Data Quality Dimensions

Transcription

1 Stakeholder s Perceptions on Data Quality Dimensions A case study in a Health Care Environment Supervisor: E- mail: Dr. M.A. Jeusfeld manfred.jeusfeld@uvt.nl Student: S.K. Pham E- mail: s.k.pham@uvt.nl ANR: Thesis: Subject: Master of Science Data Quality Dimensions 0

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3 Institution: Faculty: Department: Program: Exam Committee: Tilburg University Economics and Management Information Management Master of Science Dr. M.A. Jeusfeld Dr. B.A. van de Walle Warandelaan 2 P.O. Box LE TILBURG The Netherlands 0

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5 FOREWORD This Master Thesis is the final piece of the Information Management Master programme at Tilburg University. The subject matter of this thesis is data quality dimensions. This subject was chosen because of my participation in the MIS/ data warehouse team at the company. I realized that the success of the data warehouse depends on two very important variables. First, it is the data quality of the data warehouse and second, the stakeholders. That was the starting point to do research about this subject and my thesis is the result of a long period of research that combines theory with practical data. It has been very tough to combine a fulltime job and doing the Master thesis. Without the guidance, patient and understanding of my supervisor Manfred Jeusfeld, this thesis would never come to an end. Therefor I would like to thank my supervisor for his efforts and advices during this period. Beside my supervisor, I would like to thank my colleagues at work for helping me collecting the data and participation in this research. Without their input, some sources of evidence would be missed and the research would be incomplete. Last but certainly not least, I would like to thank my parents, sisters, brothers, family, friends, former teachers and classmates, former colleagues and supervisors from previous employers for their support and believe in me. It has almost been a decade, but now I finally see the end of my study career at Tilburg University. Thank you all for your support and believe in my study career at Tilburg University!! S.K. Pham Eindhoven,

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7 This thesis is dedicated to my mother, she makes me accomplish things I thought it was impossible. 0

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10 TABLE OF CONTENTS FOREWORD TABLE OF CONTENTS INTRODUCTION Problem introduction Problem statement Research question Conceptual model and propositions Explaining the variables THEORETICAL BACKGROUND Data warehouse Data quality Data quality dimensions The stakeholders RESEARCH APPROACH Research method The theoretical base Literature study The research design and framework The four quality tests Collecting the evidence Data collection: s Data collection: Data collection: Direct observations Data collection: Incidents and changes THE CASE COMPANY Dutch Health Care Company The four major applications The data warehouse environment The data warehouse architecture Management Information System and Reports MIS Support and Maintenance THE CASE STUDY The Human Resource Management Application Conducting the case Master Thesis IM

11 5.1.2 Applying the framework Results The Health Care Application Conducting the case Applying the framework Results The Customer Relationship Management Application Conducting the case Applying the framework Results The Management Information System Conducting the case Applying the framework Results The final results CONCLUSION Conclusion and Discussion Contributions to research and practice Limitations and future research REFERENCES Literature Company sources APPENDICES Appendix 1: Data Quality Definitions Appendix 2: Pattern Matching by Tony Hak & Jan Dul Appendix 3: Data Warehouse Architecture Appendix 4: Standard questions for interview Appendix 5: Appendix 6: Observations Master Thesis IM

12 1 INTRODUCTION Data quality is important within the context of data warehouses. The search about this topic in a data warehouse environment gives results from several perspectives. This chapter introduce the stakeholder s perspective from a data quality point of view in a data warehouse environment. 1.1 Problem introduction In search for literature about quality several articles are found. There are a range of researches from various backgrounds that have already studied the subject quality. Some of these are Total Quality Management, Information Quality, Service Quality, System Quality and Data Quality. In this research the focus is on Data Quality. In this context there are also various perspectives, such as costs, architecture, impact of data quality on decision- making, etc. The most interesting perspective for this research is the stakeholder s perspective from Giannoccaro, Shanks, Darke [2]. The paper discussed data quality from a stakeholder s perception in a data warehouse environment. The authors proposed a framework for understanding stakeholder data quality requirements that is used to define associations between stakeholder types and specific categories of data quality dimensions. A number of associations were proposed and then examined empirically in a case study. The case study led to a deeper understanding of stakeholder data quality and emergence of implications for data warehouse practitioners. During the literature review seems that almost all data warehouse initiatives have data quality problems and that it is a challenge for all practitioners to understand data quality for a successful implementation, use and maintenance of the data warehouse [1,2,3,4]. It is clear that data quality has a huge influence on the success of a data warehouse and on the other hand, stakeholders have associations with data quality. This concludes that stakeholders and data quality are important variables for the success of a data warehouse. With this in mind, the starting point of the thesis is set and the research can take- off. 1.2 Problem statement The research done by Giannoccaro, Shanks, Darke [2] is valuable for all data warehouse practitioners. Despite its value, none current research had been done lately about this topic. The article is more than 10 years old and need to be revised. Stakeholder s perception on data quality is important because if practitioners do not consider data to Master Thesis IM

