  45. 45. Detection Time Response Time Repair Time Recovery Time Down Time Observe Follow Us: #ITSMSummit! Orient Decide Restore Recover Repair Diagnosis Outage Detection Incident Life Cycle Act
  46. 46. Anatomy of an Outage !2! ! 5:45-ish pm: CICS ABENDS start flooding the console but not high enough to ticket! 6:00-ish pm: MQ flows start are interrupted and are alerting in Flow Diagnostics! !1! ! Database! WAS! Load Balancer! zOS! CICS! Firewall! 6:04pm: Synthetic transactions fail at and 6:14 the Ops Center confirms the issue Follow Us: #ITSMSummit! and creates a P0 Incident! Message! Queue! WAS! Database! 6:54pm: Support teams investigate the interrupted flows and determine it is a “back-end” problem! ! ! ! ! ! ! ! ! ! ! 3! Web! Servers! 4! ! ! ! ! Corporate! LANs & VPNs! DB2! 5! zOS! MQ! 10:29pm: Support teams investigate MQ and ultimately and rule it out and ultimately decide to reset CICS to resolve the issue!
  47. 47. Why did this happen?! hKp://www.ithakabound.com/wp-­‐content/uploads/2010/02/DC-­‐Snow-­‐men-­‐pushing-­‐car.jpg   Follow Us: #ITSMSummit!
  48. 48. The Problem Why aren’t operations teams preventative today? § Too much data to analyze manually § Existing analytic techniques, such as standard thresholds, are not up to the task § They cannot detect problems while they are emerging (before business impact) § Set threshold too high, insufficient warning before total failure. § Set threshold too low, too much noise, everything is ignored If no there is no ‘early detection’ before the outage, operations teams can only react while outage is already in effect and already losing money... Follow Us: #ITSMSummit!
  49. 49. Processing Streams Real-Time Event Streams Situational Awareness Engine Patterns from Historical Data Follow Us: #ITSMSummit! Detected and Predicted Situations Causal Relationship from Past RCAs Adapted from http://www.slideshare.net/TimBassCEP/getting-started-in-cephow-to-build-an-event-processing-application-presentation-717795
  50. 50. Complex Event Processing Event Queries A Data Events Control Event Other Events B C Event Filter Time Window Feedback Loop Event Pipeline Follow Us: #ITSMSummit! Event Intelligence Scenarios Action Events
  51. 51. One Integrated Environment CMDB Paging Presentation Framework Service Desk Knowledge 3rd Party Providers Asset Mgmt Enrichment & Correlation Event API Event Pool Predictive Business Telemetry Mainframe Distributed Follow Us: #ITSMSummit! Database Network Middleware Storage Operational! Data Warehouse! Event Catalog
  52. 52. Integrate Your Processes Audit Information and Suspicious Activity Automated Discovery Status Indications Trend-Related Faults Discovered Problems Availability Management Performance Management Asset Management & Topology Database Configuration Management Change Management Topology Snapshots Historical Data Configuration Discrepancies Security Management Incidents Change Activity Aggregation and Analysis “Enriched” Events Enrichment Data Enterprise Data Sources Business Activity Data Business Telemetry Information Business Activity Data Enrichment Data Follow Us: #ITSMSummit! Presentation Framework
  53. 53. Service Provider Managed Monitoring System! Vendor Managed Monitoring System! Automated Action! KM Entries! Triage! Archive and Report! Notification and Escalation! Business Impact Analysis! Root Cause Analysis! Automated Provisioning System! Correlation and Event Suppression! Predictive Analysis! Automated Action Tools! Meta-Data Integration Bus! Distributed Collectors! Automated Change Reconciliation! Enrichment! Element Manager! Service Center and Enterprise Notification Tool! Topology And Relationship Database! Common Event Format! Element Manager! Distributed Collectors! Element Manager! Business Telemetry Data! Distributed Collectors! LOB Managed Monitoring System! Follow Us: #ITSMSummit! Service Center! Security Management! Yammer! CMDB! CVOL! APM! Visualization! Framework! xMatters!
  54. 54. Optimized Performance Track,  Op3mize,  and  Predict   capacity  and  performance  needs   over  3me   Perform •  Track capacity and performance of applications and services in classic and cloud environments • Optimize resource deployment with what-if and best fit planning tools •  Escalate capacity and performance problems before they cause critical failures     Predictive Outage Avoidance Ensure  availability  of   applica3ons  and  services   Predict •  Use learning tools to augment custom best practices   •  Leverage statistical methods to  maximize predictive warning •  Improve problem detection across IT silos Faster Problem Resolution Find  &  correct  problems  faster   with  tools  that  determine  ac3ons   required  to  resolve  issues   Resolve •  Identify problems quicker with insight to large unstructured repositories     •  Isolate problems quicker by bringing relevant unstructured data into problem investigations •  Repair problems quicker with the right details quickly to hand. Automated Analytics helps lower IT Administration Costs: Improved Insight Enhance  visibility  into  systems   resource  rela3onships  while   increasing  customer  sa3sfac3on     Know •  Determine what resources are interdependent to assess impact of failures   •  Gain insight into what is important to your customer   •  Decrease customer churn and acquisition costs while increasing customer retention and satisfaction • Performance and Capacity planning tools monitor appropriately and escalate, reducing time consuming report browsing • Learning tools reduce customization and best practices investment on initial deployment • Log Analysis helps speed problem resolution to be able to do more with less Follow Us: #ITSMSummit!
  55. 55. Let’s keep the conversation going… APWhite@us.ibm.com! Andrew.P.White@Gmail.com! @SystemsMgmtZen! SystemsManagementZen.Wordpress.com! systemsmanagementzen.wordpress.com/feed/! ReverendDrew! ReverendDrew! 614-306-3434! Follow Us: #ITSMSummit!
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