DATA MODELING FUNDAMENTALS (P14) docx

Data Model Reviewers. Of course, a quality assurance program cannot get off the ground without good reviewers. Depending on the size and scope of the data modeling project, you may need more than one reviewer. If a few data modeling projects are running concurrently in the organization, a team of reviewers may rotate among the various projects. The qualification and skills of the reviewers are important. They must know data modeling well enough to be able to look deeply for quality problems in a data model. Good reviewers come out of the ranks of good, experienced data modelers. Standards. The data modelers and the reviewers must have very definitive guidelines and rules about how a data model must be created in your organization. Standards must be devel- oped with utmost care. The standards must encompass all aspects of data modeling. Standards lay down the confines within which the modeling effort must be made. They set the boundaries. They define the tolerance limits for deviations. Both data modelers and data model reviewers need to use the standards manual in carrying out their responsibil- ities. A good standards document serves as a good training tool for data modelers and analysts on the pr oject. Review and Action Document. As soon as the initial review of a data model takes place, the reviewer prepares a review and action document in cooperation with the data modeler. The document incorporates information on the review process. In subsequent reviews of the data model, this document will get updated continually. This document will be part of the overall data model documentation. The review and action document provides a general description of how the review was conducted. It lists the findings of the reviewer. It includes suggested actions to rectify any quality problems cited. It maintains a diary for follow-up. Stages of Quality Assurance Process The quality assurance process is based on a systematic approach of planning and execution. Based on the quality assurance plan, every quality assurance process has defi- nite stages of activities. The plan would indicate the frequency of the quality assurance reviews. In a typical data modeling project of reasonable size, at least two reviews are necessary: One at the midway point, and one when the modeling effort is nearing com- pletion. But many organizations, depending on the availability of resources, opt for three review sessions: One when the project has proceeded one-third of the way, another when two-thirds of the model is done, and the final review when the modelers have nearly completed their work. Whatever may be the frequency of the quality assurance reviews in your organizations, each review goes through the three stages. Each review session gets initiated with a planned document discussed at an initiation meeting. Then the expert reviewers perform the actual review. Finally, the review effort is deemed to be complete when action takes place on the findings and issues raised in the review. We will quickly go through each of these three stages. Review Initiation. The reviewers sit down in a meeting with the data modeling team. Appropriate user liaison persons should also be encouraged to attend the meeting. The meeting must have a definite agenda of topics and issues to be discussed and agreed to 366 CHAPTER 10 ENSURING QUALITY IN THE DATA MODEL at the meeting. A model review checklist is a useful tool in the entire review process. Figure 10-5 is a sample model review checklist. You may alter the checklist to suit the requirements of your organization and the specific data model to be reviewed. Review initiation meeting marks the beginning of the quality assurance review session. If quality assurance in your particular situation consists of three reviews, then each review gets initiated with a separate initiation meeting. Of course, the review initiation for the very first review would be more elaborate. By the second and third reviews, the reviewers and the modeling team would have learned to work and collaborate together. The preparation for these subsequen t reviews would be less and takes less time. The following are the main tasks of review initiation: Model Review Checklist. The reviewers and the modeling team go over the items listed in the checklist. Generally, the list indicates all the materials and resources necessary for the review. The checklist is used to collect the materials through the modeling team and the user liaison person. Model Building Standards. The reviewers and the modeling team go over the standards and parameters in the organization for creating data models. The modeling team informs FIGURE 10-5 Model review checklist. QUALITY ASSURANCE PROCESS 367 the reviewers how these standards have been applied in the efforts. This is especially important if the reviewers are recruited from outside to conduct the reviews. Model Status. The reviewers ascertain from the modeling team the extent of completion of the data model. The reviewers get a sense of the general state of the data model. If this is a second or third review, the modeling team indicates to the reviewers what new modeling components have been added or modified subsequent to the previous review. Specific Issues. The data modeling team informs the reviewers of any specific issues of concern. If some areas of the model are sensitive to specific user groups, the reviewers get to know about these. Also, the user groups relevant to important areas of the data model are highlighted. Next Phases. The modeling team also brings the reviewers up-to-date on the immediate next phases of the modeling effort. Data Model Review. This is the important phase of the reviewing process consuming most of the time. This has to be done thoroughly. The model review checklist will be used continually during this stage to check off review items completed. At the same time, during the review process, the reviewers will prepare a separate document to record their findings. Figure 10-6 is an example of a format for recording the findings and issues. The next subsection deals with data model review in more detail. FIGURE 10-6 Model review: findings and issues. 