queensland university of technology...live data historical data process model differences,...
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© 2017 Hitachi Ltd. All rights reserved. Proprietary and confidential.
Prof Marcello La RosaIT & Services, Process Intelligence
Queensland University of
Technology
© 2017 Hitachi Ltd. All rights reserved. Proprietary and confidential.
Professor Marcello La Rosa leads a high-performing research group in
business process management at QUT, with a strong focus on developing
and applying process mining technology to practice. He has successfully
implemented BPM solutions in large and complex organisations such as
insurance companies, banks, airports, hospitals and government agencies.
He leads the development of Apromore - an open source process analytics
platform, and coordinates QUT’s professional training program on BPM.
Marcello has taught BPM to practitioners and students in Australia and
overseas for over ten years.
Based on this experience, he co-authored the first, comprehensive textbook
on BPM, which has influenced the curriculum of over 150 universities in the
world. Using this book, he developed a series of MOOCs on the subject,
which have attracted over 20,000 participants to date.
Prof Marcello La Rosa
IT & Services,
Process Intelligence
Queensland University
of Technology
Marcello La RosaBPM Discipline
Queensland University of Technology
From Data to Process:A Journey of IntelligenceHitachi Social Innovation Forum 2017
From Data to Process:A Journey of Intelligence
Big Data?
Big Data?“Big Data is like teenage sex:
everyone talks about it, nobody really knows about it,everyone thinks everyone else is doing it, so everyone claims they are doing it.
Dan Ariely
The value of Big Data
Understand your business as it is
• Not as you imagine them
Back your hypotheses with evidence
• Not only with intuitions and beliefs
Quantify the impact of improvement options
• Before and after
1. Data collection
2. Local analytics
3. Global analytics
The three phases of Big Data development
=Business process level
The business process level
BreedBack-
groundFeed Process
Distri-bute
Explore Mine MillDistri-bute
Requi-lify
Animal farming
Resource mining
Mine Mill
1. Improved production by managing processes more efficiently
2. Cost reductions due to more efficient deployment of people an equipment resources in “optimized” plans
3. Higher reliability of operational performance and safety due to rigorous, auditable integrated planning
4. Improved compliance and ability to deploy changes in regulation, in turn leading to improved safety
Expected benefits in resource mining
✓/processintelligencealgorithms
live data
historical data
process model
differences,root-causes…
conformancereport
processperformance
A ⇒ B
“actionable”process
knowledge
What type of global analytics?Business process intelligence
15
4,318
14
14
858
13
7,128
26
3,794
32
31
734 28
6,212
9
1,526
941
4,324
258
186
4,360
4,360
Created
4,360
Waiting for Support
12,587
Waiting for Customer
8,681
Resolved
5,023
Closed
4,360
Waiting for Internal
923
Escalation
42
Waiting for Approval
14
Waiting for Triage
31
Pinpointing remarkable deviations
Breathing life into your process models
Where is process intelligence used?
HSPI, Process Mining: A Database of Applications, 2016
Where’s next?
Non-traditional “manual” business processes
How to get there? Hitachi Process Intelligence
BPM Discipline, IS School
Science & Engineering Faculty
Queensland University of Technology
marcellolarosa.com
@mlr80
