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Data Quality Assessment

The data quality in enterprises can be improved by the use of Data Quality Management Practices for governing and managing the activities related to the improvement of the operations (Peer, Vosgerau and Acquisti 2014). The improvement of the data quality management can be done by the following practices,

Data Quality Assessment: The data quality assessment can be done for analysing the data and its state for the management of the data existence. The information that contains errors, replicas, inconsistencies, or gaps in completeness is not easily identified (Schmidt et al. 2015). However, the use of the data quality assessment can help in developing the understanding of the data management and quality assessment. The independent analysis would be helpful for developing appropriate solution to the issues of the data quality assessment.  The Enterprise system can formulate a strategy for increasing the efficiency of the operations for the development of the quality of the data. The information quality evaluation should be possible for dissecting the information and its state for the administration of the information presence. The free examination would be useful for creating suitable answer for the issues of the information quality evaluation Saha and Srivastava 2014. The Enterprise framework can define a procedure for expanding the proficiency of the operations for the improvement of the nature of the information.

Data Quality Firewall: The data can be referred to as a strategic information asset and the enterprise system had been treating it same for developing their operations and functions (Weiskopf and Weng 2013). The data quality firewall is a virtual system that blocks inappropriate data and channel the right data for the operations. The inaccurate data feeds would result in catastrophic impact on the data analysis. The independent analysis would be helpful for developing appropriate solution to the issues of the data quality firewall.  The Enterprise system can formulate a strategy for increasing the efficiency of the operations for the development of the quality of the data. The erroneous information sustains would bring about disastrous effect on the information examination. The autonomous investigation would be useful for creating suitable answer for the issues of the information quality firewall (Saha and Srivastava 2014). The Enterprise framework can detail a technique for expanding the proficiency of the operations for the improvement of the nature of the information.

Integrated Data solution: The integration of the data on a unified platform would help in developing the appropriate solution to the implication of the advanced process of the development of the processes (Lehne  et al. 2015). The integration of the data with the help of modified management of operations would help in developing the support to the data management and modification. The integrated data solution would be able to work and develop the situation for the development of the system management for the data analysis. The issue of going through the data one by one is time taking and integration would make it easy to develop the solution. The incorporation of the information with the assistance of altered administration of operations would help in building up the support to the information administration and change. The incorporated information arrangement would have the capacity to work and build up the circumstance for the advancement of the framework administration for the information investigation (Weidema  et al. 2013). The issue of experiencing the information one by one is time taking and combination would make it simple to build up the arrangement.

Data Quality Firewall

In the books of Gartner Hype Cycle, the Logical Data Warehouse can be defined as a new process for the data management and processing analytics (Gartner 2014). The data analytics combines the strength of the traditional repository and it would be helpful for the development of the implementation model. The development of the improved technologies had been helpful for the dat management and the implementation of the organization. The development of the operations would be helpful for the implication of the improved technologies and processes. The Logical Data Warehouse would be implied for the development of the improved practices and processes (Loyola et al.  2014). The Enterprise development had been helpful for the implication of the development of the operations. The expansion of the data virtualization would be evaluated for the implication of the developed tools and development. The development of the improved operations would be improved for the development of the implied tools. The improvement of the data modification would be helpful for the implication of the tools. The development of the improved operations would be implied for the development of the operations for the improved technology and processes. The data analytics combines the strength of the traditional repository and it would be helpful for the development of the implementation model (Aljumah, Ahamad and Siddiqui 2013).  The development of the improved technologies had been helpful for the data management and the implementation of the organization. The development of the operations would be helpful for the implication of the improved technologies and processes. The Logical Data Warehouse would be implied for the development of the improved practices and processes. The Enterprise development had been helpful for the implication of the development of the operations. The expansion of the data virtualization would be evaluated for the implication of the developed tools and development. The development of the improved operations would be improved for the development of the implied tools. The improvement of the data modification would be helpful for the implication of the tools (Siddiqui and Naahid 2013). The development of the improved operations would be implied for the development of the operations for the improved technology and processes. The integrated data solution would be able to work and develop the situation for the development of the system management for the data analysis. The issue of going through the data one by one is time taking and integration would make it easy to develop the solution. The incorporation of the information with the assistance of altered administration of operations would help in building up the support to the information administration and change. The incorporated information arrangement would have the capacity to work and build up the circumstance for the advancement of the framework administration for the information investigation.  

