Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks

2021 ◽  
pp. 113715
Author(s):  
Dmitry Zhdanov ◽  
Sudip Bhattacharjee ◽  
Mikhail Bragin
2018 ◽  
Vol 28 (5) ◽  
pp. 1489-1496
Author(s):  
Branislav Stanisavljević

Research carried out in the last few years as the example of companies belonging to the category of medium-size enterprises has shown that, for example, typical enterprises, of the total number of data processed in information of importance for its business, seriously takes into consideration and process only 10% of the observed firms. It is justifiable to ask whether these 10% of the processed and analyzed business information can have an adequate potential or motive power to direct the organization to success that is measured by competitive advantages and on a sustainable basis? Or, the question can be formulated: what happens to the rest, mostly 90% of the information that the enterprise does not transform into a form suitable for business analysis and decision-making. It is precisely the task of business intelligence to find a way to utilize all the data collected and processed in the business decision-making process. In this regard, we can conclude that Business Intelligence is, in fact, the framework title for all tools and / or applications that will enable the collection, processing, analysis, distribution to decision-making bodies in the business system in order to derivate from this information valid business decisions - as the most important and / or most important task of the manager. Of course, from an economic point of view, the best decisions are management decisions that provide a lasting competitive advantage and achieve maximum financial performance. This means that business intelligence actually allows a more complete and / or comprehensive view of the overall business performance of all its parts and subsystems. But the system functions can be measured essential and positive economic and financial performance, as well as the position in the branch of the business to which it belongs, and wider, within the national economy. (Of course, today the boundaries of the national economy have become too crowded for many companies, bearing in mind globalization and competitiveness in the light of organization of work and business function). The advantage of business intelligence as a model, if accepted at the organization level, ensures that each subsystem in the organization receives precisely the information needed to make development decisions, but also decisions regarding operational activities. So, it should be born in mind that business intelligence does not imply that information is shared on some key words, on the contrary, the goal is to look at the context of the business, or in general, and that anyone in the further decision hierarchy can manage exactly the same information that is necessary for achieving excellent business performance. Because, if the insight into the information is not complete, the analysis is based on the description of individual parts, i.e. proving partial performance in the realization of individual information, which can certainly create a space for the loss of the expensive time and energy. Illustratively, if the view, or insight into the information, is not 100%, then all business decision-making is like the song of J.J. Zmaj "Elephant", about an elephant and a blindmen, where everyone feels and act only on the base of the experienced work, and brings judgment on what is what or what can be. As in this song for children, everyone thinks that he touches different animals and when they make claims about what they feel, everyone describes a completely different life. Therefore, business intelligence implies that information is fully considered and it is basically the basis or knowledge base, and therefore the basis of business excellence. In doing so, the main problem is how information is transformed into knowledge and based on it in business decision making. It is precisely in this segment that the main advantage of business intelligence is its contribution to the knowledge and business of the company based on power of knowledge. Therefore, for modern business conditions, it is characteristic that the management of the company is realized on the basis of partial knowledge about stakeholders (buyers, suppliers, competitors, shareholders, governments, institutional framework, legislation), and only a complete overview of managers at the highest level in all these partial interest groups allows managers to have a “boat” called the organization of labor leading a safe hand through the storm, Scile and Haribde threatens to endanger business, towards a calm sea and a safe harbor - called a sustainable competitive advantage based on power and knowledge.


Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 288 ◽  
Author(s):  
Elena A. Mikhailova ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Mark A. Schlautman ◽  
Gregory C. Post

