SlideShare uma empresa Scribd logo
1 de 29
How can digital technologies
improve organizational
performance?
©2013 L. SCHLENKER
•LHST works with senior managers to
leverage networks, processes and
technology to enhance individual and
corporate performance.
• The client portfolio in the ICT industry
includes Microsoft, Apple, Ernst & Young,
France Telecom, HP, IBM, Oracle and SAP
.
•The work with the IT industry in Europe
has included fifty partner and customer
conferences, a dozen case studies, and
various marketing support activities.
Prof. Lee SCHLENKER,
Professeur ESC Pau- EMLYON
Managing Director, LHST
Web : www.leeschlenker.com
Objectives Information
Systems
The
Problem
Data and
Information
The
Deliverables
3
Focus Improve Knowledge Leverage Measure
Process
Centric
? ? ? ? ?
Social
Networks
? ? ? ? ?
Search ? ? ? ? ?
Mobility ? ? ? ? ?
Objectives Information
Systems
The
Problem
Data and
Information
The
Deliverables
http//:newcastlemba.com
• Course slides
• Recommended reading
• Course deliverables
• Student input
Objectives Information
Systems
The
Problem
Data and
Information
The
Deliverables
 A curation page – 20 percent
 An written case study– 50
percent
 A video presentation– 30 percent
Objectives Information
Systems
The
Problem
Data and
Information
The
Deliverables
What Morgan called « the management of meaning »
©2010 LHST sarl
Intro Perspective MirrorValue Deliverables
• What does enterprise IT mean?
• What are you trying to improve?
• What do you need to learn?
• What does better mean?
• How do you measure success?
Focus Improve Knowledge Leverage Measure
• Economic transformation: The transformation from a manufacturing-
based economy to a services-based economy now underway throughout
the developed world will accelerate.
• One World of Business. Political and economic dynamics are forging a
single global market, a global workforce, global customers, partners, and
suppliers.
• Always On, Always Connected. The challenges of the “always on, always
connected” world will be converting information into insights; managing time
and staying focused on high priority tasks
• The Transparent Organization. The systems that make organizations
more agile also make them more accountable.
• NetGen Meets Baby Boom. Workers who will be delivering the innovations
and productivity growth of tomorrow, this technology not only won’t come as
a surprise, it will be a positive expectation.
• Competing for Talent in a Shrinking Workforce: Because demographics
show an aging, shrinking workforce in most of the developed world over the
next 50 years, maximizing the productivity of the workers that are available
is critical.
Intro Perspective MirrorValue Deliverables
©2013 L. SCHLENKER
• Globalization : the increasingly circulation of
information across borders.
• Technical progression: the transformation of
communication « atoms to bits »
• Economic integration: vertical and horizontal
integration to profit from economies of scale
• Social innovation: human attempts to create new
forms of expression
• Multitasking : individual efforts to use multiple
communication platforms
Henry JenkinsIntro Perspective MirrorValue Deliverables
• The assumption of order
• The assumption of rational
choice
• The assumption of intentional
capacity
• The assumption of identity
©2010 LHST sarl
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
• Study the fundamentals of an
Information System
• Analyze the constraints and possibilities
of « structured » information
• Explore how the potential links between
an IS and innovation
• Analyze the potential value of digital
transformation
Objectives Information
Systems
The
Internet
Data and
Information
The
Problem
The
Challenges
What is the link between data
and action?
• Understanding the implications between
« structured » and « unstructured data »
• Analyzing the difference between the data and
reality
• Understanding how the data fits together
• Exploring the difference between data and
action
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
• From an objective point of view, information
refers to date in context that conveys meaning
to an individual.
• From a subjective point of view, we could
suggest that it’s the individual’s perspective of
the data that implies meaning.
• Given these definitions what meaning do
Wikileaks, Facebook or Whatapp have?
Assane, The Conversation
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
• Structured data refers to data that can be easily represented in textual/numeric
form and stored in a database.
• Structured data is often logically organized around a data model or data object.
• Such models permit companies to compare and aggregate data in databases,
datamarts and data warehouses.
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
• Data is considered « non-structured » if we
can’t predefine its attributes and store it in a
table or data base
• Examples of this kind of data include press
clippings, videoclips, and songs
• In reality, this data isn’t « non-structured » - its
just that its attributes involve « complex »
relationships
http://ean.marie.gouarne.online.fr/bi.html
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
What meaning do we attach to
the data?
Frame
Cloud
Figure (s)
Oracle
Antonello da Messina
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
Results
Actions
Knowledge
Context
Data
Process
Interprets
Decisions
Measures
Obtain
Define
Require
Drive
The ladder of initiatives™
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
A business information system is an organized set of
resources (platforms, applications, procedures, data and
people) that capture the meaning of work
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
To help us understand the motivations, experience and objectives of the
internal and external clients of the organization
 ROI
 Real time data
 ...
Stockholders
 Competition
 “made in” “made by”
 ...
The State
 Peu de barrières d’entrée
 Acquisitions, OPA...
Partners
 Loyalty
 Real costs
 ...
Clients
The Enterprise
 Mobility
 Empowerment
 ...
Employees
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
©2013 L. SCHLENKER
Technicity
Reflection
Imagination
Cooperation
Method
Action
John Holland
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
• Nicolas Carr compares IT to previous tech revolutions such as
railroads and electricity. In what ways is IT different?
• What proof can you offer that information technology in
business no longer provides competitive advantage?
• Does the pervasiveness of IT mean there will be less innovation
now?
• Hasn't competitive advantage come from unique use of the
technology, not just from the technology itself? What examples
can you give?
• Do recent advances in Cloud Computing and Mobile
Applications confirm or contradict Nicolas Carr's claims?
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Digital Transformation
1. Everyone Will Have the Web
2. The Browser Will Be the Operating System
3. Business Will Live in the Cloud
4. Everything Will Be Social
5. Software Will Eat the World
Marc Andreessen
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Work (productivity)
• Harder, better, faster…
• Mechanized productivity
• Knowledge productivity
• Continuous Productivity
Steven Sinofsky
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
• Ordered domain: Known
causes and effects.
• Ordered domain: Knowable
causes and effects.
• Un-ordered domain: Complex
relationships.
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Grading Scale
The marks in this module will be based upon contributions in three areas :
• Curation: 20 possible points based on the quality of each individual
student’s on-line and in-class participation
• Design your School case study: 50 possible points based on the
number and quality of the story.
• Webcast: 30 possible points based on the quality the presentation of
your perspective
• Total points possible: 100
http://www.newcastlemba.com
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Written assignment
• How can a business school become an idea factory?
•
The vision - how can a phygital space promote your theme?
The design - what types of equipment (furniture, supplies, technologies, and
decorations) will support this vision.
The events - which specific events (conferences, workshops, coffee breaks, etc.) will
be held in the Idea Factory to encourage the exploration and appropriation of
your theme?
The guests - which specific skills and competences (deep thinking, deep reading,
visioning, project management....) will the Factory develop?
The results - how do you recommend evaluating the results of your vision (participant
comments, usage, number of ides produced...)?
Total points possible: 50
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables
Can you make a difference?
• Individual assignment
• What have you learned from your case?
• Themes : trends, convergence, fragmentation
• Delivery: video analysis
• Evaluation criteria : personalization, insight,
dissonance
• Length : minimum four minutes
• Exploring digital intermediation
Total points possible: 30
Introduction Information
Systems
The
Problem
Data and
Information
The
Deliverables

