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INFS 5075 Information Governance

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INFS 5075 Information Governance

0 Download8 Pages / 1,905 Words

Course Code: INFS 5075
University: University Of South Australia

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Country: Australia

Question:

To review literature on any of the following topics.

Data quality issues and data analytics
Causes of IT governance failure

Your assignment should have at least 10 references and they should be post year 2012. These references should be from journal articles only.

Answer:

Introduction
In recent years, the importance of collecting accurate and highly reliable data has increased substantially. With the growth of data democratisation and data socialisations, corporations focus on collecting, organisation, sharing and making available crucial information for their employees in an efficient manner. There are a number of organisations which has generated a competitive advantage in the industry by using reliable data whereas other companies face issues relating to quality and data used by them (Hazen et al., 2014). With the advancement in technologies such as artificial intelligence, internet of things, automation and others, the importance of reliable and quality data has increased. Data analytics enable corporations in evaluating the crucial data based on which they gain market insights which allow the management to form business policies which contribute to their success. The significance of reliable data has increased due to the popularity of big data and cloud computing technologies. The companies face various data quality issues while collecting and organising their data which affects the quality of their research and overall operations. In this report, various key issues relating to data quality will be discussed. Furthermore, the impact of data quality issues on data analytics will be analysed in the report as well.
Literature Review
The problems relating to data quality can result in increasing difficulties for an enterprise. In order to tackle challenges such as low customer satisfaction, uninformed decision making, missed opportunities and non-compliance sanctions, the management focuses on making data quality a priority of their data management programs. A study has shown that over 88 percent of corporations directly see the impact of inaccurate data on their business due to which they result in losing 12 percent of their revenue (Davis, 2014). A similar study conducted by Database Marketing provided that the sales of an enterprise can be increased by 29 percent based on corrected customer data since it enables the management in forming business strategies which are focused towards their needs (Humphris, 2014). There are a number of factors which result in rising challenges relating to data quality while collecting, organising and using data. Due to incorrect data, the corporations are unable to form business strategies which are focused towards the demands of their customers. As per Kwon, Lee and Shin (2014), the strategies formed based on false data did not address the needs of customers, and the challenges faced by the enterprise which results in adversely affecting the enterprise in the long run. Due to these factors, improving the quality of data collected and used by the enterprise has become the top priority for the database management system in the company. However, there are various issues relating to data quality which result in adversely affecting the information and data analytics by influencing the quality of results.
The ability of the business to reach to their potential customers efficiently and systematically is crucial since it creates new business opportunities for them; however, the data quality challenge is that customer data touches every aspect in an enterprise and its flows through each phase of the customer lifecycle. According to Chen et al. (2017), the key issue is that the customer data is collected by a company at various places such as purchase/order placement, social media marketing, cross-sell offers and others. It makes it difficult for the corporation to identify which data is reliable to use while collecting insights regarding the purchasing behaviour of customers. It makes it difficult for the enterprise to find new segment of customers by relying on such data. As per Gunasekaran et al. (2017), the data collected from different sources affect the effectiveness of data analytics which results in showing false results for the company. In order to address this issue, the corporation is required to understand that there are various sources of common errors which result in affecting the quality of data such as missing digits, incomplete phone numbers and others. Chinnaswamy et al. (2015) provided that after identifying these areas, the team can clean up such areas and capture standards which should be evaluated by all parties which needed to reach a consensus standardised formatting to maintain the data quality standards.
Duplicate copies of same records adversely affect storage and computation, but, it also results in producing incorrect and skewed insights when they go undetected. As per Smith et al. (2016), the contact data is static due to which the issues such as duplicate and obsolete data have become common data quality challenges. Everyday people change their names, move to new locations, marry and change their jobs due to which the importance of effective data verification methods has increased at each collection point. Papadatos et al. (2015) provided that this strategy is particularly important for companies that collect information from multiple sources during customer lifecycles such as websites, retail locations and call centres. Due to obsolete and duplicate data, it has become nearly impossible for the corporations to communicate with prospects and customers effectively. According to Ainsworth and Russell (2018), due to these inaccuracies, the marketing process of the company is affected negatively which increases the risk to decrease customer satisfaction. Furthermore, this issue leads to frustration and distrust from potential customers of the organisation if they receive multiple mails from the organisation under different names. In order to address this issue, the corporations are required establish a program called ‘data deduplication’. In this program, they are required to blend algorithms, data processing and human insight in order to help identify duplicate data to reduce them from the batch and improve the overall quality of the data.
