how data and analytics have changed our thinking about organizations umuc analytics summit
TRANSCRIPT
How Data and Analytics Have Changed Our Thinking About Organizations
UMUC Analytics Summit
A Quiet Revolution
• The advent of data and data systems to drive change and achieve goals
• Why has this happened?
• Entry point – the problem of accurate information– F. Hayek’s “Fallacy of Complete Information”– Information Distortion
Information DistortionThe inability to ascertain “ground truth”
What is this Revolution?
• Not about absolute availability of information
• Tidal wave in total amount of data
• What began as incremental change has reached a point of state change
• Change in how we think about these things not simply how we do things
• An Epistemological Revolution
Characteristics of Analytics Systems
• Granularity• Utility• Comprehensiveness• Timeliness• Interconnectedness• Accuracy (Quality of data)
Relative sophistication for advanced data use
• Technology
• Data
• People
• Processes
Highest
Lowest
“Culture”
Relative sophistication for advanced data use
• Technology
• Data
• People
• Processes
Highest
Lowest
“Culture”
Practice and Practicality
• Technology integration into daily activities
• Technology far out-strips our systems for use
• Why change what works?
• Answer: This is how you do this.
Information revolution: 1519
Change is being driven by what people can do now that they could not do before
People, Systems and the Problems they address will dramatically lag technology’s capabilities
Problem 1• How do we reduce the lag between
technological capabilities and ability of people and processes to incorporate those capabilities?
Goals• Integrated human and information systems• Creating self-evident answers to “how to do this?”
Lurking problem 1.5• How do we help people begin to ask the new
questions?
Deduction
1st 2nd 3rd 4th 5th0
1
2
3
4
5
Induction
1st 2nd 3rd 4th 5th0
1
2
3
4
5
Grey areas must be deduced from existing Pattern
Black areas must be induced from within all data
The Move from Deduction and Induction
The Role of Analytics
• “Analytics” (or something looking much like it)
• Discerning signal from noise• Aid to thinking about problems
• Not about answering pre-planned questions
Problem 2
• How do we think rigorously and inductively to maximize value added by new aids to thinking?
• Look to those who have already thought about the new thinking
Models
How have we learned to integrate vast new data into our thinking
• Evidence-Based Medicine
• Supply Chain Management
PatientExpectations
ExternalEvidence
ClinicalExpertise
EBM
“The EBM Triad”
Source: Sackett et al. 1996
Evidence-based Medicine
Source: Koutsoukis et al. 2000
Supply Chain Management
MSDEK-12
DLLRWorkforce
MHECPost-Secondary
LEAsCommun
ityColleges
Universities
Work-places
“Data Warehouse”
MLDS Governing Board
LEAS and Post-secondary Institutions
provide linking identification
information through transcripts
Agencies conduct current and planned data collections and upload data set to Data Warehouse
Data Repository de-identifies data ,
warehouses data , provides for cross-
agency reporting and oversees daily use
MLDS Governing Board oversees
compliance, coordination and research agenda
Reporting and Research
A CENTRAL REPOSITORY FOR STUDENT AND WORKFORCE DATA
Problem 2
• How do we think rigorously and inductively to maximize value added by new aids to thinking?
• Understanding what is and is not being captured in even vast data webs
• Focused use, aids to thinking of certain problems or classes of problems
Problem 3
• How do we use these tools and systems to make good decisions?
• Information symmetry and cooperative games• The problem of power asymmetry– Competitive Games– Micromanagers
Traditional Data Integration into Decision Making
Executive Leadership
“Experts”
Data environment
LeadershipFrame
Theory /AnalysisFrame
Operational DecisionsOrganization Policies,Plans,Programs,Regulation,Legislation
Outside information
Emerging Data Integration into Decision Making
“Experts”
“Outside” Information
Data environment
LeadershipFrame
Operational DecisionsOrganization Policies,Plans,Programs,Regulation,Legislation
Technology-AssistedAnalysisFrame
Executive Leadership
Problem 3
• How do we use these tools and systems to make good decisions?
• Understanding when not to act
• Common goals shared across the organizations
• Opportunities to consider institutional change
Our Problems
• How do we reduce the lag between technological capabilities and ability of people and processes to incorporate those capabilities?
• How do we think rigorously and inductively to maximize value added by new aids to thinking?
• How do we use these tools and systems to make good decisions?
Practical Takeaways
1. Interoperations teams2. Data training for non-technical staff– Integrating their functional activities with analysis– Data is not opinion but it is imperfect and mediated
3. Understanding limitations and power of Information systems
4. Focus on your problem(s)5. Train leaders to lead in the new environment6. Create transparent and consensus-oriented outcomes
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