The Experts below are selected from a list of 6366 Experts worldwide ranked by ideXlab platform
Ronald E. Mcgaughey - One of the best experts on this subject based on the ideXlab platform.
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Accounting for Data: A Shortcoming in Accounting for Intangible Assets
Academy of Accounting and Financial Studies Journal, 2006Co-Authors: Keith Atkinson, Ronald E. McgaugheyAbstract:ABSTRACT In today's economy and in the future, intellectual capital and the proper use of information will be critical to the success of many firms. Data has been called the raw material of information. Data can be aggregated, disaggregated, sorted, and subjected to a variety of mathematical and logical manipulations. Data can be added to a system, removed, bought, sold and used. Data can be enhanced making it more valuable and it can be made less valuable with repeated use and with the passage of time. Because data is a critical firm resource, one expects the cost of data to be accounted for and to appear on the firm's balance sheet with other firm assets. However, depending on the data's genealogy, the cost of the data may never be shown as an asset. If data is purchased it finds its way to the balance sheet, however, if the data is developed internally Accounting Principles Board (APB) Opinion No. 17 prevents it from being capitalized as an asset. The authors argue that the exclusion of internally developed assets, (particularly data) from the balance sheet can mislead investors, and managers. The authors believe that the exclusion is unnecessary - that data meets the definition of an asset. Problems persist in the management of data. The authors believe that placing a value on data and including it in the firm's balance sheet will contribute to management's capability to mange it. INTRODUCTION In most twenty-first century firms data is a critical resource. This fact is substantiated by The National Archives & Records Administration study, 2001Cost of Downtime Survey Results (2002). The study reported that ninety-three percent of companies losing their data center for 10 days filed for bankruptcy within one year, and that forty-percent of the companies surveyed reported their survival was at risk if their data center was lost for seventy-two hours. Financial statement users have come to expect critical resources to be represented on a firm's balance sheet as an asset. However, it is very likely that if the critical resource is internally developed data, it will not be reported anywhere on the company's balance sheet or in its financial statements. In fact, stockholders and other financial statement users may be unaware of the data's existence. This exclusion of a critical firm resource from the balance sheet seems to be at conflict with Financial Accounting Standards Board (FASB) Concepts Statement No. 1, "Objectives of Financial Reporting by Business Enterprises, " that states financial reporting should provide information that is useful to present and potential investors and creditors in making rational investment, credit and similar decisions (1978). Data is a critical firm resource and is just one component of an information system. Data is separable from the information system used to process it. Lawrence (1999, p. 3) defines data as "symbols, images, sounds and ideas that can be encoded, stored and transmitted." More generally, data are facts about an Entity when an Entity is a person, place or thing. Facts about a Customer Entity are categorized as attributes of the Entity. The attribute of interest may include the Customer's name, address, telephone number, age, sex, income, any number of purchasing habits and more. Data are aggregated, disaggregated, sorted, and/or subjected to a variety of mathematical and logical manipulations. Processed data presented to a user in a meaningful context becomes information. Information is clearly produced from data, making data the raw material in an information manufacturing process (Goodhue, Quillard and Rockart, 1988; Sabherwal and King, 1991; Levitin and Redman, 1998). The value of information is a function of how the information is used and the subsequent outcome. Because information has value, data also has value. Data can be added to a system, removed, bought, sold, and used. Data can be enhanced, making it more valuable, and it can be made less valuable with repeated use and with the passage of time. …
Keith Atkinson - One of the best experts on this subject based on the ideXlab platform.
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Accounting for Data: A Shortcoming in Accounting for Intangible Assets
Academy of Accounting and Financial Studies Journal, 2006Co-Authors: Keith Atkinson, Ronald E. McgaugheyAbstract:ABSTRACT In today's economy and in the future, intellectual capital and the proper use of information will be critical to the success of many firms. Data has been called the raw material of information. Data can be aggregated, disaggregated, sorted, and subjected to a variety of mathematical and logical manipulations. Data can be added to a system, removed, bought, sold and used. Data can be enhanced making it more valuable and it can be made less valuable with repeated use and with the passage of time. Because data is a critical firm resource, one expects the cost of data to be accounted for and to appear on the firm's balance sheet with other firm assets. However, depending on the data's genealogy, the cost of the data may never be shown as an asset. If data is purchased it finds its way to the balance sheet, however, if the data is developed internally Accounting Principles Board (APB) Opinion No. 17 prevents it from being capitalized as an asset. The authors argue that the exclusion of internally developed assets, (particularly data) from the balance sheet can mislead investors, and managers. The authors believe that the exclusion is unnecessary - that data meets the definition of an asset. Problems persist in the management of data. The authors believe that placing a value on data and including it in the firm's balance sheet will contribute to management's capability to mange it. INTRODUCTION In most twenty-first century firms data is a critical resource. This fact is substantiated by The National Archives & Records Administration study, 2001Cost of Downtime Survey Results (2002). The study reported that ninety-three percent of companies losing their data center for 10 days filed for bankruptcy within one year, and that forty-percent of the companies surveyed reported their survival was at risk if their data center was lost for seventy-two hours. Financial statement users have come to expect critical resources to be represented on a firm's balance sheet as an asset. However, it is very likely that if the critical resource is internally developed data, it will not