Discussion 15: All Students Must Review One Group PowerPoint

Discussion 15 4all Students Must Review One 1 Group Powerpoint Pre

Discussion 15 4all Students Must Review One 1 Group Powerpoint Pre

DISCUSSION 15 4 All students must review one (1) Group PowerPoint Presentation from another group and complete the follow activities: 1. First each student (individually) must summarize the content of the PowerPoint of another group in 200 words or more. 2. Additionally each student must present a detailed discussion of what they learned from the presentation they summarized and discuss the ways in which they would you use this information in their current or future profession. PowerPoint is attached separately Management Information Systems Campbellsville University Week 15: PowerPoint Presentation Topic: Data Group: E GROUP MEMBERS FULL NAME Data Data can be defined as a specific piece of information or a basic building block of information. Data is stored in files or in databases. Data can be presented into tables, graphs or charts, so that legitimate and analytical results can be derived from the gathered information. An authentic data is very important for the smooth running of any business organizations. It helps IT managers to make effective decisions. Data helps to interpret and enhance overall business processes (Cai & Zhu, 2015). Uses of Data The main purpose of data is to keep the records of several activities and situations. Gathering data helps to better understand the interest of customers which can enhance the sales of organization (Haug & Liempd, 2011). Relevant data assists in creating strong business strategies. Use of big data helps to promote service support to the customers. It also helps organizations to find new markets and new business opportunities. After all, data plays a great role in running the company more effectively and efficiently. Data Management Data management is the implementation of policies and procedures that put organizations in control of their business data regardless of where it resides. Data management is concerned with the end-to-end lifecycle of data, from creation to retirement, and the controlled progression of data to and from each stage within its lifecycle (Dunie, M. 2017). Data Management Information technology has evolved to deal with the most important data management computer science which helps the computer leads to the advantage of a navigable and transparent communication space. Large volumes of data can be processed and managed with the help of management systems through the methods of algebra with applications in economic engineering especially in public domains(CARINA-ELENA STEGÄ‚ROIU 2016). Data Management is essential to overcome the management, analytics and application issues due to large scale data, real time streaming, different data formats and data uncertainity. Data Management Challenges Due to the increasing Technology and adapting it to the data management techniques is a key Challenge involving complex decision making circumstances.(Lubis Muharman, Arif Ridho Lubis, Lubis Bastian, & Lubis Asmin( 2018)). Administrative process is simplified using Data Management Techniques. Building a Data Architecture helps in managing large volume of data collected to run the business. Creating groups or filters for dividing bulk data collected help in defining a process for the management by dividing within teams helps in producing qualitative analysis of data. Data Management Challenges DataRes and iCamp projects are implemented to create a Standard Data Analysis for US agencies Council on Library and Information Resources(CLIR) to provide a Data Management Solution. (Martin Halbert. (2013)). In these Projects the Data Management process is analyzed to meet the user requirement. They Categorized the process by creating multiple policy to divide the data in order to receive a quick response for any query to retrieve the data from the restore point. Data Management Challenges They also conducted Surveys to provide standardization of the research data collected and documented the response. They build the focus groups and scheduled Interviews to know the opinion of the user who contribute the data and make the necessary changes or improvements for their projects. Collecting the best practices to be implemented for the data management digitally and provide the required frame work to build a standard data analysis tool can help in managing the data following the standard defined in the iCamp Project. Data Management Principles Data Management Strategy Data management Strategy basically describe the way to deal with the purchased data. Strategy include meeting with the board to introduce the data and to suggest the government and management the data over time. From the strategy the source from where data comes and with what frequency it comes is known. Information whether to store the metadata is known. Utilization of data in which departments is also planned. Time, up to which the data remain relevant is finalized. Data Management Principles Ownership of data: With analytics, data is used to make business decisions. Data managers for project groups like customer, product are created to manage and own data. Data Governance for analytics: Data governance relies on metadata and the quality and lifecycle of data. Quality of data and data life cycle are based on the requirements. Some analytical projects require data in it’s rawest form. Data Management Principles Metadata collection, storage and dissemination for analytics Metadata tells us the navigation, structure, usage and definition of data. It also tells us where the data came from, frequency of updates. Storing this information for data life cycle management is very important to the organization. Metadata can answer questions about when the data was last used, as well as when to retire specific analytical data. Benefits Of Data Management Minimize Data Movement Improve productivity Reuse Data management Techniques Improve records Governance Share precious capabilities Allows for easy challenge management Clarifies wished price range Shows responsibility Benefits Of Data Management Good facts control will make your enterprise extra effective. On the flip aspect, poor records control will lead to your company being very inefficient Another gain of proper records