Marketing Information Systems:
part 2
Course code: PV250
Dalia Kriksciuniene, PhD
Faculty of Informatics, Lasaris lab.,
ERCIM research program
Autumn, 2012
Timetable
Part 1: Oct.22 Mon 14:00–17:50 C525
Part 2: Oct.23 Tue 8:00–11:50 G101
Part 3: Nov. 05 Mon 14:00–17:50 C525
Part 4: Nov. 05 Tue 8:00–11:50 G101
Part 5: Dec.10 Mon 14:00–17:50 C525
Part 6: Dec.11 Tue 8:00–11:50 G101
Assessment session: 1-2nd week of January
Dalia Kriksciuniene, MKIS 2012, Brno
Syllabus 2
∞ Types and functions of management information
systems
∞ Their usage for the marketing purposes:
operational, analytical, OLAP, expert, executive,
decision-support systems.
∞ Applying ERP, business intelligence, integrated
software for marketing tasks.
∞ Cloud based and open source solutions
∞ Big Data issues
∞ Dashboards (static & dynamics)
∞ ERP, BI demo (MS Axapta Dyn)
Tools &software: Sugar CRM
Lab work training for cloud-based marketing
application Sugar CRM
Dalia Kriksciuniene, MKIS 2012, Brno
Interrelationship of MIS, MkIS and IT concepts
Marketing IS concepts Management IS creation
concepts (O‘Brien, 1990)
IT concepts (O‘Brien,
1990), Zikmund et al 2003)
1 Integration of functional
modules
Management operations
processing
Transactional processing
2 Project and campaign Creating strategic
advantage
ERP (enterprise resource
planning)
CRM
Analytic applications,
EAI (enterprise application
integration), CRM
3 Value chain system
4 Competitive system
5 End-user „ad hoc“
support
6 Support for marketing
management processes
7 Marketing intelligence
system
8 Multidimensional MkIS
Decision making
support (DSS)
Expert systems (ES)
Executive information
systems (EIS)
Business intelligence
systems (BI)
data warehouses, data
mining, OLAP (online
analytical processing)
Dalia Kriksciuniene, MKIS 2012, Brno
Systems
• Definition
• a collection of interrelated parts which
taken together forms a whole such that:
• The collection has some purpose.
• A change in any of the parts leads to
or results from a change in some
other part(s).
• Characteristics
• inputs, outputs, processes, storage
• Control: feedback, feedforward
Dalia Kriksciuniene, MKIS 2012, Brno
Feedback control
Figure 9.1 Feedback control
Dalia Kriksciuniene, MKIS 2012, Brno
Feedforward control
Figure 9.2 Feedforward control
Dalia Kriksciuniene, MKIS 2012, Brno
Systems
• Systems objectives
• objective(s)
• measure of performance
• Inputs and outputs
• one system’s output is another’s input
• Systems environment
• Boundary
• Open and Closed Systems
Dalia Kriksciuniene, MKIS 2012, Brno
Analyzing Data and Information
• Decision support systems (DSS)
• Expert systems (ES)
• Executive information systems (EIS)
• Group decision support (GDS)
• Transaction processing systems (TPS)
• Document management systems
• Digital dashboards
• OnLine analytical processing (OLAP)
• Data warehousing,
• Data miningDalia Kriksciuniene, MKIS 2012, Brno
Analyzing Data and Information
Dalia Kriksciuniene, MKIS 2012, Brno
Simon’s model of Decision Making
Figure 1.3 Stages in making a decision
Dalia Kriksciuniene, MKIS 2012, Brno
Phases of the Decision-Making Process
Dalia Kriksciuniene, MKIS 2012,
Brno
Decision Making:
The Implementation Phase
Dalia Kriksciuniene, MKIS 2012,
Brno
How Decisions Are Supported
• Support for the intelligence phase
• The ability to scan external and internal information
sources for opportunities and problems and to interpret
what the scanning discovers
• Web tools and sources are extremely useful for
environmental scanning
• Web browsers provide useful front ends for a variety of
tools (OLAP, data mining, data warehouses)
• Internal data sources may be accessible via a
corporate intranet
• External sources are many and varied
Dalia Kriksciuniene, MKIS 2012, Brno
How Decisions Are Supported
• Support for the design phase
• The generation of alternatives for complex problems
requires expertise that can be provided only by a human,
brainstorming software, or an ES
Dalia Kriksciuniene, MKIS 2012, Brno
How Decisions Are Supported
• Support for the choice phase
• DSS can support the choice phase through what-if and
goal-seeking analyses
• Different scenarios can be tested for the selected option to
reinforce the final decision
• KMS helps identify similar past experiences
• CRM, ERP, and SCM systems are used to test the
