Data Analytics Projects Research Articles

Requirements Template for Analytics Projects

Data analytics projects have become a common accomplishment in many enterprises. However, establishing a data analytics project requires consideration of many factors that are not always recognized at the very beginning of the project. This study seeks to identify what generic requirements must be defined for data analytics projects and what analytics project attributes need to be addressed by these requirements. It proposes a requirements template for the generic requirements of the analytics projects. The template is intended to be used to reduce the complexity of starting the analytics projects by providing a checklist of requirements to be considered at the beginning of the project. The template is derived from analyzing 16 data analytics project reports for descriptive, diagnostic, predictive, and prescriptive analytics tasks. The template is then validated by analyzing its compliance with 20 analytics projects within the real estate domain using the corresponding research articles.

Capstone Project Data Analytics Untuk Menigkatkan Penjualan Secara Efektif

Amidst the fierce competition in the coffee shop industry in New York, many businesses have experienced a decline in sales, especially in certain products. This research aims to discuss the Capstone Data Analytics project to increase product sales at coffee shops in New York, focusing on the decline in sales of packaged chocolate products. Through a descriptive quantitative analysis approach, sales data from three branches of the coffee shop were evaluated to understand the factors influencing the sales decline. Findings showed that high prices and stagnant sales trends were the main causes of sales decline. Recommendations include price optimization, increased promotion, product diversification, and understanding market trends to increase sales by 2-5% in the following year.

Challenges and Issues in Implementing & Operationalising Big Data Analytics Capabilities in a major Australian Railway Organisation: A Case Study

This study examines the implementation of a Big Data Analytics (BDA) project within a major Australian freight and railway organisation. It also identifies the issues and challenges with data collection, data cleaning, data modelling, and data science software, and implements these models to deliver tangible business results. In addition, the project highlights the potential gains that a data analytics project, integrated with a data-driven culture, can provide through significant operational efficiencies and financial gains. Prior to 2019, the company had little exposure to Predictive Analytics. This study shows how the development of data science capability enables the creation of advanced predictive models, particularly in this case study, for the prediction of train wheel wear, and therefore a significant reduction in maintenance expenses Furthermore, a Data Analytics Maturity Assessment was conducted to determine the requirements to become a data-driven organisation. The outcome of the assessment was compared to recent global studies, and it was found that the organisation examined was significantly behind its counterparts in the areas of resources and analytic capabilities, and therefore required investment in these areas. Further studies to examine the degree of Data Analytics maturity within the Australian context are suggested. Organisations striving to become more data-driven need to plan and allocate resources for capability development in infrastructure, data management, employee quantitative skills, and governance.

Unravelling Transportation Trends with Data Engineering and Analytics

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

DAPS diagrams for defining Data Science projects

BackgroundModels for structuring big-data and data-analytics projects typically start with a definition of the project’s goals and the business value they are expected to create. The literature identifies proper project definition as crucial for a project’s success, and also recognizes that the translation of business objectives into data-analytic problems is a difficult task. Unfortunately, common project structures, such as CRISP-DM, provide little guidance for this crucial stage when compared to subsequent project stages such as data preparation and modeling.ContributionThis paper contributes structure to the project-definition stage of data-analytic projects by proposing the Data-Analytic Problem Structure (DAPS). The diagrammatic technique facilitates the collaborative development of a consistent and precise definition of a data-analytic problem, and the articulation of how it contributes to the organization’s goals. In addition, the technique helps to identify important assumptions, and to break down large ambitions in manageable subprojects.MethodsThe semi-formal specification technique took other models for problem structuring — common in fields such as operations research and business analytics — as a point of departure. The proposed technique was applied in 47 real data-analytic projects and refined based on the results, following a design-science approach.

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

49 Leveraging Big Data Analytics to Improve Disease Surveillance and Welfare in Pig Production

Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.