Integrated Data solution

The Enterprise framework can plan a methodology for expanding the productivity of the operations for the improvement of the nature of the information (Braga et al. 2014). The Logical data warehouse can be used for improving the quality of the data for all scales of Enterprises. The explanation of the benefits of Logical data warehouse for large scale Enterprises is given below,

Use of Data processing for operations: The most important prospect of using improved data quality is for the assistance in the development of the operations of the organization. The improved functions of Logical Data Warehouse would form the support to the improvement of the existing facilities for supporting the development of the organizational development (Li and Offutt 2014,). The use of the data quality assessment would help in developing smarter decision making abilities for the organizations. It would also integrate the operations for forming the support to the development of the enterprises. The most critical prospect of utilizing enhanced information quality is for the help with the advancement of the operations of the association. The enhanced capacities would shape the support to the change of the current offices for supporting the improvement of the authoritative advancement. The utilization of the information quality appraisal would help in creating more quick witted basic leadership capacities for the associations. It would likewise coordinate the operations for shaping the support to the improvement of the ventures.

Provide benefit to the customers: The attenuation of the high class customer satisfaction would be helpful for the development of the customers and implementation of the improved functions. The improved data quality would help in developing improved operations and functions of Logical Data Warehouse for the organization (Kanehisa et al. 2013). It would help in developing their activities supporting the achievement of customer’s satisfaction. The development of the improved operations would comprise the implementation of the requirements of the customers. The constriction of the high class consumer loyalty would be useful for the advancement of the clients and execution of the enhanced capacities. The enhanced information quality would help in creating enhanced operations and capacities for the association. It would help in building up their exercises supporting the accomplishment of consumer loyalties. The advancement of the enhanced operations would contain the usage of the prerequisites of the clients.

Compliance of Cisco regulations: The regulatory compliance is followed for ensuring that the operations of the organization is being achieved by following the norms and rules set by the Cisco. The development of Logical Data Warehouse would help in development of the operations for the enterprises (Goetsch and Davis 2014). The regulatory compliance is being achieved by forming the improved data analysis and quality. The regulatory compliance is being formed for developing the operations of the enterprises such that all government bodies would tend to approve the activities of the Enterprise. The administrative consistence is taken after for guaranteeing that the operations of the association are being accomplished by taking after the standards and principles set by the Cisco administering bodies. The advancement of enhanced capacities would help being developed of the operations for the ventures. The administrative consistence is being accomplished by framing the enhanced information investigation and quality. The administrative consistence is being framed for building up the operations of the ventures with the end goal that all administration bodies would have a tendency to endorse the exercises of the Enterprise.

Use of Data processing for operations

The Enterprise framework can plan a methodology for expanding the productivity of the operations for the improvement of the nature of the information. The Logical Data Warehouse can be used for improving the quality of the data for all scales of Enterprises. The explanation of the benefits of Logical data warehouse for small scale Enterprises is given below,

Improved Security Measures: The data quality improvement of Logical Data Warehouse had also helped in forming the security measures and protection for the Enterprises. The development of Logical Data Warehouse would help in development of the operations for the enterprises (Fonarow et al. 2014). The privacy protection is being achieved by forming the improved data analysis and quality for ensuring the safety of the data stored. The data quality improvement had also helped in forming the privacy protection for the Enterprises. The development of improved functions would help in development of the operations for the enterprises. The privacy protection is being achieved by forming the improved data analysis and quality for ensuring the safety of the data stored.

Employee’s Enlargement: The attenuation of the high class Staff satisfaction would be helpful for the development of the Staffs and implementation of the improved functions (Boswell et al. 2015). The improved data quality with Logical Data Warehouse would help in developing improved operations and functions for the organization. It would help in developing their activities supporting the achievement of Staff’s satisfaction. The development of the improved operations would comprise the implementation of the requirements of the Staffs. The constriction of the high class consumer loyalty would be useful for the advancement of the clients and execution of the enhanced capacities.