impacts of decisions in establishing their value, leading to
an intelligent choice
• An ES can be used to assess the desirability of certain
solutions and to recommend an appropriate solution
• A GSS can provide support to lead to consensus in a
group
Dalia Kriksciuniene, MKIS 2012, Brno
How Decisions Are Supported
• Support for the implementation phase
• DSS can be used in implementation activities such as
decision communication, explanation, and justification
• DSS benefits are partly due to the vividness and detail of
analyses and reports
Dalia Kriksciuniene, MKIS 2012, Brno
How Decisions Are Supported
• New technology support for decision making
• Mobile commerce (m-commerce)
• Personal devices
• Personal digital assistants [PDAs]
• Cell phones
• Tablet computers
• :aptop computers
Dalia Kriksciuniene, MKIS 2012, Brno
Levels of Decision Making
• Strategic Planning
• Tactical Planning and Control
• Operational Planning and Control
Dalia Kriksciuniene, MKIS 2012, Brno
DSS
Personal
computer
with
access to
databases
and
analytical
methods
MARKETING
MANAGER
Formulates question
Generates response
Acts or formulates new question
Acts or formulates new question
Generates response
Generates response
A DECISION SUPPORT SYSTEM (DSS)
Dalia Kriksciuniene, MKIS 2012,
Brno
Decision Support Systems
• Interactive support
• what if?
• goal seeking
• optimization
• Flexible access to data
• DSS are often fragmented systems
• DSS development and end users
Dalia Kriksciuniene, MKIS 2012, Brno
Types of
Decision Support Systems
• Data retrieval and analysis
• simple entry and enquiry systems
• data analysis systems
• accounting information systems
• Computational support for structured decisions
• Modelling
• spreadsheet models
• probabilistic models
• optimization modeling
Dalia Kriksciuniene, MKIS 2012, Brno
The Major Tools and Techniques
of Managerial Decision Support
• Data management
• Reporting status
tracking
• Visualization
• Business analytics
• Strategy and
performance
management
• Communication and
collaboration
• Knowledge
management
• Intelligent systems
• Enterprise systems
Computerized Tools for Decision Support
Dalia Kriksciuniene, MKIS 2012,
Brno
The Major Tools and Techniques
of Managerial Decision Support
• Tools-Web connection
• All of these tools are available in both web-based
and non web-based formats
• Hybrid (integrated) support systems
A support system that uses several tools and
techniques to assist management in solving
managerial or organizational problems and assess
opportunities and strategies
Dalia Kriksciuniene, MKIS 2012, Brno
Spreadsheets in DSS
• Rows and columns format
• What if analysis?
• Standard and advanced mathematical
functions
• Linked spreadsheets, worksheets
• Report production (e.g. P&L, balance sheet)
Dalia Kriksciuniene, MKIS 2012, Brno
Spreadsheet Design
Figure 7.3 The five main areas of spreadsheet design
(b)
Dalia Kriksciuniene, MKIS 2012, Brno
The Internet and DSS
• Heterogeneous information sources
• structured data (e.g. database table)
• semi-structured data (e.g. HTML web pages)
• unstructured (e.g. word processed document)
• Integrating data from different sources
Dalia Kriksciuniene, MKIS 2012, Brno
Group Decision Support
• Group Decision Support Systems
• decision networks
• decision rooms
• tele/computer conferencing
• Software support
• brainstorming
• voting
• policy formation
Dalia Kriksciuniene, MKIS 2012, Brno
Group Decision Support –shared expertise
Dalia Kriksciuniene, MKIS 2012, Brno
Enterprise knowledge taxonomy
Knowledge
taxonomy can be
extended by
adding “wisdom” .
Wisdom is a new
stage of
knowledge,
created out of
previous
knowledge.
Dalia Kriksciuniene, MKIS 2012,
Brno
Model of Semiotic process
• The participants of the process: sender(s) and receiver
• The problem of receiver: interpretation of the received message.
• The message can be assigned to various levels of knowledge
taxonomy. The receiver needs high level of expertise to bring it to the
level of knowledge and wisdom for making decision
Dalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012,
Brno
KM Processes
KM Mechanisms
KM Infrastructure
KM Technologies
Organization
Culture
Analogies and metaphors
Brainstorming retreats
On-the-job training
Face-to-face meetings
Apprenticeships
Employee rotation
Learning by observation
….
