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1. Companies Create Business Records of Many Types and Store the Electronic Files Using an Electronic Records Management (ERM) System. Explain Why an ERM is a Senior Management Issue and Not Simply an IT Issue?
Storing electronic files using an electronic records management (ERM) system is a senior management. While it addresses the documentation of all information underlining activities in a given institution, managers define the scope of the tool. Essentially, in determining the expanse of ERM, managers identify the information to be stored. They also decide on the various roles that will be played by the staff in maintaining the system. Such actions point to the indispensability of the management in the integration of ERM. Other than the identification and distribution of available resources to oversee the successful integration of the system, the management is also tasked with the duty of defining the scope of access (Gilliland-Swetland, 2005). Essentially, they hold the key on the degree of access that employees are allowed. As the defining factor in the determination of employee activities around the ERM systems, managers hold the key which guides interactions between the human and electronic factors.
As an IT issue, the staff only perform the role of engaging the system. Manning the storage databases and securing the platform is not similar to the definition of the security measures to be employed (Gilliland-Swetland, 2005). Given that the managers have the power to define the scope of ERM usage, the IT staff only act to implement the decrees made by the managerial department.
2. Describe Each of the Four V’s of Data Analytics, Variety, Volume, Velocity, and Veracity.
Variety in data analytics explains the presence of diverse structures of data. Mainly, data is manifested in various forms depending on the intended use and the source of the information. Some examples of data varieties include structured, unstructured, and traditional data. The varieties of data explain the nature of information and the stakeholders involved in the initiatives (Zikopoulos & Eaton, 2011). Alternatively, data volume alludes to the quantity of data at any given time. In data analytics, the success of the initiative may be determined by the volume of data that is available. The growth of information on an annual basis calls for the engagement of data analytic tools that embrace and examine more information. The volume of data is defined in bytes. Subsequently, there are kilobytes, megabytes, gigabytes and terabytes. The bigger the data, the better the analysis. Essentially, findings generated from a bigger data quantity tend to be more generalizable.
The third V in data analytics relates to velocity. The tern allude to the frequency by which data is received and transmitted. The speed of data transmission also influences the processing processes. An increase in the data being processed calls for the involvement of more efficient data analytic tools. Velocity of data receival may jeopardize or augment operations in an institutional setting. In handling the velocity of data, the applied tool should be able to address the increasing and shifting volume of data. Lastly, veracity is the validity of data being processed. Such trustworthiness results from the credibility of the source of data. A valid source often leads or produces relevant and applicable data. Data that can be verified is more appropriate in data analytics.
3. Identify the Primary Functions of a Database and a Data Warehouse. Explain Why Enterprises Need Both of These Data Management Technologies.
There are several core functions of a database and a data warehouse. Still, the primary function of a database is to collect, store and allow accessibility to data. Databases act as the storage point in data analytics (Velicanu & Matei, 2007). Alternatively, data warehouse performs the role of analyzing and computing output from the stored data. Unlike a database, a data warehouse contains tools that can be used to process information. Both data management technologies are critical in the realization of ultimate enterprise success.
The first step in analytics with an enterprise entails the collection of data. Such initiative facilitates the storage of information that could be used to define output and influence decision making. The next process entails the retrieval of the stored information. A database enables data users to access information that had been stored (Velicanu & Matei, 2007). Subsequently, the tool acts a safety deposit of data where information is secured against unauthorized access. A data warehouse allows enterprises to make use of stored data. After accessing information from databases, it has to be converted into a useful output. The realization of such an outcome calls for efficient computation of data. Decision making is a critical part of the enterprises. Failed decision-making mechanisms may lead to retrogression and redundancy. Equally, decision-making that is pegged on wrong and inaccurate data may also inspire negative outcomes. The integration of data into enterprise functions provides a medium through which efficiency can be streamlined across all institutional activities. Mainly, data warehousing and databases enable the management and transformation of data into a useful product. From the intervention, enterprises are able to generate viable decisions.
Gilliland‐Swetland, A. (2005). Electronic records management. Annual Review of Information Science and Technology, 39(1), 219-253.
Velicanu, M., & Matei, G. (2007). Database Vs Data Warehouse. Revista Informatica Economică, 91-95.
Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.
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