Competitive advantage from Data Mining: some lessons learnt in the  Information Systems field Mykola Pechenizkiy , Seppo P...
Outline <ul><li>Introduction and What is our message? </li></ul><ul><li>Part I: Existing frameworks for DM </li></ul><ul><...
What is  Data Mining Data mining  or  Knowledge discovery   is the  process  of finding previously unknown and potentially...
H.   Information Systems   <ul><ul><li>H.0 GENERAL  </li></ul></ul><ul><ul><li>H.1 MODELS AND PRINCIPLES  </li></ul></ul><...
I. Computing Methodologies <ul><ul><li>I.5  PATTERN RECOGNITION </li></ul></ul><ul><ul><ul><li>I.5.0 General  </li></ul></...
G. Mathematics of Computing <ul><ul><li>G.3  PROBABILITY AND STATISTICS  </li></ul></ul><ul><ul><ul><li>Correlation and re...
Our Message <ul><li>DM is still a technology having great expectations to enable organizations to take more benefit of the...
Our Message <ul><li>Currently the maturation of DM-supporting processes which would take into account human and organizati...
Part I <ul><li>Existing Frameworks for DM </li></ul><ul><ul><li>Theory-oriented </li></ul></ul><ul><ul><ul><li>Databases; ...
Theory-Oriented Frameworks
Database Perspective   <ul><li>DM as application to DBs </li></ul><ul><ul><li>“ In the same way business applications are ...
Reductionism Approach <ul><li>Two basic Statistical Paradigms </li></ul><ul><ul><li>“ Statistical Experiment” </li></ul></...
Machine Learning Approach <ul><li>“ let the data suggest a model ” can be seen as a practical alternative to the statistic...
Data Compression Approach <ul><ul><li>Compress the data set by  finding some structure  or  knowledg e for it, where knowl...
Other Theoretical frameworks for DM   <ul><li>Microeconomic view   </li></ul><ul><ul><li>the key point is that data mining...
Process-Oriented Frameworks
Knowledge discovery as a process Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.,  Advances in Knowledge Disc...
CRISP-DM http://www.crisp-dm.org/
KDD: “Vertical Solutions” Reinartz, T. 1999,  Focusing Solutions for Data Mining .  LNAI 1623, Berlin Heidelberg.
Conclusion on different frameworks   <ul><ul><li>Reductionist approach of viewing data mining as statistics has advantages...
Part II <ul><li>Where we are? </li></ul><ul><li>Rigor and Relevance in DM Reseach </li></ul>
So, where are we? <ul><li>Lin in Wu  et al.  notices that a new successful industry (as DM) can follow consecutive phases:...
Rigor vs Relevance in DM Research
Where is the focus? <ul><li>Still! … speeding-up, scaling-up, and increasing the accuracies of DM techniques. </li></ul><u...
Part III <ul><li>Towards the new framework for DM research </li></ul>
DMS in the Kernel of an Organization  <ul><li>DM is fundamentally application-oriented area motivated by business and scie...
The ISs-based paradigm for DM Ives B., Hamilton S., Davis G. (1980). “A Framework for Research in Computer-based MIS”  Man...
DM Artifact Development Adapted from: Nunamaker, W., Chen, M., and Purdin, T. 1990-91, Systems development in information ...
Research methods in a paper on DM <ul><ul><li>Theoretical approach: theory creating  </li></ul></ul><ul><ul><ul><li>Hypoth...
The Action Research and Design Science Approach to Artifact Creation  Design Knowledge Awareness of business problem Actio...
DM Artifact Use: Success Model 1 of 3 Adapted from D&M IS Success Models System Quality Information Quality Use User Satis...
DM Artifact Use: Success Model 2 of 3 <ul><li>What are the key factors   of success ful use and impact of DMS both at the ...
DM Artifact Use: Success Model 3 of 3 <ul><li>Hermiz communicated his beliefs that there are the four critical  success fa...
KM Perspective <ul><li>A knowledge-driven approach  to enhance  the  dynamic integration of DM strategies  in knowledge di...
New Research Framework for DM Research
Further Work <ul><li>Definition of  Relevanc e  concept in DM research </li></ul><ul><li>The revision of the book chapter ...
