The role of accountants in enterprise architecture, technology sourcing, data analytics, data science, and data management

This is a self-archived version of an article published in Collaborative Librarianship. The content of this article is not different from what is in the journal (found here: http://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4)Recommended CitationSteeves, Vicky (2017) "Reproducibility Librarianship," Collaborative Librarianship: Vol. 9 : Iss. 2 , Article 4. Available at: https://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4Over the past few years, research reproducibility has been increasingly highlighted as a multifaceted challenge across many disciplines. There are socio-cultural obstacles as well as a constantly changing technical landscape that make replicating and reproducing research extremely difficult. Researchers face challenges in reproducing research across different operating systems and different versions of software, to name just a few of the many technical barriers. The prioritization of citation counts and journal prestige has undermined incentives to make research reproducible.While libraries have been building support around research data management and digital scholarship, reproducibility is an emerging area that has yet to be systematically addressed. To respond to this, New York University (NYU) created the position of Librarian for Research Data Management and Reproducibility (RDM & R), a dual appointment between the Center for Data Science (CDS) and the Division of Libraries. This report will outline the role of the RDM & R librarian, paying close attention to the collaboration between the CDS and Libraries to bring reproducible research practices into the norm.

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PolyFrame

Proceedings of the VLDB Endowment ◽

10.14778/3476249.3476281 ◽

2021 ◽

Vol 14 (11) ◽

pp. 2296-2304

Author(s):

Phanwadee Sinthong ◽

Michael J. Carey

Keyword(s):

Data Management ◽

Data Analytics ◽

Data Science ◽

Database Systems ◽

Query Languages ◽

Machine Learning Techniques ◽

Data Management System ◽

Management Systems ◽

Learning Techniques ◽

Scaling Out

In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages.

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Data Science for Finance: Best-Suited Methods and Enterprise Architectures

Applied System Innovation ◽

10.3390/asi4030069 ◽

2021 ◽

Vol 4 (3) ◽

pp. 69

Author(s):

Galena Pisoni ◽

Bálint Molnár ◽

Ádám Tarcsi

Keyword(s):

Big Data ◽

Financial Services ◽

Data Science ◽

Enterprise Architecture ◽

Data Driven ◽

Financial Company ◽

Desk Research ◽

Qualitative Literature ◽

Analyze Data

We live in an era of big data. Large volumes of complex and difficult-to-analyze data exist in a variety of industries, including the financial sector. In this paper, we investigate the role of big data in enterprise and technology architectures for financial services. We followed a two-step qualitative process for this. First, using a qualitative literature review and desk research, we analyzed and present the data science tools and methods financial companies use; second, we used case studies to showcase the de facto standard enterprise architecture for financial companies and examined how the data lakes and data warehouses play a central role in a data-driven financial company. We additionally discuss the role of knowledge management and the customer in the implementation of such an enterprise architecture in a financial company. The emerging technological approaches offer opportunities for finance companies to plan and develop additional services as presented in this paper.

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The Role of Big Data, Data Science and Data Analytics in Financial Engineering

Proceedings of the 2019 International Conference on Big Data Engineering (BDE 2019) - BDE 2019 ◽

10.1145/3341620.3341630 ◽

2019 ◽

Author(s):

Venkamaraju Chakravaram ◽

Vidya Sagar Rao G. ◽

Jangirala Srinivas ◽

Sunitha Ratnakaram

Keyword(s):

Big Data ◽

Financial Engineering ◽

Data Analytics ◽

Data Science

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Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection

Advances in Data Mining and Database Management - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics ◽

10.4018/978-1-7998-3053-5.ch011 ◽

2021 ◽

pp. 203-227

Author(s):

James Osabuohien Odia ◽

Osaheni Thaddeus Akpata

Keyword(s):

Big Data ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Fraud Detection ◽

Economic Crime ◽

Current Increase ◽

Forensic Accounting ◽

Global Increase

The chapter examines the roles of data science and big data analytics to forensic accountants and fraud detection. It also considers how data science techniques could be applied to the investigative processes in forensic accounting. Basically, the current increase in the volume, velocity, and variety of data offer a rich source of evidence for the forensic accountant who needs to be familiar with the techniques and procedures for extracting, analysing, and visualising such data. This is against backdrop of continuous global increase in economic crime and frauds, and financial criminals are getting more sophisticated, taking advantage of the opportunities provided by the unstructured data constantly being created with every email sent, every Facebook post, every picture on Instagram, or every thought share on Twitter. Consequently, it is important that forensic accountants are constantly abreast with developments in data science and data analytics in order to stay a step ahead of fraudsters as well as address evolving vulnerabilities created by big data.

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