Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies

Pisoni, Galena; Molnár, Bálint; Tarcsi, Ádám

doi:10.1007/s13132-023-01607-z

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Companies that have invested in digital innovation in recent years now find themselves in need of an alignment of their internal competencies to optimize return on investment, recognising that they need to adapt rapidly to new market conditions (Abele et al, 2015) and adapt to a data-driven approach to decision-making (Tiwari & Raju, 2022) using data and data analytics to inform business decisions (Pisoni et al, 2023).…”

Section: Digital Transformationmentioning

confidence: 99%

RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace

Gómez Gandía,

Gavrila Gavrila,

de Lucas Ancillo

et al. 2024

J Knowl Econ

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Companies are immersed in a process of digitalization that transforms business models and creates value due to the increase in technology. The adoption of new technologies has a great impact on organizations, not only at an economic level but also on their products, processes, and human resources. This process will result in a series of necessary changes to align with their internal competencies and optimize the investment made. This digitalization generates a digital transformation that affects both large companies and SMEs, with the result that new technologies are subject to continuous change, requiring the development and training of workers with the necessary skills to cope with it. Within this transformation, the automation of processes is a constantly growing topic in the business world, as it generates a series of benefits for organizations that they would not otherwise be able to acquire. Process automation reduces the workload in repetitive processes and provides more time for employees to attend to end-customer requests. The adoption of this technology will provide the company to be adapted to a changing world experiencing an increase in productivity, effectiveness, and efficiency. This research focuses on how the process automation provides the organization with a wide range of benefits such as workload reduction and increased productivity for most of the company. Although process automation can bring many benefits to the workplace, it is important to recognize that its use does not always automatically lead to a systematic improvement of workers’ skills. In this context, it is also important to note how employee training is necessary to face this new reality. Employee training and adaptation is critical to the organization’s sustainability. Training will need to be aimed at equipping the employee with technical skills to enable them to effectively use and implement technology and to assimilate it as a complement and not as a threat. To analyse the individual’s awareness of the digitization of the workplace, the automation of tasks and the advantages or disadvantages that may result from the introduction of technology, a questionnaire was developed, and 103 valid responses were obtained and analysed. This has resulted in a series of hypotheses that have been tried to be validate throughout the research work. These results have important implications for organizations seeking to implement automation and provide a basis for future research in this constantly evolving field.

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Section: Digital Transformationmentioning

confidence: 99%

RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace

Gómez Gandía,

Gavrila Gavrila,

de Lucas Ancillo

et al. 2024

J Knowl Econ

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“…This process involves the use of cognitive information systems, collaborative tools, and knowledge-based agents to facilitate effective decision-making. By employing advanced techniques such as deep learning and ML, organizations are able to process and analyze large sets of data, thereby generating valuable insights and information that are crucial for informed decision-making (Pisoni et. al., 2023).…”

Section: Introductionmentioning

confidence: 99%

Knowledge Management Approach in Comparative Study of Air Pollution Prediction Model

ROHAJAWATI,

SETYODEWI,

TRESNANTO

et al. 2024

Appl. Comput. Sci.

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This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge management can improve the decision-making process for predicting models that involved datasets, such as air pollution. Currently, air pollution has become a serious global issue, impacting almost every major city worldwide. As the capital and a central hub for various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic and elevated air pollution levels. The comparative study aims to measure the accuracy levels of the naïve bayes, decision trees, and random forest prediction models. Additionally, the study uses evaluation measurements to assess how well the machine learning performs, utilizing a confusion matrix. The dataset’s duration is three years, from 2019 until 2021, obtained through Jakarta Open Data. The study found that the random forest achieved the best results with an accuracy rate of 94%, followed by the decision tree at 93%, and the naïve bayes had the lowest at 81%. Hence, the random forest emerges as a reliable predictive model for prediction of air pollution.

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Unlocking financial innovation through strategic investments in information management: a systematic review

Garad,

Riyadh,

Al-Ansi

et al. 2024

Discov Sustain

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Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies

Cited by 4 publications

References 39 publications

RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace

RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace

Knowledge Management Approach in Comparative Study of Air Pollution Prediction Model

Unlocking financial innovation through strategic investments in information management: a systematic review

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