13 be of high quality, they use the data warehouse less or not at all [2]. DeLone & McLean [15], Berg [27] stated that system use is an appropriate measure for systems implementation success. Based on these findings, the assumption is that system implementation success is determined by the perception of stakeholders on data quality. Now the research question can be defined. 1.3 Research question From the problem statement the following research question can be formulated: Why is data quality important for a successful data warehouse implementation? Conceptual model and propositions To answer the research question the variables data quality and stakeholders have to be researched. Next to this, a clear definition of the variables must be defined. Because of the time limit for the research and data quality exists of 15 dimensions, only the dimensions accuracy, timeliness and completeness are selected for the research. These dimensions are randomly selected, within the categories intrinsic and contextual. The variables are explained and defined in the following chapters. Accuracy Timeliness Perception of DQ By Data Consumers Completeness Figure 1: Conceptual model Dependent variable is Independent variable is Independent variable is Independent variable is Y: Perception of data quality by data consumers X1: Dimension Accuracy X2: Dimension Timeliness X3: Dimension Completeness Master Thesis IM

14 Propositions P1: Dimension accuracy has influence on the perception of data quality by data consumers P2: Dimension timeliness has influence on the perception of data quality by data consumers P3: Dimension completeness has influence on the perception of data quality by data consumers Explaining the variables The independent variables Independent variables in this model are selected data quality dimensions. The dimensions are explained further in this section according to the definitions of Wang & Strong [8]. Accuracy: data are certified error- free, accurate, correct, flawless, reliable, errors can be easily identified, the integrity of the data, precise. Timeliness: age of data. Completeness: breadth, depth, and scope of information contained in the data. The dependent variable Perception of data quality by data consumers is the dependent variable in this model. Master Thesis IM

15 2 THEORETICAL BACKGROUND Data warehouse and the importance of data quality will be addressed in this chapter. Information will be gathered from several articles to form a theoretical base. 2.1 Data warehouse A data warehouse is an analytical database used for decision support. Data is extracted from other production databases and reorganized to be loaded in the data warehouse. With on- line analytical processing (OLAP) technologies, management information is extracted to make better and faster decisions [1,6]. For decision support there are different kinds of data needed than data stored in operational databases. For instance, understanding trends historical data is needed, whereas operational databases store current data. Data is collected from heterogeneous sources and therefore might consist of inconsistencies. Extracted data must be reorganized and cleaned first before loading into the data warehouse. The data in the warehouse are than used (run complex queries) by decision makers. For good and fast decisions the query throughput, response time and quality of data are very important [1,2,3,6]. 2.2 Data quality Many organisations develop data warehouses because of several reasons. Some of them are cost reduction, support business process and provision of data. In almost every research the authors mentioned how important data quality is for data warehousing, therefore the success of data warehousing initiatives depends on data quality [1,2,3,4]. Poor data quality can have substantial social and economic impacts [2,8,16]. Best examples of these impacts are mentioned by Wang & Strong [8] about the New York bank with incomplete data of its credit- risk management system and a manufacturing company that could not access it sales data for a single user. Furthermore, data quality is more than just the accuracy of data. Other mentioned dimensions are completeness and accessibility of data. All data dimensions will be reviewed in paragraph See appendix 1: Data Quality Definitions, for an extensive explanation of each definition Data quality dimensions From all the papers that are found about data quality dimensions, the one from Wang & Strong [8] is the most extensive and has been cited by several authors in their research about data quality. This paragraph is based on the Wang & Strong research about Data Quality Dimensions. All dimensions will be mentioned and explained if necessary. Master Thesis IM

16 Wang & Strong start their paper by explaining the importance of data quality and its social and economic impacts. They define data quality as follows: Data that are fit for use by data consumers In addition, data quality dimension is a set of data quality attributes that represent a single aspect or construct of data quality. Wang & Strong summarized all data quality attributes and categorise them into categories and dimensions. According to them, data quality is categorized into intrinsic, contextual, representational and accessibility. Within these categories there are several dimensions that together forms the data quality category. This is summarized in the conceptual framework of data quality in figure 2. For this research the data quality definition of Wang & Strong is used. Data Quality Intrinsic Data Quality Contextual Data Quality Representational Data Quality Accessibility Data Quality Believability Accuracy Objectivity Reputation Value- added Relevancy Timeliness Completeness Appropriate amount of data Interpretability Ease of understanding Representational consistency Concise representation Accessibility Access security Figure 2: Conceptual Framework of Data Quality Intrinsic data quality Believability: believable Accuracy: data are certified error- free, accurate, correct, flawless, reliable, errors can be easily identified, the integrity of the data, precise. Objectivity: unbiased, objective. Reputation: reputation of the data source, reputation of the data. Contextual data quality Value- added: data give you a competitive edge, data add value to your operations. Master Thesis IM

