Discussion Board Replies
Each reply must be at least 250 words. These include one in-text citation, and one biblical integration citation. All posts must strictly comply with APA style standards. All sources used except the textbook required sources must be published within the past five years.
Reply to Venkata
Managing data is a vital part of big data analytics. Bartlet (2013, pp. 239-240) pointed out that data management is more valuable when focused on customer needs by constantly engaging customers. It is challenging to align the stakeholders from data providers and data consumers as they tend to have different cultures and viewpoints. A collaborative approach between data providers and data consumers would greatly help the program succeed. Customer-focused data management needs to deal with the main business goals to be accomplished from data, data maintenance and cleaning, monitoring of data sequence, and value of data improvements. Periodic inspections of data followed by necessary corrections and quality measurement are critical to prevent incorrect, inaccurate, or obsolete data. Maintaining quality should be part of ongoing organizational strategy, not just a tactical task as part of the analysis. Chaudhary, Aujla, Kumar, and Rodrigues (2018) proposed that software defined networking (SDN) technology can automate data management to a reasonable extent. SDN offers a programmable network that monitors and optimizes multi-streaming dynamic data flow. Some advantages of using SDN technology include its flexibility of networking services, modularizable software, data consistency, optimum resource usage, optimized routing, and fast-forwarding.
According to Onyeabor and Ta’a (2019), data quality is the fitness of the data for using and meeting the desired purpose of the user. There has been a lot of research focusing on data quality, but there is further scope for research in this area. Data quality can be accomplished by data-driven methods such as cleansing for better quality. Data quality also can be achieved by the process-driven strategy to identify sources of poor quality data and reengineer the data collection process. Cleansing of big data needs to be done before the analysis of the data. Data quality issues tend to be higher with data collected from multiple sources. Cleansing frequency has to match the incoming speed of the data. Cost of managing the data quality for organizations increases with larger data quantity, higher arrival velocity, and more variety of data. Abdallah (2019) noted that data quality management involves five primary dimensions: people in the organization involved with big data analytics, statistical data profile of data accuracy, data quality rules defined, data reporting, and repair of the data.
Ahmed and Pathan (2019, p. 376) noted that big data offers the storage of valuable data in cloud infrastructure to analyze and make data-driven decisions. One potential issue that owners and users face is security threats to this precious information. To ensure the data is secured the network system needs to be constantly monitored using intrusion detection systems (IDSs). The detection systems for the distributed cloud environments need to be more robust than conventional intrusion detection systems as they do not have the capability to identify coordinated cyber attacks. Collaborative intrusive detection (CID) systems are designed to detect and analyze any suspicious activity gathered from all the IDSs in the distributed system. Large-scale coordinated cyber attacks such as distributed denial-of-service (DDoS) and worm outbreaks attempt to damage multiple networks at the same time. High vigilant Collaborative detective systems are required to be used by organizations to monitor cloud environments and prevent such coordinated attacks.
Just as data quality is to be constantly monitored and clean up the dirt to ensure high-quality data, we need to maintain spiritual purity in our lives, cleaning all the dirt we may have accumulated. Biblical King Hezekiah was chosen by God for such a revival. Merida (2015, pp. 276-277) stated that God raised up king Hezekiah to restore the nation that his ancestors ruined. The evilest of his ancestors was his father, “He even made his son pass through the fire, imitating the detestable practices of the nations (2 Kings 16:3). God brought back a refreshing season through Hezekiah. We can refresh our lives by seeking the Lord as Hezekiah did.
Abdallah, M. (2019, February). Big data quality challenges. In the 2019 International Conference on Big Data and Computational Intelligence (ICBDCI) 1-3.
Ahmed, M., & Pathan, A. K. (2019). Data analytics. Concepts, Techniques, and Applications. CRC Press, Taylor & Francis Group. ISBN-13: 978-0367570989.
Bartlett, R. (2013). A Practitioners Guide to Business Analytics: Using data analytics tools to improve your organization’s decision-making and strategy (1st ed). McGraw-Hill. ISBN: 9780071807593.