The availability of big data has increased with the popularity of social media sites, smartphone users and others mediums. Wamba et al. (2015) stated that big data sets enable enterprises in processing large number of data sets based on which they can collect market insights from a large audience. However, it is difficult for companies to focus on specific points while using big data sets because they include information regarding different aspects. Moreover, organisations have to comply with various data security and compliance requirement while collecting, organising and using data. As per Akter and Wamba (2016), the requirements include corporate requirements to government’s mandatory policies to ensure that security of parties while using the data security. Failure to comply with these regulations results in increasing challenges for the companies due to which they have to face legal consequences. Lenca and Lallich (2015) recommended that the corporations have to ensure that they know regarding all the necessary legal requirements while conducting data analytics process to avoid legal consequences. In order to focus the big data sets for specific purposes, the corporations are required to check the sources of collect of big data and improve the technologies which are used by them while analysing the big data sets.
Inconsistency in the formats of data results in increasing the issues for the quality of data stored. According to Grabowski et al. (2015), data is collected in inconsistent formations, and the systems which are used by corporations for data analytics and storing the data might misinterpret it. For example, if a company is collecting and maintaining the data of its customers, then the format in which the data is collected should be pre-determined in order to avoid confusion and increase consistency in data. Verweij et al. (2015) provided that due to the lack of consistency, the quality of data is compromised; thus, taking precautionary measures to maintain the consistency in data is significant for enterprises. Furthermore, system upgrades also result in increasing the change of information getting lost or corrupt. Thus, corporations should make several backups of their data in order to avoid losing it after a system upgrade.
Conclusion
In conclusion, there are various issues faced by corporations relating to the quality of data which affects the strategies form by them. Organisations can face various negative consequences if they conduct research based on inaccurate data which hinders the effectiveness of their strategies as well. There are various data quality issues which result in influencing the quality of data collected by the companies such as duplicates, incomplete data, system upgrade, compliance issues, inaccurate data and others. These issues increase the effectiveness of data analytics based on which parties have to face serious consequences in their business. Various recommendations are given in the report which can assist the organisations in improving the reliability of their data and addressing the issues of data quality.
References
Ainsworth, S. and Russell, J.M. (2018) Has hosting on science direct improved the visibility of Latin American scholarly journals? A preliminary analysis of data quality. Scientometrics, 115(3), pp.1463-1484.
Akter, S. and Wamba, S.F. (2016) Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), pp.173-194.
Chen, W., Zhou, K., Yang, S. and Wu, C. (2017) Data quality of electricity consumption data in a smart grid environment. Renewable and Sustainable Energy Reviews, 75, pp.98-105.
Chinnaswamy, A.K., Balisane, H., Nguyen, Q.T., Naguib, R.N., Trodd, N., Marshall, I.M., Yaacob, N., Santos, G.N., Vallar, E.A., Galvez, M.C.D. and Shaker, M.H. (2015) Data quality issues in the GIS modelling of air pollution and cardiovascular mortality in Bangalore. International Journal of Information Quality, 4(1), pp.64-81.
Davis, B. (2014) The cost of bad data: stats. [Online] Available at: https://econsultancy.com/blog/64612-the-cost-of-bad-data-stats [Accessed on 17th August 2018].
Grabowski, A., Selke, S.E., Auras, R., Patel, M.K. and Narayan, R. (2015) Life cycle inventory data quality issues for bioplastics feedstocks. The International Journal of Life Cycle Assessment, 20(5), pp.584-596.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S. (2017) Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, pp.308-317.
Hazen, B.T., Boone, C.A., Ezell, J.D. and Jones-Farmer, L.A. (2014) Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, pp.72-80.
Humphries, W. (2016) New Research: Your Sales Database Is Killing Your Bottom Line. [Online] Available at: https://www.internalresults.com/4-signs-your-sales-database-needs-an-update [Accessed on 17th August 2018].
Kwon, O., Lee, N. and Shin, B. (2014) Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.
Lenca, P. and Lallich, S. (2015) Guest editor’s introduction: special issue on quality issues, measures of interestingness and evaluation of data mining models. Journal of Intelligent Information Systems, 45(3), pp.295-297.
Papadatos, G., Gaulton, A., Hersey, A. and Overington, J.P. (2015) Activity, assay and target data curation and quality in the ChEMBL database. Journal of computer-aided molecular design, 29(9), pp.885-896.
Smith, S.M., Roster, C.A., Golden, L.L. and Albaum, G.S. (2016) A multi-group analysis of online survey respondent data quality: Comparing a regular USA consumer panel to MTurk samples. Journal of Business Research, 69(8), pp.3139-3148.’
Verweij, L.M., Tra, J., Engel, J., Verheij, R.A., de Bruijne, M.C. and Wagner, C. (2015) Data quality issues impede comparability of hospital treatment delay performance indicators. Netherlands Heart Journal, 23(9), pp.420-427.
Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015) How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234-246.

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