17 Relevancy: applicable, relevant, interesting, usable. Timeliness: age of data. Completeness: breadth, depth and scope of information contained in the data. Appropriate amount of data: the amount of data. Representational data quality Interpretability: interpretable. Ease of understanding: easily understand, clear, readable. Representational consistency: data are continuously presented in same format, consistently represented, consistently formatted, data are compatible with previous data. Concise representation: well- presented, concise, compactly represented, well- organized, aesthetically pleasing, form of presentation, well formatted, format of the data. Accessibility data quality Accessibility: accessible, retrievable, speed of access, available, up- to- date. Access security: data cannot be accessed by competitors, data are of a proprietary nature, access to data can be restricted, secure. 2.3 The stakeholders From various articles, only Giannoccaro, Shanks and Darke [2] have done research about data warehouse from a stakeholder s perspective. This paragraph will summarize all stakeholders, their involvement with data warehouse and views on data quality based on paper [2]. Giannoccaro, Shanks and Darke indicated that each stakeholders type have a different view on data quality and therefor has a different level of importance between data quality dimensions. But all stakeholders confirm the importance of data quality. Giannoccaro, Shanks and Darke [2] categorize stakeholders into four types. These are data producers, data custodians, data consumers and data managers. Data producers: Are those who create or collect data for the data warehouse. The following data quality dimensions are important: - Concise representation - Accuracy - Believability Master Thesis IM

18 - Relevancy The following data quality dimensions are less or not important - Contextual Data custodians: Are those who design, develop and operate the data warehouse. The following data quality dimensions are important: - Accuracy - Relevancy - Reputation - Timeliness The following data quality dimensions are less or not important - Not mentioned Data consumers: Are those who use the data in their work activities. The following data quality dimensions are important: - Timeliness - Relevancy - Accessibility - Access security - Representational consistency - Accuracy The following data quality dimensions are less or not important - Not mentioned Data managers: Are those who are responsible for managing the entire data warehousing process. The following data quality dimensions are important: - Relevancy - Accuracy - Concise representation - Accessibility - Completeness The following data quality dimensions are less or not important - Not mentioned Master Thesis IM

19 3 RESEARCH APPROACH This chapter discuss the research method, how it is conducted and the four validity tests to assure the quality of the research. 3.1 Research method The case study method is selected for this research. As a research strategy, case study method has proved its value in the scientific field. Next to this, the case study method is interesting because of its possibility to compare theory and practical evidence. This thesis follows the Yin s case study method. Because of the possibility to get real data and cooperation with stakeholders in a real setting, the case study method seems to be the most appropriate method to apply for this research. The purpose of the case study is to investigate the perception of a certain stakeholders group about the data quality dimensions and to test the empirical validity of the conceptual model and its propositions. The research itself consists of three phases. The first phase is building the theoretical base as a starting point. The second phase is building the research design for case studies. The last phase of this research is conducting the case study itself. 3.2 The theoretical base Literature study The research starts with a background literature study to form a solid foundation about the subject. To be aware and prevent misinterpretation, it is important to define data quality, it s dimensions, the stakeholders and the data warehouse. This phase has passed the review in chapter The research design and framework Based on the literature study and the Yin case study method for conducting a case study, a framework has been designed. This framework is the base for conducting the research. All variables to conduct the case study have been put into the framework. See figure 3: The framework. All data quality dimensions are included in the framework; this is done to check which dimensions have or have no influence on stakeholder s perceptions. Next, the sources of evidence will be collected from several applications. This is to check and compare the findings if there are differences between data quality perceptions between applications. Master Thesis IM