Soil ecosystem services (ES) (e.g., provisioning, regulation/maintenance, and cultural) and ecosystem disservices (ED) are dependent on soil diversity/pedodiversity (variability of soils), which needs to be accounted for in the economic analysis and business decision-making. The concept of pedodiversity (biotic + abiotic) is highly complex and can be broadly interpreted because it is formed from the interaction of atmospheric diversity (abiotic + biotic), biodiversity (biotic), hydrodiversity (abiotic + biotic), and lithodiversity (abiotic) within ecosphere and anthroposphere. Pedodiversity is influenced by intrinsic (within the soil) and extrinsic (outside soil) factors, which are also relevant to ES/ED. Pedodiversity concepts and measures may need to be adapted to the ES framework and business applications. Currently, there are four main approaches to analyze pedodiversity: taxonomic (diversity of soil classes), genetic (diversity of genetic horizons), parametric (diversity of soil properties), and functional (soil behavior under different uses). The objective of this article is to illustrate the application of pedodiversity concepts and measures to value ES/ED with examples based on the contiguous United States (U.S.), its administrative units, and the systems of soil classification (e.g., U.S. Department of Agriculture (USDA) Soil Taxonomy, Soil Survey Geographic (SSURGO) Database). This study is based on a combination of original research and literature review examples. Taxonomic pedodiversity in the contiguous U.S. exhibits high soil diversity, with 11 soil orders, 65 suborders, 317 great groups, 2026 subgroups, and 19,602 series. The ranking of “soil order abundance” (area of each soil order within the U.S.) expressed as the proportion of the total area is: (1) Mollisols (27%), (2) Alfisols (17%), (3) Entisols (14%), (4) Inceptisols and Aridisols (11% each), (5) Spodosols (3%), (6) Vertisols (2%), and (7) Histosols and Andisols (1% each). Taxonomic, genetic, parametric, and functional pedodiversity are an essential context for analyzing, interpreting, and reporting ES/ED within the ES framework. Although each approach can be used separately, three of these approaches (genetic, parametric, and functional) fall within the “umbrella” of taxonomic pedodiversity, which separates soils based on properties important to potential use. Extrinsic factors play a major role in pedodiversity and should be accounted for in ES/ED valuation based on various databases (e.g., National Atmospheric Deposition Program (NADP) databases). Pedodiversity is crucial in identifying soil capacity (pedocapacity) and “hotspots” of ES/ED as part of business decision making to provide more sustainable use of soil resources. Pedodiversity is not a static construct but is highly dynamic, and various human activities (e.g., agriculture, urbanization) can lead to soil degradation and even soil extinction.


1958 ◽  
Vol 3 (3) ◽  
pp. 307 ◽  
Author(s):  
R. M. Cyert ◽  
W. R. Dill ◽  
J. G. March

2011 ◽  
Vol 71-78 ◽  
pp. 2895-2898
Author(s):  
Jian Min Xie ◽  
Qin Qin

In the process of developing e-commerce system, enterprises are able to accurately understand and grasp the needs of the user enterprise that is the key to the successful implementation of e-commerce. Based on this, the article proposed a new method which was based on the process of developing consumer demand for e-business decision-making though a rough set theory. This new approach is built using rough set decision model to calculate the different needs of the impact on consumer satisfaction, come to an important degree of each demand and the demand reduction order. This method overcomes the traditional rough set method cumbersome bottlenecks, and helps operating; cases studies show that the proposed method is simple and effective.


2021 ◽  
Author(s):  
Shir Dekel ◽  
Micah Goldwater ◽  
Dan Lovallo ◽  
Bruce Burns

Previous research found that anecdotes are more persuasive than statistical data—the anecdotal bias effect. Separate research found that anecdotes that are similar to a target problem are more influential on decision-making than dissimilar anecdotes. Further, previous investigations on anecdotal bias primarily focused on medical decision-making with very little focus on business decision-making. Therefore, we investigated the effect of anecdote similarity on anecdotal bias in capital allocation decisions. Participants were asked to allocate a hypothetical budget between two business projects. One of the projects (the target project) was clearly superior in terms of the provided statistical measures, but some of the participants also saw a description of a project with a conflicting outcome (the anecdotal project). This anecdotal project was always from the same industry as the target project. The anecdote description, however, either contained substantive connections to the target or not. Further, the anecdote conflicted with the statistical measures because it was either successful (positive anecdote) or unsuccessful (negative anecdote). The results showed that participants’ decisions were influenced by anecdotes only when they believed that they were actually relevant to the target project. Further, they still incorporated the statistical measures into their decision. This was found for both positive and negative anecdotes. Further, participants were given information about the way that the anecdotes were sampled that suggested that the statistical information should have been used in all cases. Participants did not use this information in their decisions and still showed an anecdotal bias effect. Therefore, people seem to appropriately use anecdotes based on their relevance, but do not understand the implications of certain statistical concepts.


2021 ◽  
Author(s):  
Pang william panggantara

Fourth wave of industrial revolution is marked by the use of information technology, artificial inteligence (A.I), and automatic engines. Competitive advantage has become a necessity for every business actor when they wants to competing in the global market. The current condition definitely encouraging the occurence of massive transformation at all business levels and units this condition happens because every business actor can enter from and any other countries markets easily. this condition making professionalism of every business actor is highly prioritized like many case in the business decision making and continous innovation.


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