Chaudhary, R., Aujla, G. S., Kumar, N., & Rodrigues, J. J. (2018). Optimized big data management across multi-cloud data centers: Software-defined-network-based analysis. IEEE Communications Magazine, 56(2), 118-126.
Merida, T. (2015). Christ-centered exposition: Exalting Jesus in 1 & 2 Kings. Retrieved from https://ebookcentral-proquest-com.ezproxy.liberty.edu
Onyeabor G.A., Ta’a A. (2019) A Model for Addressing Quality Issues in Big Data. In: Saeed F., Gazem N., Mohammed F., Busalim A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, 843. Springer. Retrieved from https://doi.org/10.1007/978-3-319-99007-1_
Reply to Stephanie Leach
Data management includes the process in which organizations store, organize, and preserve the data that they create and gather. An organization needs to establish effective data management because it is a critical to the systems that run business applications. Effective data management will also provide analytical information to decision makers and end users which will drive smarter operational decision-making and strategic planning (Stedman, 2019). Data management includes supporting sufficient access to data, safeguarding the integrity of data, ensuring the security of data, providing well-organized storage, and provide insight into future data investments (Bartlett, 2013). The implementation of effective data management will assist with minimizing potential data errors and reducing the harm that will be caused by bad and incomplete data.
Organizations will utilize databases to store and assist with the management of data. The database will contain a collection of data that is systematized so it can be easily accessed, updated, and managed. In a database, data warehouses can be programmed to store consolidated data sets from operational systems for business intelligence and analytics (Stedman, 2019). The management of databases is a critical component of appropriately handling and supplying data (Bartlett, 2013).
Organizations can implement database enhancements to assist with their data management. There are two valuable physical assets organizations can obtain which contain metadata. These physical enhancements include data encyclopedias and data dictionaries. A database encyclopedia can be purchased or internally developed and acts as an appendix for dataset inventory. A data dictionary assists organizations with completing intricate analyses or scrupulous data cleaning. A data dictionary will need to be developed for each dataset (Bartlett, 2013).
Internal and External Data
An organization can acquire data from either internal or external data sources. The internal data source can provide valuable information and assist an organization with gaining a competitive advantage (Bartlett, 2013). Organizations recognize that there are valuable insights that can be gained by analyzing the data that is generated from internal operational activities. The internal data that is generated can leave informational gaps, and organizations are progressively attempting to integrate new, untraditional, and external sources of data into their data analysis. The external data can contain almost anything including historical demographic and private business information (“How third-party information can enhance data analytics”, 2019). External data can be acquired from outside suppliers. The convenience, service, and cost of external suppliers are advantageous reasons for organizations to seek external data.
1 Corinthians 14:40 (ESV) states, “But all things should be done decently and in order.” The data management process has a specific order of events that must occur and be effectively completed. The order of data management will be specific to ensure its efficiency of managing the data life cycle. By proactively managing data an organization can increase the quality of data and the information produced. The quality of data is essential to success of decision making in an organization. To advance and sustain data quality a process-driven data management strategy will be vital (Glowalla & Sunyaev, 2014).
Bartlett, R. (2013). A practitioner’s guide to business analytics: Using data analysis tools to improve your organization’s decision making and strategy (1st ed). New York, McGraw-Hill. ISBN: 9780071807593
Glowalla, P., & Sunyaev, A. (2014). Process-driven data quality management. Journal of Data and Information Quality, 5(1-2), 1-30. https://doi.org/10.1145/2629568
How third-party information can enhance data analytics. Harvard Business Review. (2019). Retrieved 10 May 2021, from https://hbr.org/sponsored/2019/04/how-third-party-information-can-enhance-data-analytics.
Stedman, C. (2019). What is data management and why is it important?. Search Data Management. Retrieved 10 May 2021, from https://searchdatamanagement.techtarget.com/definition/data-management.