Improving data quality in Enterprise-Free-Samples for Students

Question: Discuss about the Data Quality. Answer: Introduction Acceptable quality of data is crucial to processes related to operational and transactional in any organization for the purpose of ensuring the reliability of business analytics or business intelligence reporting. Generally, quality of data is affected by the way such data is entered, managed and stored. Assurance related to data quality is the process of verifying the effectiveness and reliability of data. For the purpose of maintaining the quality of data it is necessary to going through the data on periodic basis and also scrubbing of data. Usually, it involves updating of data, standardizing the data, and also duplicating records for the purpose of creating the single view of the data even though such data is stored in multiple separate systems. There are number of organizations which still conduct long term data quality improvement practices for the purpose of achieving short term goals. It must be noted that various organizations understand the adverse effects of not addressing the poor quality of data. In this paper, firstly we discuss the importance of improving data quality in organization, and then we choose Software-defined security from hype cycle to understand its role in data quality improvement. Subsequently, effect of SDS is considered on large and small organizations. Lastly, this paper is concluded with brief conclusion. Improving data quality in Enterprise: In many areas of life and work quality is considered as personal and subjective judgment, but in case of organizational data quality has defined specifically, but some aspects of quality are subject to personal interpretation and some of these aspects are objectively derived. This can be understood through example such as data validity, conformance, integrity, accuracy, and completeness and all these metrics can be measured at granular level. Other aspects are broader in nature and they also provide measurement related to the entire business data element such as data is available for the consumers and timeliness (Moss, Adai, and Adelman, 2005). It is not possible that two organizations are exactly same and their ways to create, share and transform data are also completely different. Therefore, some fundamental steps are defined below for the purpose of improving data quality: Organization must establish their data quality process which means organization must initiate the process with profiling the existing data, but it is not fortunate because number of data quality teams stops at this stage. In actual team must followed profiling phase by an assessment process which is robust and comprehensive in nature, and team also uses the outcomes of the profiling process for the purpose of determining what outliners and anomalies exists. In case tools of data improvement of the organization are not in place then it becomes necessary for the organization to conduct experiments with different tools, and choose any one tool which fits best with the needs of the organization. It must be noted that all these tools are agnostic because every tool has its own pros and cons. Therefore, it is recommended to use Design-of-Experiments (DoE) framework for the purpose of ensuring that experiments conducted by organization is more useful rather than just determining how the organization works. It must be noted that well designed DOE framework will be used for measuring the effectiveness of different combination of people, roles, and technologies. Organization must take measures to develop mechanism which is analytical in nature for the purpose of determining the quality scores into visualizations which also provide control-chart like capabilities. This mechanism also makes sure that organization can become proactive on data quality scores which are actually trending towards the critical deviation limit. These measures also provide reason to the executives for supporting the efforts made by team (Vendeventer, 2015). About Software-defined security: Software defined security fall under the type of security models through which the information security in the environment of computers are implemented, controlled, and managed by security software. It is generally considered as software managed, driven of policy, and governed security where number of security controls such as intrusion detection, network segmentation, and access controls are monitored and handled through software. This system is mainly implemented in IT environments in which security dependence is not there or very minimum such as infrastructure related to virtualization and cloud computing. It must be noted if any new device is introduced within the environment then it is automatically controlled by the base security policy. This system further ensures easy security reach and scalability of the underlying environment and it also moves with the increase of resources and infrastructure related to environment. However, it becomes easy to manage security and environment can be migrated to other data center and it does not affect security policy and controls in place (catbird, n.d.). It must be noted that new category is introduced for security for the next generation environments and this category is a named as software defined security. This mainly aims to provide the network security enforcement by differentiate between the plan related to security control and plans related to security processing and forwarding plans, and similarly this process helps in abstracting the network control plan from the forwarding plan. Implementation of SDS results in system which is dynamically distributed and helps in virtualizing the network security enforcement function and it also managed as single logical system. This system is the example of network functions virtualization (NFV) which actually offers various new and different ways to design, deploy, and manage the services relating to networking by decoupling the network function such as firewalling and intrusion detection from appliances of proprietary hardware for the purpose of running the software. SDS also consolidates and delivers the networking components for the purpose of supporting the virtualized infrastructure. Therefore, SDS introduces various simple methods to the world of network security, and in this model protection is provided through logical policies which are not tied