Analisis Human-Centric Data Retrieval dalam Lingkungan Data Silo

Talakua, Abraham Mauritz (2024) Analisis Human-Centric Data Retrieval dalam Lingkungan Data Silo. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5026201141-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (7MB) | Request a copy

Abstract

Penurunan kualitas data dalam organisasi berdampak signifikan pada bidang ekonomi dan social suatu organisasi. Sebuah konsekuensi serius muncul dari krisis manajemen kualitas data, antara lain kegagalan proyek integrasi data dan biaya tambahan yang mencapai miliaran USD. Fenomena "data silo," di mana data diproses secara redundan dalam sistem IT terisolasi menyebabkan ketidakkonsistenan dalam definisi, format, dan nilai data, menghambat proses data retrieval. Penelitian ini mengeksplorasi patterns of action untuk data retrieval dalam lingkungan data silo dengan metode observasi dan wawancara. Langkah-langkah untuk mengeksplorasi patterns of action termasuk dalam bagian metodologi yang memanfaatkan narrative network. Maka dari itu, peneliti akan melakukan: Identifikasi dan perumusan masalah, studi literatur, perancangan instrumen penelitian, mengumpulkan data dengan cara observasi lapangan dan wawancara. Selanjutnya, peneliti akan melakukan kodifikasi data, kemudian apabila informasi yang didapatkan sudah cukup, peneliti melanjutkan dengan pemodelan data. Apabila belum, langkah mengumpulkan data dapat diulangi. Setelah pemodelan dilakukan, peneliti akan melakukan analisis data melalui identifikasi workarounds yang akan dapat menghasilkan patterns of action. Patterns of action akan didapatkan dengan menggambarkan rutinitas organisasional melalui assignment aksi dan aktor, Analisis workarounds akan dilakukan, yang akan menghasilkan hipotesis dan hasil analisis. Penelitian ini diharapkan dapat memberikan kontribusi pada literatur patterns of actio n, menjawab panggilan penelitian sebelumnya, dan sebagai referensi untuk penelitian selanjutnya. Secara praktis, penelitian ini juga dapat memberikan solusi bagi organisasi dalam mengatasi masalah ketersediaan data, menggunakan patterns of action sebagai dasar untuk meningkatkan efisiensi operasional, dan memberikan panduan kepada pembuat kebijakan untuk mendukung pengembangan workarounds yang bermanfaat dan mencegah risiko terkait. Setelah melakukan penelitian, peneliti mendapatkan temuan yang membagi 18 narasumber agar berasal dari 2 jenis organisasi temporary. Kedua jenis organisasi tersebut diberi kodifikasi organisasi berjenis “A” yang merupakan organisasi temporary yang memiliki umur sangat pendek (kurang dari 3 bulan) dan biasa disebut sebagai “kepanitiaan” dan organisasi berjenis “B” yang merupakan organisasi temporary yang memiliki umur kurang dari 1 tahun dan biasa disebut sebagai “himpunan.” Peneliti menemukan 3 pola yang berkaitan dengan workarounds yang muncul dari kegiatan data retrieval, antara lain pola penanganan data salah/kurang, pola manipulasi bukti laporan pertanggungjawaban, dan pola penanganan dokumentasi kurang memuaskan. Peneliti juga mengemukakan penyebab munculnya masing-masing pola. Kesimpulan dari penelitian ini terdapat pada 3 poin utama, antara lain di poin workarounds yang menyatakan organisasi berjenis A melakukan workarounds untuk melengkapi sistem yang ada; organisasi B cenderung melakukan tampering, poin kendali yang menyatakan BPH dalam pola-pola yang ditemukan mengendalikan aktivitas penting, sementara bagian operasional sebagai penyedia data. Poin terakhir di konteks manipulasi data yang menyatakan bahwa organisasi berjenis B lebih sering memanipulasi data untuk bertahan hidup, terutama oleh BPH, dibandingkan organisasi A
======================================================================================================================================
The decline in data quality in an organization has a significant impact on the economic and social aspects of an organization. A serious consequence arises from the data quality management crisis, including the failure of data integration projects and additional costs reaching billions of USD. The phenomenon of “data silos,” where data is processed redundantly in isolated IT systems causes inconsistencies in data definitions, formats, and values, hampering the data retrieval process. This research explores patterns of action for data retrieval in a data silo environment using observation and interview methods. Steps to explore patterns of action are included in the methodology section which utilizes narrative networks. Therefore, researchers will carry out: Identification and formulation of problems, literature studies, designing research instruments, collecting data by means of field observations and interviews. Next, the researcher will codify the data, then if the information obtained is sufficient, the researcher will continue with data modeling. If not, the data collection steps can be repeated. After modeling is carried out, researchers will carry out data analysis through identifying workarounds that will produce patterns of action. Patterns of action will be obtained by describing organizational routines through assignment of actions and actors. Analysis of workarounds will be carried out, which will produce hypotheses and analysis results. It is hoped that this research can contribute to the patterns of action literature, answer calls for previous research, and serve as a reference for further research. Practically, this research can also provide solutions for organizations in overcoming data availability problems, use patterns of action as a basis for improving operational efficiency, and provide guidance to policy makers to support the development of useful workarounds and prevent related risks. After conducting research, researchers obtained findings that divided the 18 sources so they came from 2 types of temporary organizations. These two types of organizations are codified as type "A" organizations which are temporary organizations which have a very short lifespan (less than 3 months) and are usually referred to as "committees" and type "B" organizations which are temporary organizations which have a lifespan of less than 1 year. and is usually referred to as a “set.” Researchers found 3 patterns related to workarounds that emerged from data retrieval activities, including incorrect/inadequate data handling patterns, accountability report evidence manipulation patterns, and unsatisfactory documentation handling patterns. Researchers also stated the causes of the emergence of each pattern. The conclusions from this research are in 3 main points, including the workarounds point which states that type A organizations carry out workarounds to complement existing systems; organization B tends to perform tampering, control points that state the BPH in patterns found to control critical activities, while the operational section is a data provider. The final point in the context of data manipulation states that type B organizations manipulate data more often to survive, especially by BPH, compared to organization A

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Patterns of action, Data retrieval, Data quality, Workarounds, Organisasi temporary; Patterns of action, Data retrieval, Data quality, Workarounds, temporary organization.
Subjects:	T Technology > T Technology (General) > T59.7 Human-machine systems.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Abraham Mauritz Talakua
Date Deposited:	26 Jul 2024 00:39
Last Modified:	26 Jul 2024 00:39
URI:	http://repository.its.ac.id/id/eprint/108940

Actions (login required)

View Item