Not So Fuzzy Audit Analytics

2020 ◽  
Author(s):  
Jamie Hoelscher ◽  
Trevor Shonhiwa
Keyword(s):  
Data Analytics ◽  
Microsoft Excel ◽  
Comprehensive Data ◽  
Using Data ◽  
Data Analytic

In this case, students are introduced to audit analytics, specifically the use of the fuzzy lookup tool available in Microsoft Excel.  Students will use both conditional formatting and the fuzzy lookup tool to examine a dataset for possible instances of fictitious vendor fraud, a common and often costly type of fraud.  The case takes students through the comprehensive data analytics cycle as students are instructed how to test for fictitious vendors using data analytic techniques.  Students will then rely on the underlying data to analyze potential relationships and trends and communicate results via a memorandum, while being introduced to other common preventive and detective controls related to mitigate the risk of fictitious vendors.

Comprehensive Data Analytics Project Using Excel and Tableau for the Sales and Purchases Cycles

2021 ◽  
Author(s):  
Karen Schuele ◽  
Elizabeth Felski
Keyword(s):  
Data Model ◽  
Data Analytics ◽  
Microsoft Excel ◽  
Comprehensive Data ◽  
Accounting Courses ◽  
The Common ◽  
The Right

Using materials from PwC’s data analytics case study with a fictitious company, Pixystems Toy Company, Inc. (PwC, 2017), henceforth Pixystems Toys, the authors developed a comprehensive, multi-part data analytics project applicable in a variety of accounting courses.  The project follows the common data analytics framework (ask the right questions, extract, transform and load (ETL) the data, perform appropriate analyses and present the results).  Students apply this framework to the sales and purchases cycles.  For each students develop relevant questions, build a data model and perform other ETL procedures, perform analytics and prepare a presentation to convey insights and recommendations.  For the sales cycle, Microsoft Excel is the analytics tool; for the purchases cycle, Tableau is used. This project provides an opportunity for students to gain experience with two analytics tools, understand the process of building a data model, and wrestle with how to convey the results of their analysis.

Nodal screw compressor failure analysis using data analytics—CSG application

2015 ◽  
Vol 55 (1) ◽  
pp. 59
Author(s):  
Prashant Parulekar
Keyword(s):  
Statistical Analysis ◽  
Data Analytics ◽  
Screw Compressor ◽  
Cause Analysis ◽  
Root Cause ◽  
Statistical Measures ◽  
Using Data ◽  
Filter Signal ◽  
Data Analytic ◽  
Insight Into

An engine-driven oil-injected screw compressor in CSG service failed catastrophically. Instrumentation provided on the package was ineffective in predicting or detecting the failure. As part of the Root Cause Analysis (RCA) process, a statistical analysis of the logged instrument data, as measured across a period of six months prior to the failure, was carried out. This paper uses data analytic methods to process instrument data, data visualisation techniques, advanced statistical analysis of the instrument data, and techniques to filter signal noise. The analysis recognised the multivariate behaviour and interrelationships between various operating parameters. The paper further provides insight into the interpretation of statistical measures and how to draw conclusions that explain the failure mechanism. The outcomes of the analysis presented in this paper then provided insights into establishing operating envelopes, proposed instrumentation upgrades to be provided in future and helped establish an operation and maintenance regime that should assist in preventing such failures in future.

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

2015 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge ◽  
Chad Syverson ◽  
Elbert S. Huang ◽  
Min-Woong Sohn
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Pharmaceutical Regulation ◽  
Using Data

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

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