Master Data and Data Warehousing and Business Intelligence Management - Essay Example

Master data and data warehousing and business intelligence management First Task: A of the activities associated with the Reference and Master Data Management data management function 6.1 Understand Reference and master Data Integration Needs (P) Diverse industries require specific earth science statistics such as severe weather risks and related socio-logical data such as ethnicity, which is usually supplied from external sources, which highlights the need for data integration (Cervo 2011).Data integration in a hospital setting requires precise information, because a minor element may have critical outcomes, and probably a permanent casualty in terms of patient death. Reference data requirement is a sophisticated function, because it requires understanding the needs across an enterprise, understanding and analyzing them to ensure quality data is made available for master data integration. This project necessitates different activities performed by various departments; sequence of these activities and the necessary requirements in each activity before a patient receives the services of the doctor (Berson 2011). Reference data will include patient name, age, ethnicity, past medical records, body temperature, blood pressure and any other relevant individual data deemed necessary before a patient receives treatment. Taking the above into account, there is need to understand the relevant personnel require to gather the necessary data, feed it into a system and order in which such data is gathered. 6.2 Identify Reference Data Sources and Contributors (P) Data Sources, Contributors and Data Integration Architecture; Data sources may be primary or secondary in reference to the situation at hand (ibm, R 2012). Main sources of data in this integration model are patient records, parts of which are filled prior to the treatment and others during treatment. Other sources include first-hand information provided by patients, and other observable traits such as skin colour, hair texture and colour among others. Main contributors of data in this model are the patients being attended too. They are the primary core contributors, without them the organization will not be functioning. Others contributors include employees who attend to these patients, because their services make it possible to interact with the sources. 6.3 Define and Maintain the Data Integration Architecture (P) Data convolution is the main reason why data integration needs architecture (Cervo 2011). Architecture engaged in this system should allow data integration effects in which data flows from diverse sources, through multiple transformations as it gets ready to load on the target system such as patient data hubs. Staging areas should be made possible because data does not flow uninterrupted, that is in a straight line (Berson 2011). 6.4 Implement Reference and Master Data Management Solutions (D Data integration architecture is better understood to mean the pattern developed when servers relate through interfaces. Architecture developing standards necessitates application of multiple systems, the results is simple data integration, which in turn leads to increased consistency, and harmony between common infrastructure and differing problems. 6.5 Define and Maintain Match Rules (P) Match rules involve matching, merging and linking of data, which is an on-going challenge in master data management system (ibm, R 2012). Match rule in this model will be social security number, a socio-logical element, which is distinct for every individual. This will contribute to confidence in matching and reducing data redundancy and there will be no conflict of individuals sharing names and similar or almost similar street addresses. 6.6 Establish Golden Records (C) Golden records are techniques employed to ensure that most accurate and complete reference data are distinct from techniques used in providing a more accurate and complete master data. On this model, global IDs need to be developed to reconcile and link matched records about an individual from different sources, which will act as the unique golden records. 6.7 Define and Maintain Hierarchies and Affiliations (C) This model Hierarchy should be such that each activity is performed in a locking phase, ensuring that the preceding activity cannot take place until the prior activity happens. This will allow activity affiliation to be clearly observed once system learn is done, and results observed. 6.8 Plan and Implement Integration of New Sources (D) New sources integration into this model will be easy, done through entering of data into master data, which is updating of the master data to ensure it remains updated. Master data replication and distribution will be possible through providing three basic servers, one is development server for testing new developments before they are incorporated in existing data base, second is quality server for testing actual results observed when these systems are run. 6.9 Replicate and Distribute Reference and Master Data (O) Confidence levels of data integration are also done at this level, and lastly is the final server referred to as production server. This is the server with real-life data, which is employed in running day to day affairs of the health organization. Data from this server should be copied to quality and development servers, every time it is updated, to providing a platform for people using this database system to test real life situations in hypothetical settings before they employ them in real life situations where they might result to permanent casualties. 