IT
Infrastructure
Common
Knowledge
ExternalizationCombination RoutinesSocialization Exchange DirectionInternalization
Knowledge
Capture
Knowledge
Sharing
Knowledge
Application
Decision support systems
Web-based discussion groups
Repositories of best practices
Artificial intelligence systems
Case-based reasoning
Groupware
Web pages
…
Physical
Environment
Organization
Structure
Knowledge
Discovery
KM Systems
Knowledge
Capture
Systems
Knowledge
Sharing
Systems
Knowledge
Application
Systems
Knowledge
Discovery
Systems
Enterprise Knowledge Management Solutions
Enterprise-Wide Knowledge Management Systems
Dalia Kriksciuniene, MKIS 2012, Brno
Expert Systems
• Knowledge area or domain
• Mimic ‘expert behaviour’
• Interconnected rules
• Reasoning rather than computation
• Development through
• programming language
• expert system shell
Dalia Kriksciuniene, MKIS 2012, Brno
Expert systems
Dalia Kriksciuniene, MKIS 2012, Brno
Capturing Knowledge: Expert
Systems
Knowledge Base: Model of human knowledge
The task: to elicit information and expertise from other
professionals and translate it into set of rules for an
expert system
Rule-based Expert System: Collection in an AI system
represented in the the form of IF-THEN
Capturing knowledge in natural setting (by enterprise
storytelling, observing, application of concept mapping)
AI shell: programming environment
Inference Engine: strategy used to search through the rule
base
Forward Chaining: strategy for searching the rules base
that begins with the information entered by user and
searches the rule base to arrive at a conclusion
Backward Chaining: Strategy for searching the rule base in
an expert system that acts as a problem solverDalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012, Brno
Concept Map about Concept Maps:
Based on Ausubel’s learning psychology theory
Example: A Concept Map Segment from
Nuclear Cardiology Domain
Dalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012, Brno
Example: Knowledge Capture Systems:
CmapTools
• To capture and formalize knowledge resulting in context rich
knowledge representation models to be viewed and shared
through the Internet
• Alleviates navigation problem with concept maps
• Serve as the browsing interface to a domain of knowledge
• Icons below the concept nodes provide access to auxiliary
information
• Linked media resources and concept maps can be located
anywhere on the Internet
• Browser provides a window showing the hierarchical
ordering of maps
Dalia Kriksciuniene, MKIS 2012, Brno
Example: Expert system
(Nuclear Cardiology, application of CmapTools)
Expert knowledge explanation subsystem
Dalia Kriksciuniene, MKIS 2012, Brno
Application for marketing
• How to arrange goods in the shelves
• How to guide the customer in a shop, that
he should visit more shelves
• Where to build a new store (according to
people density, type of district ,
competitors)
• How to create new promotion idea related
to pricing, buying behavior of selected
customer segment
• What values make biggest impact for
introducing new product ?
Dalia Kriksciuniene, MKIS 2012, Brno
Features of Document Management
Systems
• Hardware
• Document input
• Retrieval
• Integration and sharing
• Security
• Versioning
Dalia Kriksciuniene, MKIS 2012, Brno
Benefits of Document Management Systems
• Reduced physical storage space
• Flexible retrieval
• targeted search, speed of retrieval and availability
• Managed availability of documentation distribution
• different entitlements for different users for different
classes of documentation can easily be specified
• Improved security of access
• compliance with data legislation
• Enhanced internal communication and operations
• Ability to implement workflow for the production and
approval of documents
• Share knowledge within the enterprise
Dalia Kriksciuniene, MKIS 2012, Brno
The Major Theories and
Characteristics of Business Intelligence
• online transaction processing
systems (OLTP)
Systems that handle a company’s
routine ongoing business
• online analytic processing
(OLAP)
An information system that enables
the user, while at a PC, to query the
system, conduct an analysis, and so
on. The result is generated in
secondsDalia Kriksciuniene, MKIS 2012, Brno
OLAP
• Involves trend analysis and forecasting
• Uses summarized historical data (from
operational databases)
• Entails complex queries, often building very large
tables
• Is read-intensive
• The decisions it informs are strategic, so
response is time-critical
• The users are managers/analysts
Dalia Kriksciuniene, MKIS 2012, Brno
Data Warehousing : cube and processes
• Cube is a subset of highly interrelated data that
is organized to allow users to combine any
attributes in a cube (e.g., stores, products,
customers, suppliers) with any metrics in the
cube (e.g., sales, profit, units, age) to create
various two-dimensional views, or slices, that
can be displayed on a computer screen
• Processes:
• Pivoting
• Roll-up
• Drill-down, drill-through, drill-across
• Slice and diceDalia Kriksciuniene, MKIS 2012, Brno
Data Warehousing:Process Overview
Dalia Kriksciuniene, MKIS 2012,
Brno
Data Warehousing (Processes)
Pivoting data to provide different perspectives
(a)
Dalia Kriksciuniene, MKIS 2012, Brno
Data Mining
The non-trivial extraction of implicit, previously
unknown, and potentially useful information from
data.’
