Volume 9, Issue 2 (9-2022)
Human Information Interaction 2022, 9(2): 21-37 |
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Daniali S, Seddighi A H. Data Governance Evaluation in Organizations:A Hybrid AHP-TOPSIS Approach. Human Information Interaction 2022; 9 (2)
URL: http://hii.khu.ac.ir/article-1-3144-en.html
URL: http://hii.khu.ac.ir/article-1-3144-en.html
Iranian Research Institute for Information Science and Technology (IranDoc)
Abstract: (3159 Views)
Background and Aim: Data as a strategic asset in any organization requires proper and effective management in order to provide transformative opportunities for the organization. In addition, the increase in the volume of data has forced organizations to move towards collecting, classifying and analyzing data so that they can identify the customer's needs and respond to them at the right time and in the right way. On the one hand, to manage such a volume of data and on the other hand, to maximize the business value resulting from the analyses based on these data, a concept called data governance has been introduced. Data governance is a system for determining the responsibilities, policies and standards used in connection with data-driven processes at the organization level, which tries to take steps to transform organization's data into business values while maintaining and increasing the quality of data. From this point of view, data governance is considered as a strategic program that aims to guide and monitor the various data dimensions of the organization in order to solve internal problems around data, and to improve collaboration between business and information technology departments. It will lead to increase productivity in data management and use, and help generate value by pushing the organization towards data-driven decisions. Considering the importance and role of data governance, organizations need to have a clear picture of their situation in this field. Therefore, the need for an approach that can evaluate data governance in organizations is strongly felt. For this purpose, this research tries to find an answer to this need by developing a model for evaluating data governance.
Method: In this research, first, a set of criteria for evaluating data governance is extracted from the literature, and according to the structure of the problem and the opinion of experts, a hierarchical structure is developed for evaluating data governance. Then the evaluation method is established using the proposed structure and a hybrid approach based on Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In the following, the proposed approach is used in a case study in the food industry.
In the evaluation method, first, the weight of each criterion is calculated with the help of AHP. In this regard, the following steps are taken.
Method: In this research, first, a set of criteria for evaluating data governance is extracted from the literature, and according to the structure of the problem and the opinion of experts, a hierarchical structure is developed for evaluating data governance. Then the evaluation method is established using the proposed structure and a hybrid approach based on Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In the following, the proposed approach is used in a case study in the food industry.
In the evaluation method, first, the weight of each criterion is calculated with the help of AHP. In this regard, the following steps are taken.
- Form a hierarchy
- For the elements of each level of the hierarchy, perform pairwise comparisons according to the expert opinion.
- Calculate the inconsistency rate for each matrix of pairwise comparisons.
- If the inconsistency rate for the matrix was more than 0.1, review the values of the paired comparisons of that matrix.
- Calculate the relative weight of the elements corresponding to each matrix of pairwise comparisons.
- The product of the relative weight of each criterion and the relative weight of all elements related to it, at all higher levels of the hierarchy, gives the final weight of that criterion.
- Now, with the help of the weights calculated for each criterion and using the TOPSIS method, the alternatives will be ranked. The TOPSIS method is based on choosing the alternative that has the smallest distance from the positive ideal solution and the largest distance from the negative ideal solution. In this method, it is assumed that there are m alternatives (organizations) that must be evaluated based on n criteria. The steps of this method are as follows.
- Evaluate the alternatives with respect to each criterion according to the expert opinion.
- Normalize the decision matrix so that the data are on the same scale.
- Obtain the normalized weighted decision matrix by multiplying each component of the normal matrix by the weight of similar criteria.
- Calculate positive ideal and negative ideal solutions.
- Calculate the distance of each alternative from the ideal positive and ideal negative solutions.
- Obtain the closeness coefficient of the alternatives.
- Rank the alternatives in decreasing order of the closeness coefficient.
- Finally, the proposed evaluation method is used to evaluate data governance in five organizations active in the food industry.
Findings: According to the results of this research, data governance can be evaluated at the first level from three different dimensions, which are data quality, internal organizational effects, and external organizational effects. Then, at the second level, different criteria are considered for each dimension of data governance. Data quality evaluation criteria include data accuracy, data completeness, data consistency, data availability, data timeliness, and data uniqueness. The data accuracy measure refers to issues such as the percentage of incorrect information, the percentage of the need for manual corrective actions on the data, and the percentage of change in the retrieval of incorrect information after the implementation of data governance. The data completeness criterion seeks to ensure the completeness of various dimensions of information that the organization must use in its business line, and is related to issues such as the percentage of information filled in the required fields, the percentage of usable information, and the percentage of incomplete information. Data consistency refers to ensuring that the data is consistent and aligned with the policies, rules and values set for the data in the business. Data availability seeks to assess the time the business group has access to critical information and data elements.
