Data Science Methodologies: Current Challenges and Future Approaches

Martı́nez, Iñigo; Viles, Elisabeth; Olaizola, Igor G.

doi:10.1016/j.bdr.2020.100183

Cited by 63 publications

(34 citation statements)

References 47 publications

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“…There are three foundation stones for DS projects success: project, team, and data & information management. This is a constantly evolving and improving framework to adapt to new challenges in DS, generating uncertainty (Martinez et al, 2021).…”

Section: How Harvard's Case Methods Can Help Bridge the Gap Between Ds And Decision Makingmentioning

confidence: 99%

“…It has employed great efforts in developing advanced analytics, improving data models, and cultivating new algorithms. However, there are organizational and socio-technical challenges that arise when executing a DS project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects, and the ambiguity of roles in DS are among these challenges (Chkoniya et al, 2020;Martinez et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Success Factors for Using Case Method in Teaching Applied Data Science Education

Chkoniya

2021

European Journal of Education

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In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities. Data science combines multiple fields like statistics, scientific methods, and data analysis to extract value from data, being an umbrella term used for multiple industries, such as data analytics, data mining, machine learning, big data, business intelligence, and predictive analytics. This paper gives an overview of success factors for using the Case Method in teaching Applied Data Science education. Showing that close analysis provides a deeper understanding of implications, connects theory to practice, and classes unfold without a detailed script when successful instructors simultaneously manage content and process. This synthesis of current research can be used by Applied Data Science educators to more effectively plan the use of the Case Method as one possible teaching method.

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Section: How Harvard's Case Methods Can Help Bridge the Gap Between Ds And Decision Makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Success Factors for Using Case Method in Teaching Applied Data Science Education

Chkoniya

2021

European Journal of Education

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“…However, both data and automatic analysis are the base and more common denominator: the data science approach. Several methodologies exist to drive data projects, but in general that consists in fourth steps: to know the problem, to understand the data, to extract features and to model an analyze [14]. Data engineering is complementary and fundamental to achieve implementations: from data capture to the action over the physical system.…”

Section: Componentsmentioning

confidence: 99%

Trends and Opportunities of Industry 4.0 in Wood Manufacturing Processes

Ramos-Maldonado¹,

Aguilera-Carrasco²

2022

Engineered Wood Products for Construction

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Wood industry is key for sustainability and an important economic activity in many countries. In manufacturing plants, wood variability turns operation management more complex. In a competitive scenario, assets availability is critical to achieve higher productivity. In a new fourth industrial revolution, Industry 4.0, data engineering permits efficient decisions making. Phenomena difficult to model with conventional techniques are turned possible with algorithms based on artificial intelligence. Sensors and machine learning techniques allow intelligent analysis of data. However, algorithms are highly sensitive of the problem and his study to decide on which work is critical. For the manufacturing wood processes, Industry 4.0 is a great opportunity. Wood is a material of biological origin and generates variabilities over the manufacturing processes. For example, in the veneer drying, density and anatomical structure impact the product quality. Scanners have been developed to measure variables and outcomes, but decisions are made yet by humans. Today, robust sensors, computing capacity, communications and intelligent algorithms permit to manage wood variability. Real-time actions can be achieved by learning from data. This paper presents trends and opportunities provided by Industry 4.0 components. Sensors, decision support systems and intelligent algorithms use are reviewed. Some applications are presented.

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“…However, many of these projects fail to deliver the expected value ( Martinez, Viles & Olaizola, 2021 ). For example, VentureBeats (2019) noted that 87% of data science projects never make it into production and a NewVantage survey ( NewVantage Partners, 2019 ) reported that for 77% of businesses, the adoption of big data and artificial intelligence (AI) initiatives is a big challenge.…”

Section: Introductionmentioning

confidence: 99%

“…This is due, at least in part, to that fact that data science teams generally suffer from immature processes, often relying on trial-and-error and Ad Hoc processes ( Bhardwaj et al, 2015 ; Gao, Koronios & Selle, 2015 ; Saltz & Shamshurin, 2015 ). In short, big data science projects often do not leverage well-defined process methodologies ( Martinez, Viles & Olaizola, 2021 ; Saltz & Hotz, 2020 ). To further emphasize this point, in a survey to data scientists from both industry as well as from not-for-profit organizations, 82% of the respondents did not follow an explicit process methodology for developing data science projects, and equally important, 85% of the respondents stated that using an improved and more consistent process would produce more effective data science projects ( Saltz et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Current approaches for executing big data science projects—a systematic literature review

Saltz

Krasteva

2022

PeerJ Computer Science

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There is an increasing number of big data science projects aiming to create value for organizations by improving decision making, streamlining costs or enhancing business processes. However, many of these projects fail to deliver the expected value. It has been observed that a key reason many data science projects don’t succeed is not technical in nature, but rather, the process aspect of the project. The lack of established and mature methodologies for executing data science projects has been frequently noted as a reason for these project failures. To help move the field forward, this study presents a systematic review of research focused on the adoption of big data science process frameworks. The goal of the review was to identify (1) the key themes, with respect to current research on how teams execute data science projects, (2) the most common approaches regarding how data science projects are organized, managed and coordinated, (3) the activities involved in a data science projects life cycle, and (4) the implications for future research in this field. In short, the review identified 68 primary studies thematically classified in six categories. Two of the themes (workflow and agility) accounted for approximately 80% of the identified studies. The findings regarding workflow approaches consist mainly of adaptations to CRISP-DM (vs entirely new proposed methodologies). With respect to agile approaches, most of the studies only explored the conceptual benefits of using an agile approach in a data science project (vs actually evaluating an agile framework being used in a data science context). Hence, one finding from this research is that future research should explore how to best achieve the theorized benefits of agility. Another finding is the need to explore how to efficiently combine workflow and agile frameworks within a data science context to achieve a more comprehensive approach for project execution.

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Data Science Methodologies: Current Challenges and Future Approaches

Cited by 63 publications

References 47 publications

Success Factors for Using Case Method in Teaching Applied Data Science Education

Success Factors for Using Case Method in Teaching Applied Data Science Education

Trends and Opportunities of Industry 4.0 in Wood Manufacturing Processes

Current approaches for executing big data science projects—a systematic literature review

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