Mais conteúdo relacionado

Mais procurados

Emm Introduction 2013
Emm Introduction 2013Emm Introduction 2013
Emm Introduction 2013Lee Schlenker
 
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...Earley Information Science
 
Agile Project Management for Nonprofits
Agile Project Management for NonprofitsAgile Project Management for Nonprofits
Agile Project Management for Nonprofits501 Commons
 
A next generation introduction to data science and its potential to change bu...
A next generation introduction to data science and its potential to change bu...A next generation introduction to data science and its potential to change bu...
A next generation introduction to data science and its potential to change bu...InnoTech
 
IBM Watson Ecosystem roadshow - Chicago 4-2-14
IBM Watson Ecosystem roadshow - Chicago 4-2-14IBM Watson Ecosystem roadshow - Chicago 4-2-14
IBM Watson Ecosystem roadshow - Chicago 4-2-14cheribergeron
 
Enhancing productivity: ICT that supports digital proficiency in the communit...
Enhancing productivity: ICT that supports digital proficiency in the communit...Enhancing productivity: ICT that supports digital proficiency in the communit...
Enhancing productivity: ICT that supports digital proficiency in the communit...Connecting Up
 
Cognitive Era and Introduction to IBM Watson
Cognitive Era and Introduction to IBM WatsonCognitive Era and Introduction to IBM Watson
Cognitive Era and Introduction to IBM WatsonSubhendu Dey
 
Storytelling using Data
Storytelling using DataStorytelling using Data
Storytelling using Data501 Commons
 
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?How COVID-19 is Accelerating Digital Transformation in Health and Social Care?
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?NUS-ISS
 
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...Earley Information Science
 
O'Reilly ebook: Machine Learning at Enterprise Scale | Qubole
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleO'Reilly ebook: Machine Learning at Enterprise Scale | Qubole
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleVasu S
 
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"DATAVERSITY
 
NBS8053 Introduction 2012
NBS8053 Introduction 2012NBS8053 Introduction 2012
NBS8053 Introduction 2012Lee Schlenker
 
Collaborative Data Management: How Crowdsourcing Can Help To Manage Data
Collaborative Data Management: How Crowdsourcing Can Help To Manage DataCollaborative Data Management: How Crowdsourcing Can Help To Manage Data
Collaborative Data Management: How Crowdsourcing Can Help To Manage DataEdward Curry
 
Fraud Management_CAS_Presentation_Oct2016
Fraud Management_CAS_Presentation_Oct2016Fraud Management_CAS_Presentation_Oct2016
Fraud Management_CAS_Presentation_Oct2016Mark Jones
 
What creates real Real Estate value?
What creates real Real Estate value?What creates real Real Estate value?
What creates real Real Estate value?Dan Kamminga
 
Lessons Learned Deploying Cloud Services in Emerging Markets
Lessons Learned Deploying Cloud Services in Emerging MarketsLessons Learned Deploying Cloud Services in Emerging Markets
Lessons Learned Deploying Cloud Services in Emerging MarketsFrancisco "Cocoy" Claravall
 
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...Steven Callahan
 

Mais procurados (20)

Emm Introduction 2013
Emm Introduction 2013Emm Introduction 2013
Emm Introduction 2013
 
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...
Earley Executive Roundtable on Data Analytics - Session 2 - Mining Business I...
 
Agile Project Management for Nonprofits
Agile Project Management for NonprofitsAgile Project Management for Nonprofits
Agile Project Management for Nonprofits
 
A next generation introduction to data science and its potential to change bu...
A next generation introduction to data science and its potential to change bu...A next generation introduction to data science and its potential to change bu...
A next generation introduction to data science and its potential to change bu...
 
IBM Watson Ecosystem roadshow - Chicago 4-2-14
IBM Watson Ecosystem roadshow - Chicago 4-2-14IBM Watson Ecosystem roadshow - Chicago 4-2-14
IBM Watson Ecosystem roadshow - Chicago 4-2-14
 
Enhancing productivity: ICT that supports digital proficiency in the communit...
Enhancing productivity: ICT that supports digital proficiency in the communit...Enhancing productivity: ICT that supports digital proficiency in the communit...
Enhancing productivity: ICT that supports digital proficiency in the communit...
 
Cognitive Era and Introduction to IBM Watson
Cognitive Era and Introduction to IBM WatsonCognitive Era and Introduction to IBM Watson
Cognitive Era and Introduction to IBM Watson
 
Storytelling using Data
Storytelling using DataStorytelling using Data
Storytelling using Data
 
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?How COVID-19 is Accelerating Digital Transformation in Health and Social Care?
How COVID-19 is Accelerating Digital Transformation in Health and Social Care?
 
Keynote Dubai
Keynote DubaiKeynote Dubai
Keynote Dubai
 
Emm introduction
Emm introductionEmm introduction
Emm introduction
 
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...
Earley Executive Roundtable Using Business Analytics to Drive Higher ROI and ...
 
