Requirements Template for Analytics Projects

Agile values or plan-driven aspects: Which factor contributes more toward the success of data warehousing, business intelligence, and analytics project development?

Analytics Canvas – A Framework for the Design and Specification of Data Analytics Projects

The global disruption of 2020-2022 forced organizations to reassess their resilience strategies, with executives agreeing to prioritize project management, data analytics, and problem-solving among the top 10 skills for their workforce. In this context, the role of the Project Management Office (PMO) in building resilience and achieving project success has been critically examined. While PMOs are assumed to enhance governance and structured guidance, it remains unclear whether their presence significantly influences resilience-building during disruptions. This study investigates this issue by conducting multi-group analysis (MGA) using data from 170 practicing project managers worldwide who navigated the global disruption. The study examines how Data Analytics Capability (DACP) and Project Management Maturity (PMMM) impact Project Resilience (PRES) and Project Success (PSUC), employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical validation. Results indicate that both DACP and PMMM significantly enhance resilience, with DACP demonstrating a comparatively stronger effect. Project resilience, in turn, has a robust positive impact on project success, highlighting its strategic value beyond mere survival. Notably, while multi-group analysis shows no statistically significant differences between PMO-supported and non-PMO-supported projects, this does not undermine the importance of PMMM and PMO support. Instead, it reinforces that structured governance and maturity practices are considered baseline expectations in high-reliability industries, rather than distinct differentiators of project resilience.

Efficient Secondary Use of Representative Social and Health Care Data In Finland: Isaacus Data Lake, Analytics and Knowledge Management Pre-Production Project

The case presented in this article can be used as a surrogate experiential learning exercise in an introductory MBA course on data science and business analytics. Students learn about the life cycle of data analytics projects by alternating between study of analytical techniques and participation in a data analytics project running through the course. The project begins as a simple business question which, through sequential discovery, expands into rich discussions and experiences, and culminates with presentations that test students’ ability to translate computational results into insights for various stakeholders. In the process, students learn about effective information gathering and discovery, identifying major stakeholders, problem framing and mapping, model identification, data visualization, and basic model implementation. They also learn how to present their findings to different audiences with the greatest possible impact. This approach elicits student engagement, teamwork, and multiple perspectives as students are asked to view themselves and evaluate their colleagues from the point of view of various roles in the company. Teaching Note: Interested Instructors please see the Instructor Materials page for access to the restricted materials. To maintain the integrity and usefulness of cases published in ITE, unapproved distribution of the case teaching notes and other restricted materials to any other party is prohibited.

Architectural Technical Debt (ATD) is a metaphor used to describe consciously decisions taken by software architects to accomplish short-term goals but possibly negatively affecting the long-term health of the system. However, difficulties arise when repayment strategies are defined because software architects need to be aware of the consequences of these strategies over others decisions in the software architecture. This article proposes REBEL, a semi-automated model-driven approach that exploits natural language processing, machine learning and model checking techniques on heterogeneous project artifacts to build a model that allows to locate and visualize the impact produced by the consciously injected ATD and its repayment strategy on the other architectural decisions. The technique is illustrated with a data analytics project in Colombia where software architects are unaware of the consequences of the repayment strategies. This proposal seeks to support teams of architects to make explicit the current and future impact of the ATD injected as a result of decisions taken, focusing on the architectural level rather than code level.

Assessing the Variables, Resources, and Deliverables of Your Data Analytics Project

PurposeThe purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.Design/methodology/approachThe study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants.FindingsThe authors found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.Practical implicationsThe study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., “one set of numbers”.Originality/valueEven though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall. The central contribution of the paper, therefore, is to show that if a PA project is initiated by the accounting department, data integration becomes more likely. It contributes also to the discussion related to the appropriateness of data integration in the context of forecasting.

In the real estate domain, investment decisions rely heavily on spatial and economic context, yet most digital platforms still provide static listings with limited personalization or geographic intelligence. The primary objective of this paper is to introduce and validate a spatially enriched recommendation system for real estate investment that integrates Artificial Intelligence (AI), Geographic Information Systems GIS), and big data analytics. Evaluated on over 70,000 property listings, the system leverages historical property trends, spatial amenity density, and price deviation metrics to identify undervalued or highgrowth-potential properties across urban areas. It combines location-sensitive scoring models with price per square foot analysis and Z-score based outlier detection to recommend listings that deviate positively from local price norms while offering strong amenity access. By evaluating properties based on proximity to hospitals, schools, banks, parks, transit, and other infrastructure, the model delivers context-aware investment insights. Key findings show the proposed model achieves a 70% match accuracy with expert evaluations, significantly outperforming baseline models. The implications of this work include a new framework for data-driven decision-making that can improve market efficiency, particularly in fragmented real estate markets like those in South Asia.

