Organizations continue to gather and store a large amount of data in their data centres. A study by Tallon (2013) indicates that “companies such as Wal-Mart, Google, and Intel have petabytes of data that are individually hundreds of times larger than the data in the US Library of Congress” (pp.32). The first study analyzed in this paper is by Tallon (2013), whose main contributions were about how data value can be increased by developing structures and policies that balance the risk and reward of storing data in large quantities. The key features of the Tallon (2013) study are that it calculated the value of data by examining two main factors; risks and benefits associated with data. Tallon (2013) suggested that most organizations measure value of their data through scenarios where losses occur due to data breach or inaccessibility for a period of time. The study suggested that companies can identify costs of storing data through the use of total cost ownership model, which calculates the costs such as energy, maintenance, and other hardware storage costs.
Tallon (2013) study is relevant to our topic because it clearly indicates methods of calculating the value of data in storage for companies. Another model of calculating value of data as suggested by Tallon (2013) is the frequency of use. Increased rate of data usage means that data is valuable in a company. This model is supported by Weber, Otto, and Österle (2009) study that argue data governance policies and structures are important in responding to strategic and operational challenges that need high-quality corporate data. Therefore, this study is related to the topic in that organizations understand how to calculate data storage costs and adjust appropriately to gain maximum value. However, the weakness of the Tallon (2013) study is that the suggestions made were theoretical.
Another paper reviewed in this study is by Miller and Mork (2013) that discussed the data value chain, which is achieved through three key major steps. Miller and Mork (2013) described steps in the data value chain as data discovery, integration, and exploitation. For organizations to use data for informed decision-making process, they are supposed to identify relevant data sources, collect and store appropriate information. Key characteristics of Miller and Mork (2013) are how they clearly described the steps that are taken in the data value chain, and how organizations can achieve high value for the data stored. First, organizations have to identify completeness, consistency, validity, accuracy, and timeliness to ensure that the data they collect is of quality. After data collection, organizations have to ensure that data is accessible only to the relevant bodies to avoid data breach risks. Furthermore, data value is maintained by identifying structure, syntax, and semantics of the data source for easy integration.
Data integration involves mapping and representation of data that facilitates the analytical process. Data integration includes relational databases that are useful in tabular data whereas semantic web is compatible with nontabular data (Miller & Mork, 2013). The final step of data value chain is exploitation of the collected data. This process involves analyzing data to identify patterns and trends that help create value in an organization. The shareholders can then use such visualized results to reward good behaviors and eliminate negative behaviors in a company (Warren Jr, Moffitt, & Byrnes, 2015). Therefore, the contributions of Miller and Mork (2013) study are on how companies can use data value chain in calculating data value in companies as it helps to identify the weaknesses and strengths of the organization. The weakness of the Miller and Mork (2013) study only offered descriptive information on what data value chain does in a company.
Third study reviewed in this paper is by Sydler, Haefliger, and Pruksa (2014), which indicate that knowledge is perceived as company’s main resource because of how a firm creates, transfers, and uses knowledge affects the performance within a specified industry. Knowledge in a company creates a competitive advantage and fosters performance in numerous ways. Therefore, companies invest a lot in research and development to collect information from external sources and integrate it with a company’s processes to boost knowledge (Peltier, Zahay, & Lehmann, 2013). Sydler et al. (2014) study identified two common intellectual capital valuation methods according to the purposes they serve. The first valuation approach is internal management approach that calculates intellectual capital to measure the internal efficiency in a company. This valuation approach helps the management in crucial areas such as decision-making processes. The second valuation approach identified by Sydler et al. (2014) is an external reporting approach, which focuses on offering insights into investments and creating transparency.