Thank You! <ul><li>Book chapter draft is available on request from </li></ul><ul><li>Mykola Pechenizkiy </li></ul><ul><li>...
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  • ACM classification system for the computing field: DM is a subject of database applications (H.2.8), database management (H.2), and information systems field (H.)
  • SPSS whitepaper [4] states that “Unless there’s a method, there’s madness”. It is accepted that just by pushing a button someone should not expect useful results to appear. An industry standard to DM projects CRISP-DM is a good initiative and a starting point directed towards the development of DM meta-artifact (methodology to produce DM artifacts). However, in our opinion it is just one guideline, which is too general-level, that every DM developer follows with or without success.
  • In fact, the study of development and use processes was recognized to be of importance in the IS fields many years ago, and therefore it has been introduced into the different IS frameworks.
  • Nevertheless, so far in the DM community there exist too few research activities directed towards the study of a DM system as an artifact aimed to enable certain DM tasks in a certain context (Figure 1). In the IS discipline two research paradigms – the behavioral-science paradigm and design-science paradigm – have
  • The first efforts in that direction are the ones presented in the DM Review magazine [9, 21], referred below. We believe that such efforts should be encouraged in DM research and followed by research-based reports.
  • Lin in Wu et al. [43] notices that in fact there have been no major impacts of DM on the business world echoed. However, even reporting of existing success stories is important. Giraud-Carrier [18] reported 136 success stories of DM, covering 9 business areas with 30 DM tools or DM vendors referred. Unfortunately, there was no deep analysis provided that would summarize or discover the main success factors and the research should be continued.
  • In order to distinguish between the knowledge extracted from data and the higher-level knowledge (from the KDS perspective) required for managing techniques’ selection, combination and application we will refer to the latter as meta-knowledge .
  • Competitive advantage from Data Mining: some lessons learnt ...

    1. 1. Competitive advantage from Data Mining: some lessons learnt in the Information Systems field Mykola Pechenizkiy , Seppo Puuronen Department of Computer Science University of Jyväskylä Finland Alexey Tsymbal Department of Computer Science Trinity College Dublin Ireland PMKD’05 Copenhagen, Denmark August 22-26, 2005
    2. 2. Outline <ul><li>Introduction and What is our message? </li></ul><ul><li>Part I: Existing frameworks for DM </li></ul><ul><ul><li>Theory-oriented: Databases; Statistics; Machine learning; etc </li></ul></ul><ul><ul><li>Process-oriented: Fayyad’s, CRISP, Reinartz’s </li></ul></ul><ul><li>Part II: Where we are? – rigor vs. relevance in DM </li></ul><ul><li>Part III: Towards the new framework for DM research </li></ul><ul><ul><li>DM System as adaptive Information System (IS) </li></ul></ul><ul><ul><li>DM research as IS Development: DM system as artefact </li></ul></ul><ul><ul><li>DM success model: success factors </li></ul></ul><ul><ul><li>KM Challenges in KDD </li></ul></ul><ul><ul><li>One possible reference for new DM research framework </li></ul></ul><ul><li>Further plans and Discussion </li></ul>
    3. 3. What is Data Mining Data mining or Knowledge discovery is the process of finding previously unknown and potentially interesting patterns and relations in large databases (Fayyad, KDD’96) Data mining is the emerging science and industry of applying modern statistical and computational technologies to the problem of finding useful patterns hidden within large databases (John 1997) Intersection of many fields : statistics, AI, machine learning, databases, neural networks, pattern recognition, econometrics, etc.
