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Information & System Quality. Considering and assuring quality dimensions in architecture design "Drowning in data, yet starved of information" (Ruth Stanat , 1990, in 'The Intelligent Corporation’ ). Ir. Nitesh Bharosa | [email protected] 11-02-2010 . Who am I?. Nitesh Bharosa

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Information system quality

Information & System Quality

Considering and assuring quality dimensions in architecture design

"Drowning in data, yet starved of information"(Ruth Stanat, 1990, in 'The Intelligent Corporation’ )

Ir. Nitesh Bharosa | [email protected]

11-02-2010


Who am i

Who am I?

  • Nitesh Bharosa

    • PHD candidate at the ICT Section (finishing in January 2011)

    • M.Sc. in Systems Engineering, Policy Analysis and Management Thesis: Enterprise Architecture at Siemens

  • Research interest

    • information & system quality

    • orchestration & coordination

    • enterprise-architecture, SOA, SAAS,

    • public safety and disaster management

  • Courses:

    • SPM3410 Web information Systems and Management

    • SPM4341 Design of Innovative ICT-infrastructures and services,

    • guest lectures e-business and management of technology


Today s goals

Today’s goals

  • Understand the concepts of information and system quality in multi-actor environments

  • Be able to distinguish multiple information quality dimensions

  • Be able to distinguish multiple systems quality dimensions

  • Understand principles for assuring information and system quality

  • Introduction to “Master of Disaster Game”


Further reading

Further reading

  • Strong, Lee & Wang. (1997). Data quality in context. Communications of the ACM.

  • Nelson et al (2002). Antecedents of information and system quality. Journal of Management Information Systems.

  • Bharosa, N., et al (2009). Identifying and confirming information and system quality requirements for multi-agency disaster management. In the ISCRAM 2009 proceedings.


Agenda

Agenda

  • Background and relevance

  • Concepts and definitions

  • Hurdles for IQ and SQ in practice

  • Complex multi actor case: Disaster management

  • How do we assure information and system quality in the architecture?

  • Summary and conclusions


When was the last time you were encountered with wrong information

When was the last time you were encountered with wrong information?


Information systems success theory

Information Systems Success theory*

Information

Quality

System

Quality

*Delone & Mclean (1992). Information Systems Success: the quest for the dependent variable. Information Systems Research, 3(1), pp.60-95


Relevance of poor iq for the typical enterprise

Relevance of poor IQ for the typical enterprise*

  • Operational Impacts:

    • Lowered customer satisfaction

    • Increased cost: 8–12% of revenue in the few, carefully studied cases

    • For service organizations, 40–60% of expense

    • Lowered employee satisfaction

  • Typical Impacts:

    • Poorer decision making: Poorer decisions that take longer to make

    • More difficult to implement data warehouses

    • More difficult to reengineer

    • Increased organizational mistrust

  • Strategic Impacts:

    • More difficult to set strategy

    • More difficult to execute strategy

    • Contribute to issues of data ownership

    • Compromise ability to align organizations

*based on Redman (2002)


What is information quality

What is information quality?


The concept of quality in information systems

The concept of quality in Information systems

  • Quality is not a new concept in information systems management and research

  • What is ‘new’ is the explosion in the quantity of information and the increasing reliance of most segments of society on that information

  • Challenges: defining and improving quality for a specific context

  • Information systems researchers have attempted to define data quality, information quality software quality, system quality, documentation quality, service quality, web quality and global information systems quality


Some definitions for iq

Some definitions for IQ

  • Quality information is information that meets specifications or requirements (Khan & Strong, 1999)

  • IQ is the characteristic of information to meet the functional, technical, cognitive, and aesthetic requirements of information producers, administrators, consumers, and experts (Eppler, 2003)

  • Information of high IQ is fit for use by information consumers (Huang, Lee, Wang, 1999, p. 43)

  • IQ as set of dimensions describing the quality of the information produced by the information system (Delone & Mclean, 1992).

  • Quality of information can be defined as a difference between the required information (determined by a goal) and the obtained information (Gerkes, 1997)


Iq frameworks 1

IQ Frameworks * 1

*Lesca & Lesca (1995)


Iq frameworks 2

IQ Frameworks * 2

*Redman (1996) Data Quality for the information age


Iq frameworks 3a

IQ Frameworks* 3a

*Strong, D. M., Lee, Y. W., & Wang, R. Y. 1997.

Data Quality in Context. Communications of the ACM, 40(5): pp.103-110.


Iq frameworks 3b

IQ frameworks* 3b

*Strong, D. M., Lee, Y. W., & Wang, R. Y. 1997.

Data Quality in Context. Communications of the ACM, 40(5): pp.103-110.


