The document discusses complex problem solving and the use of big data analytics. It describes characteristics of complex problems like having multiple goals and interconnected factors. Complex problems are also unpredictable and their causes are only apparent in retrospect. The document advocates combining effective problem solving methods like event mapping and risk analysis with intelligent tools that can identify trends, patterns and correlations in large data sets. This helps prevent issues by predicting and addressing risks prior to any incidents occurring.

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Complex
Problem
Solving
And Big Data
Analytics

2. Who am I ?

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Complex Problem Solving
World Economic Forum – Future Of Jobs

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Perception in the Netherlands
Dutch businesses also recognize the
increasing complexity of problems
77%

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What makes a problem complex?
4 characteristics of a complex problem:
1. Polytelie (there are multiple goals)
2. Interconnectedness (the information points are connected to eachother)
3. Intransparency (the relation between information points is not clear)
4. Dynamics (the situation, information or goals change continually)
Scientific point of view
In a complex problem the relation between cause
and effect is not apparent and can only be deduced
in retrospect
Cynefin point of view

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What makes a problem complex?
 Multi vendor sourcing
 Virtualization
 Black boxes
 Decentralized organization
 Off shore, multi cultural
 Complex technology
 More business dependencies
 Big Data
 Automation
 Workforce ageing
Practical point of view in ICT

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Sometimes a problem seems to be complex ..

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What is complex?
Unclear: you don’t know
 No information, incomplete or incorrect information, or
disagreement about the information
 Wrong approach and expectations
Incomprehensible: you don’t understand it, unclear factors.
 Open system, too many factors, unverifiable information
 Investigation changes the system
Unpredictable: no control, no fixed solution
 Problem, system and context changes
 Cause - effect relation is variable, unpredictable
Complex Problem Solving (CPS)
Orden information /
facts: visualize!
Breakdown into single
issues: subject &
deviation
Apply structured
problem analysis
methods and skills
Apply Risk Analysis
Look for trends, patterns and
correlation, also in symptoms:
use big data and intelligent
tools!
Prioritize hypothesis, apply Risk
Analysis and check one by one

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Approaching Complex Problems
Combining effective problem solving approach with intelligent tools

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Preventing Big Problems with Big Data
Operations data
Planned changes
Big Data Analytics
Machine Learning
Prediction
Prevention
No
Yes
Solving &
Root Cause
Analysisincident?Analysis
Trends, patterns, correlation
Change classification & risk
Logs, performance, capacity
Standard & non-standard changes
Celebrate succes!
Learn from succes!
Overview: how do intelligent tools and the RATIO approach combine?

11. CoThink RATIO approach
11
Reflect – Prevent starting with a vague problem description
Analyse – Prevent jumping to conclusions
Target – Prevent jumping to solutions
Implement – Prevent more issues due to implementation
Observe – Prevent wrong assumptions about results

12. 3 RATIO Methods:
12
Event Map:
visualizing all causal
facts about the
incident or problem,
including effects,
causes, broken
barriers and
contributing
circumstances
Visualize all facts of a problem with Event Map

13. 3 RATIO Methods:
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Problem Analysis:
powerful definition of
what the problem IS
and what it IS NOT,
including verification
against possible
causes
Finding the root cause with Problem Analysis

14. 3 RATIO Methods:
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Risk Analysis:
structured analysis
of risks for all critical
steps in a (change)
plan, defining
preventive and
contingent
measures for each
identified risk
Preventing problems with Risk Analysis:

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How is problem solving changing?

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How is problem solving changing?

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Trends, patterns, correlation
Finding what you’re not looking for ..

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Preventing Big Problems with Big Data
Operations data
Planned changes
Big Data Analytics
Machine Learning
Prediction
Prevention
No
Yes
Solving &
Root Cause
Analysisincident?Analysis
Trends, patterns, correlation
Change classification & risk
Logs, performance, capacity
Standard & non-standard changes
Celebrate succes!
Learn from succes!
Overview: how do intelligent tools and the RATIO approach combine?

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Questions?
For more info, please contact:
Martijn Maas
Complex Problem Solver & senior Consultant at CoThink
martijn.maas@cothink.nl
+31 6 20 60 9663
www.cothink.nl

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Innovative Incident Reduction cycle
Operations data
Planned changes
Big Data Analytics
Machine Learning
No
Yes
incident?
Trends, patterns, correlation
Change classification & risk
Logs, performance, capacity
Standard & non-standard changes
Identify the most severe
or common incidents
Analyze incidents and
identify warning signs*
and/or causing change**
* Experience teaches that there are predictive warning signs preceding over 90% of incidents
** On average > 80% of incidents are caused by a planned action or change
Add areas of most
relevant warning signs to
tooling for automatic
analysis and prediction
Well planned actions to
prevent predicted
incidents. Avoid jumping
to conclusions and
solutions
Let machine learning
identify which actions or
changes form high risks.
Perform effective Risk
Analysis to identify risks
and counter measures
Prediction
Prevention
Solving &
Root Cause
Analysis
Celebrate succes!
Learn from succes!
Analysis