O'Reilly ebook: Machine Learning at Enterprise Scale | Qubole
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleO'Reilly ebook: Machine Learning at Enterprise Scale | Qubole
O'Reilly ebook: Machine Learning at Enterprise Scale | Qubole
 
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"
 
NBS8053 Introduction 2012
NBS8053 Introduction 2012NBS8053 Introduction 2012
NBS8053 Introduction 2012
 
Collaborative Data Management: How Crowdsourcing Can Help To Manage Data
Collaborative Data Management: How Crowdsourcing Can Help To Manage DataCollaborative Data Management: How Crowdsourcing Can Help To Manage Data
Collaborative Data Management: How Crowdsourcing Can Help To Manage Data
 
Fraud Management_CAS_Presentation_Oct2016
Fraud Management_CAS_Presentation_Oct2016Fraud Management_CAS_Presentation_Oct2016
Fraud Management_CAS_Presentation_Oct2016
 
What creates real Real Estate value?
What creates real Real Estate value?What creates real Real Estate value?
What creates real Real Estate value?
 
Lessons Learned Deploying Cloud Services in Emerging Markets
Lessons Learned Deploying Cloud Services in Emerging MarketsLessons Learned Deploying Cloud Services in Emerging Markets
Lessons Learned Deploying Cloud Services in Emerging Markets
 
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...
20140826 I&T Webinar_The Proliferation of Data - Finding Meaning Amidst the N...
 

Semelhante a NBSintro2013

Technologies and Innovation - Introduction
Technologies and Innovation - IntroductionTechnologies and Innovation - Introduction
Technologies and Innovation - IntroductionLee Schlenker
 
Networked Business Initiative partner presentation 2014
Networked Business Initiative partner presentation 2014Networked Business Initiative partner presentation 2014
Networked Business Initiative partner presentation 2014Networked Busniess Initiative
 
Lessons from the front line: Next generation knowledge management using socia...
Lessons from the front line: Next generation knowledge management using socia...Lessons from the front line: Next generation knowledge management using socia...
Lessons from the front line: Next generation knowledge management using socia...Velrada
 
Analytics in Action - the Digital Economy
Analytics in Action - the Digital EconomyAnalytics in Action - the Digital Economy
Analytics in Action - the Digital EconomyLee Schlenker
 
Scaling Training Data for AI Applications
Scaling Training Data for AI ApplicationsScaling Training Data for AI Applications
Scaling Training Data for AI ApplicationsApplause
 
Technology and Innovation - Introduction
Technology and Innovation - IntroductionTechnology and Innovation - Introduction
Technology and Innovation - IntroductionLee Schlenker
 
Keynote: Graphs in Government_Lance Walter, CMO
Keynote:  Graphs in Government_Lance Walter, CMOKeynote:  Graphs in Government_Lance Walter, CMO
Keynote: Graphs in Government_Lance Walter, CMONeo4j
 
Digital transformation
Digital transformationDigital transformation
Digital transformationLee Schlenker
 
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...Data Science Society
 
Matt McIlwain opening keynote
Matt McIlwain opening keynoteMatt McIlwain opening keynote
Matt McIlwain opening keynoteSeattleSIM
 
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ...
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ..."Developments in Accessibility of Information" - Access Israel 's 6th Annual ...
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ...Ricardo Garcia Bahamonde
 

Semelhante a NBSintro2013 (20)

Gem Intro
Gem IntroGem Intro
Gem Intro
 
Introduction
IntroductionIntroduction
Introduction
 
Estrat digital2014
Estrat digital2014Estrat digital2014
Estrat digital2014
 
Quantified digital
Quantified digitalQuantified digital
Quantified digital
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction
IntroductionIntroduction
Introduction
 
Technologies and Innovation - Introduction
Technologies and Innovation - IntroductionTechnologies and Innovation - Introduction
Technologies and Innovation - Introduction
 
Networked Business Initiative partner presentation 2014
Networked Business Initiative partner presentation 2014Networked Business Initiative partner presentation 2014
Networked Business Initiative partner presentation 2014
 
Lessons from the front line: Next generation knowledge management using socia...
Lessons from the front line: Next generation knowledge management using socia...Lessons from the front line: Next generation knowledge management using socia...
Lessons from the front line: Next generation knowledge management using socia...
 