Further, other valuation methods discussed by Sydelr et al. (2014) include return of assets, market capitalization methods, and direct intellectual capital methods that are useful in estimating the monetary value of intangible assets (Sydler et al., 2014). Sydler et al. (2014) study suggest that research and development, advertising, and labor expenditures represent part of intangible asset capitalization that helps in boosting firms’ performance. However, creating value from such intangible assets is determined by how companies manage their data and tools they use to reflect on their thinking model (Kianto, Ritala, Spender & Vanhala, 2014; Sydler et al., 2014). The relevance of Sydler et al. (2014) study to our topic is on how intellectual capital can play a useful role in promoting performance of the company. The study also identified better method of valuing data such as return of assets and direct intellectual capital methods. The weakness of the Sydler et al. (2014) study is that it involved a sample of 69 companies, which is small that means their findings cannot be generalized.
The final study reviewed in this paper is by Love, Matthews, Simpson, Hill, and Olatunji (2014) study that focused on the benefits that companies enjoy by investing in building information modeling (BIM). According to Love et al. (2014), investing in BIM does not create value unless the company utilizes the systems well to enhance their operations. Therefore, unique characteristics of Love et al. (2014) contribution is how value of BIM can be achieved when organizations and employees are able to perform their duties differently and efficiently. For instance, Love et al. (2014) indicated that business owners can use the building information model to improve inventory management, workflow, and safety of workers, production capacity, and maintenance. In addition, Love et al. (2014) study indicate that for clear valuation of information systems, companies have to create clear mandate and scope of the systems, lines of accountability, and the performance measurement. Performance measurement would be useful in helping the company understand if they gain value in implementing the systems. Love et al. (2014) work on BIM is ongoing, therefore, the framework cannot be used to calculate value of the data in an organization. The Love et al. (2014) study is relevant to our topic as it helps in data valuation in tangible ways such as increased productivity and efficiency in workplace.
In conclusion, the most appropriate evaluation model for hotel data sets is the use of the method discussed in Sydler et al. (2014) study. The use of internal management and external reporting approaches would help the hotel to understand the value that dataset offers by observing internal efficiencies and market capitalization. The strength in those models is that they assess both internal and external efficiencies of data. The companies would effectively analyze the benefits realized from data analysis such as improved productivity, efficiency, and effectiveness of the processes. On the other hand, a company would effectively understand the market segment, where to invest, the adverts to use, and how to effectively target the intended audience by analyzing relevant data.
References
Kianto, A., Ritala, P., Spender, J. C., & Vanhala, M. (2014). The interaction of intellectual capital assets and knowledge management practices in organizational value creation. Journal of Intellectual capital, 15(3), 362-375. DOI 10.1108/JIC-05-2014-0059
Love, P. E., Matthews, J., Simpson, I., Hill, A., & Olatunji, O. A. (2014). A benefits realization management building information modeling framework for asset owners. Automation in construction, 37, 1-10. Doi: 10.1016/j.autcon.2013.09.007
Miller, H. G., & Mork, P. (2013). From data to decisions: a value chain for big data. It Professional, 15(1), 57-59. Doi: 10.1109/MITP.2013.11
Peltier, J. W., Zahay, D., & Lehmann, D. R. (2013). Organizational learning and CRM success: a model for linking organizational practices, customer data quality, and performance. Journal of Interactive Marketing, 27(1), 1-13. Doi: 10.1016/j.intmar.2012.05.001
Sydler, R., Haefliger, S., & Pruksa, R. (2014). Measuring intellectual capital with financial figures: Can we predict firm profitability?. European Management Journal, 32(2), 244-259. Doi: 10.1016/j.emj.2013.01.008
Tallon, P. P. (2013). Corporate governance of big data: Perspectives on value, risk, and cost. Computer, 46(6), 32-38. Doi: 10.1109/MC.2013.155
Warren Jr, J. D., Moffitt, K. C., & Byrnes, P. (2015). How Big Data will change accounting. Accounting Horizons, 29(2), 397-407. Doi: 10.2308/acch-51069
Weber, K., Otto, B., & Österle, H. (2009). One size does not fit all—a contingency approach to data governance. Journal of Data and Information Quality (JDIQ), 1(1), 4. Doi: 10.1145/1515693.15156
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