Uses machine learning, statistical and visualization
techniques to discover and present knowledge in a
form which is easily comprehensible to humans
• Decision tables
• Nearest neighbour classification
• Neural networks
• Rule induction
• K-means clusteringDalia Kriksciuniene, MKIS 2012, Brno
Operation (transaction processing system
Dalia Kriksciuniene, MKIS 2012,
Brno
Transaction processing system: context
diagram (system in the context of its environment)
Dalia Kriksciuniene, MKIS 2012,
Brno
Data Integration and the Extraction,
Transformation, and Load (ETL) Process
Enterprise application integration (EAI) technology for
pushing data from source systems into data warehouse
Enterprise information integration (EII) tool space that
enables real-time data integration from a variety of sources,
such as relational databases, Web services, and
multidimensional databases
Dalia Kriksciuniene, MKIS 2012, Brno
Real-Time Data Warehousing
Dalia Kriksciuniene, MKIS 2012,
Brno
Analytic applications
• Analytic applications (AA) are the packaged software
products that provide value along three dimensions :
• Process support: structuring and automating business
tasks for optimization of business operations and
discovering opportunities
• Separation of function: functioning independently of
organizations core transactional applications, yet
dependent on transactional data and able to send results
back to these applications.
• Time-oriented, integrated data: integrating data from
multiple sources (internal or external to business), able of
time-basis analysis. The integration of heterogeneous data
enables organization to measure its performance against
its own stated goals or industry benchmarks.Dalia Kriksciuniene, MKIS 2012, Brno
The Business Analytics (BA)
Dalia Kriksciuniene, MKIS 2012,
Brno
Business analytics: Gartner
• Business intelligence (BI) platforms enable all types
of users from IT staff to consultants to business users
to build applications
• that help organizations learn about and understand
their business. Gartner defines a BI platform as a
software platform that delivers
• the 14 capabilities listed in the document *. These
capabilities are organized into three categories of
functionality: integration, information delivery and
analysis.
* http://businessintelligence.info/docs/estudios/Magic-
Quadrant-for-Business-Intelligence-Platforms-2012.pdf
Dalia Kriksciuniene, MKIS 2012, Brno
Business analytics: Microstrategy
5 categories of business analytics :
(www.strategy.com)
• Scorecards & Dashboards
• Enterprise Reporting
• OLAP Analysis
• Adv. & Predictive Analysis
• Alerts & Notification
Dalia Kriksciuniene, MKIS 2012, Brno
Reports and Queries
• Reports
• Routine reports
• Ad hoc (or on-demand) reports
• Multilingual support
• Scorecards and dashboards
• Report delivery and alerting
• Report distribution through any touchpoint
• Self-subscription as well as administrator-based
distribution
• Delivery on-demand, on-schedule, or on-event
• Automatic content personalization
Dalia Kriksciuniene, MKIS 2012, Brno
Data Visualization
A graphical, animation, or video presentation of data and
the results of data analysis
• The ability to quickly identify important trends in
corporate and market data can provide competitive
advantage
• Check their magnitude of trends by using predictive
models that provide significant business advantages in
applications that drive content, transactions, or
processes
• Mainstream computing, where it is integrated with
decision support tools and applications
• Intelligent visualization, which includes data
(information) interpretation
Dalia Kriksciuniene, MKIS 2012, Brno
Data Visualization problem: how many
dimensions is it possible to show in 2-dim space?