Introduction
IntroductionIntroduction
Introduction
 
Analytics in Action - the Digital Economy
Analytics in Action - the Digital EconomyAnalytics in Action - the Digital Economy
Analytics in Action - the Digital Economy
 
Scaling Training Data for AI Applications
Scaling Training Data for AI ApplicationsScaling Training Data for AI Applications
Scaling Training Data for AI Applications
 
Digital Economics
Digital EconomicsDigital Economics
Digital Economics
 
Technology and Innovation - Introduction
Technology and Innovation - IntroductionTechnology and Innovation - Introduction
Technology and Innovation - Introduction
 
Keynote: Graphs in Government_Lance Walter, CMO
Keynote:  Graphs in Government_Lance Walter, CMOKeynote:  Graphs in Government_Lance Walter, CMO
Keynote: Graphs in Government_Lance Walter, CMO
 
Digital transformation
Digital transformationDigital transformation
Digital transformation
 
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
 
Matt McIlwain opening keynote
Matt McIlwain opening keynoteMatt McIlwain opening keynote
Matt McIlwain opening keynote
 
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ...
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ..."Developments in Accessibility of Information" - Access Israel 's 6th Annual ...
"Developments in Accessibility of Information" - Access Israel 's 6th Annual ...
 
Digital Economics
Digital EconomicsDigital Economics
Digital Economics
 

Mais de Lee Schlenker

Data, Ethics and Healthcare
Data, Ethics and HealthcareData, Ethics and Healthcare
Data, Ethics and HealthcareLee Schlenker
 
AI and Managerial Decision Making
AI and Managerial Decision MakingAI and Managerial Decision Making
AI and Managerial Decision MakingLee Schlenker
 
Les enjeux éthique de l'IA
Les enjeux éthique de l'IALes enjeux éthique de l'IA
Les enjeux éthique de l'IALee Schlenker
 
Technologies and Innovation – Ethics
Technologies and Innovation – EthicsTechnologies and Innovation – Ethics
Technologies and Innovation – EthicsLee Schlenker
 
Technologies and Innovation – Decision Making
Technologies and Innovation – Decision MakingTechnologies and Innovation – Decision Making
Technologies and Innovation – Decision MakingLee Schlenker
 
Technologies and Innovation – The Internet of Value
Technologies and Innovation – The Internet of ValueTechnologies and Innovation – The Internet of Value
Technologies and Innovation – The Internet of ValueLee Schlenker
 
Technologies and Innovation – Digital Economics
Technologies and Innovation – Digital EconomicsTechnologies and Innovation – Digital Economics
Technologies and Innovation – Digital EconomicsLee Schlenker
 
Technologies and Innovation – Innovation
Technologies and Innovation – InnovationTechnologies and Innovation – Innovation
Technologies and Innovation – InnovationLee Schlenker
 
Group 5 - Narayana Health
Group 5 -  Narayana HealthGroup 5 -  Narayana Health
Group 5 - Narayana HealthLee Schlenker
 
Analytics in Action - Introduction
Analytics in Action - IntroductionAnalytics in Action - Introduction
Analytics in Action - IntroductionLee Schlenker
 
Analytics in Action - Storytelling
Analytics in Action - StorytellingAnalytics in Action - Storytelling
Analytics in Action - StorytellingLee Schlenker
 
Analytics in Action - Data Protection
Analytics in Action - Data ProtectionAnalytics in Action - Data Protection
Analytics in Action - Data ProtectionLee Schlenker
 
Analytics in Action - Smart Cities
Analytics in Action - Smart CitiesAnalytics in Action - Smart Cities
Analytics in Action - Smart CitiesLee Schlenker
 
Analytics in Action - Health
Analytics in Action - HealthAnalytics in Action - Health
Analytics in Action - HealthLee Schlenker
 

Mais de Lee Schlenker (20)

Trust by Design
Trust by DesignTrust by Design
Trust by Design
 
Ethics schlenker
Ethics schlenkerEthics schlenker
Ethics schlenker
 
Data, Ethics and Healthcare
Data, Ethics and HealthcareData, Ethics and Healthcare
Data, Ethics and Healthcare
 
AI and Managerial Decision Making
AI and Managerial Decision MakingAI and Managerial Decision Making
AI and Managerial Decision Making
 
Les enjeux éthique de l'IA
Les enjeux éthique de l'IALes enjeux éthique de l'IA
Les enjeux éthique de l'IA
 
Technologies and Innovation – Ethics
Technologies and Innovation – EthicsTechnologies and Innovation – Ethics
Technologies and Innovation – Ethics
 
Technologies and Innovation – Decision Making
Technologies and Innovation – Decision MakingTechnologies and Innovation – Decision Making
Technologies and Innovation – Decision Making
 
