Data Spectrum Interpretation
Data Spectrum
International Institute For Advanced Data Management Study · Knowledge Sharing
With the rapid advancement of information technology and the Internet, a multitude of diverse information systems and platforms have emerged. Achieving interoperability and integration among different systems has become a key issue. Data, scattered across various organizations and systems, has resulted in the phenomenon of data silos. As cross-domain collaboration demands increase, data sharing and exchange between different sectors become increasingly crucial.
The advent of the big data era has brought data's value and applications into the spotlight, rendering data a vital resource driving innovation and decision-making support. Consequently, interoperability of data has emerged as a significant area of study in the data domain, striving to address the challenges of data integration and sharing, thereby optimizing data utilization and realizing its value.
The International Academy for Advanced Data Management is dedicated to tackling the complexities of interoperability between different industries. Innovatively, it has introduced the concept of the data spectrum, extending it to the notion of datawaves, which illustrate the potential and level of standardization for data interoperability across various industries. This article will delve into the relevant knowledge surrounding the data spectrum
Definition of Data Spectrum
Data Spectrum
The Data Spectrum is a dynamic hierarchical diagram that represents the varying degrees of difficulty in data interoperability among different industries. An illustrative figure of the data spectrum follows below.
In the spectrum, the central point represents accounting data, and each concentric circle layer contains multiple points signifying different industries. The closer a layer is to the center, the easier it is to convert the industry data represented by the points within that layer to accounting data; conversely, the farther away a layer is from the center, the more challenging the conversion becomes. Within the same layer, data interoperability between two industries is relatively straightforward to achieve;crossing to another layer incurs a certain cost, and the higher the number of layers crossed, the greater the cost and difficulty of interoperability.
Data Waves
Within the layers of the data spectrum, the size of each point represents the score of the industry's data standard. Clicking on a point reveals the industry's data wave chart (as shown in the accompanying figure). On this chart, the horizontal axis denotes the nodes of the industrial ecosystem (industries), arranged according to their sequence, importance, and relevance within the industrial supply chain. The vertical axis indicates the current standard scores for each respective node.
According to the Data Spectrum and Data Waves, we can assess the ease or difficulty of data interoperability among different industries and evaluate the level of data standardization within various sectors within those industries.
Data Sources
The Data Spectrum draws its information from industry group standards. After classifying, compiling statistics, and assigning weighted scores to these group standards, the data wave scores for each industry and sector are calculated, resulting in the creation of the data wave charts. When there is a sufficient number of industries, based on the data scores, they can be stratified and used to construct the Data Spectrum diagram.
Significance of the Data Spectrum
The Data Spectrum provides a measurement criterion to gauge the difficulty of data interoperability across diverse industries, which helps estimate the costs and challenges involved, thereby guiding targeted efforts.
An industry's position within the Data Spectrum reflects the degree of its data standardization, aiding businesses within the industry in identifying strategic directions and potential collaboration partners.
Simultaneously, Data Waves shed light on developmental disparities and innovative potentials among different sectors within the same industry. This insight facilitates the identification of priority development areas and drives the establishment of new standards, promoting interoperability and data sharing.
Enterprises can identify areas of underdevelopment within their own industry's data wave chart, pinpoint growth potential zones, and comprehensively embark on R&D innovation, patent asset accumulation, and other endeavors. By doing so, they enhance their market competitiveness and foster the emergence of more specialized, innovative, and niche-leading enterprises, such as specialized, sophisticated, and 'little giant' companies.
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International Institute for Advanced Data Management Studies (IIADMS) is a non-profit, vendor-neutral institution dedicated to fostering collaboration among technology and business professionals. IIADMS is committed to advancing research in data and data management-related fields, consistently seeking new insights and best practices in the data landscape.
IIADMS is to establish itself as a preeminent global platform for knowledge exchange on theoretical and practical aspects of data management. The institution is eager to engage in diverse partnerships with prestigious domestic and international forums, both directly and indirectly addressing traditional and cutting-edge topics in data management. Through these collaborations, IIADMS aims to disseminate its research findings and contribute to the collective understanding within these forums.
The Global Data Forum 50 (GDF50) is a non-profit platform for international exchange on data management theory and practice, established under the auspices of organizations such as DAMA China. Its legal entity is authorized by the International Institute for Advanced Data Management Studies Limited, with the Forum serving as the representative responsible for the establishment, administration, and advancement of research at domestic centers.
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