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Towards Proactive Enterprise Intelligence
Prof. Gregoris Mentzas Director, Information Management Unit
National Technical University of Athens
www.imu.ntua.gr
twitter: gmentzas
FInES Workshop, Future Internet Assembly Aalborg, Denmark, 9th May, 2012
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Everything generates data
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Enterprise ‘Big Data’
A 2011 study by MIT found that effective use of data and analytics correlated
with a 5 to 6 percent improvement in productivity, profitability & market value. Erik Brynjolfsson, Lorin M. Hitt, and Heekyung Hellen Kim, “Strength in numbers: How does data-driven decision making
affect firm performance?” Social Science Research Network (SSRN), April 2011.
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Source: McKinsey
Global Institute (2011)
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Proactive enterprise capabilities
Ability for early recognition and exploitation of
opportunities.
ability to act agilely to changes in demand for a product
respond to an emerging customer need
Ability to alter the likelihood that negative
outcomes will occur by intervening prior to their
occurrence
prevent the loss of a customer to a competitor
initiate measures to mitigate costs of interruptions in
processes
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Challenges for Proactive Enterprise Intelligence
Fuzzy Decision-making
Process Agility
Predictive Analytics
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Fuzzy Decision-making
Process Agility
Predictive Analytics
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Challenges for Proactive Enterprise Intelligence
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
From Business Analytics to Predictive Analytics
Next generation analytics will expand beyond measuring and
describing the past to predicting what is likely to happen, and
optimizing what should happen
Need for analytic tools that are self-directed
Unlike services triggered by a person, a device or software application
and implement continuous intelligence
they run nonstop and incorporate mobile, social and collaborative consumer
interactions
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Source: Gartner (2012) “Advanced Analytics: Predictive, Collaborative and Pervasive” Feb. 2012
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Research Challenges for Predictive Analytics
Future Internet Enterprises need to
be able to adopt data-driven processes in order to comprehend and
predict changes in their business environment.
Research challenges include:
Big Data and Text Analytics
Social Network mining
Predictive modelling
Machine learning techniques
Processing of structured and unstructured data
Semantic services based on big and linked data
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
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Bothos, E., D. Apostolou, G. Mentzas (2010) Using Social Media to Predict Future Events with Agent-based
Markets, IEEE Intelligent Systems, November-December, 2010, pp. 50-58
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Fuzzy Decision-making
Process Agility
Predictive Analytics
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Challenges for Proactive Enterprise Intelligence
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
The inherent uncertainty of proactive business
Predicting business issues cannot lead to exact solutions
Uncertainty whether a business situation actually occurred or on
the predicted value of a specific information item, increases the
need for adopting probabilistic approaches
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Augmenting decision-making processes
with fuzzy and linguistic approaches
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Research Challenge III: Fuzzy Decision Making
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Research challenges to augment decision-making:
Uncertain data management
Fuzzy Multiple-Criteria Evaluation
Probabilistic multi-criteria decision making
e.g. Hidden Markov processes, Bayesian Belief Networks etc
Statistical inference and learning approaches
e.g. Markov Decision Processes, Markov Learning Networks
Fuzzy Semantic Web techniques
Linguistic modeling techniques
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Fuzzy linguistic approaches in service selection
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Patiniotakis, I., D. Apostolou, G. Mentzas (2011) Fuzzy UTASTAR: A method for discovering utility functions from fuzzy data, Expert Systems with Applications, Vol. 38, 12, Nov-Dec 2011, 15463-15474
Patiniotakis, I., D. et al (2012) Linguistic Multi-criteria Decision Making for Web service Selection, ICCS working paper, 2012
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Fuzzy Decision-making
Process Agility
Predictive Analytics
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Challenges for Proactive Enterprise Intelligence
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Integrating analytics for agile process flows
Predictive capability as a mode of operation used to rapidly
produce analytical information based on event data from
business processes in order to support decision making
Businesses aim to use analytics for business process innovation and
differentiation
Organizations effective in the integration of predictive analytic
capabilities and fuzzy decision making within business
processes will be seeing lower costs and greater business
impact
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Research Challenges for Process Agility
Process agility represents a set of techniques and technologies
that incorporate BPM, Complex Event Processing, and
Artificial Intelligence
Research challenges include:
Semantics in event-driven processing
Event stream processing in business processes
Events in human-oriented tasks
Event-driven business process management
Context perception and situational awareness
Situation-driven process adaptation
Goal-oriented event-driven process agility
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Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Process agility in logistics
Patiniotiakis, I., et al (2011) A Framework for Situation-Aware Adaptation of Service-Based Applications, 4th
European ServiceWave Conference, Poznan, 26-28 /10/11
Magoutas, B. D. Apostolou, G. Mentzas (2012) An event-driven Framework for Business Awareness
Management, ICCS working paper, February 2012
Logistics managers are tapping early capabilities to get knowledge of changing
conditions and increase their ability to make proactive route adjustments
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
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Process Agility
Fuzzy Decision-making
Predictive Analytics
Challenges for Proactive Enterprise Intelligence
Information Management Unit / ICCS of NTUA www.imu.iccs.gr
Capabilities of Proactive Enterprise Intelligence
Capability to reveal insights and extract -
previously hidden – meaningful patterns from
structured and unstructured ‘big data’ from a
multitude of sources
sensors and actuators embedded in objects
customer transactions
social interactions, GPS trails, etc.
Capability to be proactive, i.e. to be able to
develop predictions and implement
respective actions
recognize possible opportunities or threats
before these actually happen and
trigger appropriate actions
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Contact
Prof. Gregoris Mentzas
Director, Information Management Unit
National Technical University of Athens
W: www.imu.ntua.gr
http://www.imu.ntua.gr
Thank you for your attention!