000 03127nam a22002777a 4500
003 OSt
005 20250710161709.0
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040 _aTUPM
_bEnglish
_cTUPM
_dTUPM
_erda
050 _aBTH T 58.5
_bB67 2025
100 _aBorja, Samuel.
_eauthor
245 _aA framework for dynamic metrics tracking and data visualization/
_cBorja, Samuel.--
260 _aManila:
_bTechnological University of the Philippines,
_c2025.
300 _ax, 131pages:
_c29cm.
336 _2rdacontent
337 _2rdamedia
338 _2rdacarrier
500 _aBachelor's thesis
502 _aCollege of Science.--
_bBachelor of science in information technology:
_cTechnological University of the Philippines,
_d2025.
504 _aIncludes bibliographic references and index.
520 _aThe integration of technology in modern businesses and organizations has become significant, and having a reliable way to track metrics and access insights in real-time is fundamental for making informed, apt, and data-driven business decisions. As a response to these advancements, the study entitled, “A Framework for Dynamic Metrics Tracking and Data Visualization” explores the development of a structured framework for dynamic metrics tracking and data visualization using a powerful out-of-the-box tool from Microsoft Power BI which provides functionalities for data integration, transformation, and presentation. The system consolidates data from multiple enterprise platforms such as SharePoint, Jira, OpsGenie, and SQL databases into unified, interactive dashboards customized based on business needs. These dashboards created in a unified platform provide actionable insights into key performance indicators (KPIs), and this enables decision-makers, executives, and even non-technical users to identify inefficiencies, monitor trends, and make timely interventions that support the annotations team and continuous improvement team. Python and Power BI are the essential tools used to develop this framework following a structured methodology for implementing visualization strategies, building data models, and improving reporting processes. A survey instrument based on ISO 25010, an evaluation instrument based on Functional Suitability and Reliability to identify the level of adequacy of the system was used to assess the final output. The results from the live working environment highlighted that the system met expectations in terms of ease of access, data accuracy, and visualization quality. The framework was rated with an overall mean of 4.8 described as “HIGHLY ACCEPTABLE” by the stakeholders who regularly interact with the metrics, indicating its usefulness in enhancing transparency, organizational responsiveness, and performance monitoring. This study provides a model and guide for organizations that want to utilize and maximize business intelligence tools for data governance and operational administration.
650 _aData integration
650 _aOpsGenie
650 _aFunctional suitability
942 _2lcc
_cBTH COS
_n0
999 _c30271
_d30271