20 The purpose is to collect sources of evidences from the major applications within the case company and match the sources with the dimensions. Matching is based on the pattern matching logic technique, see also appendix 2: Pattern Matching. If a source match with a dimension, than it will be considered as has influence, if no sources are found that match a certain dimension means there is no influence. Matching the sources and dimension is based on the definition of the dimension. For example, in the source documentation it is noted that data/information in any kind of form (such as table, report, etc.) must be available for only managers. This notation matches with the dimension access security. All dimensions have been explained earlier so there will be no misinterpretation issues. All sources that match with a dimension will be noted just once. This way of working will guarantee the research quality and no misinterpretations are made. This research design and framework will be conducted in chapter The four quality tests To test the validity of this research the four commonly used quality tests for empirical research will be validated. These four validity tests are: construct, internal, external and reliability. If the results of these tests are positive than the quality of the research can be confirmed. These tests will be viewed and concluded in the final chapter. Stakeholders Applications Sources Dimensions Application 1 Application 2 Application 3 Application 4 Source 1 Source 2 Source 3 Source 1 Source 2 Source 3 Source 1 Source 2 Source 3 Source 1 Source 2 Source 3 Intrinsic Data Quality Believability Accuracy Objectivity Reputation Contextual Data Quality Value- added Relevancy Timeliness Completeness Appr. Amount of data Representational Data Quality Interpretability Ease of understanding Representational consistency Concise representation Accessibility Data Quality Accessibility Access security Figure 3: The Framework Master Thesis IM

21 3.3 Collecting the evidence To collect the evidence, several different resources will be investigated in order to apply data triangulation. Triangulation is about using multiple sources to come to findings for analysis. The strong aspect of triangulation is that the findings are more convincing and accurate because it s based on several different sources of information. For the case study the following sources of evidence will be used: - s o Minutes of meetings from several projects o Contracts from several software suppliers o Reports from several projects o Memo s during projects - o Data consumers - Direct observations o Field visits from several departments - Incidents and changes of the application o Request for changes o Issues For empirical validity, at least four (sub) sources from major applications within the company will be collected. For each source of evidence a well- defined process for collecting the data is prepared. In the following sections the process of data collection for each source of evidence is defined Data collection: s s mentioned earlier will be collected from the major applications. Most documents are digital available except the minutes of meetings. For each major application a folder will be composed for the collected documentations. These documentations will be used for later analyses Data collection: It will be guided interviews of an hour with a focus to the subject data quality about a certain application. The preparation for the interviews is selecting the application, interviewees and preparing standard questions for the interviews in advance. See also appendix 4: Standard questions for interview. All interviews will start with explaining the interviewees about the purpose of the research, the interview and data quality with its dimensions. Open ( how and why ) and follow- up questions will be asked. For Master Thesis IM

22 confirmation, closed ( yes or no ) questions will be asked. Sometimes certain questions will be repeated but asked differently to test if the interviewee gives the same answer. When there is doubt if the interviewee understands the question or the reason why a certain question is asked, an explanation will be given for a clear understanding. This is a guided interview, which means the interviewer guide the conversation and cut the conversation if it takes to long about a certain topic, question or it leads to a wrong direction. At least ten users of several departments with several IT skills will be interviewed. All interviews will be noted for later analyses. See also appendix 5: Data collection: Direct observations There will be field visits to observe behaviours of the actors. The purpose is to observe the actors during their daily work with a certain application. The procedure is simple, just watch and hear, no questions will be asked. The observer will note the behaviours of the actors how they administer the application and if they encounter certain problems or issues about the application. The observations will be noted for later analyses. See also appendix 6: Observations Data collection: Incidents and changes The data collected here are change requests and issue reporting lists of several applications. There is no certain procedure for this. Just collecting the request for changes and issues for certain applications. These data can be used for corroboration or contradiction with other sources. Master Thesis IM

23 4 THE CASE COMPANY The case study concerns data quality requirements in a data warehouse project within a large Dutch health care company with over 1500 employees in This chapter introduces the case company and the data warehouse. 4.1 Dutch Health Care Company The case company is a large Dutch health care company in Noord Brabant, The Netherlands with over 1500 employees in The last few years there have been significant reductions in employees because of reorganization and collaborations with other health companies. Because of the decrease in financial income, changing government regulations, changing demands of customers and aging of the Dutch population, the company has to do more with the minimal financial and human resources it has. Therefor, the company is moving towards a more business driven approach to deliver their health services. The strategic aim of the company was to create an organizational structure, which is flexible enough for change to fulfil the company s goals. The new structure must lead to a more effective leadership, decision- making, communications and performance monitoring. Several projects are started and some are still on going to achieve this aim. One of these projects is developing a data warehouse, which is the Management Information System (MIS) of the company in order to provide high quality information for internal, external report obligations and support business decisions. The MIS project team consist of an external project manager/advisor, data warehouse system administrator, the financial controller and the team coördinator. Data producers are not directly part of the team. An external supplier takes care for the design and development of the data warehouse. Next to the MIS project, several other (software) projects have been started, to rethink and automate business processes. Some of these projects are implementing the new HRM and Health Care Applications. One of the goals of implementing these applications is to achieve flexibility and performance improvements of the administrative processes. Implementing these applications brings changes to the company, e.g. way of working, employee reduction, etc. The business issues that comes along with implementing these applications is not in the scope of this thesis. Master Thesis IM

24 Because of confidential data and information, the case company will be named TWINGATE. Therefor all data, information, interviewees and resources have been renamed to keep everything anonymous. 4.2 The four major applications This chapter will introduce the major applications of the company. The purpose is to collect evidence from the four most important applications. The degree of importance is based on manager s value of the application, number of users, amount of data the application has and the costs of implementing the application. The amount of data is based on the size of the application s database. Taken this into consideration, the four major applications of TWINGATE are: The HRM Application, Health Care Application, Financial Application and the Management Information System. The numbers of users noted in the table is just an approximate and not the exact number. The database size can change during time, therefor the numbers noted in the table are just current snapshots. The costs are an average amount, which the company paid for implementing the application. Management value is based on how many senior managers and board members are involved in the process of software selection, implementation and degree of communication within the company. If three or more senior managers involved in the project, the management value is noted as high. See table 1 for a summary. HRM HCA FA MIS Mngt value High High High High Users < Data > 15GB > 15GB ± 10GB > 40GB Costs > > N/A > Table 1: The 4 Major Applications 4.3 The data warehouse environment The data from the major applications are extracted, transformed and load into the data warehouse. With several tools the data will be aggregated and published for the end- users. TWINGATE use the MICROSOFT SQL SERVER (BI 2008) for developing, configuring and managing the databases. This is also for the data warehousing process of Extraction, Transformation and Load. In the following sections the architecture of TWINGATE s data warehouse will be viewed in more detail. Master Thesis IM

25 4.3.1 The data warehouse architecture The input sources for the data warehouse are the relational databases of the HRM Application, Financial Application and the Health Care Application. In this research these relational databases are called: HRM DB, FA DB and HCA DB. OLE DB is used to access the HRM DB and HCA DB, FA DB can be accessed with ODBC. To be sure the data are correct, it has to be cleaned first. This is done with SQL scripts. After cleaning, the data are set up and prepared for extraction, transformation and loading into the staging database. Transformation is needed so the database tables fit in the data warehouse schema. This process is done with SSIS, a service within the MS SQL BI The staging database is part of the data warehouse environment called MISERVER 2. In the MISERVER 2 environment there are three databases coupled with its cubes: BI Control (Cube 1), BI (Cube 2) and BI - 1 (Cube 3). These databases have the same structure and extract direct or indirectly data from the staging but are used differently. BI Control is for the daily- actualized reports. Data is directly from the staging. BI is for the monthly- accountability- reports from previous months till the last month, financially approved by the department Finance & Control. Data is copied directly from BI Control. BI - 1, is the same data as BI 1, minus 1 month. This is used for quarterly analyses. Data is accessible for up to three years, which means the data from this year, previous year and previous year - 1 (2 years ago) can be accessed on detail level. The MISERVER 2 is modelled as a star schema, which exists of a fact table, and several dimension tables. This way of modelling makes its possible to view financial data from diverse dimensions. This way of modelling is presented in the cube for multidimensional analyses. For financial data with the dimensions year, period, version and department the schema could look like this: Dim_period Dim_version Fact_financial Dim_year Dim_department For the front- end, TWINGATE use the Reporting Server (SSRS), a part of the MS SQL BI solution and SHAREPOINT SERVER solution. With SSRS, data from the cubes are presented in reports and published as a sharepoint (web) site for the end users. The Reporting Server and Sharepoint Server are both on the MISERVER 1 environment. On the client side, end- users make use of client front- end browsers such as Microsoft Master Thesis IM

26 Internet Explorer to approach the sharepoint site. Advanced users make use of the extended tool from Excel to open data directly with Excel web services or with Pivot tables. Financial controllers mostly use Pivot tables, which have a direct connection with the cubes. For the more advanced users the MISERVER 1 is not appropriate, it takes to much loading time. The data warehouse architecture is summarized in figure 4. See also appendix 3: Data Warehouse Architecture Management Information System and Reports The MISERVER 1 is to publish reports for managers to check and control their management information. This information, called management information (e.g. KPI s, complaints information, personnel information, etc.) in the form of reports, is aggregated data and financially checked by the Financial & Control department. MISERVER 1 in combination with the client side is called the MIS. For the end- users the MIS is the sharepoint site where they can approach management information reports. Depends on a user s credential, he/she can zoom in/out the report. For example: Division managers can see information of the whole division and can zoom in at individual (employee) level if the cube is modelled to show the information. A department manager only has credentials to view information on department level. Not all reports will be published in the MIS. Only management information will be published and not operational information. The process of requirements analyses will take place upfront between the manager and the financial controller and only if the financial controller approve that the report that is required by the manager is indeed necessary for control and decision making on management level than the report will be build and published by the IT department or external supplier MIS Support and Maintenance The IT department is responsible for authorizing users in the system and maintenance of the MIS. Depends on the users function and responsibilities, the financial controller authorize the user on functional level and the administrator takes care of the technical level. Every day the MIS system administrator(s) has to check if the MIS is up and running. Other checks includes if the source data are extracted and loaded into the data warehouse. All these activities are automated in the form of jobs (a functionality within MS SQL SERVER). If a job is not running, the administrator has to start it manually. Besides checking the jobs, the administrator also has the responsibility to update the reports. A change in the star schema or table in one of the source databases leads to a dysfunctional report. When new reports are requested, the administrator is responsible Master Thesis IM

27 for publishing on MISERVER 2 after the report is built by the supplier. The supplier uses the MISERVER 3, a test environment that has the same structure and data as the MISERVER 2. It is an exact copy of MISERVER 2 where the supplier built and tests the reports before publishing on the production environment (MISERVER 2). Almost every month the data on MISERVER 2 has to be frozen and copied to BI and BI- 1. The technical part of this action is also the responsibility of the administrator. The financial & control department gives the administrator a sign when they have closed the monthly booking. This action includes backing up and restoring the cubes and databases. The data from the first cube and database will be back- upped and restored to the second and the cube and database from the 2 nd will be back- upped and restored on the 3 rd cube and database. Next to the monthly closing there is also the yearly closing. The process structure is almost the same, only the data is for a whole year.!!!!!!!!!!!!!!!!!!!!!!!!!!!!client'!!!!!miserver'1'!!!!!!!!!!!!!!!!!!!!!!!miserver'2'!!!!!!!!!!!!data'transformation'!!!!!!!!!!!!!!!sources' Figure 4: Data Warehouse Architecture Master Thesis IM

28 5 THE CASE STUDY The results of the case study are presented in this chapter. The case study follows the framework of chapter 3 and is conducted within four applications. A small modification is made. Instead of the four major applications, just three of them are included. The Financial Application (FA) is exchanged for the Customer Relationship Management Application (CRM). This is because there were not enough sources of evidence to collect for the Financial Application. The following chapters will discuss the four applications that have been studied. Each chapter will start with an introduction of the application and the framework. The results will be presented in a table with reference to the source of evidence. 5.1 The Human Resource Management Application The HRM Application is an integrated software solution for Human Resource Management. This software makes HR processes and activities available to the managers and employees as a Self Service. This makes it possible for employees to register and/ or change its personal data without the need for an HR officer. The HR processes and obligations still exist and need to be followed. These HR processes and obligations are integrated in the application. At this moment TWINGATE is implementing this application for a company wide use where managers and employees follows the HR procedures built in the application Conducting the case The stakeholders in this case are the IT department, HR department and managers. The data collected during the research are documentations, direct observations, request for changes and issues. Figure 5.1 shows the research framework for this application. Data collected from this project/application is presented in following section. These sources have been scanned if any of the dimensions are mentioned. If a dimension is mentioned, it will be noted in the table in chapter During the matching process the interpretations of the findings are translated into terms that fit in the context of data quality and its dimensions. Master Thesis IM

29 Stakeholders Applications Sources Dimensions Research Framework HRM Application Direct observation Intrinsic Data Quality Contextual Data Quality Representational Data Quality Accessibility Data Quality Figure 5.1: Research framework HRM Applying the framework The selected sources are from this year (January - August 2012). Table below presents the results of the matching between the sources of evidence and dimensions. Dimensions Evidence Source Believability (0) - - Accuracy (6) 1. Functional design report 2. Activity report June 3. Activity report June 4. Issue Observation Observation Observation Observation Objectivity (0) - - Reputation (0) - - Value- added (1) 7. Observation Observation Relevancy (0) - - Timeliness (3) 8. Functional design report 9. Issue Observation Observation Completeness (6) 11. Functional design report 12. Activity report September 13. Issue Issue Issue Observation Observation Appropriate amount of data (1) 17. Functional design report Interpretability (0) - - Ease of understanding (0) - - Representational consistency (2) 18. Issue Master Thesis IM

30 19 Observation Observation Concise representation - - Accessibility (5) Access security (7) 20. Functional design report 21. Issue Issue Issue Issue Functional design report 26. Activity report June 27. Activity report August 28. Issue Issue Issue Observation Observation Results The previous table shows that the top 3 dimensions access security (22.58%), completeness (19.35%) and accuracy (19.35%) are noted the most. Together they noted 61.30% and exist of the following sources: 13, 11 documentations and 7 observations. See table below for the results. DIMENSIONS Observation Sources/ HRM RFC/ issues TOTAL Perc. Believability ,00% Accuracy ,35% Objectivity ,00% Reputation ,00% Value- added ,23% Relevancy ,00% Timeliness ,68% Completeness ,35% Appr. Amount of data ,23% Interpretability ,00% Ease of understanding ,00% Representational consistency ,45% Concise representation ,00% Accessibility ,13% Access security ,58% TOTAL ,00% Master Thesis IM

31 5.2 The Health Care Application The Health Care Application is truly a major application within the company. As a health care company, this application provides functionalities for the care professionals to register, plan and keep track of patient records in a so- called client profile. The application is web based and suitable for mobile devices such as the ipad. The implementation phase of the application has ended but did not reached maturity yet. Lot of development issues are still on going Conducting the case The stakeholders in this case are the IT department, Finance department, Client department, Health Care personnel, Key- users and managers. The data collected during the research are documentations, direct observations, request for changes and issues. Figure 5.2 shows the research framework for this application. Data collected from this project/application is presented in following section. These sources have been scanned if any of the dimensions are mentioned. If a dimension is mentioned, it will be noted in the table in chapter During the matching process the interpretations of the findings are translated into terms that fit in the context of data quality and its dimensions. Stakeholders Applications Sources Dimensions Research Framework HCA (Health Care) Application Direct observation Intrinsic Data Quality Contextual Data Quality Representational Data Quality Accessibility Data Quality Figure 5.2: Research framework HCA Applying the framework The selected sources are from 2010 till July Table below presents the results of the matching between the sources of evidence and dimensions. Dimensions Evidence Source Believability (2) 1. Issue Observation Observation Accuracy (7) 3. Functional Analyses Report 4. Minutes of meeting Nov Master Thesis IM

32 5. Minutes of meeting Dec Minutes of meeting Feb Issue Issue Observation Observation Objectivity (0) - - Reputation (1) 10. Minutes of meeting Feb Value- added (1) 11. Functional Analyses Report Relevancy (1) 12. Functional Analyses Report Timeliness (1) 13. Functional Analyses Report Completeness (1) 14. Functional Analyses Report Appropriate amount of data (1) 15. Decision report Oct Interpretability (0) - - Ease of understanding (0) - - Representational consistency (1) 16. Observation Observation Concise representation (1) 17. Functional Analyses Report Accessibility (4) Access security (4) 18. Functional Analyses Report 19. Minutes of meeting Feb Issue Observation Functional Analyses Report 23. Minutes of meeting Nov Minutes of meeting Jan Issue RFC/ Issues Observation Results The previous table shows that the top 3 dimensions accuracy (28%), access security (16%) and accessibility (16%) are noted the most. Together they noted 60% and exist of the following sources: 16 documentations, 5, and 4 observations. See table below for the results. DIMENSIONS Observation Sources/ HCA RFC/ issues TOTAL Perc. Believability ,00% Accuracy ,00% Objectivity ,00% Reputation ,00% Value- added ,00% Relevancy ,00% Timeliness ,00% Master Thesis IM

33 Completeness ,00% Appr. Amount of data ,00% Interpretability ,00% Ease of understanding ,00% Representational consistency ,00% Concise representation ,00% Accessibility ,00% Access security ,00% TOTAL ,00% 5.3 The Customer Relationship Management Application The CRM Application is a customer relationship management system. This system has functionalities to register and keep track of customer s interests. The relation- administrative department uses this system to register and arrange its customers and services. Other functionalities are invoicing, reporting and mailings. In the context of company value, it ranks much lower than the other three major applications. See table 2 for a summary. The implementation of the first module Relationship Management has just finished and is in the phase of aftercare. The data collection is based on this module. CRM Mngt value Low Users 10+ Data < 1GB Costs < Table 2: The CRM Application Conducting the case The stakeholders in this case are the IT department, relation- administrative department, Finance department and managers. The data collected during the research are documentations, direct observations, request for changes and issues. Figure 5.3 shows the research framework for this application. Data collected from this project/application are presented in the following section. These sources have been scanned if any of the dimensions are mentioned. If a dimension is mentioned, it will be noted in the table in chapter During the matching process the interpretations of the findings are translated into terms that fit in the context of data quality and its dimensions. Master Thesis IM

