Pengembangan Business Intelligence Dashboard Untuk Monitoring Key Performance Indicator Perusahaan di WWMusik Malang
Kata Kunci:
KPI, Business Intelligence, four step design methodology, data warehouse, dashboard, AHP, DATUSAbstrak
Efisiensi dan efektivitas merupakan aspek krusial dalam mencapai keberhasilan bisnis. Kedua aspek ini dapat diukur melalui Key Performance Indicators (KPI) yang dirumuskan berdasarkan objektif bisnis. WWMusik Malang, sebuah toko retail alat musik yang telah beroperasi sejak tahun 1994, hingga kini belum pernah memanfaatkan data transaksinya untuk menganalisis performa toko. Pengambilan keputusan manajerial hanya bergantung pada intuisi dan pengalaman. Hal ini menjadi hambatan, sebab proses pengambilan keputusan tidak didukung oleh data. Penelitian ini mengusulkan pengembangan dashboard Business Intelligence (BI) untuk memantau kinerja berdasarkan KPI. BI dirancang menggunakan metode four-step design methodology yang diperkenalkan oleh Kimball, termasuk salah satu tahapannya yaitu pengembangan data warehouse. Data operasional terlebih dahulu melalui tahapan Extract-Transform-Load (ETL) menggunakan Talend Open Studio, kemudian dimuat dalam Data warehouse dengan mekanisme penjadwalan pemutakhiran data setiap minggunya. Data warehouse menjadi sumber data utama dari komponen visualisasi pada dashboard, yang ditentukan berdasarkan tingkat krusial KPI oleh CEO perusahaan menggunakan metode Analytical Hierarchy Process (AHP) dengan kriteria SMART. Dashboard diimplementasikan menggunakan Microsoft Power BI dengan koneksi ODBC dan On-Premises data gateway untuk melakukan pembaharuan semantic model. Hasil dari implementasi dashboard diuji menggunakan metode pengujian usability DATUS. Hasil pengujian menunjukkan tingkat kepuasan pengguna yang relatif tinggi, dengan skor keseluruhan 89,6 dari 100, dan interpretasi dari nilai rata-rata yang memuaskan
Referensi
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Golfarelli, M., S. Rizzi, and J. Cella. 2004. Beyond data warehousing: What's next in business intelligence?. Proceedings of 7th International Workshop on Data Warehousing and OLAP (DOLAP 2004). Washington DC.
Johnston, R., S. Brignall, and L. Fitzgerald. 2002. ‘Good enough’ performance measurement - a trade-off between activity and action. Journal of Operational Research Society. 53: p. 256-262.
Kimball, R., Ross, M., 2013. The Data Warehouse Toolkit. 3rd ed. John Wiley and Son, Inc.
Likert R. A. 1932. Technique for the measurements of attitudes. Archives of psychology. 140(22). p.5-55.
Olszak, C. 2020. Business intelligence and big data: Drivers of organizational success. CRC Press.
Podgórski, Daniel. 2015. Measuring operational performance of OSH management system – A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/J.SSCI.2014.11.018.
Ponniah, Paulraj. 2010. Data Warehousing Fundamentals for IT Professionals. 2nd Edition. John Wiley and Son, Inc
Potineni, Padmaja. 2022. Oracle Database Data Warehousing Guide. 12c Release (12.1). Oracle
Skyrius, R., & Skyrius, R. 2021. Business intelligence information needs: Related systems and activities. Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture, 27–50.
Turban, Efraim. Sharda, Rhamesh. Delen, Dursun. 2011. Decision Support and Business Intelligence Systems. 9th ed. New Jersey. Pearson/Prentice Hall.
Wind, Y., Saaty, T. Marketing Applications of the Analytic Hierarchy Process. Management Science. 26(7)., p.641-658.
Antunes, S. 2020. DATUS: Dashboard Assessment Usability Model-a case study with student dashboards.
Cheung, S.O., H.C.H. Suen, and K.K.W. Cheung. 2004. PPMS: a Web-based construction Project Performance Monitoring System. Automation in Construction. 13(3) p. 361-376.
Davenport, T., & Harris, J. 2017. Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Press.
Golfarelli, M., S. Rizzi, and J. Cella. 2004. Beyond data warehousing: What's next in business intelligence?. Proceedings of 7th International Workshop on Data Warehousing and OLAP (DOLAP 2004). Washington DC.
Johnston, R., S. Brignall, and L. Fitzgerald. 2002. ‘Good enough’ performance measurement - a trade-off between activity and action. Journal of Operational Research Society. 53: p. 256-262.
Kimball, R., Ross, M., 2013. The Data Warehouse Toolkit. 3rd ed. John Wiley and Son, Inc.
Likert R. A. 1932. Technique for the measurements of attitudes. Archives of psychology. 140(22). p.5-55.
Olszak, C. 2020. Business intelligence and big data: Drivers of organizational success. CRC Press.
Podgórski, Daniel. 2015. Measuring operational performance of OSH management system – A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/J.SSCI.2014.11.018.
Ponniah, Paulraj. 2010. Data Warehousing Fundamentals for IT Professionals. 2nd Edition. John Wiley and Son, Inc
Potineni, Padmaja. 2022. Oracle Database Data Warehousing Guide. 12c Release (12.1). Oracle
Skyrius, R., & Skyrius, R. 2021. Business intelligence information needs: Related systems and activities. Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture, 27–50.
Turban, Efraim. Sharda, Rhamesh. Delen, Dursun. 2011. Decision Support and Business Intelligence Systems. 9th ed. New Jersey. Pearson/Prentice Hall.
Wind, Y., Saaty, T. Marketing Applications of the Analytic Hierarchy Process. Management Science. 26(7)., p.641-658.
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