6.10 Manage Changes to Reference and Master Data (C Any changes to reference and master data should begin in development server, if satisfactory the changes will be moved to quality server for end users to tests the changes, and once the end user is satisfied they should be moved to production server (Berson 2011). Second Task: A description of the activities associated with the Data Warehousing and Business Intelligence Management data management function 7.1 Understand Business Intelligence Information Needs (P) Business intelligence refers to sets of technologies used to increase the understanding of business processes and their associated data. In this model, business intelligence provides set of solutions that correspond to each of the steps outlined in an integrated process that enables for continuous business improvement (Moss, 2010). Needs for business intelligence starts with data, from which information is created to achieve an understanding of the processes. This information is then used to build a base of knowledge known as data warehouse, update information while changing business processes. Therefore, business intelligence allows for deeper knowledge and continuous change of business processes in the future. Data marts should be set for every server in isolation (Sherman 2014). 7.2 Define the Data Warehouse/BI Architecture Business Intelligence architecture is defined as a framework that provides standards, best practices and policies that help in analyzing business data. It will include both structured and unstructured data, as well as both internal and external sources. 7.3 Implement Data Warehouses and Data Marts Data warehouses holds numerous subject areas, holds comprehensive information, works on incorporated data sources and does not essentially use a dimensional model but feeds dimensional models such as those found in a data mart. 7.4 Implement Business Intelligence Tools and User Interface (D) The main business intelligence tools required here are reporting and querying software for extracting, sorting, summarizing and presenting selected data, OLAP, data mining, data warehousing, digital dashboard and local information systems. Key deliverables that quantify what the end users desire from this model defines the user interface to be employed, and this model proposes to use Sisense to accommodate all users even those without prior knowledge with BI software (Stackwak et al 2007). 7.5 Process Data for Business Intelligence Processing and analysing are the central blocks of business intelligence, and the arena in which most Business intelligence compete by adding and refining features to their solutions. Processing data for Business intelligence necessitates gathering data, organizing, tuning it into meaningful information and making actionable decisions aimed at fulfilling a strategic goal. 7.6 Monitor and Tune Data Warehousing Processes (C) Monitoring and tuning data warehousing processes is necessary due to factors beyond the control of the data warehousing team. ETL processes should be tuned on as much as possible, query processing should be optimized because users typically lose interest after 30 seconds of waiting for a report to run, and report delivery should be tuned to ensure there are no significant delays arising from factors such as traffic jam, server setup or any other factor. Tuning will involve adjustments of databases, application servers to ensure server memory and connection settings are as require for better performance and adjustments of web servers for maximum performance (Moss 2010). 7.7 Monitor and Tune BI Activity and Performance Monitoring performance means regularly checking the status of the business intelligence model, its’ installation and resources. Using intelligence business model cognos provides metrics for checking the performance of the system, dispatchers, services and servers (Moss 2010).This may require configuring the system to notify anyone who should be made aware of the problem when a performance issue occurs. This gives rise to the need to monitor and tune business intelligence activity and performance regularly. b. The effectiveness of the activities Overtime, business intelligence environment changes owing to factors such as user population growth increase in processing requests and complexity among others (Sherman 2014). As time lapses, more powerful servers, rescaling horizontally by adding servers and balancing the processing load among servers will be necessary to increase business intelligence activities and yield maximum results. Administering healthcare and achieving clinical amalgamation in contemporary payment setting is a national distress which can only be eased through use of technology to make sure quality healthcare and cost control. Given the opportunity to re-do subpart A, this paper would examine business intelligence use in healthcare business, address significant challenges and discover the role of business intelligence to promote organization capabilities. Furthermore, this paper would explore to provide a means to make certain a robust and methodical approach to healthcare administration with the definitive goal of lasting impact on value improvement and cost control. References Berson, A., &Dubov, L. (2011). Master data management and data governance. New York: McGraw-Hill. Bulusu, L, Open Source Data Warehousing and Business Intelligence, CRC Press, 2012 Cervo, D., & Allen, M. (2011). Master data management in practice: Achieving true customer MDM. Hoboken, NJ: Wiley. Ibm, R. (2012). Smarter modeling of ibm master data management solutions. S.l.: Vervante. Krishnan, K, Data Warehousing in the Age of Big Data, The Morgan Kaufmann Series on Business Intelligence,Newnes, 2013 Lans, R, Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses, Elsevier, 2012 Moss, L. T., &Atre, S. (2010). Business intelligence roadmap: The complete project lifecycle for decision-support applications. Boston, Mass: Addison-Wesley. Mundy, J and Thornthwaite, W, The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset, John Wiley & Sons, 2011, Rainardi, V, Building a Data Warehouse: With Examples in SQL Server, Apress, 2007 Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics. Stackowiak, R. and Rayman, J, Rick Greenwald, Oracle Data Warehousing and Business Intelligence Solutions, John Wiley & Sons, 2007 Stackowiak, R., Rayman, J., & Greenwald, R. (2007). Oracle data warehousing and business intelligence solutions. Hoboken, N.J: Wiley. Read More