M816 Data Management

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M816 Data Management

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M816 Data Management

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Course Code: M816
University: The Open University

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Country: United Kingdom

Questions:
Organisation are increasingly challenged by the quantity of data being generated on a daily basis. Disk space seems to no longer limit how much data can be generated and store, but managing huge volumes of data requires a systematic approach, regardless of the size of the organization. Further, data can be located within a variety of devices including personal devices not under the control of the organization.
 
1.What are some of the approaches an organisation might use to manage data, data structures and databases? Share experiences from your own working environment, where possible.2.Once you place your data in the cloud, are you really in charge of who it is shared with and how long it is held? Do you think there are enough laws in place governing these decisions?
 
3.Analyse or comment the following:
 
Organizations today have many different sources of data. Traditionally data stored in organizations has been mainly structured data that is generated as a result of business processes within the organization and primarily stored in applications utilizing relational databases, Excel sheets and other forms of files that contain records of data that have been created, processed and stored for a specific process and are thus already converted to useful information and not just raw data. These forms of data are stored in a structured manner, meaning the data is organized according to aspecific schema. Data stored in this way is easy to access, manage and manipulate for a variety of purposes.
Increasingly sources of data that may be useful for organizations to collect, store and process are unstructured data. Data that is collected from a variety of external sources that are, sometimes freely, available on the Internet for example, such as social media feeds, and data collected organizations that sell streams of large amounts of unstructured data that have been previously or often even live, collected and distributed for further processing. A lot of data can be collected by tapping into APIs of other Internet accessible applications that allow the unconditional or conditional download of this data into another system for further use. Large amounts of data can also be generated by IoT sensors installed for example in machinery, household appliances, vehicles, and mining equipment. The amount of data generated by all sensors in a modern mid-sized sedan every hour is approximately 25TB per hour (Hunt, 2017).
 
4.Analyse or comment the following:
 
Business owners increasingly realize the value of information and how it can affect the operations, business processes, and growth of the organization. Collecting information can help an organization increase their efficiency, productivity, and profitability by better serving their customers. Innovative systems using data analytics can also help an organization gain a competitive advantage.
According to research found online (State of Information Global Results, 2012), enterprises store 100.000 terabytes of data on average. Information and information security systems, smart products, e-commerce, and customer relationship management (CRM) systems are some of the types of systems that are used in an organization, independent of their size or industry, and produce a vast amount of information. Furthermore, organizations are replacing paper-based systems with electronic systems and standalone systems with interrelated systems, these are systems which feed each other, and all of them together analyze and present data to the customer. Data exists in different types such as structured, unstructured and semi-structured (Kannan, 2018), as well as in many different forms such as content data, meta-data (Michael, 2012), and often reside in different repositories such as relational databases, data structures, files, mainframe systems, and clouds.
This ongoing transformation increases the complexity and size of the IT environments, creating a great challenge for data holders. If either the data or the data management process in the organization is not well defined, the user and customer needs would be impossible to be met, the data could be misused, and could also pose excessive risks to the organization and potential compliance infringements.
In the company where I work for, we are oriented towards our customer’s outcomes and often use a certain methodology for creating data management plans, which includes the data collection, analysis, and conceptual framework creation phases. In such methodology, similar to what is described by Murti et al. (2018), interviews with IT staff and users of information systems are conducted, the tables structure of the application is being reviewed, data types are sized appropriately, pertinent documents are analyzed, business process flow diagrams are being created together with a conceptual framework. Many times we need to adjust the data management plans, to our growing and changing needs. Moreover, we often make changes based on how much more we can do with the data. As an example of changing data management plans, I remember when we designed an internal reporting solution which parsed security vulnerability data from third-party tools and transformed them according to our vulnerability reporting standards. The solution was designed and developed in an MS Excel spreadsheet which represented a very simple form of a database. The decision to proceed with that approach was made by considering the very basic functional requirements that existed, the limited resources available to develop and maintain the solution as well as the minimum skills needed to operate the solution among other. During the years, however, the solution has grown to include additional functionality and methods in order to be able to collect data from various tools in different formats (i.e flat-files CSV, XML), to calculate vulnerability severity scores based on different risk rating models (i.e OWASP, CVSSv2, CVSSv3), to support multiple users and also different backup levels (i.e configuration, database, mappings).
The original architecture and data management plans had to change to sustain this new functionality since the size and memory requirements of the solution have been increased while the performance has been significantly degraded. As a result, an application server and a relational database have been integrated into the solution and special methods for reading, writing and analyzing data have been developed.
Answers:

1.Most of the global organizations have huge volumes of structured and unstructured data which are needed to be managed so that growth and progress of the organization is maintained. Most of those data are managed with the help of the big data solutions (Baek et al. 2015). Location and efficiency of the data is maintained if the big data solutions are professionally maintained in the work places. The efficiency of different units of the business can be improved significantly with the use of big data solutions. New business needs are also identified with the help of the big data solutions (Storey and Song 2017). Adaptations to new changes is one of the new findings of the big data solutions. The different complexities during the auditing time can be solved purpose fully with the help of the big data solutions. The business opportunities can be hugely capitalized with the help of the database management structure. The IT functionalities of the global organizations can be hugely benefitted from the use of the DBMS which helps in maintaining the structures of the organization (Jiang et al. 2014). The structure of the data which are getting transmitted from one unit of the system to another is also managed with the help of efficient business models based on DBMS. Databases are the most important business aspects of the global organizations which can be solved purposefully with the use of the relational database management system (Yan et al. 2016). The big data analytics can be embedded into the advanced devices which is the main reason behind its incorporations across most of the successful organizations.
2.Hosting is defined as defined as the type of method by which organizations can store their valuable information. There are different types hosting techniques. This unit of the paper will be focusing on the shared hosting service, during this service multiple users can share same data file. Dedicated hosting in the cloud services is completely different from the shared hosting services. The validity of the users managing their data can be either defined by the users or else by the cloud service departments (Rickscloud.com 2018). When the data is successfully stored in the cloud we can choose about the type of package we want to store the data. There is different type of cloud services present which helps in protecting the data which is stored by the users such as the Clarifying Lawful Overseas Use of Data act which was passed in the February, 2018. There are different regional laws all over the world which helps in protecting data stored in the cloud computing services (Findlaw 2018). The sector specific laws are also very much useful in managing the data in cloud services.
3.Analysis of given scenario
The data management system which was incorporated in an organization in Japan is very much efficient is storing and accessing data with an increased speed. Both structured and unstructured data can be professionally managed by this platform architecture. The complexities faced by the organizations in terms of downloading data from unreliable sources (Arasu et al. 2016). The data which are generated from different types of sources are very much important to be managed in an efficient way as there are lots of issues associated with the processing of those data in terms of their transfer speeds. The storage capacity of the proposed system is very much important for most of the organizations. Management of the data requires a variety of software’s such as the Cassandra, Spark worker and log stash and also ulta fast storage based on NVM e solid stage storage waste similar to SRAM. Redis and Cuda is very much useful for the machine learning system, which again requires GPU assisted CPU nodes (Tang et al. 2016). In the internal mechanism the data considered in the architecture considers process able SQL relational database using the presto. Visualization is useful in returning the data from the platform.
4.Analysis of given scenario
The value of information is getting increased day in the global organizations. Huge volumes of data are transferred from one unit of the organization to another and there are lots of security issues associated with the data (Baller, Dutta and Lanvin 2016). Data is considered as an asset in most of the global organizations which is available in different types of form such as the semi structured data, structured data and the unstructured data each of the data is dedicated for specific units of the business as any kinds of security issues mat result in huge financial loss for the business organizations (Laudon and Laudon  2015). The customers of the organization can also be managed with the help of the effective data management procedures. Effective data management plans are required to minimize the vulnerabilities associated with the data revolved in the organization. Calculation of the vulnerabilities is much easier with the use of the data management plans as there are lots of complexities associated with the data considering the future extensible infrastructures. Thus effective management of data is required to minimize the risks associated with the data considering the availability, integrity and confidentiality.
References
Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U. and Widom, J., 2016. Stream: The stanford data stream management system. In Data Stream Management (pp. 317-336). Springer, Berlin, Heidelberg.
Baek, J., Vu, Q.H., Liu, J.K., Huang, X. and Xiang, Y., 2015. A secure cloud computing based framework for big data information management of smart grid. IEEE transactions on cloud computing, 3(2), pp.233-244.
Baller, S., Dutta, S. and Lanvin, B., 2016. Global information technology report 2016. Geneva: Ouranos.
Findlaw (2018) Cloud Computing and the Law: The Basics – FindLaw [online] [Accessed 24th December 2018].
Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F. and Xu, B., 2014. An IoT-oriented data storage framework in cloud computing platform. IEEE Transactions on Industrial Informatics, 10(2), pp.1443-1451.
Laudon, K.C. and Laudon, J.P., 2015. Management information systems (Vol. 8). Prentice Hall.
Rickscloud.com (2018) Laws and Regulations Governing the Cloud Computing Environment | Rick’s Cloud [online] [Accessed 24th December 2018].
Storey, V.C. and Song, I.Y., 2017. Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering, 108, pp.50-67.

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