management may be that it need to allow your business enterprise to keep away from pointless duplication Includes measures for keeping the integrity of the records, ensuring that they may be no longer lost because of technical mishaps, and that the proper human beings can get entry to the records at the suitable time Spend time on security, cost, time to increase your values in sharing you data to others so they can access the right data. Develop a Data Map Data Mapping is a process of mapping the data from various data models and sources to create a viable information. Mapping the data is an important process in an organization to govern, to evaluate the business data from different data models. Developing a data map is carried when the data is at rest in the data base and when the data is stream continuously from various sources and is being mapped before storing in the database. For the secure communication of the messages, data mapping is used in Steganography to secure data in an organization by concealing the data in a digital medium (Zakaria & Hussain, 2018). Develop a Data Map Developing the data model with data mapping is a continuous process and not a one time work to have an updated information. Mapping data is process which have to be monitored in an organization regularly. It helps organization to get various critical information regarding the flow of data of where is the source, how the data travels with structured data models. The scientific and engineering tasks will be heavily benefited with effective mapping and in generating effective data models (Xin Li & Iyengar, 2015). Segment Data Data Segmentation is the process of taking the existing organizational data and breaking down into smaller volumes of data and if required, grouping the smaller chunks of data to perform any operations. Data segmentation is the practice of identifying, categorizing, labeling, and processing specific elements or sections of electronic data in order to provide precise control over who may use, view, access, or manipulate specific bits of data(Gormanns & Reckow, (2012)). Segmented Data enables any organization to filter on their analysis based on certain factors taking into consideration. Segment Data Effective data segmentation can be achieved by conducting thorough analysis of existing data which is already in organizational database and team had to decide on which group or portion of data they want to segment based on current market trend. Data mining process plays a vital role in reefing the data that can be used for performing the segmentation(Tollerson, Gamble (2017)). By taking consideration of customer database, data segmentation can be divided into: Demographical, Attitudinal and Behavioral segmentations. By adapting the latest commercial platforms for advance data segmentation will help the organization to better reach out to target group of customers for data. Data Hygiene Data Hygiene is a process in which cleanliness of data is ensured. It is also called Data Cleansing. Factors affecting the hygiene of data are: Duplicate Records – Duplicate data is of no use and would increase the storage size unnecessarily. Outdated Data – The data which is no longer useful for business operations and can be deleted any time. This data has no dependency on other contents of the database. Parsing Errors – Parsing errors occur when erroneous data is fed and processed resulting in unexpected output. Data Hygiene Process Planning- identify high priority data. This data is crucial and never be deleted. Analyze- find errors and gaps in the data to decide what can be deleted. Automation- run scripts to clean the data once the data is identified. Monitoring- keep monitoring the data always to find new errors that can become major if not cleaned properly. References Abdul Alif Zakaria, Mehdi Hussain, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Norli Anida Abdullah, & Ki-Hyun Jung. (2018). High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution. Applied Sciences, (11), 2199. Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality & Data Quality Assessment. Data Science Journal, 13(4), 15-24. CARINA-ELENA STEGÄ‚ROIU. (2016). The Importance of Information Systems in the Management and Processing of Large Data Volumes in Public Institutions. Analele Universităţii Constantin BrâncuÅŸi Din Târgu Jiu : Seria Economie, (Special Issue ECO-TREND), 140. Retrieved from References Cody, R. P., & SAS Institute. (2008). Cody’s Data Cleaning Techniques Using SAS (Vol. 2nd ed). Cary, N.C.: SAS Institute. Retrieved from Dunie, M. (2017). The importance of research data management: The value of electronic laboratory notebooks in the management of data integrity and data availability. Information Services & Use, 37(3), 355–359. Gormanns, P., Reckow, S., Poczatek, J. C., Turck, C. W., & Lechene, C. P. (2012). Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest. References Haug, A., & Liempd, D.V. (2011). The Costs of Poor Data Quality. Journal of Industrial Engineering and Management, 7(9), . Kahn, D.L. Rumelhart, and B.L. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Indianapolis, IN: Wiley. Retrieved from References Lubis Muharman, Arif Ridho Lubis, Lubis Bastian, & Lubis Asmin. (2018). Incremental Innovation towards Business Performance: Data Management Challenges in Healthcare Industry in Indonesia. MATEC Web of Conferences, 04015. Martin Halbert. (2013). The Problematic Future of Research Data Management: Challenges, Opportunities and Emerging Patterns Identified by the DataRes Project. International Journal of Digital Curation, (2), 111. . Moorthy, V. S., Roth, C., Olliaro, P., Dyed, C., & Paule Kieny, M. (2016). Best practices for sharing information through data platforms: establishing the principles. Bulletin of the World Health Organization, 94(4), 234–234A. References R. R. Downs. (2017). Implementing the Group on Earth Observations Data Management Principles: Observations of a Scientific Data Center. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 51. Tollerson, C. D., Chin, W. W., Gamble, G. O., Murray, M. J., & Chun-Chia Chang. (2017). Segment Data Decision-Usefulness Model: An Exploration. Journal of Accounting & Finance (), 17(8), 71–96. XIN LI, & IYENGAR, S. S. (2015). On Computing Mapping of 3D Objects: A Survey. ACM Computing Surveys, 47(2), 34:1-34:45.