    4. 4. H. Information Systems <ul><ul><li>H.0 GENERAL </li></ul></ul><ul><ul><li>H.1 MODELS AND PRINCIPLES </li></ul></ul><ul><ul><li>H.2 DATABASE MANAGEMENT </li></ul></ul><ul><ul><ul><li>H.2.0 General </li></ul></ul></ul><ul><ul><ul><ul><li>Security, integrity, and protection </li></ul></ul></ul></ul><ul><ul><ul><li>H.2.8 Database Applications </li></ul></ul></ul><ul><ul><ul><ul><li>Data mining </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Image databases </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Scientific databases </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Spatial databases and GIS </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Statistical databases </li></ul></ul></ul></ul><ul><ul><ul><li>H.2.m Miscellaneous </li></ul></ul></ul>http://www.acm.org/class/1998/ valid in 2003
    5. 5. I. Computing Methodologies <ul><ul><li>I.5 PATTERN RECOGNITION </li></ul></ul><ul><ul><ul><li>I.5.0 General </li></ul></ul></ul><ul><ul><ul><li>I.5.1 Models </li></ul></ul></ul><ul><ul><ul><ul><li>Deterministic </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Fuzzy set </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Geometric </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Neural nets </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Statistical </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Structural </li></ul></ul></ul></ul><ul><ul><ul><li>I.5.2 Design Methodology </li></ul></ul></ul><ul><ul><ul><ul><li>Classifier design & evaluation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Feature evaluation & selection </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Pattern analysis </li></ul></ul></ul></ul><ul><ul><ul><li>I.5.3 Clustering </li></ul></ul></ul><ul><ul><ul><ul><li>Algorithms </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Similarity measures </li></ul></ul></ul></ul><ul><ul><ul><li>I.5.4 Applications </li></ul></ul></ul><ul><ul><ul><ul><li>Computer vision </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Signal processing </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Text processing </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Waveform analysis </li></ul></ul></ul></ul><ul><ul><ul><li>I.2 ARTIFICIAL INTELLIGENCE </li></ul></ul></ul><ul><ul><ul><ul><li>I.2.0 General </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Cognitive simulation </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Philosophical foundations </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.1 Applications and Expert Systems </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.2 Automatic Programming </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.3 Deduction and Theorem Proving </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.4 Knowledge Representation Formalisms and Methods </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.5 Programming Languages and Software </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.6 Learning </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Analogies </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Concept learning </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Connectionism and neural nets </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Induction </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Knowledge acquisition </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Language acquisition </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Parameter learning </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.7 Natural Language Processing </li></ul></ul></ul></ul><ul><ul><ul><ul><li>I.2.m Miscellaneous </li></ul></ul></ul></ul>
    6. 6. G. Mathematics of Computing <ul><ul><li>G.3 PROBABILITY AND STATISTICS </li></ul></ul><ul><ul><ul><li>Correlation and regression analysis </li></ul></ul></ul><ul><ul><ul><li>Distribution functions </li></ul></ul></ul><ul><ul><ul><li>Experimental design </li></ul></ul></ul><ul><ul><ul><li>Markov processes </li></ul></ul></ul><ul><ul><ul><li>Multivariate statistics </li></ul></ul></ul><ul><ul><ul><li>Nonparametric statistics </li></ul></ul></ul><ul><ul><ul><li>Probabilistic algorithms (including Monte Carlo) </li></ul></ul></ul><ul><ul><ul><li>Statistical computing </li></ul></ul></ul>
    7. 7. Our Message <ul><li>DM is still a technology having great expectations to enable organizations to take more benefit of their huge databases. </li></ul><ul><li>There exist some success stories where organizations have managed to have competitive advantage of DM. </li></ul><ul><li>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. </li></ul><ul><li>Research in the IS discipline has strong traditions to take into account human and organizational aspects of systems beside the technical ones. </li></ul>
