A Method and IR4I Index Indicating the Readiness of Business Processes for Data Science Solutions

2017 ◽  
pp. 21-34 ◽  
Author(s):  
Maxim Shcherbakov ◽  
Peter P. Groumpos ◽  
Alla Kravets
Keyword(s):  
Data Science ◽  
Business Processes

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Analytics-as-a-Service (AaaS)

2017 ◽  
pp. 26-45
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Data Science Techniques in Knowledge-Intensive Business Processes

2020 ◽  
Vol 1 (1) ◽  
pp. 52-67
Author(s):  
Matthias Lederer ◽  
Joanna Riedl
Keyword(s):  
Fuzzy Logic ◽  
Business Intelligence ◽  
Data Science ◽  
Business Processes ◽  
Due Diligence ◽  
Fuzzy Logic Systems ◽  
Investment Bank ◽  
Front Office ◽  
Management Techniques ◽  
Knowledge Intensive

The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

scholarly journals Opportunities to observe and measure intangible inputs to innovation: Definitions, operationalization, and examples

2018 ◽  
Vol 115 (50) ◽  
pp. 12638-12645 ◽  
Author(s):  
Sallie Keller ◽  
Gizem Korkmaz ◽  
Carol Robbins ◽  
Stephanie Shipp
Keyword(s):  
Open Source ◽  
Administrative Data ◽  
Open Source Software ◽  
Data Science ◽  
Business Processes ◽  
Organizational Innovation ◽  
Innovation Process ◽  
Sources Of Information ◽  
First Case ◽  
Science Framework

Measuring the value of intangibles is not easy, because they are critical but usually invisible components of the innovation process. Today, access to nonsurvey data sources, such as administrative data and repositories captured on web pages, opens opportunities to create intangibles based on new sources of information and capture intangible innovations in new ways. Intangibles include ownership of innovative property and human resources that make a company unique but are currently unmeasured. For example, intangibles represent the value of a company’s databases and software, the tacit knowledge of their workers, and the investments in research and development (R&D) and design. Through two case studies, the challenges and processes to both create and measure intangibles are presented using a data science framework that outlines processes to discover, acquire, profile, clean, link, explore the fitness-for-use, and statistically analyze the data. The first case study shows that creating organizational innovation is possible by linking administrative data across business processes in a Fortune 500 company. The motivation for this research is to develop company processes capable of synchronizing their supply chain end to end while capturing dynamics that can alter the inventory, profits, and service balance. The second example shows the feasibility of measurement of innovation related to the characteristics of open source software through data scraped from software repositories that provide this information. The ultimate goal is to develop accurate and repeatable measures to estimate the value of nonbusiness sector open source software to the economy. This early work shows the feasibility of these approaches.

scholarly journals Digital trace data as a tool for assessing competencies: the case of the Gazprom neft

2021 ◽  
Vol 6 (2) ◽  
pp. 91-98
Author(s):  
T. A. Lezina ◽  
T. A. Khorosheva ◽  
A. V. Korosteleva
Keyword(s):  
Professional Growth ◽  
Work Performance ◽  
Data Science ◽  
Business Processes ◽  
New Technologies ◽  
Theory And Practice ◽  
Digital Data ◽  
Professional Competencies ◽  
Trace Data ◽  
Digital Footprints

Large companies can use the analysis of employees’ digital trace data to increase the efficiency and objectivity of business processes of assessment of employee competencies. New technologies allow to accumulate data on the activities of employees related to their work performance in the information systems of companies. The results of employees training, protocols of their interaction on professional issues, the results of recruiting procedures form their digital footprints and can be used to regularly assess their professional growth. A significant problem in applying the idea of using digital footprints to assessing competencies is the choice of assessment metrics. At present, there are no described methods of using digital footprints of personnel. The objective of the work is to describe the case of using the digital footprints to assess the level of professional competencies of data science specialists from Gazprom Neft and describe the approach to assessing the professional competencies of employees using their digital data. Gazprom Neft has chosen as the assessment metric the level of competence employee development, which is determined through a set of “activities” of employees confirmed by digital artifacts, information about which is entered into the information system. The method for assessing the professional competencies of employees described in the article, was used as the basis for an approach to assessing competencies using digital data. This approach makes it possible to increase the efficiency of business processes in HR and can be used in companies of various industries and scales. The key advantages of the approach are its universality and objectivity. The results of the research can be used in companies that use a competency-based approach to the assessment of professional competencies of personnel, and form the first step in the development of the theory and practice of using digital traces of employees in company’s management.

scholarly journals Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio

2021 ◽  
Vol 4 ◽  
Author(s):  
Thomas Gramespacher ◽  
Jan-Alexander Posth
Keyword(s):  
Data Science ◽  
Business Processes ◽  
Ad Hoc ◽  
Target Function ◽  
Point Of View ◽  
Economic Point ◽  
Banking And Finance ◽  
Event Prediction ◽  
Regulatory Constraints ◽  
Regulatory Bodies

In the recent years, data science methods have been developed considerably and have consequently found their way into many business processes in banking and finance. One example is the review and approval process of credit applications where they are employed with the aim to reduce rare but costly credit defaults in portfolios of loans. But there are challenges. Since defaults are rare events, it is—even with machine learning (ML) techniques—difficult to improve prediction accuracy and improvements are often marginal. Furthermore, while from an event prediction point of view, a non-default is the same as a default, from an economic point of view much more relevant to the end user it is not due to the high asymmetry in cost. Last, there are regulatory constraints when it comes to the adoption of advanced ML, hence the call for explainable artificial intelligence (XAI) issued by regulatory bodies like FINMA and BaFin. In our study, we will address these challenges. In particular, based on an exemplary use case, we show how ML methods can be adapted to the specific needs of credit assessment and how, in the case of strongly asymmetric costs of wrong forecasts, it makes sense to optimize not for accuracy but for an economic target function. We showcase this for two simple and ad hoc explainable ML algorithms, finding that in the case of credit approval, surprisingly high rejection rates contribute to maximizing profit.

scholarly journals Challenges in Designing Curriculum for Trans-Disciplinary Education: On Cases of Designing Concentration on Informing Science and Master Program on Data Science

10.28945/4300 ◽  
2019 ◽  
Vol 22 ◽  
pp. 019-030
Author(s):  
Dimitar Grozdanov Christozov ◽  
Katia Rasheva-Yordanova ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Professional Education ◽  
Data Science ◽  
Business Processes ◽  
Business Environment ◽  
Working Environment ◽  
Future Research ◽  
Master Program ◽  
Analytical Skills ◽  
Training Professionals ◽  
Science Methodology

Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

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