Data timeliness refers to the degree to which the data represents reality at a particular point in time.
Data uniqueness means that no information item is recorded more than once in the data set.
The second dimension of data governance evaluation focuses on its internal organizational effects,andthe main criteria in this dimension are data governance efficiency, data governance productivity, and business cost savings. These criteria seek to evaluate the level of involvement, participation and impact of data governance in the organization. Among the important things in evaluating the efficiency of data governance are the number of business lines, functional areas, system areas, project teams, and other parts of the organization that have come together to support monitoring and providing resources for data governance, and in addition, categorizing and tracking the status of all issues that fall within the scope of data governance tasks. The data governance productivity considers the impact of data governance in relation to the amount of support and investment in this area, and includes issues such as the amount of reduction in resources required to coordinate members, products and other entities in data systems, the amount of reduction in work required to solve existing data problems, the percentage of projects or initiatives within the organization that have been identified and eliminated as redundant by the data governance program, and the number of redundant systems eliminated in order to create a single definition of customer, product, or other master data. Finally, the cost savings measure reflects the business value of data governance in terms of internal organizational impacts.
The third dimension of data governance evaluation considers the effects that go beyond the internal boundaries of the organization and affect the entire business of the organization. The criteria considered in this dimension include obtaining and improving customer satisfaction, complying with laws and creating business opportunities, which express the main motivations and drivers of the organization to adopt data governance in the current competitive environment. The customer satisfaction criterion measures the fruit of the efforts made to govern and manage data and turn it into a real business value. On the other hand, there are laws and regulations that are defined in relation to data and depending on the type of business at different national, regional and international levels, and failure to comply with them, in addition to monetary fines, will sometimes result in the suspension or even termination of the organization's business. Therefore, the level of compliance with laws is one of the key criteria in the evaluation of data governance from the perspective of external organizational effects. Finally, it is expected that high-quality data along with analysis and reporting systems will lead to informed decisions and data-driven insights and provide new business opportunities for the organization, which is the subject of the last criterion in this dimension.
Finally, five organizations active in the food industry were examined from the perspective of data governance. According to the information collected from these organizations and using expert opinion, pairwise comparisons were made at different levels of the proposed hierarchy. Then, using AHP, the weight of each dimension and criteria was calculated. According to the results, it can be seen that the external organizational effects is the most important dimension of data governance evaluation in organizations. In addition, customer satisfaction was chosen as the most important evaluation criterion, and compliance with laws and productivity were placed in the next positions. Then, the solutions and the distance of each organization from these solutions were calculated. studied organizations were scored based on the data governance criteria on a scale between zero and ten, and using the TOPSIS method, ideal positive and ideal negative Afterwards, the closeness coefficient and accordingly the rank of each organization was obtained.The results show that the third organization has the best performance in the field of data governance among other organizations, and
the second and fourth or ganizations are placed in the next places with a slight difference from each other.
The fifth organization has a much weaker performance and the first organization is in the last place by a large margin. These results emphasize the applicability of the proposed approach for evaluating data governance and show the steps to perform such an evaluation in a case study in the field of food industry. Such an evaluation in the target organizations can be used as a measure to determine the current state of data governance on the one hand, and on the other hand, it can be used to set goals to reach the desired state in data governance. Moreover, considering the comprehensive and general nature of the proposed approach, it enables its application in other organizations, regardless of their size and type, which is one of the advantages of this approach.
Conclusion: The volume of data is exploding in the last decade and its complexity is continuously increasing. Moreover, organizations have become more adept at using data, which has created new demands that require different methods to combine, change, store and present information. Leading organizations are finding that traditional solutions for data management are becoming more expensive and unable to truly manage the business. Therefore, organizations need to solve these data problems in another way and by implementing an effective data governance. Data governance, by monitoring data quality and aligning it with business goals, is one of the causes of internal organizational changes, such as increased productivity. It has external impacts like increasing customer satisfaction and creating new business opportunities. Therefore, organizations need to use, implement, and evaluate data governance in their business to maintain competitive advantage and comply with laws and regulations. The present study tried to provide an applicable approach to evaluate data governance in organizations. For this purpose, the dimensions of data governance and different criteria for their evaluation were determined using the literature review and the opinion of experts. Then, a hierarchical structure was proposed to evaluate data governance. This structure considers data governance from the three dimensions of data quality, internal organizational effects and external organizational effects of data governance. In the following, for each of these dimensions and depending on their nature, different criteria were introduced and explained. Then the evaluation method was developed based on the obtained structure and using a hybrid AHP and TOPSIS approach. In the next step, the proposed approach was used in a case study to evaluate data governance in five organizations active in the food industry. This study, while showing the implementation steps of the proposed approach, specifies its applicability and generalizability in other organizations. In addition, the results of this evaluation can help organizations to improve the state of data governance and while ensuring customer satisfaction and compliance with laws in the field of data, provide a platform for organizational excellence and new business opportunities.