368 CHAPTER 10 ENSURING QUALITY IN THE DATA MODEL Actions on Findings. The record of findings and issues indicates who would be responsible for the resolutions of the items. Sometimes, user represen tatives may be named as those for taking particular actions. A rep ort of actions taken on the findings will be added as a supporting document to the findings document. The completion of all actions for the resolution of issues and findings marks the end of the model review at this point. If this is the first review of a data model, the reviewers have an opportunity to give specific pointers and guidance to the project team. Data Model Review This subsection describes the actual data model review stage in sufficient detail. It lists the major activities of the stage. The list given orders the activities in their logical sequence of how these should take place. As indicated earlier, the model review checklist is used extensively and continually in this stage. The goal is to complete all the items listed in that checklist. However, the hand- ling of the items on the list happens in a systematic manner. Preliminary Model Review. This is just a quick glimpse of the data model—nothing detailed at this point. The reviewers perform a quick walk-through of the data model diagram with the modeling team. They also scan through the contents of accompanying data model document. Again, this is not an elaborate step. If the data model is quite small or moderate, this step may also be done during the model review initiation. Assessment of Modeling Team. During the model review initiation, the reviewers get a chance to get acquainted with the modeling team and other user representatives. The reviewers need to build up lasting relationships with the team in order to complete their work. The reviewers get to understand the level of the skills and experience of the memb ers of the modeling team. This will help the reviewers to match up the team’s background with the particular data model and help them to concentrate more on specific parts of the mod- eling effort. If the team members are not sufficiently strong on identifying relationships among categories of relationships, then this is an area for particular attention by the model reviewers. Review of Model Standards and Management. The data modeling team has to follow approved standards in the organization and manage its data modeling effort accord- ingly. Data model reviewers have a responsibility to ensure that this had happened. The data model reviewers study the standards and procedures of the organization care- fully. If there is a pool of approved standard entity types and attributes, then the data mod- eling team must draw their components from this pool as far as feasible. If the current data model is an add-on to existing data models that had been implemented, then the mo del reviewers need to know the standards for integrating data models. Documentation Study. This is an important prerequisite for performing the data model assessment effectively. Before launching a very detailed assessment of the data model, the model reviewers study various documents and materials in great detail. QUALITY ASSURANCE PROCESS 369 The following indicate the materials and documents to be studied: . Other data models already completed and in use . The place of the organization in the industry . Organization’s core business . Organization’s overall business operations . Business plans . Relevant policy documents . Applicable business rules . Notes from interviews and group sessions held by the modeling team Data Model Assessment So far, we have covered the preliminary activities that lead to the detailed review and assessment of the data model itself. The outcome of the assessment would be a series of findings of the data model reviewers. Proper actions to resolve the issues and rectify errors pointed out as findings measure the success of the entire review and assessment process. Data model assessment consists of several tasks. If you have transformed your concep- tual data model into a logical data model in the form of a relational system or any other prevalent types, then data model assessment becomes more intricate and involved. However, if the model to be assessed is a conceptual data model at a higher level, then model assessment becomes comparatively easier. In order to discuss model assessment in a more intricate form, we will take up the assessment of a logical data model. Once you understand the principles for a logical data model, then applying the principles of model assessment to a generic conceptual model would be simpler. For a relational database system, remember the logical model is the relational data model. Data is represented as two-dim ensional tables. The following gives an indication how data model assessment proceeds and which tasks are normally performed. Data Model Subdivision. The first task is to make the model assessment task manage- able. Subdivide the data model into logical subsets. Then the assessment of each subset could become easier. A few methods are available for subdividing a data model. If the parts of the model can be clearly connected with the responsibilities of particular user groups, then each such subset may be handled separately. The data model reviewers will work with the modeling team in determining the proper method for subdividing the model. Once the model is subdivided using the best approach, then the reviewers can arrive at a sequence for assessing individual submodels. Component Clusters. If the data model does not subject itself to subdivision by user groups, another popular method is to subdivide the model into component clusters. You check for clusters of entity types that are linked together by entity dependencies. Here, we need to assume that all many-to-many relationships have been resolved into one-to-many relationships. The structures are in the Boyce –Codd normal form. Optional attributes have been pushed down to the subtype entities in generalization and specialization. 370 CHAPTER 10 ENSURING QUALITY IN THE DATA MODEL While adopting the entity-dependency method for identifying component clusters, use the following steps: . Review the entire data model and identify the entity types that have no children. These will be seen as end points in the data model diagram. . From each of the end points, trace back to the parent entity types, one step at a time. Stop when you reach entity types that have no parents. These would typically be inde- pendent entity types. While you trace back, all the entity types that were touched along the way would form a family or cluster of compon ents. . Name each cluster of entity types and note for model assessment. Later on, in the assessment and documen tation, these names may be used for reference. . Note and mark the attributes, identifiers, and relationships in each cluster as a complete unit for assessment. . Determine the ideal sequence for assessing the clusters, one at a time. Figure 10-7 shows a partial data model diagram and notes how a cluster is identified. Data Model Evaluation. As soon as the clusters are identified, the actual evaluation activity ensues. The reviewer takes each cluster, object by object, and begins the evalu- ation. All the supporting documents provide the necessary information for performing the evaluation. The following tasks make up data model evaluation. Syntax Verification. Begin by reviewing and evaluating independent entity types. Evalu- ate the attributes of each of these independent entity types. Check the relationships that emanate from these entity types. Next, do the same tasks for dependent entity types. FIGURE 10-7 Model assessment: identification of clusters. QUALITY ASSURANCE PROCESS 371 Reverification. In order to confirm the verification, perform a backward pass. Trace back the same paths starting from the end points. Evaluate the entity types that were not eval- uated in the forward pass. Business Rules Repre sentation. Go through documentation of business rules govern- ing relationships among entity types. Evaluate the data model to ensure that these are properly represented. Conceptual Review. Use the same set of component clusters to perform this task. Here, the task ensures that every business concept and statement found in the requirements definition finds an expression in the data model. Findings and Actions. The cluster references may be used to record the findings of the quality assurance process. The following tasks comprise the documentation of findings, actions on findings, and termination of the quality assurance process. Recording Findings and Issues. Reference each cluster evaluated and record full details of results of the evaluation. If any CASE tool is available in the organization for performing this task, take advantage of the availability. Keeping Track of Evaluations. Use the findings documentation to keep track of all reviews, findings, and actions. Resolution of Issues. Arrange to provide assistance to the modeling team for resolving issues and errors that surfaced during the evaluation process. Also, provide methods for documenting how each issue gets resolved. FIGURE 10-8 QA: findings and actions. 372 CHAPTER 10 ENSURING QUALITY IN THE DATA MODEL Termination Meeting. Reviewers conduct a final meeting for each review with the project team and any user liaison persons. They go through the findings document and all settle on how follow-up will be done. Figure 10-8 shows a sample findings document and notes how findings and actions are documented. CHAPTER SUMMARY . Quality is important in the data model as well as in the definitions of individual components. . A data model must be of high quality for it to serve as an effective tool for commu- nicating with the users and to be an efficient blueprint for database construction. . Good definitions of data model components have the following characteristics: correctness, completeness , clearness, and right format. . Correct definitions imply the following: reviewed and confirmed, and consistent with organizational understanding. Complete definitions are not too broad, not too narrow, are self-contained, and are supplemented with examples. Clear definitions do not repeat the obvious, contain obscure terminology, or use unknown abbreviations. . Data model quality dimensions: correctness (syntactic and conceptual), completeness (syntactic and conceptual), and proper organizational context. . Stages of quality assurance process: review initiation; data model review and assess- ment; action on findings. . Phases of data model review: preliminary review; assessment of modeling team; study of standards; and documentation study. Phases of data model assessment: data model sub- division; ascertaining component clusters; data model evaluation; findings and actions. REVIEW QUESTIONS 1. True or false: A. Users can underst and a good data model diagram intuitively. B. A definition of a model object may be considered good if it conveys a general overall idea about the object. C. It is not necessary for all definitions to be reviewed and confirmed by domain experts. D. Good definitions must not be too broad or too narrow. E. Data model quality implies correctness and completeness. F. If the correct symbols are used in a data model, the model is said to be concep- tually correct. G. Responsibility of a quality assurance coordinator is mostly administrative. H. Every data modeling project must have three data model review cycles. I. Preliminary model review is usually a detailed examination of a data model by the reviewers. J. Broad definitions intentionally use ambiguous words in order to avoid conflicts. REVIEW QUESTIONS 373 2. Do you agree that quality of a data model is of paramount importance? Give your reasons and explain. 3. Describe the meaning and role of definitions of data model components. What are the aspects of quality definitions? 4. When can you say a definition is correct and complete? List the factors. 5. Name any three characteristics of a good data model. Give examples of each. 6. Discuss the quality dimensions of a data model. Differentiate between correctness and completeness. 7. Describe the role of the quality assurance coordinator for a data modeling project. 8. Data model quality control and assurance is a mindset. Discuss. 9. List the quality assurance phases of data model review and data model assessment. Describe the detailed activities in any two of the phases. 10. The success of quality assurance completely depends on the data model reviewers. Discuss this statement giving your reasons. 374 CHAPTER 10 ENSURING QUALITY IN THE DATA MODEL 11 AGILE DATA MODELING IN PRACTICE CHAPTER OBJECTIVES . Introduce the agile movement . Review the principles of agile software development . Understand agile data modeling . Explore basic and auxiliary principles of agile modeling . Examine primary and additional practices of agile modeling . Discuss agile documentation . Learn to recognize agile data models . Study evolutionary data modeling in detail The adoption of agile software development methodology is a recent phenomenon. The benefits derived from the practice of this method have propelled this new set of principles and practices to wider acceptance. More and more organizations have begun using the new methodology. It has now permeated every aspect of software development—analysis, design, data modeling, generating code, and even project management. Lately, several books have appeared on the scene, not ably by Scott W. Ambler and Sanjiv Augustine. I am indebted to these authors for the material in this chapter. Note the reference to these publications and others in the bibliography at the end of the book. As this methodology is likely to be unfamiliar to many readers, we will begin with an introduction to the agile movement itself. As you will see, the methodology is not a set of “how-to’s.” It actually consists of overarching guidelines for the practice of some funda- mental principles. Practicing agile principles requires a certain mindset; a certain willing- ness to be flexible and nimble in software development. Change is real and change must 375 Data Modeling Fundamentals. By Paulraj Ponniah Copyright # 2007 John Wiley & Sons, Inc. [...]... principles of agile modeling Pick out any two of these principles and explain how these two improve the data modeling process 8 Describe how the data modeling process will change when you adopt agile modeling Give a simple example 9 How is evolutionary data modeling different from the traditional approach? Describe briefly 10 List the benefits of evolutionary data modeling 12 DATA MODELING: PRACTICAL... additional practices in agile modeling Again, these are not methods or techniques These indicate the way overall modeling is carried out when you adopt agile modeling These practices are underlying guidelines for agile modeling Primary Practices Primary agile modeling practices include the following 382 CHAPTER 11 AGILE DATA MODELING IN PRACTICE Use Simple Tools CASE modeling tools are useful, but... evolutionary data modeling As modeling is performed in small, manageable increments, the modeling task is simplified Iterative modeling fine-tunes the model at each iteration It promotes review and feedback by users at frequent and regular intervals It enables changes to requirements to be incorporated in the model as the iterations continue FIGURE 11-3 Evolutionary data modeling 388 CHAPTER 11 AGILE DATA. .. adjustments Philosophies The agile data (AD) method applies to all activities relating to data modeling and design of the database system The agile principles stated above act as guidelines for the agile data method The principles are rooted in certain underlying philosophic considerations Agile data philosophies include the following Significance of Data An organization’s data is the centerpiece of all... possible Emphasize Simple Components Use the simplest modeling components for the purpose at hand Adopt Incremental Modeling Create partial data models in small increments Share Ownership Allow all data modelers collective ownership of the models Promote Collaborative Modeling Ensure that data modelers can work together and cooperate Apply the Right Modeling Artifact Use the proper component for the specific... norm, agile modeling provides practices to accommodate changes and revisions Strong Support of AM Enthusiast You need the complete support of an AM champion at a sufficiently high executive level, especially if agile development is new to your organization EVOLUTIONARY DATA MODELING Evolutionary data modeling is a key for the adoption of agile modeling principles and practices Evolutionary modeling allows... Evolutionary Modeling When you perform data modeling in an evolutionary manner, a key element in the whole process is the accommodation of feedback and change You do modeling in small increments You pause and look for feedback You incorporate changes based on feedback and reiterate The cycle continues until the data model is right up till then FIGURE 11-2 Flexibility in agile development EVOLUTIONARY DATA MODELING. .. main focus in this book, namely, data modeling and how agile development principles would apply to data modeling Let us begin our discussions of agile modeling Agile development has a wider connotation The term may be applied to a collection of values, philosophies, and practices for requirements, analysis, architecture, and design We will narrow these down to the data modeling effort in a development... development principles and practices apply to data modeling We will conclude with a close look at evolutionary data modeling a direct outcome of adopting agile development principles THE AGILE MOVEMENT Although principles of agile development cover the wider aspects of software development, our primary focus is on the data- oriented issues, and particularly on data modeling Software development like most... chapter, we will expand the suggestions to logical data modeling Remember, these suggestions and tips may not apply exactly to your particular circumstances Nevertheless, they will provide insights into how you can best adapt them and use them Data Modeling Fundamentals By Paulraj Ponniah Copyright # 2007 John Wiley & Sons, Inc 391 392 CHAPTER 12 DATA MODELING: PRACTICAL TIPS TIPS AND SUGGESTIONS Although . your organization. EVOLUTIONARY DATA MODELING Evolutionary data modeling is a key for the adoption of agile modeling principles and practices. Evolutionary modeling allows for the creation of the data model in. Model Standards and Management. The data modeling team has to follow approved standards in the organization and manage its data modeling effort accord- ingly. Data model reviewers have a responsibility. assurance coordinator for a data modeling project. 8. Data model quality control and assurance is a mindset. Discuss. 9. List the quality assurance phases of data model review and data model assessment. Describe