Dalia Kriksciuniene, MKIS 2012,
Brno
Data Visualization
• Dashboards and scorecards
• Visual analysis
• Financial data visualization
• Interactive visualization
• Dynamic visualization
Dalia Kriksciuniene, MKIS 2012, Brno
Geographic Information Systems (GIS)
An information system that uses spatial data, such as
digitized maps. A GIS is a combination of text, graphics,
icons, and symbols on maps
• As GIS tools become increasingly sophisticated and
affordable, they help more companies and governments plan
service for customer, the best distribution channels and gain
competitive intelligence
• Some firms are deploying GIS on the Internet for internal use
or for use by their customers (locate the closest store
location, find hotels. Also link to mobile devices.
• E.g. ESRI solutions http://www.esri.com/ aim to use maps for market
research, mapping business competitors, resources, customer density,
etc Dalia Kriksciuniene, MKIS 2012, Brno
Real-Time BI, Automated Decision
Support, and Competitive Intelligence
ADS are most suitable for decisions that must be made
frequently and/or rapidly, using information that is
available electronically
Rapidly build rules-based applications and deploy them
into almost any operating environment
Inject predictive analytics into rule-based applications,
provide services to legacy systems
Combine business rules, predictive models, and
optimization strategies flexibly into state-of-the-art
decision-management applications
Ensure learning from decision criteria into strategy
design, execution, and refinement
Dalia Kriksciuniene, MKIS 2012, Brno
Real-Time BI, Automated Decision
Support, and Competitive Intelligence
Tasks:
• Product or service configuration, yield (price) optimization
• Routing or segmentation decisions
• Corporate and regulatory compliance
• Fraud detection
• Dynamic forecasting, operational control
Software companies provide these components to ADS:
• Rule engines
• Mathematical and statistical algorithms
• Industry-specific packages
• Enterprise systems
• Workflow applications
Dalia Kriksciuniene, MKIS 2012, Brno
BA,Web Intelligence and Analytics
• The application of business analytics activities to Webbased
processes, including e-commerce
• Clickstream analysis
The analysis of data that occur in the Web environment.
• Clickstream data
Data that provide a trail of the user’s activities and show
the user’s browsing patterns (e.g., which sites are visited,
which pages, how long)
• Social network data
• Data emanating from natural communication among
people, their personal interrelationships, attitudes to
businesses and products. It is expected to be enormous
wealth for marketing, however requiring new analytical
skils
Dalia Kriksciuniene, MKIS 2012, Brno
The Executive Information System
Dalia Kriksciuniene, MKIS 2012,
Brno
• Executive information and support systems
Provides rapid access to timely and relevant information
aiding in monitoring an organization’s performance
• A firm's EIS usually includes executive workstations
networked to a central. Some executives prefer more
detail, so EIS designers build in flexibility so their systems
fit the preferences of all executives, whatever they are
• One approach is to provide a drill-down capability, giving
executives the ability to bring up a summary display and
then display successively greater levels of detail
Executive systems
Dalia Kriksciuniene, MKIS 2012, Brno
A Framework for
Business Intelligence
Dalia Kriksciuniene, MKIS 2012,
Brno
A Framework for
Business Intelligence (BI)
Dalia Kriksciuniene, MKIS 2012,
Brno
Business intelligence
Dalia Kriksciuniene, MKIS 2012, Brno
MkIS for decision makingMkIS for decision making
Data Data warehouse
Decision making
by OLAP
User reports
Meta data
(data about data)
Decision making
metadata
Dalia Kriksciuniene, MKIS 2012,
Brno
Digital Dashboards
Figure 7.5 An example of a digital dashboard
Dalia Kriksciuniene, MKIS 2012, Brno
Performance dashboard sample
Dalia Kriksciuniene, MKIS 2012, Brno
Performance Dashboards
• What to look for in a dashboard
• Use of visual components (e.g., charts,
performance bars, sparklines, gauges, meters,
stoplights) to highlight, at a glance, the data and
exceptions that require action.
• Transparent to the user, meaning that they require
minimal training and are extremely easy to use
• Combine data from a variety of systems into a
single, summarized, unified view of the business
Dalia Kriksciuniene, MKIS 2012, Brno
Performance Dashboards
• Dashboards versus scorecards
• Three types of performance dashboards:
1. Operational dashboards
2. Tactical dashboards
3. Strategic dashboards
• Dashboard design
• “The fundamental challenge of dashboard design is
to display all the required information on a single
screen, clearly and without distraction, in a manner
that can be assimilated quickly" (Few, 2005)
Dalia Kriksciuniene, MKIS 2012, Brno
Performance Dashboards
• What to look for in a dashboard
• Enable drill-down or drill-through to
underlying data sources or reports
• Present a dynamic, real-world view with
timely data refreshes, enabling the end user
to stay up-to-date with any recent changes in
the business.