be reported anywhere on the company's balance sheet or in its financial statements. In fact, stockholders and other financial statement users may be unaware of the data's existence. This exclusion of a critical firm resource from the balance sheet seems to be at conflict with Financial Accounting Standards Board (FASB) Concepts Statement No. 1, "Objectives of Financial Reporting by Business Enterprises, " that states financial reporting should provide information that is useful to present and potential investors and creditors in making rational investment, credit and similar decisions (1978). Data is a critical firm resource and is just one component of an information system. Data is separable from the information system used to process it. Lawrence (1999, p. 3) defines data as "symbols, images, sounds and ideas that can be encoded, stored and transmitted." More generally, data are facts about an Entity when an Entity is a person, place or thing. Facts about a Customer Entity are categorized as attributes of the Entity. The attribute of interest may include the Customer's name, address, telephone number, age, sex, income, any number of purchasing habits and more. Data are aggregated, disaggregated, sorted, and/or subjected to a variety of mathematical and logical manipulations. Processed data presented to a user in a meaningful context becomes information. Information is clearly produced from data, making data the raw material in an information manufacturing process (Goodhue, Quillard and Rockart, 1988; Sabherwal and King, 1991; Levitin and Redman, 1998). The value of information is a function of how the information is used and the subsequent outcome. Because information has value, data also has value. Data can be added to a system, removed, bought, sold, and used. Data can be enhanced, making it more valuable, and it can be made less valuable with repeated use and with the passage of time. …
Yao Yu-feng - One of the best experts on this subject based on the ideXlab platform.
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Application of data mining in Customer name disambiguation of insurance field
Application Research of Computers, 2012Co-Authors: Yao Yu-fengAbstract:This paper researched the solution to Customer name disambiguation of the field of insurance.Aiming at the former name disambiguation methods such as text clustering method need to be considered in a lot of useless words,manually set the threshold,and gave he numbers of type,and the method of character-related properties of similarity based on information extraction depends on the character information,proposed a new name disambiguation method.Firstly,applied the same attribute matching,merging the idEntity of a successful match and then used link analysis,analyzed structural analysis of Customers network,the entities had the same idEntity and classified cooperate with the same policy to a Customer Entity,merged the same cooperating information.Finally,analyzed cluster analysis cluster.Experiment results show that the proposed method can optimize the chinese text string matching process and have the high implementation efficiency,especially suitable for large amounts of data to the insurance sector is characterized by digestion of the same name.
B. Tak - One of the best experts on this subject based on the ideXlab platform.
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Entity search techniques for expediting entitlement resolution in technology support services
Journal of Reproduction and Development, 2017Co-Authors: S. Sarkar, B. TakAbstract:“Entity search” addresses the problem of finding data objects most accurately identified by multidimensional attributes, where individual attributes span multiple data sources with duplicate or inconsistent values. We have applied efficient Entity search techniques to expedite entitlement validation in IBM's Technology Support Services (TSS) division. Entitlement validation enables a Customer Entity to obtain services—e.g., repair or replacement of a product Entity—under warranty or contract. Expedited verification ensures Customer satisfaction but is challenging when a single critical Customer-provided attribute error results in a validation failure, and resolution requires exploring a large search space to find alternate entitlements that are most similar to the original request to be viable alternatives. We have built an Entity search tool (consisting of data field interpretation, approximate matching, and weighted scoring) that has accelerated alternate entitlement search in TSS from minutes to seconds. Core concepts include the use of error correction/exploration techniques (where different Entity idEntity attributes are varied based on nearness measures), entitlement repositories searched using the variations, and attribute-level fuzzy matching used to compute similarity scores between the original request and alternate entitlements and return the highest scoring results. Our experiences with deploying these tools in a large worldwide organization are described.
S. Sarkar - One of the best experts on this subject based on the ideXlab platform.
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Entity search techniques for expediting entitlement resolution in technology support services
Journal of Reproduction and Development, 2017Co-Authors: S. Sarkar, B. TakAbstract:“Entity search” addresses the problem of finding data objects most accurately identified by multidimensional attributes, where individual attributes span multiple data sources with duplicate or inconsistent values. We have applied efficient Entity search techniques to expedite entitlement validation in IBM's Technology Support Services (TSS) division. Entitlement validation enables a Customer Entity to obtain services—e.g., repair or replacement of a product Entity—under warranty or contract. Expedited verification ensures Customer satisfaction but is challenging when a single critical Customer-provided attribute error results in a validation failure, and resolution requires exploring a large search space to find alternate entitlements that are most similar to the original request to be viable alternatives. We have built an Entity search tool (consisting of data field interpretation, approximate matching, and weighted scoring) that has accelerated alternate entitlement search in TSS from minutes to seconds. Core concepts include the use of error correction/exploration techniques (where different Entity idEntity attributes are varied based on nearness measures), entitlement repositories searched using the variations, and attribute-level fuzzy matching used to compute similarity scores between the original request and alternate entitlements and return the highest scoring results. Our experiences with deploying these tools in a large worldwide organization are described.
