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Organizational Decisions in the Age of Data Science (Decisions …
Organizational Decisions in the Age of Data Science
Decisions
challenging
too few too many information
uncertain future
different interpretation of reality
inconsistent and conflicting goals
Decision Process
Intelligence
What is the problem?
Design
What are the alternatives?
Choice
Which alternative is best?
Implementation
How do we make it happen?
Structure
Unstructured decisions
unique, complex
developed from scratch
Structured decisions
repetetive, routine decisions
standardized
Model-based decision making
vs.
Data-driven decision making
Data Science
Extract useful knowledge from data
Turn data into value
Interdisciplinary term
Strategic Asset
Data Science vs. Business Analytics
Broader term
Data Science = new methods, new data, more scientific
Examples
Demand forecast in retail
Enterprise Architecture Project
New Data Sources
Cyber Physical Systems
Internet of Things
Smart Objects
Real-Time Business Intelligence
Self-Service BI
Facilties allow BI users become more self-reliant
which are less dependant on IT organization
make BI tools easy to use
easy to acces data
DW solution fast to employ and easy to manage
BI results easy to consume and enhance
From traditional BI to Data discovery
Reasons:
constantly changing business needs
IT does not meet user demand
need to be more analytic driven
Traditional BI
IT department
Top-Down, IT-initiatives
Stack centricity
Dashboard
for reporting
provided by IT department
Data discovery
Business
Bottom-Up, user-centric
Ease-of-use and agility
Interactivity visualization
Analysis
User builds and finds his own solutions