• Require little, if any, customized coding to
implement, deploy, and maintain
Dalia Kriksciuniene, MKIS 2012, Brno
Example: Microstrategy dashboards
http://www.microstrategy.com/software/business-
intelligence/dashboards-and-scorecards/
The criteria:
Align the Organization
Polished and personalized
High Scale and Performance
Empower Operational Workers
Replace Dense Reports
Embedded Analytical Workflows
Dalia Kriksciuniene, MKIS 2012, Brno
Performance Dashboards
• Dashboards versus scorecards
• Performance dashboards
Visual display used to monitor operational
performance
• Performance scorecards
Visual display used to chart progress against strategic
and tactical goals and targets
Performance dashboard is a multilayered application
built on a business intelligence and data integration
infrastructure that enables organizations to measure,
monitor, and manage business performance more
effectively (Eckerson)Dalia Kriksciuniene, MKIS 2012, Brno
Balanced scorecard model
The model suggested by Kaplan & Norton (1996).
The results of the Fortune 500 company survey revealed the
“strategy gap” and importance to measure executing strategy:
Dalia Kriksciuniene, MKIS 2012, Brno
The Balanced Scorecard Model: four
perspectives, cause – effect principle
Dalia Kriksciuniene, MKIS 2012, Brno
The Balanced Scorecard Model: four
perspectives, cause – effect principle, several
views for reporting
• Defining vision, mission, strategy
• Decomposing strategy to strategic themes
• Defining objectives for each theme – building “strategy map”
• Designing measures
• Defining target values - building “balanced scorecard”
• Planning strategic initiatives for improving measures (e.g.by
marketing promotion, launching product, implementing IS) or
for measuring them (e.g. survey for evaluation of customer
satisfaction or customer characteristics (e.g. VALS survey)
• Balancing the scorecard – if it sufficient to achieve strategy
• Cascading balanced scorecard for each department, each
workerSource: Adapted with permission from Harvard Business School Press. From The Balanced Scorecard: Translating Strategy into Action, by Kaplan, R. S. and Norton, D. P., Boston,
MA 1996. Copyright ©1996 by the Harvard Business School Publishing Corporation; all rights reserved
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: Application of balanced scorecard for
marketing strategic approach
Dalia Kriksciuniene, MKIS 2012, Brno
Composing balanced scorecard
The initial view of the balanced scorecard is “strategy map”. Its
structure- four perspectives, strategic themes with their objectives
expressed in “bubbles”, linked by causal relationships.
Each enterprise is advised to have not more than 20 -25 strategic objectives
in the 4 perspectives
“strategy
map” is
further
transformed
into
“balanced
scorecard”
by creating
measures for
each
objective
Dalia Kriksciuniene, MKIS 2012,
Brno
How to design measures for the objectives
Each objective can have
several measures
Each measure is derived from
variables found in the transaction
systems, ERP , composed by
formulas or evaluated by
surveys.
Distinct feature- each measure
has several targets for its
evaluation:
Current value
Forecasted value
Intolerable value
Best value existing in the
business branch
Desired ideal value (search
for new ideas)
Value presentation methods:
numeric, textual, color,
graph, etc.
Dalia Kriksciuniene, MKIS 2012, Brno
Components: objectives, measures, targets, initiatives
Dalia Kriksciuniene, MKIS 2012, Brno
Example of strategy map of consumer bank
Dalia Kriksciuniene, MKIS 2012, Brno
Linking strategy map with balanced scorecard
and action plan (initiatives)
Dalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012, Brno
Example of balanced scorecard
BSC: Planning strategy
Source: Planning and Budgeting Web
Seminar with Dr. Robert KaplanDalia Kriksciuniene, MKIS 2012, Brno
BSC: Balancing the strategic plan
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: Strategic initiatives for measurement or
improving the indicators
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: Strategy map
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: measurement process
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: Example of strategy map
Dalia Kriksciuniene, MKIS 2012, Brno
BSC: Example- linking to measures
Dalia Kriksciuniene, MKIS 2012, Brno
Cascading balanced scorecard:
builds common understanding from strategic to
operational level persons
Dalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012, Brno
Building computerized systems for
Balanced Scorecard
• Software solutions are numerous, fully of
only partly following the method.