Unified Data Analytics Platform For Financial Sector Using Big Data

Organizations in today's data-driven digital economy are seeking ways to leverage the immense value of massive amounts of information so as to make more informed decisions. They are able to not only uncover new prospects, but also learn more and improve their performance thanks to big data analytics. Although many businesses have poured resources into big data analytics projects, most have failed to reap the benefits. While there has been a lot of research into the topic of big data analytics, relatively little is known about the strategies that organizations use to merge the various aspects of this discipline. The authors of this paper attempt to solve the problem by developing a comprehensive big data analytics maturity model that can be used by executives to evaluate their own proficiency levels and plan for future development. In this research, we combined traditional quantitative techniques with qualitative meta-synthesis. To begin, we conducted an extensive literature search to catalog the skills and procedures associated with big data analytics maturity. Later, using a quantitative survey method, experts' perspectives on the proposed fundamental skills and practices were analyzed and graded. Finally, a focus group was used to allocate the capabilities to maturity levels according to their priority of deployment, taking into account the architecture of the big data analytics maturity model. We propose a model with four primary capabilities, nine key dimensions (KDs), and five stages of development. Its framework is CMMI-based, which stands for "capability maturity model integration." Questionnaires and focus groups were used to illustrate the big data maturity model. As a guide for effective adoption of big data analytics, we provide the capabilities and KDs, together with their suggested deployment order and weight in the maturity model. Using the offered methodology, businesses may assess their current big data analytics capabilities and steer themselves toward the most effective paths for improvement. Because of this, it enables managers to assess their strengths and weaknesses and establish investment objectives. This work uses a meta-synthesis, which hasn't been done in this subject before, to create an extensive maturity model. The proposed method makes substantial contributions to large data research, and it is both descriptive and prescriptive. A framework is presented in this paper for evaluating different big data analytics projects and settling on a coherent plan of action for their future growth and development.

Measuring benefits from big data analytics projects: an action research study

Measuring benefits from big data analytics projects: an action research study

Data Storytelling to Communicate Big Data Internally – a Guide for Practical Usage

Purpose: Data is collected from all aspects of our lives. Yet, data alone is useless unless converted into information and, ultimately, knowledge. Since data analysts, in most cases, are not the ones in charge of making decisions based on their findings, communicating the results to stakeholders is crucial to passing on information of data-driven insights. That is where the discipline of data storytelling comes into play. Often, data storytelling is considered an effective data visualization. Creating data stories is a structured approach to communicating data insights as an interplay of the three elements data, visuals, and narrative. Sharing data-driven insights to support better business decisions require data storytellers skilled in the “art of storytelling”.
 Design/Method/Approach: In this paper, the authors discuss the use of data storytelling in business to communicate data to stakeholders for improving decision-making. The findings are derived from (1) an extensive literature review and (2) a qualitative analysis of 13 expert interviews with people incorporating data storytelling into their daily work within their jobs in international companies.
 Findings: These interviews revealed the importance of providing a flexible tool to support knowledge sharing for people communicating complex data to internal stakeholders. Combining literature with qualitative research enabled the authors to create the "data storytelling cheat sheet", a guide for practical data storytelling.
 Theoretical Implications: Theories like the Psychological distance or the idea of the theory of dual processing dual are used to base our research idea on. There was no new theory built in this paper.
 Practical Implications: One of the results is an implementation systematic cheat sheet that helps practitioners to implement data storytelling in their daily business.
 Originality/Value: The theory of data storytelling is overwhelming the first time to use and based on an empirical study with experts in the field a guideline for hands on use was developed under a based on a cleanly defined empirical study.
 Research Limitations/Future Research: The paper focus on internal data storytelling – maybe with external stakeholders it might be slightly different. The results the data communication part in any data analytics project.
 Paper Type: Empirical
 JEL Classification: D7, D8

DataOps Lifecycle with a Case Study in Healthcare

The DataOps methodology has become a solution to many of the difficulties faced by data science and analytics projects. This research introduces a novel DataOps lifecycle along with a detailed description of each phase. The proposed cycle enhances the implementation of data science and analytics projects for achieving business value. As a proof of concept, the new cycle phases are applied in a healthcare case study using the UCI Heart Disease dataset. Two goals are achieved. First, a dataset reduction by features analytic in which the four most effective features are selected. Second, different machine learning algorithms are applied to the dataset. The recorded results show that using the four most effective features is comparable with using the full features (thirteen features), and both approaches show high accuracy and sensitivity. The average accuracy of the highest four features is 82.32%, and the thirteen features is 84.28%. That means that the selected four features affect the applications with 97.67% accuracy. Besides, the average sensitivity of the highest four features is 87.94%, while the thirteen features are 87.12%. The study shows an interesting and significant result that data modeling needn't be done for all data science projects which reduced the dataset.