    8. 8. Our Message <ul><li>Currently the maturation of DM-supporting processes which would take into account human and organizational aspects is still living its childhood. </li></ul><ul><li>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 . </li></ul><ul><li>The DM community by becoming more amenable to research results of the IS community might be able to increase its collective understanding of </li></ul><ul><ul><li>how DM artifacts are developed – conceived, constructed, and implemented, </li></ul></ul><ul><ul><li>how DM artifacts are used, supported and evolved, </li></ul></ul><ul><ul><li>how DM artifacts impact and are impacted by the contexts in which they are embedded. </li></ul></ul>
    9. 9. Part I <ul><li>Existing Frameworks for DM </li></ul><ul><ul><li>Theory-oriented </li></ul></ul><ul><ul><ul><li>Databases; </li></ul></ul></ul><ul><ul><ul><li>Statistics; </li></ul></ul></ul><ul><ul><ul><li>Machine learning; </li></ul></ul></ul><ul><ul><ul><li>Data compression </li></ul></ul></ul><ul><ul><li>Process-oriented </li></ul></ul><ul><ul><ul><li>Fayyad’s </li></ul></ul></ul><ul><ul><ul><li>CRISP-DM </li></ul></ul></ul><ul><ul><ul><li>Reinartz’s </li></ul></ul></ul>
    10. 10. Theory-Oriented Frameworks
    11. 11. Database Perspective <ul><li>DM as application to DBs </li></ul><ul><ul><li>“ In the same way business applications are currently supported using SQL-based API, the KKD applications need to be provided with application development support.” </li></ul></ul><ul><ul><li>query KDD objects, support for finding NN s, clustering, or discretization and aggregate operations. </li></ul></ul><ul><li>Inductive databases approach </li></ul><ul><ul><li>query concept should be applied also to data mining and knowledge discovery tasks </li></ul></ul><ul><ul><ul><li>“ there is no such thing as discovery, it is all in the power of the query language” </li></ul></ul></ul><ul><ul><li>contain not only the data but the theory of the data as well </li></ul></ul>Imielinski, T., and Mannila, H. 1996, A database perspective on knowledge discovery. Communications of the ACM , 39 (11), 58-64. Boulicaut, J., Klemettinen, M., and Mannila, H. 1999, Modeling KDD processes within the inductive database framework. In Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery , Springer-Verlag, London, 293-302
    12. 12. Reductionism Approach <ul><li>Two basic Statistical Paradigms </li></ul><ul><ul><li>“ Statistical Experiment” </li></ul></ul><ul><ul><ul><li>Fisher’s version, inductive principle of maximum likelihood </li></ul></ul></ul><ul><ul><ul><li>Neyman and Pearson-Wald’s version, inductive behaviour </li></ul></ul></ul><ul><ul><ul><li>Bayesian version, maximum posterior probability </li></ul></ul></ul><ul><ul><ul><li>“ Statistical learning from empirical process” </li></ul></ul></ul><ul><ul><li>“ Structural Data Analysis” </li></ul></ul><ul><ul><ul><li>SVD </li></ul></ul></ul><ul><li>Data mining  statistics - the issue of computational feasibility has a much clearer role in data mining than in statistics </li></ul><ul><ul><li>data mining area approaches that emphasize on database integration, simplicity of use, and the understandability of results </li></ul></ul><ul><ul><li>theoretical framework of statistics does not concern much about data analysis as a process that includes several steps </li></ul></ul>
    13. 13. Machine Learning Approach <ul><li>“ let the data suggest a model ” can be seen as a practical alternative to the statistical paradigm “ fit a model to the data ” </li></ul><ul><li>Constructive Induction – a learning process, two intertwined phases: construction of the “best” representation space and generating hypothesis in the found space ( Michalski & Wnek , 1993). </li></ul><ul><ul><li>Feature transformation (PCA, SVD, Random Projection) </li></ul></ul><ul><ul><li>Feature selection </li></ul></ul><ul><ul><li>LSI </li></ul></ul>
    14. 14. Data Compression Approach <ul><ul><li>Compress the data set by finding some structure or knowledg e for it, where knowledge is interpreted as a representation that allows coding the data by using fewer amount of bits. </li></ul></ul><ul><ul><li>Theories should not be ad hoc that is they should not overfit the examples used to build it. </li></ul></ul><ul><ul><li>Occam’s razor principle,14th century. </li></ul></ul><ul><ul><ul><li>&quot;when you have two competing models which make exactly the same predictions, the one that is simpler is the better&quot;. </li></ul></ul></ul>Mehta, M., Rissanen, J., and Agrawal, R. 1995, MDL-based decision tree pruning. In U.M. Fayyad, R. Uthurusamy (Eds.) Proceedings of the KDD 1995 , AAAI Press, Montreal, Canada, 216-221.
    15. 15. Other Theoretical frameworks for DM <ul><li>Microeconomic view </li></ul><ul><ul><li>the key point is that data mining is about finding actionable patterns: the only interest is in patterns that can somehow be used to increase utility ; </li></ul></ul><ul><ul><li>a decision theoretic formulation of this principle: the goal can be formulated in finding a decision x that tries to maximise utility function f(x) . </li></ul></ul><ul><ul><li>Kleinberg, J., Papadimitriou, C., and Raghavan, P. 1998, A microeconomic view of data mining, Data Mining and Knowledge Discovery 2 (4), 311-324 </li></ul></ul><ul><li>Philosophy of Science </li></ul><ul><ul><li>logical empiricism, critical rationalism, systems theory </li></ul></ul><ul><ul><li>formism, mechanism, contextualism </li></ul></ul><ul><ul><li>dispersive vs. integrative, analytical vs. synthetic theories </li></ul></ul><ul><ul><li>subjectivist vs. objectivist, nomothetic vs. ideographic, nominalism vs. realism, voluntarism vs. determinism, epistemological assumptions </li></ul></ul><ul><ul><li>Explanation, prediction, understanding </li></ul></ul>
    16. 16. Process-Oriented Frameworks
    17. 17. Knowledge discovery as a process Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R., Advances in Knowledge Discovery and Data Mining , AAAI/MIT Press, 1997. I
    18. 18. CRISP-DM http://www.crisp-dm.org/
    19. 19. KDD: “Vertical Solutions” Reinartz, T. 1999, Focusing Solutions for Data Mining . LNAI 1623, Berlin Heidelberg.
    20. 20. Conclusion on different frameworks <ul><ul><li>Reductionist approach of viewing data mining as statistics has advantages of the strong background, and easy-formulated problems. </li></ul></ul><ul><ul><li>The data mining tasks concerning processed like clusterisation, regression and classification fit easily into these approaches. </li></ul></ul><ul><ul><li>More recent (process-oriented) frameworks address the issues related to a view of data mining as a process, and its iterative and interactive nature </li></ul></ul>
    21. 21. Part II <ul><li>Where we are? </li></ul><ul><li>Rigor and Relevance in DM Reseach </li></ul>
    22. 22. So, where are we? <ul><li>Lin in Wu et al. notices that a new successful industry (as DM) can follow consecutive phases: </li></ul><ul><ul><li>discovering a new idea, </li></ul></ul><ul><ul><li>ensuring its applicability, </li></ul></ul><ul><ul><li>producing small-scale systems to test the market, </li></ul></ul><ul><ul><li>better understanding of new technology and </li></ul></ul><ul><ul><li>producing a fully scaled system. </li></ul></ul><ul><li>At the present moment there are several dozens of DM systems, none of which can be compared to the scale of a DBMS system. </li></ul><ul><ul><li>This fact indicates that we are still in the 3rd phase in the DM area! </li></ul></ul>
    23. 23. Rigor vs Relevance in DM Research
    24. 24. Where is the focus? <ul><li>Still! … speeding-up, scaling-up, and increasing the accuracies of DM techniques. </li></ul><ul><li>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” </li></ul><ul><li>Lin claims that the R&D goals of DM are quite different: </li></ul><ul><ul><li>since research is knowledge-oriented while development is profit-oriented. </li></ul></ul><ul><ul><li>Thus, DM research is concentrated on the development of new algorithms or their enhancements, </li></ul></ul><ul><ul><li>but the DM developers in domain areas are aware of cost considerations: investment in research, product development, marketing, and product support. </li></ul></ul><ul><li>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. </li></ul>
    25. 25. Part III <ul><li>Towards the new framework for DM research </li></ul>
    26. 26. DMS in the Kernel of an Organization <ul><li>DM is fundamentally application-oriented area motivated by business and scientific needs to make sense of mountains of data. </li></ul><ul><li>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. </li></ul><ul><li>Further, the organization has its own environment that has its own interest related to DMS, e.g. that privacy of people is not violated. </li></ul>Environment DM Task(s) DMS (Artifact) Organization
    27. 27. 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.
    28. 28. DM Artifact Development 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. A multimethodological approach to the construction of an artefact for DM DM Artifact Development Experimentation Theory Building Observation
    29. 29. Research methods in a paper on DM <ul><ul><li>Theoretical approach: theory creating </li></ul></ul><ul><ul><ul><li>Hypothesis, new algorithm, etc. </li></ul></ul></ul><ul><ul><li>Constructive approach </li></ul></ul><ul><ul><ul><li>Prototype of a DM tool </li></ul></ul></ul><ul><ul><li>Theoretical approach: theory testing and evaluation </li></ul></ul><ul><ul><ul><li>Artificial, benchmark, real-world data </li></ul></ul></ul><ul><ul><ul><li>Evaluation techniques </li></ul></ul></ul><ul><ul><li>Conclusion on theory </li></ul></ul>
    30. 30. The Action Research and Design Science Approach to Artifact Creation Design Knowledge Awareness of business problem Action planning Action taking Conclusion Business Knowledge Artifact Development Artifact Evaluation Contextual Knowledge
    31. 31. DM Artifact Use: Success Model 1 of 3 Adapted from D&M IS Success Models System Quality Information Quality Use User Satisfaction Individual Impact Organizational Impact Service Quality
    32. 32. DM Artifact Use: Success Model 2 of 3 <ul><li>What are the key factors of success ful use and impact of DMS both at the individual and organizational levels. </li></ul><ul><ul><li>how the system is used, and also supported and evolved, and </li></ul></ul><ul><ul><li>how the system impacts and is impacted by the contexts in which it is embedded. </li></ul></ul><ul><li>Coppock: the failure factors of DM-related projects. </li></ul><ul><li>have nothing to do with the skill of the modeler or the quality of data. </li></ul><ul><li>But those do include: </li></ul><ul><ul><li>persons in charge of the project did not formulate actionable insights , </li></ul></ul><ul><ul><li>the sponsors of the work did not communicate the insights derived to key constituents, </li></ul></ul><ul><ul><li>the results don't agree with institutional truths </li></ul></ul>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
    33. 33. DM Artifact Use: Success Model 3 of 3 <ul><li>Hermiz communicated his beliefs that there are the four critical success factors for DM projects: </li></ul><ul><li>(1) having a clearly articulated business problem that needs to be solved and for which DM is a proper tool; </li></ul><ul><li>(2) insuring that the problem being pursued is supported by the right type of data of sufficient quality and in sufficient quantity for DM; </li></ul><ul><li>(3) recognizing that DM is a process with many components and dependencies – the entire project cannot be &quot;managed&quot; in the traditional sense of the business word; </li></ul><ul><li>(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. </li></ul>
    34. 34. KM Perspective <ul><li>A knowledge-driven approach to enhance the dynamic integration of DM strategies in knowledge discovery systems. </li></ul><ul><li>Focus here is on knowledge management aimed to organise a systematic process of (meta-)knowledge capture and refinement over time . </li></ul><ul><ul><li>knowledge extracted from data </li></ul></ul><ul><ul><li>the higher-level knowledge required for managing DM techniques’ selection, combination and application </li></ul></ul><ul><li>Basic knowledge management processes of </li></ul><ul><ul><li>knowledge creation and identification, representation, collection and organization, sharing, adaptation, and application </li></ul></ul><ul><li>DEXA’05: TAKMA WS paper&presentation are available </li></ul>
    35. 35. New Research Framework for DM Research
    36. 36. Further Work <ul><li>Definition of Relevanc e concept in DM research </li></ul><ul><li>The revision of the book chapter </li></ul><ul><li>Further work on the new framework for DM research </li></ul><ul><li>Organization of Workshop or Special Track or Working conference on </li></ul><ul><ul><li>more social directions in DM research likely with one of the focuses on IS as a sister discipline . </li></ul></ul><ul><ul><li>Few options: </li></ul></ul><ul><ul><li>IRIS Scandinavian Conference on IS is one option </li></ul></ul><ul><ul><li>Next PMKD </li></ul></ul><ul><ul><li>Workshop in Jyväskylä </li></ul></ul>
    37. 37. Thank You! <ul><li>Book chapter draft is available on request from </li></ul><ul><li>Mykola Pechenizkiy </li></ul><ul><li>Department of Computer Science and Information Systems, </li></ul><ul><li>University of Jyväskylä, FINLAND </li></ul><ul><li>E-mail: [email_address] </li></ul><ul><li>Tel.: +358 14 2602472 Fax: +358 14 260 3011 </li></ul><ul><li>http://www.cs.jyu.fi/~mpechen </li></ul><ul><li>Feedback is very welcome: </li></ul><ul><li>Questions </li></ul><ul><li>Suggestions </li></ul><ul><li>Collaboration </li></ul>

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