• Part of them are certified by consulting
company Palladium group established by
creators of method (Kaplan and Norton).
• Requirements and software providers
http://www.thepalladiumgroup.com/about/soft
warecertification/Pages/overview.aspx
• Among certified are MS Dynamics (Axapta),
SAP, Hyperion, Cognos, Peoplesoft
Example: COGNOS software
Dalia Kriksciuniene, MKIS 2012, Brno
Dalia Kriksciuniene, MKIS 2012, Brno
Example: QPR software
Dalia Kriksciuniene, MKIS 2012, Brno
Example: QPR various views of bsc (reporting)
Requirements for computation for
implementing BSC
• Defining variables for measurements and key performance
indicators, selected by managers
• Collecting data in legacy systems, external systems
• Supplementing necessary data by organizing marketing
surveys
• Collecting data from experts, internal communication
observations
• Creating new variables derived from the available data
• Processing data for research of root-cause relationships
getting analytical insights, forecasting, evaluation,
classification, indicating problems and providing alternatives
for solutions.Dalia Kriksciuniene, MKIS 2012, Brno
Cloud services
• Software-as-a-Service (SaaS) layer applications are
hosted by cloud computing providers and are available
to customers over Internet, such as CRM, ERP, project
management systems, document management systems,
office suite programs etc. These solutions are targeted
for business and home users.
• Platform-as-a-Service (PaaS) layer is targeted at
software developers’ needs, it offers both development
environment and tools as a service.
• Infrastructure-as-a-Service (IaaS) layer delivers platform
virtualization environment as a service [5]. Target users
are system administrators, who analyse the needs for
resources and ensure computing powerDalia Kriksciuniene, MKIS 2012, Brno
Cloud allows moving local software to
internet: service instead of product
Dalia Kriksciuniene, MKIS 2012, Brno
Cloud architecture
Dalia Kriksciuniene, MKIS 2012, Brno
Benefits of cloud solutions
Easy to start, as they do not require specific programming or
administration knowledge and could be especially suitable
for small and medium enterprises that lack financial and
human resources for investing to IT infrastructure: installing
and maintaining hardware infrastructure and software
applications
By using “pay as you go” subscription model the enterprise
can avoid costs of starting capital, and the running costs
can be further regulated by subscribing resources and
services that company needs at the time.
Other benefits include scalability, reliability, security, ease of
deployment, and ease of management for customers
Dalia Kriksciuniene, MKIS 2012, Brno
Problem of moving to cloud Vendor
lock-in
• If company
subscribes
some cloud
services, it
could be hard
or impossible to
make backup
of all data and
ensure that
data could be
reused of
moved to other
cloud vendor.
Dalia Kriksciuniene, MKIS 2012,
Brno
Threats and risks of cloud
• Major threats of cloud computing can be summarized to the
following risk categories :
• Policy and organizational risks: lock-in, loss of governance,
compliance challenges, loss of business reputation, and
cloud service termination or failure;
• Technical risks: unavailability of service, resource
exhaustion, intercepting data in transit, data transfer
bottlenecks, and distributed denial of service;
• Legal risks: subpoena and e-discovery, changes of
jurisdiction, data privacy, and licensing.
Dalia Kriksciuniene, MKIS 2012, Brno
Threats and risks of cloud
• Policy and organizational risks can lead to difficulty
of extracting data from the cloud service, and this
is important reason why some companies refuse
start using it . It is recomended that cloud
computing customers should have an alternative
location for services, and the cloud provider would
give proper data backup to ensure continuity even
if the cloud computing provider went broke or
acquired and swallowed up by a larger company
Dalia Kriksciuniene, MKIS 2012, Brno
Scopes of risk: many users, their specific
needs
• Salesforce.com can serve as a case study of managing company data
in cloud. In 2010 it had over 87 200 customers (a world leading CRM
provider indicated) by Gartner. It provides export possibility of all
company data once a week only for subscribers of highest priced
versions - Enterprise and Unlimited Editions, paid for other versions.
The provided backup is flat file format without any object relations.