Constellate: A Pedagogy-Focused Text Analytics Platform

Constellate is a text analytics platform offered by ITHAKA, a nonprofit organization devoted to providing platforms for educational and research purposes. It uses data sources such as JSTOR, Portico, Chronicling America, DocSouth, South Asian Open Archive, and Reveal Digital. It is a great tool for users of these data sources who would like to engage in data analytics projects without any exposure to programming languages. Because Constellate was designed primarily for pedagogical purposes, ITHAKA offers a growing number of training workshops and documentations in text analysis methods, Python programming, and data visualization. The integrated design of document search, dataset builder, and visualization reduces the learning curve for beginners.

Domain Knowledge in CRISP-DM: An Application Case in Manufacturing

To keep up with shifting technology trends and remain competitive, more manufacturing companies are investigating how to utilize data analytics to improve their processes. An issue these companies often face today is the need for more competence to perform advanced analytics projects within their departments. By using a human-in-the-loop approach and efficiently utilizing current domain knowledge in combination with data analytics, the higher success of implementation can be achieved. A common approach today to perform data analytics projects is to use the general Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. This methodology does not consider the challenges specific to manufacturing and how to include domain expertise. This paper, therefore, suggests how the CRISP-DM methodology can be adapted to compensate for these issues. The adapted methodology is demonstrated in a case study for improving quality in the machining process by using interpretable machine learning models that can be used to assist experts when performing root cause analysis. This contributes to showing how to use domain experts’ knowledge better and how data analytics can be used in conjunction with domain-specific methods.

Characterization of Usage Data with the Help of Data Classifications

Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.

Toward a Maturity Model for Big Data Analytics: A Roadmap for Complex Data Processing

In the current data-driven digital economy, organizations attempt to harness big data power to make their decisions better. The big data analytics assist them not only to identify new opportunities but extract knowledge and obtain better performance. Despite a huge investment in big data analytics initiatives, the majority of organizations have failed to successfully exploit their power. Although big data analytics have received considerable research attention, a little has been done on how organizations implement strategies in order to integrate the different dimensions of big data analytics; hence, a roadmap is required to navigate these technological initiatives. This paper is also an attempt to overcome this challenge by developing a comprehensive big data analytics maturity model to help managers evaluate their existing capabilities and formulate an appropriate strategy for further progress. A mixed-method was applied in this research using a qualitative meta-synthesis approach. For this purpose, first, a systematic literature review was conducted to identify the capabilities and practices of big data analytics maturity. Then the proposed key capabilities and practices were assessed and prioritized based on the opinions of experts using the quantitative survey method. Finally, considering the architecture of the big data analytics maturity model, the capabilities were assigned to maturity levels according to their priority of implementation using a focus group. The proposed model is comprised of four main capabilities, nine key dimensions (KDs) and five maturity levels based on the capability maturity model integration (CMMI) architecture. A questionnaire and a focus group were used to present the big data maturity model. The capabilities and KDs, as well as their implementation order and weight in the proposed maturity model are presented as a roadmap for implementing big data analytics effectively. The proposed model enables organizations to assess their current big data analytics capabilities and navigate them to select appropriate strategies for their improvement. Due to its nature, it allows managers to find their strong and weak points and identify investment priorities. This study provides a comprehensive maturity model using a meta-synthesis which has not been used in this field so far. The proposed model is both descriptive and prescriptive and has a significant theoretical contribution to big data researches. The paper provides a mechanism to benchmark big data analytics projects and develop an appropriate strategy in terms of progress.