Information system quality

Discussion:Is there a difference between data quality and information quality?An what about knowledge and wisdom?


Transitions from data to wisdom

Transitions from data to wisdom

Intelligence

Complexity

of

quality

management

Based on level of understanding & experience

Knowledge

Internalization over time (human processing, can be tacit)

Information

Processing (use of information systems)

(raw) Data

Volume


Data information knowledge and wisdom

Data, Information, Knowledge and Wisdom*

  • Data is an discrete, unitary, and indivisible element which conveys a single value. Data serves as the basis for computation and reasoning to be executed

  • Information is an aggregate of one or more data elements with certain established relationships, and it has the ability to convey a single, meaningful message

  • Knowledge is a large-scale selective combination or union of related pieces of information accumulated over a prolonged period of time, and it can be viewed as a discipline area

  • Wisdom is the new knowledge subset created when the deductive ability acquired by a person after attaining a sufficient level of understanding of a knowledge area is executed

*Adapted from Liang (1994)


Data to information processing

Data to information processing*

Al-Hakim (2007) Information Quality Function Deployment


Subjective and context dependent nature of information

Subjective and context dependent nature of information

  • “Perfect” IQ, is difficult, if not impossible, to achieve

  • but neither is it necessary!

  • If users of the data feel that its quality, which can be described by such attributes as accuracy, completeness and timeliness, is sufficient for their needs, then, from their perspective, at least, the quality of the information available to them is fine

  • Hence we need a clear understanding of user processes and their information needs in specific context


What is system quality

What is system quality?


System quality

System Quality

  • Defined as: the quality of the information system (as producing system) and not of the information (as product) (Delone & McLean, 1992)

  • Also not a ‘new’ concept in information systems

  • However, this concept has received less formal and coherent treatment than information quality

  • Trend: information systems are becoming more than just single software applications

  • SQ is also an antecedent for information system success


Examples of poor system quality 1

Examples of poor system quality 1


Examples of poor system quality 2

Examples of poor system quality 2


Complex multi actor systems

Complex multi-actor systems

  • Examples include supply chains, value networks traffic systems and crisis management networks

  • In such systems, intra- and inter organizational information flows need to be coordinated in order to achieve goals: high interdependency

  • Information systems play in critical role in the coordination process

  • Multiple echelons of coordination: strategic, tactical and operational

  • Actors operate in a complex, dynamic and unpredictable task environment


Iq sq issues during disaster response

IQ & SQ issues during disaster response

  • Chernobyl (1986)

  • Herculus (1999)

  • Enschede (2000)

  • New York (2001)

  • Singapore (2003)

  • Tsunami (2004)

  • Schiphol (2006)

  • Delft (2008)

Disaster Management


Complexity heterogeneous actors and systems during 9 11 response

Complexity: heterogeneous actors and systems during 9/11 response

*source: Comfort, L. (2002), ‘‘Rethinking Security: Organizational Fragility in Extreme Events,’’ Public Administration Review 62, Special Issue (September), 98–107


Information flows in the netherlands

Information flows in the Netherlands

Strategic Echelon

Tactical Echelon

Emergency Control room

Operational Echelon


Practice 1 distributed teams

Practice 1: distributed teams

Manual situation report generation


Practice 2 several information types formats sources and technologies

Practice 2: several information types, formats, sources and technologies


Examples of poor iq during disasters

Examples of poor IQ during disasters


Examples of poor sq during disasters

Examples of poor SQ during disasters


Main challenge assuring iq and sq in mas

Main Challenge: Assuring IQ and SQ in MAS

Information

Quality

+

?

System

Quality

+


Some generic steps in the assurance process

Some generic steps in the assurance process

  • Understand the stakeholder goals and information needs

  • Model the process and information flows

  • Define clear IQ and SQ measurement instruments

  • Analyze hurdles for IQ and SQ (symptoms) on the various architectural layers (i.e., via observations and interviews)

  • Synthesize principles for assuring IQ and SQ

  • Implement and evaluate principles (i.e., prototyping, gaming simulation)

  • Train awareness: information as a product

  • Capture feedback and start over again (continuous process)


1 stakeholder analysis

1. Stakeholder Analysis

  • Consumers/clients

  • Process architects

  • Database architects

  • Data suppliers

  • Application architects

  • Communication trainers

  • Programmers

  • Managers (CIO, CTO etc)

  • Auditors etc


2 process and information flow modeling

2. Process and information flow modeling


3a iq and sq measurement

3a. IQ and SQ measurement

  • Context dependent

  • Multidimensional constructs

  • Subjective: dependent on the user judgment

  • So, how do we measure IQ and SQ?

  • Need for multiple instruments

    • Questionnaires (paper or online)

    • Observations

    • Interviews

    • Focus groups

    • Gaming


3b iq measurement items

3b. IQ measurement items


3c sq measurement items

3c. SQ measurement items


4 hurdles in mas

4. Hurdles in MAS


5a strategies to avoid poor iq and sq

5a. Strategies to avoid poor IQ and SQ

  • Sender or source based strategies

    • e.g., rules and policies, data cleansing

  • Receiver or destination based strategies

    • e.g., filters, aggregation algorithms

  • Mediation or network based strategies

    • e.g., stewardship and “Information Orchestration”


5b conventional source based techniques for iq improvement

5b. Conventional source based techniques for IQ improvement

  • data cleansing & normalization (Hernadez & Stolfo, 1998),

  • data tracking & statistical process control (Redman, 1996),

  • data source calculus & algebra (Lee, Bressen, & Madnick, 1998)

  • data stewardship (English, 1999)

  • dimensional gap analysis (Kahn, Strong, & Wang, 2002)

  • Usually there are four steps involved

    • Profiling and identification of DQ problems

    • Reviewing and characterize of expectations (business rules)

    • Instrument development and Measurement

    • Solution proposition and implementation


5c conventional techniques for sq improvement

5c. Conventional techniques for SQ improvement

  • More/better hardware

  • More/better software

  • Reduce number of nodes in the information flow

  • Redundancy (reliability and robustness)

  • Less forms and procedures in the information exchange process


5d limitations of conventional assurance approaches

5d. Limitations of conventional assurance approaches

  • More databases and technologies include higher cost and do not solve IQ and SQ problems in coherence

  • Assume a “static” data layer

  • Do not address task environment dynamics and uncertainty

  • Reactive, do not include strategies for sensing and adapting

  • Need for proactive mechanisms to deal with dynamic information needs


5e an information orchestration approach

5e. An information orchestration approach

Offensive

Advance structuring

strategy

Dynamic adjustment

strategy

Preemptive principles (e.g., IQ auditing)

Exploitative principles (e.g., proactive sensing)

During a

disaster

Before a

disaster

Information Orchestration

Protective principles (e.g., dependency diversification)

Corrective principles (e.g., IQ rating)

Defensive

46


5f advance structuring strategy and principles

5f. Advance structuring strategy and principles

  • Examples of preemptive principles

    • Treat information as product not by-product

    • Organize IQ audits on a regular basis

    • Assign IQ roles and responsibilities across organizational units

  • Examples of protective principles

    • Maximize the number of sources for each information object

    • Define several information access and manipulation levels

    • Strive for loosely coupled application components


5g dynamic adjustment strategy and principles

5g. Dynamic adjustment strategy and principles

  • Examples of exploitative principles

    • Anticipate information needs prior to the occurrence of events

    • Exploit multi-channel and technology convergence

    • Scan the environment for complementary information

  • Examples of corrective principles

    • Maximize the number of feedback opportunities across the network

    • Develop policies for ascertaining information needs, acquiring and managing information throughout its life cycle

    • Encourage a sharing culture (data to information transformation by collective interpretation, discussion & expert analysis)


6a prototyping

6a. Prototyping


6b gaming simulation

6b. Gaming simulation


7 information as product or by product

7. Information as product or by-product*

* Source: Lee et al (2006) Journey to data quality


Iq assurance requires trade offs between

IQ Assurance requires trade-offs* between:

  • security & accessibility: the more secure an information system is, the less convenient is its access

  • timeliness & accuracy: the more current a piece of information has to be, the less time is available to check on its accuracy

  • correctness or reliability and timeliness: the faster information has to be delivered to the end-user, the less time is available to check its reliability or correctness

  • right amount of information (or scope) and comprehensibility: more detailed information can prevent a fast comprehension, because it becomes difficult “to see the big picture”

  • conciseness & right amount (scope) of information: the more detail that is provided, the less concise a piece of information or document is going to be

*based on Eppler (2003)


Sq assurance tradeoffs

SQ assurance tradeoffs

  • Flexibility versus robustness

  • Accessibility versus security

  • Security versus interoperability

  • Reliability versus flexibility

  • Availability versus cost

  • Adaptability versus accountability


Conclusions for today

Conclusions for today

  • Assuring high IQ and SQ is becoming more important and more problematic

  • The hurdles for IQ and SQ are abundant and multi-level

  • There is no one best (technical) solution for IQ problems, the solution space covers multiple architecture layers (e.g., organizational, process and technical layers)

  • Assuring IQ and SQ is an continuous process and needs to be institutionalized/embodied in the organizational culture

  • There are many information quality dimensions and not all are relevant: some tradeoffs need to be made


Questions and discussion

Questions and Discussion


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