• SugarCRM:http://www.sugarcrm.com/company-overview
company is headquartered in Cupertino, California with European
headquarters in Munich, Germany and Asia Pacific headquarters in
Sydney, Australia
* News from partner In Czech:
Akce: Integrace a inovace s SugarCRM
Datum: 20.11.2012 / 9:00-14:00
Místo: HUB Praha (Drtinova 10, Praha 5)
http://www.corenet.cz/registrace
Dalia Kriksciuniene, MKIS 2012, Brno
Example of
Sugar CRM
usage
Workload
estimation for
updating
customized
customer files
due to
development of
vendor’s cloud
environment
(Empirical data
from 15 SME
companies in LT)
Customers
Risk of updating
system: data loss
risk due to
administrative
changes
Risk of updating
system: data recovery
and preserving cost
(hours)
File
storage
Resource usage
Integration
with other
apps
Their
business
area
Number
of fiels
in new
modules
Number
of
records
in new
modules
Average
time for
updating
customized
files (hrs)
Average
time for
updating
DB (hrs)
Size of
files
stored
(MB)
Size
of
DB
(MB)
Max
amount
of
records
No.of
linked
objects
(SugarCRM)
1
Wholesale
329 37771 40.5 2.5 5000 53 26033 5
2 Fin 178 48107 42.5 3.5 200 112 105275 5
3
Advertise
72 117392 35 1 500 50 61283 3
4 Retail 96 279692 56 1 250 684 918216 6
5 Retail 144 363599 44.5 0.5 900 167 204016 7
6
Wholesale
39 4380 12.5 0.5 500 15 1802 0
7
Advertise
52 32760 57.5 0.5 100 27 7746 0
8
Advertise
120 782 13.5 0.5 50 28 12252 5
9 Service 56 4320 21 0 20 15 8634 0
10 Retail 26 420 2.5 0 220 9 1890 3
11
Wholesale
0 0 1 0 5 5 567 0
12 Service 20 28 0 0 70 7 989 0
13 Service 8 150 5 0 200 8 290 0
14 Service 68 8480 32.5 3 3000 32 16046 0
15
Wholesale
26 690 0 0 200 5 419 0
Dalia Kriksciuniene, MKIS 2012, Brno
Customer data collections become “big data”
• Facebook revealed some stats on its big
data:http://techcrunch.com/2012/08/22/how-big-is-
facebooks-data-2-5-billion-pieces-of-content-and-
500-terabytes-ingested-every-day/
If you want to
insert one
more field into
Facebook
customer
record it
would take 3
months (told at
hosting service
providers’ seminar
2012)Dalia Kriksciuniene, MKIS 2012, Brno
Assignment 2
Tools &software: Sugar CRM, MS Excel pivot
module, Statistica advanced models,
Viscovery SoMine
2nd team assignment and lab work training:
•Operational CRM (Sugar CRM)
•Analytical CRM (CRM performance
analysis by applying computational
intelligence methods: neural networks,
fuzzy rules, Kohonen self organizing
networks)
Dalia Kriksciuniene, MKIS 2012, Brno
Assignment 2
Tools &software: Sugar CRM,
lab work training:
Operational CRM (Sugar CRM)
https://demo.sugaropencloud.eu/optimus/
Masaryk
Username : sugarcrm0
Password: mkis0
(0-your number of enrolment in PV250
MkIS)
Dalia Kriksciuniene, MKIS 2012, Brno
Lab work
1. Access the system
2. Register new customer and his data
3. Register new task, related to the customer. Services
(reminder, topic categories)
4. Plan the meeting, fulfill it
5. Assign the task to your colleague
6. Create sales operation for the customer.
7. Find the sales transaction in the analytical area.
8. Define the time period of analysis
9. Create project. Define tasks for the project
10.Get acquainted to the environment, find 5 other
functions and make them.
115Dalia Kriksciuniene, MKIS 2012,
Brno
Literature
Turban, E., Aronson, J.E., Liang, T.P. Sharda, R. (2007). Decision
Support and Business Intelligence Systems, 8/E, Prentice Hall
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004
Prentice Hall
Berry, M.,J.A., Linoff, G.S. (2011), "Data Mining Techniques: For
Marketing, Sales, and Customer Relationship Management", (3rd ed.),
Indianapolis: Wiley Publishing, Inc.
Sugar CRM Implementation
http://www.optimuscrm.com/index.php?lang=en
Statsoft: the creators of Statistica http://www.statsoft.com
Viscovery Somine http://www.viscovery.net/
MS Axapta Dyn. http://www.microsoft.com/en-us/dynamics/erp-ax-
overview.aspx
Online scientific databases accessed via library.muni.cz
Kotler, Ph. Marketing management (any edition)
Dalia Kriksciuniene, MKIS 2012, Brno