Keywords: Data Governance, Business Management, Data Governance Evaluation, Analytic Hierarchy Process (AHP), TOPSIS, Data Quality
References
1. Alofaysan, S., Alhaqbani, B., Alseghayyir, R., & Omar, M. (2014). The Significance of Data Governance in Healthcare - A Case Study in a Tertiary Care Hospital. In Proceedings of the 7th International Conference on Health Informatics, Loire Valley, France, pp 178-187.
2. Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2016). A Conceptual Framework for Designing Data Governance for Cloud Computing. Procedia Computer Science, 94, 160-167. [DOI:10.1016/j.procs.2016.08.025]
3. Bradley, C. (2013). Implementing Effective Data Governance. London: IPL / Embarcadero series.
4. Cheong, L.K., & Chang, V. (2007). The Need for Data Governance: A Case Study. In Proceedings of the 18th Australasian Conference on Information System Governance, Toowoomba, Australia, pp 999-1008.
5. Experian Data Quality. (2016). The 2016 Global Data Management Benchmark Report. MA: Experian Information Solutions Inc.
6. García-Cascales, M.S., & Lamata, M.T. (2012). On Rank Reversal and TOPSIS Method. Mathematical and Computer Modelling, 56, 123-132. [DOI:10.1016/j.mcm.2011.12.022]
7. Hwang, C.-L., & Yoon, K. (1981). Multiple Attributes Decision Making: Methods and Applications A State-of-the-Art Survey. USA: Springer-Verlag. [DOI:10.1007/978-3-642-48318-9]
8. Informatica. (2012). Holistic Data Governance: A Framework for Competitive Advantage. CA: Informatica Corporation.
9. Jena, D., Lampshire, G., McCoppin, T., & Van de Water, J. (2012). The Ultimate Guide to Data Governance Metrics: Healthcare Edition. IL: Ajilitee.
10. Khatri, V., & Brown, C.V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148-152. [DOI:10.1145/1629175.1629210]
11. Mosley, M., Brackett, M., Earley, S., & Henderson, D. (2009). The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK). USA: Technics Publications, LLC.
12. O'Neal, K. (2015). Top 10 Artifacts Needed for Data Governance. CA: First San Francisco Partners.
13. Panian, Z. (2010). Some Practical Experiences in Data Governance. World Academy of Science, Engineering and Technology, 62, 939-946.
14. Prasetyo, H.N., & Surendro, K. (2015). Designing a Data Governance Model Based on Soft System Methodology (SSM) in Organization. Journal of Theoretical and Applied Information Technology, 78(1), 46-52.
15. Rifaie, M., Alhajj, R., & Ridley, M. (2009). Data Governance Strategy: A Key Issue in Building Enterprise Data Warehouse. In Proceedings of The 11th International Conference on Information Integration and Web-based Applications & Services, Kuala Lumpur, Malaysia, pp 587-591. [DOI:10.1145/1806338.1806449]
16. Saaty, T.L. (2012). Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World, 3rd Revised Edition. USA: RWS Publications.
17. Soares, S. (2010). The IBM Data Governance Unified Process - Driving Business Value with IBM Software and Best Practices. USA: MC PRESS.
18. Thomas, G. (2014). The DGI Data Governance Framework. The Data Governance Institute, http://www.datagovernance.com/wp-content/uploads/2014/11/dgi_framework.pdf.
19. Were, V., & Moturi, C. (2017). Toward a Data Governance Model for the Kenya Health Professional Regulatory Authorities. The TQM Journal, 29(4), 579-589. [DOI:10.1108/TQM-10-2016-0092]
20. Yulfitri, A. (2016). Modeling Operational Model of Data Governance in Government - Case Study: Government Agency X in Jakarta. In Proceedings of the International Conference on Information Technology Systems and Innovation, Bandung, Indonesia, pp 1-5. [DOI:10.1109/ICITSI.2016.7858207]
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