Paper For Above instruction

The PowerPoint presentation provided by the group offers an extensive overview of the critical role data plays in modern organizations, emphasizing its fundamental role as the building block of information that supports decision-making, strategic planning, and operational efficiency. It delineates the definition of data as specific pieces of information stored in files or databases, which can be visualized through charts and graphs to facilitate analytical insights. The presentation underscores the importance of authentic data in ensuring the smooth functioning of businesses, primarily through enabling IT managers to make informed decisions and optimize business processes (Cai & Zhu, 2015).

A central theme of the presentation is the diverse uses of data, including maintaining records of various activities, understanding customer interests, and enhancing organizational strategies. By leveraging big data, companies can identify new markets, improve customer service support, and increase sales, illustrating data's tremendous strategic value (Haug & Liempd, 2011). The presentation moves on to explore data management—its scope, lifecycle, and significance—highlighting that effective management involves implementing policies and procedures that control data from creation through retirement (Dunie, 2017).

Data management encompasses several challenges, particularly with the explosion of technological advancements that generate large volumes of diverse and real-time data. The presentation details these challenges, such as managing data scale, formats, uncertainties, and the need for robust architecture. Building and maintaining a data architecture that facilitates efficient data processing and retrieval is crucial, requiring organizational techniques like grouping data and filtering to produce meaningful analysis (Lubis Muharman, Arif Ridho Lubis, et al., 2018).

The presentation also discusses specific projects like DataRes and iCamp, which aim to standardize data management practices for US agencies, exemplifying efforts toward data standardization and policy formulation (Martin Halbert, 2013). These initiatives involve conducting surveys, forming focus groups, and establishing response policies to ensure data usability and reproducibility.

Moreover, the presentation emphasizes the importance of data principles, such as ownership, governance, and lifecycle management, underscoring that metadata plays a vital role in navigation, structure, and quality assurance. Effective metadata collection and storage can improve data business value by indicating data age, usage, and retirement timelines, which support data hygiene practices and reduce redundant or outdated information (Zakaria & Hussain, 2018).

Furthermore, the presentation elaborates on techniques like data mapping, segmentation, and data hygiene. Data mapping involves translating different data models to create a coherent and accessible data structure, critical for compliance and security, especially in steganography applications for secure communication (Xin Li & Iyengar, 2015). Data segmentation splits vast datasets into manageable parts for targeted analysis, enabling organizations to perform market segmentation, customer profiling, and behavioral studies—vital for crafting personalized marketing strategies (Gormanns & Reckow, 2012; Tollerson et al., 2017).

Data hygiene ensures the accuracy, reliability, and cleanliness of data by removing duplicates, outdated information, and correcting errors through automation and continuous monitoring—key practices to sustain data quality and integrity (Cody & SAS Institute, 2008). Collectively, these components highlight that effective data management not only enhances organizational productivity and decision-making but also safeguards data assets, ensures compliance, and fosters competitive advantage (Kahn, Rumelhart, & Bronson, 1977).

Overall, the presentation provides a comprehensive understanding of data’s strategic importance and the multifaceted approaches required for effective data management within organizations, illustrating that rigorous practices in data governance, quality control, and analytic techniques are essential for leveraging data as a valuable business asset in the digital age (Kimball & Caserta, 2004).

References

• Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality & Data Quality Assessment. Data Science Journal, 13(4), 15-24.
• Haug, A., & Liempd, D.V. (2011). The Costs of Poor Data Quality. Journal of Industrial Engineering and Management, 7(9).
• Lubis Muharman, Arif Ridho Lubis, Lubis Bastian, & Lubis Asmin. (2018). Incremental Innovation towards Business Performance: Data Management Challenges in Healthcare Industry in Indonesia. MATEC Web of Conferences, 04015.
• Martin Halbert. (2013). The Problematic Future of Research Data Management: Challenges, Opportunities and Emerging Patterns Identified by the DataRes Project. International Journal of Digital Curation, 2, 111.
• Xin Li, & Iyengar, S. S. (2015). On Computing Mapping of 3D Objects: A Survey. ACM Computing Surveys, 47(2), 34:1-34:45.
• Zakaria, A., & Hussain, M. (2018). High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution. Applied Sciences.
• Dunie, M. (2017). The importance of research data management: The value of electronic laboratory notebooks in the management of data integrity and data availability. Information Services & Use, 37(3), 355–359.
• Gormanns, P., Reckow, S., Poczatek, J. C., Turck, C. W., & Lechene, C. P. (2012). Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest.
• Cody, R. P., & SAS Institute. (2008). Cody’s Data Cleaning Techniques Using SAS (2nd ed.). SAS Institute.
• Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley.