Miss professor
sandesh4u
MIS_Topic_Data.pptxManagement 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 STEGĂROIU 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 facts 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. It includes data standardization and creating workflows.
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 STEGĂROIU. (2016). The Importance of Information Systems in the Management and Processing of Large Data Volumes in Public Institutions. Analele Universităţii Constantin Brâncuşi Din Târgu Jiu : Seria Economie, (Special Issue ECO-TREND), 140. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&AuthType=sso&db=edsdoj&AN=edsdoj.43294fd181c04c75b36c9f6227956ebd
References
Cody, R. P., & SAS Institute. (2008). Cody’s Data Cleaning Techniques Using SAS (Vol. 2nd ed). Cary, N.C.: SAS Institute. Retrieved from http://0-search.ebscohost.com.library.acaweb.org/login.aspx?direct=true&AuthType=sso&db=nlebk&AN=230920&site=ehost-live
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. https://doi.org/10.3233/ISU-170843
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), 183-189.
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 http://0-search.ebscohost.com.library.acaweb.org/login.aspx?direct=true&AuthType=sso&db=nlebk&AN=124355&site=ehost-live
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. https://doi.org/10.1051/matecconf/201821804015
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. https://doi.org/10.2218/ijdc.v8i2.276.
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. https://doi.org/10.2471/BLT.16.172882
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. https://doi.org/10.5194/isprs-archives-XLII-3-W2-51-2017
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 (2158-3625), 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.
