A Data Model Controversy

Size: px

Start display at page:

Download "A Data Model Controversy"

Mark Wiggins
5 years ago
Views:

1 A Data Model Controversy or 459 data modelers can t be wrong (right?) Graeme Simsion University of Melbourne 1

2 The Question: Which better characterizes data modeling? (a) Describing the data requirements of an organization or part of an organization Description or (b) Designing data structures to meet the requirements of an organization or part of an organization Design 2

3 Thesis structure (which I ll follow today) Introduction Literature Review Research Design Research Components 4. Thought-Leader Opinions Synthesis and Conclusions Overview Overview 5. Scope and Stages Environment Environment Environment Problem Background Review of Data Modeling Literature Problem Process Product Research Sub- Questions Problem Process Product M A P P I N G 6. Espoused Positions 7. Characteristics of Data Modeling 8. Diversity in Conceptual Modeling Process Product Person Synthesis of Results Person Person Synthesis of 5Ps Framework Identification of Gaps in Knowledge Environment Problem Process Product Administration Statistics Participants 9. Diversity in Logical Modeling 10. Style in Data Modeling Reflections and Conclusions Person 3

4 Context We re talking about data modeling in the context of specifying a new (relational) database 4

5 Conflict in the academic literature The objective is an accurate representation of reality (Teorey, Lightstone et al. 2006) Data modeling is generally viewed as a design activity (Srinivasan and Te'eni 1990) 5

6 Conflict in the practitioner literature We try to abstract yet such abstractions are constructed Can we, should we be passive describers when we analyze? And if we are not, are we really doing design rather than analysis? Fowler, 1999 In my view this is wrong The [data] analyst will indeed construct artifacts, but the purpose of the artifacts is to describe the fundamental structures and concepts behind the world that the business sees. Hay,

7 Does it matter? 1. Reference disciplines 2. Process and deliverables 3. Mechanizing or supporting the process 4. Arguments - one right answer? 5. Quality criteria 6. Package selection 7. Valuing the contribution - and contributor 8. The relevance of creativity 9. Teaching 10.Making sense of experience 11.Designing research 7

8 What does academe know about data modeling practice? I logged 59 empirical studies They involved 3210 participants (subjects) of whom 5% had >1 year experience Practitioners were in 9 studies only Almost half were accounted for by one study Most problems were simple 29 compared formalisms 28 involved development of a model And 21 compared it with a gold standard solution 8

9 Are academic researchers interested in data modeling? Dedicated journals and conferences Hundreds of papers Interest in data modeling techniques was strong until the mid 1990s, and has recently experienced a revival - Wand and Weber,

10 The gold standard the goal of the subject was to create a complete model containing 16 entities, 20 relationships, 40 attributes, 40 links and 80 cardinalities (Parent 1997). An employee can report to only one department. Each department has a phone number (Batra, Hoffer et al. 1990). 10

11 Participants (459 of them) location

Number of Participants Number of Participants Experience Years Experience Number of Models Produced 120 120 100 100 80 80 60

12 Number of Participants Number of Participants Experience Years Experience Number of Models Produced > >100 Years Experience Number of Models 12

13 Job Role Occupation of Participants 11% 25% Data Administrator Data Modeller Database Administrator Data Warehouse 11% Enterprise Modeller 41% Manager 1% Other 3% 8% 13

14 Job Titles 5 Designers 9 Engineers 31 (DB) Administrators 38 Managers 40 Consultants 69 Modelers 127 Architects 14

15 Number of participants Main means of learning Where they learnt books publication experience mentor TE IE Method 15

16 Notations The Winner: Crow s foot Distant Second: Relational Weak Third: UML Way, way back: Everything else 16

Steve Hoberman Karen Lopez Dawn Michels Terry Moriarty Ron

17 Interviews with thought leaders Peter Aiken Richard Barker Mike Brackett Harry Ellis Larry English Terry Halpin David Hay Steve Hoberman Karen Lopez Dawn Michels Terry Moriarty Ron Ross Bob Seiner Alec Sharp Eskil Swende Len Silverston John Zachman 17

18 A few important caveats This was five years ago! The quotes are intended to show the diversity of views but not necessarily each speakers full (or current!) position 18

19 One right answer? 19

20 Positions Position Strongly supports description 5 Somewhat supports description 1 Supports neither position more strongly than the other Position depends on language 1 Somewhat supports design 3 Strongly supports design 4 Number of Interviewees 2 20

21 Part II Practitioners perceptions of data modeling 1. Scope and stages 2. What is data modeling? 3. Characteristics of data modeling 21

22 1. Perceptions of Scope and Stages Sample of 55 (UK, USA) What are the stages in database specification? Where do 26 activities fit? Who does them? Which ones are data modeling? 22

23 Data modeling stages: Very broadly Business Requirements Conceptual (entities and relationships) Logical data modeling Physical data modeling / db design 23

24 Frequency What gets done in what stage? Bus Reqs Conceptual Logical Physical Post Design Activity 24

25 Frequency Is it data modeling? Who is responsible? DM resp DM resp DBA resp Analyst resp User Activity 25

26 2. What is data modeling? Open question: What is data modeling? Closed question: (a) Describing the data requirements of an organization or part of an organization or (b) Designing data structures to meet the requirements of an organization or part of an organization 104 responses USA 26

Number of Participants Analysis of responses to Open Question Coded Responses to Open Question 60 50 40

27 Number of Participants Analysis of responses to Open Question Coded Responses to Open Question Neither Strong Description Somew hat Description Both Somew hat Design Strong Design Response 27

28 Number of Participants Closed Question: Description or design? Responses to Closed Question Description Design Both Response 28

29 3. Perceived characteristics of data modeling 19 questions based on Bryan Lawson s characteristics of design e.g. Data modeling problems are often full of uncertainties about objectives and relative priorities Data modeling requires a high level of creative thinking. In most practical business situations, there is a wide range of possible (and workable) data models. 29

30 Overview of results 30

31 Data modelers, accountants and architects Design 31

32 Part III: Diversity in Data Models 1. Diversity in Conceptual Models 2. Diversity in Logical Models 3. Individual Style 32

Diversity in conceptual modeling A video, transcripts and forms - a real case 93 people submitted models 82% said they understood the requirement very well or

33 Diversity in conceptual modeling A video, transcripts and forms - a real case 93 people submitted models 82% said they understood the requirement very well or fairly well 76% said they made no guesses or only obvious unimportant guesses about requirements. 1 person found the problem very difficult, 22% fairly difficult 33

34 Measures of diversity Peer assessment of difference Entity names (including noun matches) Number of entities Construct decisions Generalization decisions Structural comparison External assessment of sample 34

35 Initial assessment of the models 66 models judged (by me) as workable Number of different entity names across the models: 291 Noun matches from zero to 7 35

36 Frequency Number of entities in the models Total Number of Entities Number of Entities 36

37 Pairing off and comparing models Participants Perceptions of Difference 1% 6% Models were identical 40% 53% Models were identical except for naming/agreed errors Models were structurally different in minor ways Models were structurally different in important ways 37

38 Level of Generalization: Respondent Level of Generalization No Entity Separate entities for Health Worker & Patient Respondent subtyped Respondent Person/Individual/Party Frequency Total 93 38

39 An external evaluation Focus on one component of the model: surveys, questions and responses Select ten models Map to standardized entity names Send them to experts for evaluation 39

40 Number of Evaluations Results of the evaluation Acceptability of Models Model >=Benchmark >=3 40

41 2. Diversity in logical modeling Dept # Year Approved-By Budget-First-Qtr-Mat Budget-Second-Qtr-Mat Budget-Third-Qtr-Mat Budget-Last-Qtr-Mat Actual-First-Qtr-Mat Actual-Second-Qtr-Mat Actual-Third-Qtr-Mat Actual-Total-Mat Budget-First-Qtr-Lab Budget-Second-Qtr-Lab Budget-Third-Qtr-Lab Budget-Last-Qtr-Lab Actual-First-Qtr-Lab Actual-Second-Qtr-Lab Actual-Third-Qtr-Lab Actual-Total-Lab Budget-Other Actual-Other Discretionary-Spending-Limit 41

42 The results broadly 83 responses (UK, US, Scandinavia) Most believed that the model would not change if they had more time, etc Average tables / entities: 5 Range 2-9 entities No two models the same. 42

43 What I looked at Table names Number of tables Choice of construct Table generalization decisions Column generalization decisions Perceived difference Other decisions (dates vs period ) 43

44 3. Style: comparing levels of generalization across models 1. A database to record quarterly budgets and expenditure 2. A database to record a family tree: ancestry and marriages. 3. A database to record details of various kinds of bank loans and the parties involved. Text descriptions of problem 44

45 Frequency Frequency Family Tree and Bank Loans generalization decisions Frequency of Design Option Not Generalised Generalised Customer Party Party Relationship Transaction Design Option Frequency of Design Options Not Generalised Generalised Person Relationship Parenthood Design Option 45

46 Correlation between Family Tree and Bank Loans genl n Demographic Group Kendall s Tau B > 10 models produced 0.54 < <=10 models produced Occupation = Data Modelers 0.52 < Occupation = Non Data Modelers > 6 Years Experience < 6 Years Experience Total Sample 0.51 < p 46

47 Family Tree and Bank Loans vs Annual Budget No significant correlation in generalization decisions Dang! 47

48 Part IV: Feedback from practice I confess that I thought you were a lunatic when I first heard about your conjecture many years ago, but a little self-examination and looking around convinced me you were right - Alec Sharp (Author, consultant, data modeling instructor) 48

49 and As you know I don t agree with you that data modeling is a creative discipline. I must say I am rather critical of the way you try to prove that you are right! - Eskil Swende Data modeling consultancy manager 49

50 but Now your thoughts have reached an academic level great 50

51 Critical reflection 51

52 The inevitable book 52

53 Opening quote (and my closing quote) Forget your perfect offering / There is a crack in everything / That s how the light gets in. - Leonard Cohen 53

Become a Champion Data Modeler with SQL Developer Data Modeler 3.0

Become a Champion Data Modeler with SQL Developer Data Modeler 3.0 Marc de Oliveira, Simplify Systems Introduction This presentation will show you how I think good data models are made, and how SQL Developer