Security Risk Management: The use of the data quality assessment would help in developing smarter decision making abilities for the organizations. It would also integrate the operations for forming the support to the development of the enterprises (McQuillan et al. 2016). Hence, the deployment of the risk assessment would be eased for the development of the operations with the help of Logical Data Warehouse. The Logical data warehouse can be used for improving the quality of the data for all scales of Enterprises. The use of the data quality assessment would help in developing smarter decision making abilities for the organizations. It would also integrate the operations for forming the support to the development of the enterprises. Hence, the deployment of the risk assessment would be eased for the development of the operations. The Logical data warehouse can be used for improving the quality of the data for all scales of Enterprises.

Provide benefit to the customers

References

Aljumah, A.A., Ahamad, M.G. and Siddiqui, M.K., 2013. Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University-Computer and Information Sciences, 25(2), pp.127-136.

Boswell, J.F., Kraus, D.R., Miller, S.D. and Lambert, M.J., 2015. Implementing routine outcome monitoring in clinical practice: Benefits, challenges, and solutions. Psychotherapy research, 25(1), pp.6-19.

Braga, P., Portela, F., Santos, M.F. and Rua, F., 2014. Data mining models to predict patient's readmission in intensive care units. In ICAART 2014-Proceedings of the 6th International Conference on Agents and Artificial Intelligence.

Fonarow, G.C., Zhao, X., Smith, E.E., Saver, J.L., Reeves, M.J., Bhatt, D.L., Xian, Y., Hernandez, A.F., Peterson, E.D. and Schwamm, L.H., 2014. Door-to-needle times for tissue plasminogen activator administration and clinical outcomes in acute ischemic stroke before and after a quality improvement initiative. Jama, 311(16), pp.1632-1640.

Gartner, J., 2016. Gartner’s 2014 hype cycle for emerging technologies maps the journey to digital business.

Goetsch, D.L. and Davis, S.B., 2014. Quality management for organizational excellence. Upper Saddle River, NJ: pearson.

Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M., 2013. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids research, 42(D1), pp.D199-D205.

Lehne, B., Drong, A.W., Loh, M., Zhang, W., Scott, W.R., Tan, S.T., Afzal, U., Scott, J., Jarvelin, M.R., Elliott, P. and McCarthy, M.I., 2015. A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome biology, 16(1), p.37.

Li, N. and Offutt, J., 2014, March. An empirical analysis of test oracle strategies for model-based testing. In Software Testing, Verification and Validation (ICST), 2014 IEEE Seventh International Conference on (pp. 363-372). IEEE.

Loyola, P., Staats, M., Ko, I.Y. and Rothermel, G., 2014, July. Dodona: automated oracle data set selection. In Proceedings of the 2014 International Symposium on Software Testing and Analysis (pp. 193-203). ACM.

McQuillan, R.F., Silver, S.A., Harel, Z., Weizman, A., Thomas, A., Bell, C., Chertow, G.M., Chan, C.T. and Nesrallah, G., 2016. How to measure and interpret quality improvement data. Clinical Journal of the American Society of Nephrology, 11(5), pp.908-914.

Peer, E., Vosgerau, J. and Acquisti, A., 2014. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior research methods, 46(4), pp.1023-1031.

Saha, B. and Srivastava, D., 2014, March. Data quality: The other face of big data. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on (pp. 1294-1297). IEEE.

Schmidt, M., Schmidt, S.A.J., Sandegaard, J.L., Ehrenstein, V., Pedersen, L. and Sørensen, H.T., 2015. The Danish National Patient Registry: a review of content, data quality, and research potential. Clinical epidemiology, 7, p.449.

Siddiqui, M.K. and Naahid, S., 2013. Analysis of KDD CUP 99 dataset using clustering based data mining. International Journal of Database Theory and Application, 6(5), pp.23-34.

Weidema, B.P., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C.O. and Wernet, G., 2013. Overview and methodology: Data quality guideline for the ecoinvent database version 3. Swiss Centre for Life Cycle Inventories.

Weiskopf, N.G. and Weng, C., 2013. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), pp.144-151.

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