Paper -2 Include Milestone-1 And Milestone-2 In This Paper (Milestone1 And 2 Are Attached)
Running Head: DISCOVERY TOOLS 1
DISCOVERY TOOLS 5
Data Discovery
Name: Murali Mohan Gaddameeni
Professor’s name: Dr. Patricia Pedraza-Nafziger
Data Discovery
Data discovery entails the identification and location of detailed data for adequate protection or secured removal and convenience. It is one of the essential business intelligence that has been in use in recent years for compliance in business operations. Business Intelligence utilizes user-generated content (UGC) that gives information about the trends in the business environment. Business Intelligence acquires its data from what is called data warehouses. Usually, data is sourced from various information systems after which data is analyzed, and data mining is done thereof. In business intelligence, the goal is to convert the collected data to essential information that aids in making decisions in an organization. In an attempt to consolidate their data for monitoring, businesses do get themselves in the struggle and fear of the unknown. Workers in most companies today are located in different places, better termed as remote working; these workers do conduct their operations in a cloud that entails sharing and storage of files. Doing this poses challenges to businesses that want to understand in detail about the storage location of their sensitive data.
Regarding this, different business processes tend to be interconnected, that means data goes through several system databases, applications and file sharing hence might sabotage its authentication, confidentiality, and its protection. To offset this challenge, Data Discovery steps in to identify an organization’s entire data and ensures to intervene with appropriate controls for proper security, regulatory, and compliance measures. There are a variety of issues that are associated with data discovery. Most are problematic to businesses, but the use of data discovery serves to save them from the troubles.
Firstly, a massive amount of data is a challenge to handle for organizations. According to Castellanos et al. (2009), a large amount of data streaming into an organization concerns those of an influx in new customers doing transactions and the pressure of having to send emails to several customers, say at least one thousand clients in a day. This poses a significant challenge whenever there is a need to monitor data for typical analytics in business that enables workers to evaluate data from different perspectives to discover actionable patterns. Users can utilize data discovery tools for more efficient and user-oriented ways of presenting data to make more sense out of it.
Secondly, the diversity of data in a business setting implies more security concerns. Besides the inflow of data, Catlett et al. (2014) identified that different data types in businesses make it hard for company workers to identify data that is more sensitive than the others. Catlett asserted that while some data are more vulnerable, others are less or do not appeal as sensitive at all. Therefore, this means that business persons might risk exchanging information that is of critical concern to a company, for instance, data on intellectual property, those concerning trade secrets and or a pending merger. These data could cause harm to a business if it landed in the hands of a business rival. Concerning this, data discovery provides persons handling information with tracking, securing, and purging tools to discriminate on data efficiently. Security of data spans to include several measures, for example, use of real-time data alerts to discover security breaches that may lead to loss or unauthorized access of data. Purging of data involves the process of keeping and storage of information. While companies may have vast data of importance, it may become problematic in terms of storage space. The ability to customize this data to determine which ones to store and which to do away with is an crucial function provided for by data discovery.
Thirdly, managing all operations consistently can be quite challenging, and it is a common problem in businesses. Fink et al. (2017) asserted that setting up of a standard workable business schedule while ensuring consistency at it across the organization can be difficult. Fink reviewed this on their research model that sought to establish Business Intelligence Value creation. In the process, they “incorporated both general Information Technology and specific Business Intelligence value creation tools.” Data discovery enables companies to gain a more in-depth insight into their data so they can adjust to trends in business and ensure a cognitive continuity of their operations. Companies can do this by using the following generalized procedures. Business can: gather all of their data regardless of whether it is sensitive or non-sensitive; proper location of the data and consequently condensation of information possible is key in ensuring compliance with regulations. Analysis of data is done once it has been collected; this involves separating per sensitivity. In the process, IT personnel determines what data to keep and what to discard to save on the storage spaces available. Once data is no longer necessary, it is purged. A company should set policy standards for purging data. Protection of information is critical; both physical and digital safeguarding is effected to ensure consistency in operations.
References
Castellanos, M., De Medeiros, A. A., Mendling, J., Weber, B., & Weijters, A. J. M. M. (2009). Business process intelligence. In Handbook of research on business process modeling (pp. 456-480). IGI Global.
Catlett, C., Malik, T., Goldstein, B., Giuffrida, J., Shao, Y., Panella, A., ... & Foster, I. T. (2014). Plenario: An Open Data Discovery and Exploration Platform for Urban Science. IEEE Data Eng. Bull., 37(4), 27-42.
Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56.