34 Stakeholders Applications Sources Dimensions Research Framework CRM Application Direct observation Intrinsic Data Quality Contextual Data Quality Representational Data Quality Accessibility Data Quality Figure 5.3: Research framework CRM Applying the framework The selected sources are from January - July Table below presents the results of the matching between the sources of evidence and dimensions. Dimensions Evidence Source Believability (1) 1. Test report Accuracy (8) 2. Contract 3. RFC Memo Jan RFC Memo Feb Test report 8. Observation RFC Observation Objectivity (0) - - Reputation (0) - - Value- added (1) 10. Design report Relevancy (2) 11. RFC RFC Timeliness (1) 13. RFC Completeness (7) 14. Test report 15. Memo Jan RFC RFC RFC RFC RFC Appropriate amount of data (0) - - Interpretability (1) 21. RFC Master Thesis IM

35 Ease of understanding (0) - - Representational consistency (2) 22. Test report 23. RFC Concise representation (1) 24. RFC Accessibility (4) 25. Design report 26. Observation Memo RFC Observation Access security (5) 29. Memo Jan Memo Feb Test report 32. Observation RFC Observation Results The previous table shows that the top 3 dimensions accuracy (24.24%), completeness (21.21%) and access security (15.15%) are noted the most. Together they noted 60.60% and exist of the following sources: 16, 14 documentations and 3 observations. See table below for the results. DIMENSIONS Observation Sources/ CRM RFC/ issues TOTAL Perc. Believability ,03% Accuracy ,24% Objectivity ,00% Reputation ,00% Value- added ,03% Relevancy ,06% Timeliness ,03% Completeness ,21% Appr. Amount of data ,00% Interpretability ,03% Ease of understanding ,00% Representational consistency ,06% Concise representation ,03% Accessibility ,12% Access security ,15% TOTAL ,00% Master Thesis IM

36 5.4 The Management Information System The Management Information System is already viewed in chapter Therefor no further introduction is needed in this chapter Conducting the case The stakeholders in this case are the IT department, HR department, Finance department, Health Care personnel and managers. The data collected during the project are documentations, interviews, request for changes and issues. Figure 5.4 shows the research framework for this application. Data collected from this project/application is presented in the following section. These sources have been scanned if any of the dimensions are mentioned. If a dimension is mentioned, it will be noted in the table in chapter During the matching process the interpretations of the findings are translated into terms that fits in the context of data quality and its dimensions. Stakeholders Applications Sources Dimensions Research Framework MIS Mngt Information System Intrinsic Data Quality Contextual Data Quality Representational Data Quality Accessibility Data Quality Figure 5.4: Research framework MIS Applying the framework The selected sources are from July Table below presents the results of the matching between the sources of evidence and dimensions. Dimensions Evidence Source Believability (15) 1. Requirements report Design report v35 3. RFC RFC RFC Interview 1 7. Interview 2 8. Interview 3 Master Thesis IM

37 9. Interview Interview Interview Interview Interview Interview Interview 10 Accuracy (15) 16. Requirements report Design report v RFC RFC RFC RFC RFC Interview Interview Interview Interview Interview Interview Interview Interview 10 Objectivity (0) - - Reputation (4) 31. Interview Interview Interview Interview 6 Value- added (14) 35. Design report v RFC RFC RFC Interview Interview Interview Interview Interview Interview Interview Interview 8 Master Thesis IM

38 47. Interview Interview 10 Relevancy (8) 49. Requirements report Design report v RFC Interview Interview Interview Interview Interview 8 Timeliness (10) 57. Requirements report Design report v RFC RFC Interview Interview Interview Interview Interview Interview 8 Completeness (9) 67. Requirements report Design report v RFC RFC RFC RFC Interview Interview Interview 7 Appropriate amount of data (9) 76. Requirements report Design report v RFC RFC RFC Interview Interview Interview Interview 9 Interpretability (3) 85. Requirements report 2010 Master Thesis IM

39 86. Interview Interview 10 Ease of understanding (3) 88. Requirements report Interview Interview 10 Representational consistency (4) 91. Design report v MIS Report standards 93. RFC Interview 1 Concise representation (13) 95. Requirements report Design report v MIS report standards 98. RFC Interview Interview Interview Interview Interview Interview Interview Interview Interview 10 Accessibility (12) 108. Requirements report Design report v Interview Interview Interview Interview Interview Interview Interview Interview Interview Interview 10 Access security (3) 120. Requirements report Design report v Interview 2 Master Thesis IM