Smart Database Design to Avoid Fault Data Research Paper

Smart Database Design to Avoid Fault Data - Research Paper Example This paper reveales the diverse ways of entering data into databases along with reasons of entered and stored poor quality data in databases and its impacts on the organizations. One of the reasons is improper database design, therefore in order to avoid poor quality data in databases, features of good database design along with guidelines for developing a smart database to avoid faulty data have been provided in this paper. Keywords: database design, data quality, avoiding faulty information, Garbage in Garbage out (GIGO), database normalization, smart database design. Introduction Today, each and every decision from solving particular problem to deciding future of an organization is based on availability, accuracy and quality of information. â€Å"Information is an organizational asset, and, according to its value and scope, must be organized, inventoried, secured, and made readily available in a usable format for daily operations and analysis by individuals, groups, and processes, both today and in the future† (Neilson, 2007). The organizational information is neither just bits, bytes saved in a server nor limited to client data, the hardware and the software that store it. A data or information to which an organization deals with is a process of gathering, normalizing and sharing that information to all its stakeholders. It might be difficult to manage this imperative huge information manually. This is the reason that databases are formulated and high in demand. A database facilitates to store, handle and utilize implausible diverse organization’s information easily. A database can be defined as â€Å"collection of information that is organized so that it can easily be accessed, managed, and updated† (Rouse, 2006). Developing a database is neither a complicated process nor complex for using and manipulating information stored in it. A database smoothes the progress of maintaining order in what could be an extremely chaotic informative environment. In databases, a collection of information is stored individually and its management entails preliminary indexing of existing data by categorizing the isolated saved information based on common factors (identity). It can be done through assigning values which signify appropriate condition (i.e. national identities, names, cell numbers, etc.). Undoubtedly, if the data gathering and storing process are malfunctioned, the established data will be incorrect as well; this process is known to be as Garbage in Garbage out (GIGO). Quality and accuracy of data are too critical and fundamental for a database developed/maintained by any organization, either the database is developed for achieving a small goal with limited scope or it is a multi-billion dollar information system. It can be said that the value of data is directly proportional to the quality of data. It is one of many reasons that an inadequately designed database may present incorrect information that may be complicated to utilize, or may even stop working accurately. Why Poor data Quality? As there are a number of ways to enter data in databases that include initial data conversion (data conversion from some previously existing data source), consolidating existing database with new database, manual data entry, batch feeds and real-time data entry interfaces, therefore, there are a plenty of diverse root causes currently subsist for storage of inaccurate and poor data quality in databases. Some of them are because of inappropriate database design whereas the others are due to external outage factors. The basis of these errors is a lot more than just stumble-fingered typographer (typo error). Some of the reasons of poor quality data except database design include receiving

Business Appraisal in the Professional Services Sector Dissertation

Business Appraisal in the Professional Services Sector - Dissertation Example The study made use of secondary data garnered mainly from official reports and pronouncements issued by the company for its shareholders, consumer groups, and the government. Other secondary data were acquired from investment analyses and professional publications, while the financial data were taken from official online databases. Quantitative analysis were conducted to establish relationships between research and development metrics and the firm’s profitability indicators, to determine which measures in the firm’s financial reports relating to software development impacted upon how profitably the firm performance. Qualitative analysis provided an appreciation of the software development process and the value-enhancing strategies that improved Microsoft’s comparative advantage over its rival firms. The study found that the research and development efforts of Microsoft, which principally consists of software development, impacted on the firm’s profitabilit y within two years after the software development effort had taken place. Competitively, Microsoft had benefitted from a strategy of operational competence until the present, but Apple is gradually overtaking it with a more effective method of customer intimacy as its value discipline. Table of Contents Table of Contents 3 List of Tables 5 List of Figures 6 Chapter 1: Introduction 7 1.1Chapter overview 7 1.2 Background of the study 7 1.3 Significance of the study 9 1.4 Research aim and objectives 9 1.5Research questions 10 1.6 Brief description of the data and analysis 10 1.7 Conceptual framework 11 Chapter 2: Literature Review 14 2.1 Chapter overview 14 2.2 The conceptualization of the phrase â€Å"value of Microsoft’s software development† 14 2.3 The organizational dynamics of software development 15 2.4 Microsoft’s software development strategy 17 2.5 Discerning the rationale in code development at Microsoft 20 2.6 Accounting treatment of software development costs 23 2.7 The Conceptual Framework: The Treacy & Wiersema Value Discipline Model 24 Chapter 3: Methodology 29 3.1 Chapter overview 29 3.2 Research strategy 29 3.3Description of variables used in the quantitative study 31 3.4 Statistical model and treatment used in the study 34 3.5Data collection method 35 3.6 Sampling method and size 36 3.7 Data analysis 36 3.8 Scope and Limitation 37 3.9 Ethical considerations 37 Chapter 4: Data and Discussion 39 4.1 Chapter Overview 39 4.2 Microsoft’s Product Innovation and Momentum 39 4.3 Perceived implementation of Microsoft’s corporate strategy 41 4.4 Human resources management at Microsoft 43 4.5 Assessment of Microsoft’s operating performance 44 4.6 Competitive values dimension comparison among Microsoft, Apple and Google 49 4.7 Results of statistical correlation studies 54 Chapter 5: Conclusion 62 5.1 Chapter overview 62 5.2 Summary of the research findings 62 5.3 Conclusion 67 5.4 Limitation of the findings and conc lusion 68 References: 70 Appendices 74 List of Tables Table 1: Examples of incumbent and entrant software products 18 Table 2: Criteria to measure the disruptive potential of an innovation in software markets 19 Table 3: Comparison of â€Å"Waterfall† and Microsoft development processes 20 Table 4: Revenues and income per business segment 44 Table 5: Growth rates of Microsoft’s yearend revenues and operating income for 2011 46 Table 6: Direct competitor comparison 47 Table