to nay server or security device which is special. Adaptive, virtualization, and security is achieved by polling the security resources across boundaries. Some key attributes of SDS are listed below: Abstraction: SDS abstract he security from different physical constructs such as stateful port firewalls and wire sniffers, and it also replaced by different flexible controls which are in the form of policy envelops and blanketing the assets which are virtualized in nature. It is considered as the foundation of establishing common security models which are deployed o repetitive basis without any concern for underlying the capabilities in the form of physical hardware. Automation: it must be noted that each asset is considered as spun and redeployed. It also eliminates the Concern related to inadvertent operator error. SDS further ensures that no asset can be created without being automatically put into a security trust zone. Role based controls stated in SDS also ensures that administrators related to properly privileged can make modifications. SDS automation also provides wire speed reaction to those security events which are anomalous, and policy also indicates instantly alerting and quarantining. However, traditionally security is depend on continues detection, action and administration (catbird, n.d.). Scalability and flexibility: eliminating the dependency of organization on physical hardwares, and security can be deployed on the basis of scale appropriate to each host hypervisor, and it also helps in growing the scope accommodating with business needs. Control orchestration: SDS is mainly designed to manage the range of security network controls such as intrusion detection and prevention, management of vulnerability, network segmentation, monitoring tools, etc. into a single engine which is coordinated for the purpose of intelligent analysis and action. Unlimited sources related to security input can be funneled into the system known as policy-driven orchestration which actually improves the data accuracy and attendant action. This aspect of SDS is critical for ensuring successful compliance enforcement because standards related to major compliance stated different controls as parts of the specification. Portability: in case data is governed through SDS then those assets which carry their security settings with them for the purpose of moving or scaling. This can be understood through example such as ITsec and netsec personnel can set it and forget it. Visibility: because of the virtualized infrastructure itself Software-Defined Security suddenly improves the visibility related to network activity. Administrators and security personnel related to network are able to detect the anomalous behavior which helps them in blinding the physical devices and also ensures thwart and protect with greater degree of accuracy. Therefore network securities are accompanied with this additional data and netflow mapping becomes more extensive and precise (SDXCentral, n.d.). Benefits of using SDS for improving data quality: There are number of benefits of using SDS and these benefits are stated below: Central management of security: organizations can use SDS by the way of SDN controller for the purpose of implementing, controlling, managing the threats from one single place. Reduce the security threats: SDS help the organization to reduce the security attacks by relieving their network from off ramp traffic to central location. Hardware cost reduction: because of the virtualization of network security applications in the hardware, SDS reduces the need of specialized vendor appliances (Seyf, 2016). Large organizations: These are some aspects which large organization can achieve for ensuring data quality through SDS: SDS enables the organization to use consistent and efficient security policies across the enterprise networks, and it does not matter where such resources are established. SDS also helps the organization to focus on the software aspect of data security instead of hardware, and it also helps the IT staff to focus on policies rather than just keep their machines on (Buzz, n.d.). SDS also facilitates the security automation with different technologies of security such as intrusion prevention, identity access management, data loss prevention which ultimately saves time and money. However, it must be noted that any type of virtualization includes the element of scalability and flexibility. In the security world, removing the hardware from any equation makes it quick and also makes it easy to scale the security up or down, and this is based on immediate needs of each host hypervisor and also with each business. Also geography in this case is not relevant for the purpose of securing the networks in multiple locations. In case large organizations migrates the assets to software defined, cloud infrastructures, and security for the purpose of protecting these assets and needs also emerged for shifting the software defined model (IT Glossary, n.d.). Small organizations: In case of small organizations SDS helps in various ways for improving the data quality such as it eliminates the dependency of organization hardware because of its visualization security. Hardware is expensive to buy, upgrade and manage. On the other hand SDS is considered as cost effective model. Therefore users pay only for services used by them. SDS eliminates the heavy capital expenditure (Buzz, n.d.). Conclusion: In this paper, aspects related to data quality improvement and role of Software defined security in data quality improvement is considered. From the above facts it is clear that improvement in data quality plays very important role in organizations as it improves the performance of the organization and ensures easy access to information. On the other hand SDS play important role in improving the quality of data by the way of SDN controller for the purpose of implementing, controlling, managing the threats from one single place. It also helps the organization by eliminating the dependency on hardware. Therefore, it is concluded that SDS plays very important role in data quality improvement and ensures productivity in computing environment. References: Moss, T. L. Abai, M. Adelman, S. (2005). How to Improve Data Quality. Available at: https://www.informit.com/articles/article.aspx?p=399325seqNum=4. Accessed on 21st June 2017. Vandeventer, R. 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