Why Data Mining Research Does Not Contribute to Business

1
Why Data Mining Research Does Not Contribute to
Business?
DMBiz05 Porto, Portugal October 3, 2005

• Mykola Pechenizkiy, Seppo Puuronen Department of
  Computer ScienceUniversity of Jyväskylä
  Finland
  
• Alexey Tsymbal
• Department of Computer ScienceTrinity College
  DublinIreland

2
Outline

• Introduction and What is our message?
• Where we are? rigor vs. relevance in DM
• Towards the new framework for DM research
• DM System as adaptive Information System (IS)
• DM research as IS Development DM system as
  artefact
  
• DM success model success factors
• Further plans and Discussion

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Our Message

• DM is still a technology having great
  expectations to enable organizations to take more
  benefit of their huge databases.
  
• There exist some success stories where
  organizations have managed to have competitive
  advantage of DM.
  
• Still the strong focus of most DM-researchers in
  technology-oriented topics does not support
  expanding the scope in less rigorous but
  practically very relevant sub-areas.
• Research in the IS discipline has strong
  traditions to take into account human and
  organizational aspects of systems beside the
  technical ones.

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Our Message

• Currently the maturation of DM-supporting
  processes which would take into account human and
  organizational aspects is still living its
  childhood.
• DM community might benefit, at least from the
  practical point of view, looking at some other
  older sub-areas of IT having traditions to
  consider solution-driven concepts with a focus
  also on human and organizational aspects.
• The DM community by becoming more amenable to
  research results of the IS community might be
  able to increase its collective understanding of
  
• how DM artifacts are developed conceived,
  constructed, and implemented,
  
• how DM artifacts are used, supported and evolved,
  
  
• how DM artifacts impact and are impacted by the
  contexts in which they are embedded.

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Existing Frameworks for DM

• Theory-oriented
• Databases
• Statistics
• Machine learning
• Data compression
• Process-oriented
• Fayyads
• CRISP-DM
• Reinartzs

• Reductionist approach of viewing DM as statistics
  has advantages of the strong background, and
  easy-formulated problems.
  
• The DM tasks concerning processes like
  clustering, regression and classification fit
  easily into these approaches.
  
• More recent (process-oriented) frameworks address
  the issues related to a view of DM as a process,
  and its iterative and interactive nature

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Rigor and Relevance in DM Research

• Lin in Wu et al. notices that a new successful
  industry (as DM) can follow consecutive phases
  
• discovering a new idea,
• ensuring its applicability,
• producing small-scale systems to test the market,
  
  
• better understanding of new technology and
• producing a fully scaled system.
• At the present moment there are several dozens of
  DM systems, none of which can be compared to the
  scale of a DBMS system.
  
• This fact indicates that we are still in the 3rd
  phase in the DM area!

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Rigor vs Relevance in DM Research
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Where is the focus?

• Still! speeding-up, scaling-up, and increasing
  the accuracies of DM techniques.
  
• Piatetsky-Shapiro we see many papers proposing
  incremental refinements in association rules
  algorithms, but very few papers describing how
  the discovered association rules are used
• Lin claims that the RD goals of DM are quite
  different
  
• since research is knowledge-oriented while
  development is profit-oriented.
  
• Thus, DM research is concentrated on the
  development of new algorithms or their
  enhancements,
  
• but the DM developers in domain areas are aware
  of cost considerations investment in research,
  product development, marketing, and product
  support.
• However, we believe that the study of the DM
  development and DM use processes is equally
  important as the technological aspects and
  therefore such research activities are likely to
  emerge within the DM field.

Towards the new framework for DM research
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DMS in the Kernel of an Organization
Environment

• DM is fundamentally application-oriented area
  motivated by business and scientific needs to
  make sense of mountains of data.
  
• A DMS is generally used to support or do some
  task(s) by human beings in an organizational
  environment both having their desires related to
  DMS.
• Further, the organization has its own environment
  that has its own interest related to DMS, e.g.
  that privacy of people is not violated.

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The ISs-based paradigm for DM
Ives B., Hamilton S., Davis G. (1980). A
Framework for Research in Computer-based MIS
Management Science, 26(9), 910-934.
Information systems are powerful instruments for
organizational problem solving through formal
information processing
Lyytinen, K., 1987, Different perspectives on
ISs problems and solutions. ACM Computing
Surveys, 19(1), 5-46.
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DM Artifact Development
A multimethodological approach to the
construction of an artefact for DM
Adapted from Nunamaker, W., Chen, M., and
Purdin, T. 1990-91, Systems development in
information systems research, Journal of
Management Information Systems, 7(3), 89-106.
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The Action Research and Design Science Approach
to Artifact Creation
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DM Artifact Use Success Model 1 of 3
Adapted from DM IS Success Models
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DM Artifact Use Success Model 2 of 3

• What are the key factors of successful use and
  impact of DMS both at the individual and
  organizational levels.
  
• how the system is used, and also supported and
  evolved, and
  
• how the system impacts and is impacted by the
  contexts in which it is embedded.
  
• Coppock the failure factors of DM-related
  projects.
  
• have nothing to do with the skill of the modeler
  or the quality of data.
  
• But those do include
• persons in charge of the project did not
  formulate actionable insights,
  
• the sponsors of the work did not communicate the
  insights derived to key constituents,
  
• the results don't agree with institutional truths

the leadership, communication skills and
understanding of the culture of the organization
are not less important than the traditionally
emphasized technological job of turning data into
insights
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DM Artifact Use Success Model 3 of 3

• Hermiz communicated his beliefs that there are
  the four critical success factors for DM
  projects
  
• (1) having a clearly articulated business problem
  that needs to be solved and for which DM is a
  proper tool
  
• (2) insuring that the problem being pursued is
  supported by the right type of data of sufficient
  quality and in sufficient quantity for DM
  
• (3) recognizing that DM is a process with many
  components and dependencies the entire project
  cannot be "managed" in the traditional sense of
  the business word
• (4) planning to learn from the DM process
  regardless of the outcome, and clearly
  understanding, that there is no guarantee that
  any given DM project will be successful.

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New Research Framework for DM Research
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New Research Framework for DM Research
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Further Work

• Definition of Relevance concept in DM research
• The revision of the book chapter
• Further work on the new framework for DM
  research
  
• Organization of Workshop/Working conf. or ST on
• more social directions in DM research likely
  with one of the focuses on IS as a sister
  discipline.
  
• SIAM DM 2006 Interests include
• Human Factors and Social Issues
• ? Ethics of Data ?Mining Intellectual
  Ownership? Privacy Models ? Privacy
  Preservation Techniques? Risk Analysis ?
  User Interfaces? Data and Result Visualization

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Thank You!

• Feedback is very welcome
• Questions
• Suggestions
• Collaboration

• Book chapter draft is available on request from
• Mykola Pechenizkiy
• Department of Computer Science and Information
  Systems,
  
• University of Jyväskylä, FINLAND
• E-mail mpechen_at_cs.jyu.fi
• Tel. [phone] Fax [phone]
• http//www.cs.jyu.fi/mpechen