Big data analytics assist the organizations and business managers to make decision more efficient by analyzing given data and applying some algorithms upon it. Data analytics is the process in which, we find patterns and draw conclusions about the information with the help of raw data. Mainly, three kinds of analytical approaches are existent for analysis of dataset, including descriptive analytics, predictive analytics and prescriptive analytics. In this paper, authors' focused on comparative analysis of descriptive analytical algorithms, along with their different approaches and applications in different areas. In addition to this, various types of data analytics have also been elaborated, in this paper.

ABSTRACTBI solutions are constantly being developed to support decision-making at various organisational levels. These solutions facilitate the compilation, aggregation and summarisation of large volumes of data. Consequently, the business value created by these systems is increasing as they sustain more and more advanced analytics, ranging from descriptive analytics, to predictive analytics, to prescriptive analytics. However, most organisations work primarily with internal data. Despite many references in the literature to the value hidden in external data, details on how such data can be used are scarce. In this paper, we present the results of an extensive action case study at a public waste management company. The results illustrate how external data from several external data sources, integrated into an up-and-running BI solution, are used jointly to allow for descriptive and predictive analytics, as well as prescriptive analytics. In addition, details of these analytical values are given and related to organisational benefits.

Data analytics is related to extracting information from observations, measurements, or experiments about a phenomenon or subject of interest for different purposes, such as interpretation of the data, decision making, diagnosis, and prediction [1]. A classification of the entire field of big data analytics is difficult to make and can be based on a variety of criteria. One classification related to the depth of the analysis is presented by Blackett [2], identifying three levels: descriptive, predictive, and prescriptive analytics. Descriptive analytics uses past data to analyze what has occurred, while predictive analytics focuses on forecasting future trends and determining probabilities of occurrence mainly through the application of different statistical techniques or data mining algorithms for pattern extraction. Prescriptive analytics facilitates decision making and optimization of complex systems.

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

The Unravelling Transportation Trends with Data Engineering and Analytics project delves into the correlation between driver behavior and customer ratings within ride-hailing platforms. Utilizing a dataset akin to Uber's, the study investigates the impact of acceptance rates and cancellation rates on customer satisfaction, aiming to glean valuable business insights for enhancing service quality and fostering business growth. Meticulous data collection and preprocessing ensure data accuracy and reliability, while secure cloud-based storage facilitates efficient data processing. By analyzing driver behavior metrics and customer ratings, the project uncovers pivotal patterns and trends, offering essential guidance for optimizing driver performance. This data-driven approach empowers ride-hailing services to implement targeted strategies for improving service quality, enhancing customer retention, and cultivating a loyal customer base. Keywords—Ride-hailing platforms, Driver behavior, Customer ratings, Data engineering Analytics, Service quality, Customer satisfaction, Data accuracy, Driver performance, optimization

Background:Nasopharyngeal carcinomas (NPCs) are prevalent in southeast Asia. There is a need to systematically review the current trend and status of NPC research. However, most bibliometric analyses have tended to focus on descriptive and diagnostic analytics rather than predictive and prescriptive analyses. Thus, it is necessary to use the model of the 4 (called the descriptive, diagnostic, predictive, and prescriptive analytics [DDPP]) to derive insights from the data. This study aimed to apply the DDPP model to classify article themes and illustrate the characteristics of NPCs; compare NPC researcher achievements across countries, institutes, departments, and authors; determine whether the mean citations of keywords can be used to predict article citations; and highlight articles that are worthy of reading.Methods:The Web of Science Core Collection was searched for 100 top-cited articles and reviews related to NPCs published between 2013 and 2022. As part of Microsoft Office Excel 2019, Visual Basic for Applications was used to illustrate the number of publications and scientific productivity of authors over time and to generate network/temporal heatmaps, chord/Sankey diagrams, radar/impact beam plots, and scatter/pyramid charts about collaborations among countries. The DDPP model identifies institutions, authors, and hotspots of NPC research. The category, journal, authorship, and L-index (CJAL) score was applied to evaluate individual research achievements.Results:A total of 10,564 publications were extracted from Web of Science Core Collection and screened for 100 top-cited articles and reviews related to NPCs. Despite having the highest number of publications (36%), China lags slightly behind the US in CJAL scores. CJAL was higher at Sun Yat-Sen University, Radiat Oncol department, and author Jun Ma from China. The number of article citations was significantly correlated with the number of weighted keywords (F = 1791.17; P < .0001). Six articles with significantly increasing citations over the last 4 years were recommended.Conclusion:This bibliometric study utilizes the DDPP model to analyze the scientific progress of NPC over the past decade. The whole genome is a hot topic that may prove to be a promising research area in the future. A temporal heatmap may serve as a tool for providing readers with articles that are worth reading, which could lead to additional research in bibliometrics.