Technologies and Innovation – The Internet of Value
Technologies and Innovation – The Internet of ValueTechnologies and Innovation – The Internet of Value
Technologies and Innovation – The Internet of Value
 
Technologies and Innovation – Digital Economics
Technologies and Innovation – Digital EconomicsTechnologies and Innovation – Digital Economics
Technologies and Innovation – Digital Economics
 
Technologies and Innovation – Innovation
Technologies and Innovation – InnovationTechnologies and Innovation – Innovation
Technologies and Innovation – Innovation
 
Group 5 - Narayana Health
Group 5 -  Narayana HealthGroup 5 -  Narayana Health
Group 5 - Narayana Health
 
Group 4 - DHL
Group 4 - DHLGroup 4 - DHL
Group 4 - DHL
 
Group 3 - BBVA
Group  3  -  BBVA Group  3  -  BBVA
Group 3 - BBVA
 
Group 2 - Byju's
Group 2 - Byju'sGroup 2 - Byju's
Group 2 - Byju's
 
Group 1 LinkedIn
Group 1 LinkedInGroup 1 LinkedIn
Group 1 LinkedIn
 
Analytics in Action - Introduction
Analytics in Action - IntroductionAnalytics in Action - Introduction
Analytics in Action - Introduction
 
Analytics in Action - Storytelling
Analytics in Action - StorytellingAnalytics in Action - Storytelling
Analytics in Action - Storytelling
 
Analytics in Action - Data Protection
Analytics in Action - Data ProtectionAnalytics in Action - Data Protection
Analytics in Action - Data Protection
 
Analytics in Action - Smart Cities
Analytics in Action - Smart CitiesAnalytics in Action - Smart Cities
Analytics in Action - Smart Cities
 
Analytics in Action - Health
Analytics in Action - HealthAnalytics in Action - Health
Analytics in Action - Health
 

Último

4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptxmary850239
 
How to Fix XML SyntaxError in Odoo the 17
How to Fix XML SyntaxError in Odoo the 17How to Fix XML SyntaxError in Odoo the 17
How to Fix XML SyntaxError in Odoo the 17Celine George
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxkarenfajardo43
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQuiz Club NITW
 
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...Nguyen Thanh Tu Collection
 
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxlancelewisportillo
 
4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptxmary850239
 
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...DhatriParmar
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Seán Kennedy
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDhatriParmar
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Projectjordimapav
 
Textual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSTextual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSMae Pangan
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxHumphrey A Beña
 
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
Unraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptxUnraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptx
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptxDhatriParmar
 
Multi Domain Alias In the Odoo 17 ERP Module
Multi Domain Alias In the Odoo 17 ERP ModuleMulti Domain Alias In the Odoo 17 ERP Module
Multi Domain Alias In the Odoo 17 ERP ModuleCeline George
 
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnv
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnvESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnv
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnvRicaMaeCastro1
 

Último (20)

prashanth updated resume 2024 for Teaching Profession
prashanth updated resume 2024 for Teaching Professionprashanth updated resume 2024 for Teaching Profession
prashanth updated resume 2024 for Teaching Profession
 
4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx
 
How to Fix XML SyntaxError in Odoo the 17
How to Fix XML SyntaxError in Odoo the 17How to Fix XML SyntaxError in Odoo the 17
How to Fix XML SyntaxError in Odoo the 17
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
 
Paradigm shift in nursing research by RS MEHTA
Paradigm shift in nursing research by RS MEHTAParadigm shift in nursing research by RS MEHTA
Paradigm shift in nursing research by RS MEHTA
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
 
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...
31 ĐỀ THI THỬ VÀO LỚP 10 - TIẾNG ANH - FORM MỚI 2025 - 40 CÂU HỎI - BÙI VĂN V...
 