Challenges and Obstacles Facing Data in the Big Data Environment

Big data is dubbed “today’s digital oil” and the “new raw resource of the twenty-first century”. BD is synonymous with the future of innovation, competition, and productivity. It can produce and find corporate value by analyzing data in ways that older methodologies could not. Regardless of its benefits, the development of big data continues to encounter various barriers, the most important of which are security and privacy concerns. As a result, this study is motivated by the need to address and evaluate big data challenges. Thus, by comparing and contrasting big data difficulties with available and potential solutions, users, developers, and businesses can find pertinent and timely responses to specific dangers, resulting in the best possible big data-based services. The objective of this essay is to highlight the inherent challenges of big data and some essential strategies for overcoming them.The purpose of this article was to extract and analyze significant works in order to contribute to the corpus of literature by emphasizing many critical difficulties in the big data domain and throwing light on how these challenges affect a range of domains, including users, sites, and business. Many issues such as data privacy, information sharing, failures in big data technologies and infrastructure, poor data quality management, managers and policymakers’ inability to learn and adapt, the absence of government policies and plans, the lack of successfully implemented big data analytics projects, and the lack of human experience are among the most frequently mentioned issues. Obstacles also exist in the areas of real-time data collection, as well as real-time data processing and visualization, among others.By combining previously identified solutions, this research addressed these concerns. The consequences for both researchers and practitioners have been discussed. Aiming to help scholars gain a comprehensive understanding of these issues and confirm the approaches used to address them, this study’s theoretical focus is broad. This study uses tried-and-true solutions to overcome these obstacles. Business and individuals using big data analytics systems will benefit from these solutions.

Strategies for data analytics projects in business performance forecasting: a field study

Data analytics is applied in various fields, including business performance forecasting, but companies struggle with its implementation. Following a cross-sectional field study approach, we make two contributions. First, we elaborate on the central role played by the head controller in generating trust in analytics solutions and thus, making the project successful. Second, we identify three patterns in the way companies plan, implement, and then use data analytics in the context of business performance forecasting. The two successful patterns are the ones that start with a limited but tangible objective (either in term of information precision, or rapidity of processing) that can be expended in a second time.

The Performance Benefit of Data Analytics Applications

The idea of gaining new insights about business processes from data is driving companies to invest in the area of data analytics. In particular, the opportunities to improve typical business metrics such as revenue, profit, but also market share are pivotal for the motivation. In most data analytics projects, it is a challenge to determine to what extent the existing company-wide or generally accessible data pool is sufficient for such insights. Furthermore, the use of appropriate methods and algorithms for the analysis is an ongoing issue from the perspective of the data scientist, even if the question of suitable data has been resolved. Accordingly, this paper will outline how a benefit of data and algorithms on performance indicators through results of data analytics projects. A suitable setup for determining this influence will then be defined and demonstrated.

A Peek into the US Informal STEM Landscape through Theconnectory.org

AbstractIt's well‐known that informal STEM learning is a critical component of STEM education. However, there is not a comprehensive directory, at the state or national level, listing informal STEM programs offered in the US. This makes it difficult to assess the national landscape of informal STEM programs. Theconnectory.org is by far the most comprehensive listing that aims to connect learners and volunteers to informal STEM learning opportunities. In this poster, we present the results from a data analytics project using the informal STEM program information collected through theconnectory.org API to sketch a preliminary picture of what kind of programs are offered and what audiences are targeted. Theconnectory.org has the potential to become a primary resource for STEM learners, educators, and policymakers. There are ways the ASIST community (faculty and students) can help enrich this resource.

The dark side of data ecosystems: A longitudinal study of the DAMD project

ABSTRACT Data are often vividly depicted as strategic assets that organisations can (re)use to create value for myriad purposes. However, the same qualities that make data so appreciated – that is, their volume, their value for a plurality of stakeholders and their indefinite reuse capacity – also have a dark side: data reuse can lead to deviant data use that undermines the legitimacy of data analytics initiatives. To investigate this dynamic, we build on the notion of data ecosystems and provide empirical evidence from a longitudinal, 15-year case study of the emergence, expansion and eventual collapse of a large-scale data analytics project – called DAMD – in the Danish health care sector. We demonstrate that data reuse in the evolving data ecosystem elicited a data reuse dark side that was so dominant that it eventually resulted in the project’s demise. We conceptualise the reuse dark side’s three major mechanisms as function creep, stakeholder creep and data creep. Based on these insights, we develop an empirically grounded Data Analytics Ecosystem Model that extends the current understanding of data ecosystems and provides a view of these ecosystems as having both a bright and a dark side.