TODAY’S SOCIAL MEDIA AND CLOUD COMPUTING IN BUSINESS ENVIRONMENT

MANAGEMENT INFORMATION SYSTEMS 2014 – JARI JUSSILA @JJUSSILA

OUTLINE

• Personal experience on information systems• Theory and models on how to develop and acquire social media

and cloud computing applications from systems perspective• How social media and cloud computing applications are

changing management information systems in today’s business environment

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

3

• 1996 Technical documentation specialist, Healy Chemicals Ltd. (UK)

• 1997 Data base programmer and administrator, Healy Chemicals Ltd. (UK)

• 1998 Computer sales and support, Puhetta Ltd.

• 1998 System administrator, Kuljetusliike K. Vierikko Ltd. & Kuljetus Veli Nikkilä Ltd.

• 2001 Trainee / technical documentation, Säteri Ltd.

• 2003- Entrepreneur / IS consultant, Ins. tst. Jari Jussila

• 2002- Project engineer, HAMK University of Applied Sciences,

2004 e.g. PLC, SCADA, MMS, ERP, BI

• 2007- Researcher, Tampere University of Technology

2009 e.g. Web 2.0, Scrum, Agile and Lean software development

• 2009 CEO / IS & KM & M consultant, Yoso Services Ltd.

2011 e.g. Cloud computing (SaaS and IaaS)

• 2009 Project planner, Technology Centre Innoparke.g. Social media

• 2010- Project manager, Tampere University of Technology

INFORMATION SYSTEMS BACKGROUND

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

4

WHAT IS A MANAGEMENT INFORMATION SYSTEM?

Goal: to inspire double loop learning NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

5

SINGLE-LOOP AND DOUBLE-LOOP LEARNING

Governing variables

Action strategiesResults /

consequences

SINGLE-LOOP LEARNING

DOUBLE-LOOP LEARNING

Ref. Argyris & Schön 1978; Argyris 1990

Assumptions / beliefs

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

6

HOW TO DEVELOP OR ACQUIRE (I.E. MAKE OR BUY) INFORMATION SYSTEMS IN TODAY’S BUSINESS ENVIRONMENT

Ref. Lyytinen 1987

e.g. Waterfall e.g. Agile

e.g. Lean Startup

e.g. Vanguard

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

7

WORK SYSTEM THEORY AND WORK SYSTEM METHOD

Ref. Alter 2013NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

8

ACTIVITY THEORY, ACTIVITY SYSTEMS AND EXPANSIVE LEARNING

Ref. Engeström 2001; Engeström 1987NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

9

INTERACTING ACTIVITY SYSTEMS – EXPANSIVE LEARNING IN INTERACTING ACTIVITY SYSTEMS

Instruments

Rules

Division of labour

Community

SubjectObject -> Outcome

Transformation

Motivation

Subject

Division of labour

Instruments

Community Rules

Ref. Vartiainen, Aramo-Immonen, Jussila et al. 2011

e.g. IaaS provider e.g. customer

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

10

ACTIVITY SYSTEM – CYCLE OF EXPANSIVE LEARNING

1. Questioning

2. Historical analysis & actual-empirical analysis

3. Modeling the new solution

4. New model

5. Implementing the new model

6. Reflecting on the process

7. Consolidating the new practice

WORK SYSTEM LIFE CYCLE

1. Initiation

2. Development

3. Implementation

4. Operation and maintenance

Ref. Vartiainen, Aramo-Immonen, Jussila et al. 2011 NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

11

Command and Control Thinking Systems Thinking

Top-down, hierarchy Perspective Outside-in, system

Functional Design Demand, value and flow

Separated from work Decision-making Integrated with work

Output, targets, standards: related to budget

MeasurementCapability, variation: related to

purpose

ContractualAttitude to customers

What matters?

ContractualAttitude to suppliers

Cooperative

Manage people and budgetsRole of

managementAct on the system

Control Ethos Learning

Reactive, projects Change Adaptive, integral

Extrinsic Motivation IntrinsicRef. Seddon 2011

COMMAND & CONTROL VS SYSTEMS THINKING VIEW

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

12

13

LEAN STARTUP

LITERATURE ON HOW TO CHANGE MANAGEMENT THINKING IN TODAY’S BUSINESS ENVIRONMENT

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

14

EXAMPLES OF SOCIAL MEDIA AND CLOUD COMPUTING ENVIRONMENTS

Public social media and cloud applications

Business oriented public social media and cloud applications

Private business oriented social media and cloud applications

e.g. Google Documents e.g. Yammer e.g. phpBB

e.g. WordPress e.g. ZenDesk e.g. Confluence

e.g. Facebook e.g. SharePoint Online e.g. SharePoint Server

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

15

Ref. Schubert & Jeffery 2012NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

16

• Utility computing• allow easy outsourcing of IT resources infrastructure, by moving the in-house

applications to a dedicated (public) CLOUD provider• Elasticity

• allow the environment to – ideally automatically – assign a dynamic number of resources to a task