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptxINCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
 
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
 
4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx
 
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Project
 
Textual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSTextual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHS
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
 
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
Unraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptxUnraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptx
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
 
Multi Domain Alias In the Odoo 17 ERP Module
Multi Domain Alias In the Odoo 17 ERP ModuleMulti Domain Alias In the Odoo 17 ERP Module
Multi Domain Alias In the Odoo 17 ERP Module
 
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnv
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnvESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnv
ESP 4-EDITED.pdfmmcncncncmcmmnmnmncnmncmnnjvnnv
 

NBSintro2013

  • 1. How can digital technologies improve organizational performance?
  • 2. ©2013 L. SCHLENKER •LHST works with senior managers to leverage networks, processes and technology to enhance individual and corporate performance. • The client portfolio in the ICT industry includes Microsoft, Apple, Ernst & Young, France Telecom, HP, IBM, Oracle and SAP . •The work with the IT industry in Europe has included fifty partner and customer conferences, a dozen case studies, and various marketing support activities. Prof. Lee SCHLENKER, Professeur ESC Pau- EMLYON Managing Director, LHST Web : www.leeschlenker.com Objectives Information Systems The Problem Data and Information The Deliverables
  • 3. 3 Focus Improve Knowledge Leverage Measure Process Centric ? ? ? ? ? Social Networks ? ? ? ? ? Search ? ? ? ? ? Mobility ? ? ? ? ? Objectives Information Systems The Problem Data and Information The Deliverables
  • 4. http//:newcastlemba.com • Course slides • Recommended reading • Course deliverables • Student input Objectives Information Systems The Problem Data and Information The Deliverables
  • 5.  A curation page – 20 percent  An written case study– 50 percent  A video presentation– 30 percent Objectives Information Systems The Problem Data and Information The Deliverables
  • 6. What Morgan called « the management of meaning » ©2010 LHST sarl Intro Perspective MirrorValue Deliverables • What does enterprise IT mean? • What are you trying to improve? • What do you need to learn? • What does better mean? • How do you measure success? Focus Improve Knowledge Leverage Measure
  • 7. • Economic transformation: The transformation from a manufacturing- based economy to a services-based economy now underway throughout the developed world will accelerate. • One World of Business. Political and economic dynamics are forging a single global market, a global workforce, global customers, partners, and suppliers. • Always On, Always Connected. The challenges of the “always on, always connected” world will be converting information into insights; managing time and staying focused on high priority tasks • The Transparent Organization. The systems that make organizations more agile also make them more accountable. • NetGen Meets Baby Boom. Workers who will be delivering the innovations and productivity growth of tomorrow, this technology not only won’t come as a surprise, it will be a positive expectation. • Competing for Talent in a Shrinking Workforce: Because demographics show an aging, shrinking workforce in most of the developed world over the next 50 years, maximizing the productivity of the workers that are available is critical. Intro Perspective MirrorValue Deliverables
  • 8. ©2013 L. SCHLENKER • Globalization : the increasingly circulation of information across borders. • Technical progression: the transformation of communication « atoms to bits » • Economic integration: vertical and horizontal integration to profit from economies of scale • Social innovation: human attempts to create new forms of expression • Multitasking : individual efforts to use multiple communication platforms Henry JenkinsIntro Perspective MirrorValue Deliverables
  • 9. • The assumption of order • The assumption of rational choice • The assumption of intentional capacity • The assumption of identity ©2010 LHST sarl Introduction Information Systems The Problem Data and Information The Deliverables
  • 10. • Study the fundamentals of an Information System • Analyze the constraints and possibilities of « structured » information • Explore how the potential links between an IS and innovation • Analyze the potential value of digital transformation Objectives Information Systems The Internet Data and Information The Problem The Challenges
  • 11. What is the link between data and action? • Understanding the implications between « structured » and « unstructured data » • Analyzing the difference between the data and reality • Understanding how the data fits together • Exploring the difference between data and action Introduction Information Systems The Problem Data and Information The Deliverables
  • 12. ©2013 L. SCHLENKER • From an objective point of view, information refers to date in context that conveys meaning to an individual. • From a subjective point of view, we could suggest that it’s the individual’s perspective of the data that implies meaning. • Given these definitions what meaning do Wikileaks, Facebook or Whatapp have? Assane, The Conversation Introduction Information Systems The Problem Data and Information The Deliverables
  • 13. ©2013 L. SCHLENKER • Structured data refers to data that can be easily represented in textual/numeric form and stored in a database. • Structured data is often logically organized around a data model or data object. • Such models permit companies to compare and aggregate data in databases, datamarts and data warehouses. Introduction Information Systems The Problem Data and Information The Deliverables
  • 14. ©2013 L. SCHLENKER • Data is considered « non-structured » if we can’t predefine its attributes and store it in a table or data base • Examples of this kind of data include press clippings, videoclips, and songs • In reality, this data isn’t « non-structured » - its just that its attributes involve « complex » relationships http://ean.marie.gouarne.online.fr/bi.html Introduction Information Systems The Problem Data and Information The Deliverables
  • 15. ©2013 L. SCHLENKER What meaning do we attach to the data? Frame Cloud Figure (s) Oracle Antonello da Messina Introduction Information Systems The Problem Data and Information The Deliverables
  • 16. ©2013 L. SCHLENKER Results Actions Knowledge Context Data Process Interprets Decisions Measures Obtain Define Require Drive The ladder of initiatives™ Introduction Information Systems The Problem Data and Information The Deliverables
  • 17. ©2013 L. SCHLENKER A business information system is an organized set of resources (platforms, applications, procedures, data and people) that capture the meaning of work Introduction Information Systems The Problem Data and Information The Deliverables
  • 18. ©2013 L. SCHLENKER To help us understand the motivations, experience and objectives of the internal and external clients of the organization  ROI  Real time data  ... Stockholders  Competition  “made in” “made by”  ... The State  Peu de barrières d’entrée  Acquisitions, OPA... Partners  Loyalty  Real costs  ... Clients The Enterprise  Mobility  Empowerment  ... Employees Introduction Information Systems The Problem Data and Information The Deliverables
  • 19. ©2013 L. SCHLENKER Introduction Information Systems The Problem Data and Information The Deliverables
  • 20. ©2013 L. SCHLENKER Introduction Information Systems The Problem Data and Information The Deliverables
  • 22. ©2013 L. SCHLENKER Technicity Reflection Imagination Cooperation Method Action John Holland Introduction Information Systems The Problem Data and Information The Deliverables
  • 23. • Nicolas Carr compares IT to previous tech revolutions such as railroads and electricity. In what ways is IT different? • What proof can you offer that information technology in business no longer provides competitive advantage? • Does the pervasiveness of IT mean there will be less innovation now? • Hasn't competitive advantage come from unique use of the technology, not just from the technology itself? What examples can you give? • Do recent advances in Cloud Computing and Mobile Applications confirm or contradict Nicolas Carr's claims? Introduction Information Systems The Problem Data and Information The Deliverables
  • 24. Digital Transformation 1. Everyone Will Have the Web 2. The Browser Will Be the Operating System 3. Business Will Live in the Cloud 4. Everything Will Be Social 5. Software Will Eat the World Marc Andreessen Introduction Information Systems The Problem Data and Information The Deliverables
  • 25. Work (productivity) • Harder, better, faster… • Mechanized productivity • Knowledge productivity • Continuous Productivity Steven Sinofsky Introduction Information Systems The Problem Data and Information The Deliverables
  • 26. • Ordered domain: Known causes and effects. • Ordered domain: Knowable causes and effects. • Un-ordered domain: Complex relationships. Introduction Information Systems The Problem Data and Information The Deliverables
  • 27. Grading Scale The marks in this module will be based upon contributions in three areas : • Curation: 20 possible points based on the quality of each individual student’s on-line and in-class participation • Design your School case study: 50 possible points based on the number and quality of the story. • Webcast: 30 possible points based on the quality the presentation of your perspective • Total points possible: 100 http://www.newcastlemba.com Introduction Information Systems The Problem Data and Information The Deliverables
  • 28. Written assignment • How can a business school become an idea factory? • The vision - how can a phygital space promote your theme? The design - what types of equipment (furniture, supplies, technologies, and decorations) will support this vision. The events - which specific events (conferences, workshops, coffee breaks, etc.) will be held in the Idea Factory to encourage the exploration and appropriation of your theme? The guests - which specific skills and competences (deep thinking, deep reading, visioning, project management....) will the Factory develop? The results - how do you recommend evaluating the results of your vision (participant comments, usage, number of ides produced...)? Total points possible: 50 Introduction Information Systems The Problem Data and Information The Deliverables
  • 29. Can you make a difference? • Individual assignment • What have you learned from your case? • Themes : trends, convergence, fragmentation • Delivery: video analysis • Evaluation criteria : personalization, insight, dissonance • Length : minimum four minutes • Exploring digital intermediation Total points possible: 30 Introduction Information Systems The Problem Data and Information The Deliverables

Notas do Editor

  1. Zynga, Skype, Instagram, Groupon, Foursquare and Pinterest