• Availability and Reliability• CLOUD systems build up on the internet of service principle to expose the services in

a highly accessible fashion, i.e. with minimal configuration and device requirements (generally through a browser). CLOUDs enhance this aspect further by virtualising the service / resource access, basically allowing access “anywhere, anytime”

• Ease of use• The fact is that CLOUDs can reduce the overhead for managing and administering

resources through automation and outsourcing, and should reduce the overhead for creating highly available and reliable services

PRIMARY CHARACTERISTICS OF CLOUD SYSTEMS

Ref. Schubert & Jeffery 2012NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

17

SOCIAL MEDIA GENRES

Ref. Lietsala & SirkkunenNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

18

More on social media tools and processes:• Lietsala & Sirkkunen

Ref. Lietsala & Sirkkunen 2008NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

19

ADOPTION OF CORPORATE TECHNOLOGIES

Automating transactions Enabling collaboration and participation

Adoption of ERP, CRM, SCM- Users assigned by

management- Users must comply with

rules- Often complex technology

investment

Adoption of Web 2.0 tools- User groups can form

unexpectedly- Users engage in high

degree participation- Technology investment

often lightweight overlay to existing infrastructure

Traditional IT

Web 2.0 tools

Time

20091990s

Productivity

Ref. McKinsey 2009NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

20

21

Ref. Pirttilä 2014NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

21

Changed service systems enabled by corporate technologies

Ref. Pirttilä 2014NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

22

A DATA PERSPECTIVE ON INFORMATION SYSTEMS

WEB

BIG DATA

ERP

CRM

ostotiedotmaksutiedot

segmentointitarjoustiedotasiakaskohtaamisettukikontaktit

Web logs

Offer history A / B testing

Affiliate Networks

Search marketing

Dynamic Pricing

Behavioral targeting

Dynamic Funnels

Sentiment

External Demographics

Pictures & Video

Speech to Text

Business Data Feeds

Sensor Data

Product/Service Logs

SMS/MMS

Social Network

User Generated ContentMobile Web

User Click Stream

Location Data

Ref. Yli-Pietilä & Backman 2013; Valli & Ahlgren 2013NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

23

EXAMPLE OF PREDICTION MARKETS

Ref. Wolfers & Zitzewitz 2004NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

24

• Concern over the taste of a new coffee • Social media was followed• Taste was fine, but price was too high• Price was reduced the same day

EXAMPLE OF NEW APPLICATION IN SALES FUNCTION

Ref. Kaisler et al. 2013 Introduction to Big DataNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

25

• Business challenge• Needed to gain deeper insights into the causes and combinations of circumstances

which led to warranty issues• Needed to increase customer satisfaction through increased product quality and

reduced warranty issues• Solution implemented

• Implemented a data mining capability to gain actionable insights across a wide range of warranty issues

• Feedback of issue findings into product design process for improvements and modified service patterns where these were demonstrated to have contributed to warranty issues

• Demonstrated business value• Reduced warranty cases from 1.1 to 0.85 per vehicle• 5% reduction in warranty cases• Annual savings of €30m approximately

EXAMPLE OF NEW APPLICATION IN SERVICE FUNCTION

Ref. IBM 2013 Improving Operational and Financial Results through Predictive Maintenance

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

26

BIG DATA DRIVEN AGENT-BASED MODEL IN PREDICTION BUSINESS DECISIONS

The average cumulative sales of PS3 and Xbox 360 predicted by the model against the real cumulative sales of the two consoles.

Ref. Huotari et al. 2014 Winter Simulation Conference*NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

27

Huotari, Järvi, Kortelainen et al. 2014

Batch reports

REPORTINGWHAT

happened?

ANALYZINGWHY

did it happen?

Ad Hoc,BI tools

PREDICTINGWHAT WILL

happen?

Predictivemodels

OPERATIONALIZINGWHAT IS

happening now?

Link toOperationalSystems

ACTIVATINGMAKE

it happen

AutomaticLinkages

STRATEGIC INTELLIGENCE

OPERATIONAL INTELLIGENCEFrom reporting to operational analytics

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

28

Yli-Pietilä & Backman 2013

• Using collective intelligence and wisdom of crowds in decision making• e.g. Prediction markets

• Using real-time or near real-time data in decision making• e.g. Starbucks product price optimization• e.g. Telefonica customer support

• Using increased computing power in predicting results / options in management decision making

• Automated decision making

NEW TRENDS IN MANAGEMENT INFORMATION SYSTEMS

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH

29

30

MORE CONTENT ON KNOWLEDGE MANAGEMENT:

Twitter: @Noviresearch ja @tietojohtaminen

NOVI research center’s websitehttps://www.tut.fi/novi/

Slideshare: http://www.slideshare.net/Noviresearch

YouTube: http://youtube.com/noviresearch

NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH