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"Politeknik Keuangan Negara STAN (PKN STAN) sebagai perguruan tinggi diwajibkan untuk memenuhi Standar Nasional Pendidikan Tinggi dalam rangka menjaga mutu pendidikan tinggi. Hasil penjaminan mutu digunakan oleh BAN-PT dalam menetapkan akreditasi Perguruan Tinggi. Penilaian akrediatasi, salah satunya, dilaksanakan dengan mengambil data dari Pangkalan Data Perguruan Tinggi (PDPT). Perguruan Tinggi wajib menyampaikan data dan informasi penyelenggaraan pendidikan ke PDPT. Data pendidikan tinggi meliputi data pokok, data referensi, dan data transaksional pendidikan tinggi. Data yang disampaikan ke PDPT harus memenuhi syarat kelengkapan, kebenaran, ketepatan, dan kemutakhiran. Hasil pengukuran data mahasiswa sebagai salah satu data pokok pendidikan tinggi di PKN STAN padadimensi kualitas data yang disyaratkan, yaitu Kelengkapan50.38%, Kebenaran/Ketepatan14.16%, dan Kemutakhiran100% diukur dari waktu pembuatan/pemutakhiran. Hasil tersebut belum memenuhi kriteria yang disyaratkan organisasi sebesar 90% untuk setiap dimensi data yang disyaratkan. Berdasarkan hal tersebut, penelitian ini menyusun rekomendasi peningkatan kualitas data pokok pendidikan di PKNSTAN. Rekomendasi disusun dengan melakukan penilaian manajemen kualitas data saat ini yang meliputi penilaian dimensi kualitas data padadata pokok pendidikan (dosen, mahasiswa, kurikulum, dan mata kuliah) dan penilaian tingkat kematangan manajemen kualitas data. Rekomendasi yang diberikan meliputi delapan komponen dalam Data Quality Frameworkdari David Loshin dengan menerapkan praktik terbaik manajemen kualitas data dariData Management Book of Knowledge dari DAMA Institute. Terdapat 66 rekomendasi peningkatan kualitas data pokok pendidikan di PKN STAN untuk dapat mencapai tingkat kematangan manajemen kualitas data yang diinginkan. Dari 66 rekomendasi tersebut, terdapat delapan rekomendasi yang dinilai berdampak signifikan dalam awal pelaksanaan program manajemen kualitas data di PKN STAN. Rekomendasi tersebut diharapkan dapat digunakan sebagai acuan dalam melaksanakan program peningkatan kualitas data pokok pendidikan di PKN STAN.

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State Finance Polytechnic STAN, as a higher education institution (HEI), is required to meet the National Standards of Higher Education in order to maintain the quality of higher education. The quality assurance results are used by BAN-PT in establishing university accreditation. Accreditation assessment, one of which, is carried out by taking data from the Pangkalan Data Perguruan Tinggi (PDPT).

HEIs must submit data and information on the implementation of education to PDPT. Higher education data includes basic data, reference data, and higher education transactional data. Data submitted to PDPT must meet the requirements for completeness, truth, accuracy, and currency.

The measurement results of student data as one of the primary data of higher education in PKN STAN on the required data quality dimensions, namely Completeness 50.38%, Truth/Accuracy 14.16%, and Update 100% measured from the time of creation/updating. These results do not meet the criteria required by the organization by 90% for each dimension of data required.

Based on this, the research composes recommendations for improving the quality of the basic data of education in PKN STAN. The recommendations are prepared by evaluating the current data quality management, which includes evaluating the dimensions of data quality in the basic education data (lecturers, students, curriculum, and courses) and assessing the maturity level of data quality management.

The recommendations include eight components in the David Quality Quality Framework by implementing data quality management best practices from the Data Management Book of Knowledge from DAMA Institute. PKN STAN needs to make the 66 recommendations to be able to reach the desired level of data quality management maturity. There were eight recommendations which considered to have a significant impact at the beginning of the implementation of the data quality management program in PKN STAN. This recommendation is expected to be used as a reference in implementing the basic data quality improvement program in PKN STAN."

"Setiap perguruan tinggi di Indonesia bertanggung jawab atas kelengkapan, kebenaran, ketepatan, dan kemutakhiran data pendidikan tinggi di perguruan tinggi masing-masing. Data pendidikan tinggi digunakan untuk pelaksanaan sistem penjaminan mutu pendidikan tinggi dan digunakan sebagai landasan dalam penyusunan kebijakan terkait program studi dan perguruan tinggi di Indonesia. Hasil pengukuran kualitas data menunjukkan bahwa terdapat permasalahan pada data pendidikan tinggi di Politeknik Statistika STIS yaitu belum memenuhi kriteria kelengkapan, kebenaran, ketepatan, dan kemutakhiran. Pengukuran tingkat kematangan manajemen kualitas data telah dilakukan dengan menggunakan Loshins Data Quality Maturity Model dimana hasilnya berada pada kisaran level 1 dan 2. Hanya komponen dimensi kualitas data yang telah mencapai target yang diharapkan. Untuk itu, rekomendasi disusun berdasarkan kerangka kerja DAMA-DMBOK. Adapun aktivitas yang perlu dilakukan adalah mengembangkan dan mempromosikan kesadaran terhadap kualitas data; mendefinisikan kebutuhan kualitas data; melakukan profiling, analisis, dan penilaian kualitas data; mendefinisikan aturan bisnis (business rules) kualitas data; menetapkan dan mengevaluasi tingkat layanan kualitas data (data quality service levels); mengelola permasalahan terkait kualitas data; merancang dan mengimplementasikan operasional prosedur untuk manajemen kualitas data; dan memantau operasional dan performa prosedur manajemen kualitas data.

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Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for the implementation of the higher-education quality assurance system and is used as a basis to formulate policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic STIS did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshins Data Quality Maturity Model which the result are in level 1 to level 2 of maturity. Only data quality dimensions component has achieved the expected target.

Thus, recommendations have been proposed based on the DAMA-DMBOK framework. The activities needed to be carried out are developing and promoting awareness of data quality; defining data quality requirements; profiling, analyzing, and evaluating data quality; define business rules for data quality, establish, and evaluate the data quality services levels, manage problems related to data quality, design and implement operational procedures for data quality management, and monitor operations and performance of data quality management procedures."

"Pusat Data dan Statistik Pendidikan dan Kebudayaan (PDSPK) merupakan unit kerja di Kementerian Pendidikan dan Kebudayaan yang bertugas dalam mendukung tugas Kementerian di bidang data dan statistik pendidikan dan kebudayaan. PDSPK melakukan proses verifikasi dan validasi terhadap data dari hasil pendataan DAPODIK yang bersifat data referensi. Proses verifikasi dan validasi ini dilakukan untuk menjamin kualitas data yang memenuhi persyaratan identitas tunggal untuk dapat melakukan integrasi data pendidikan. Integrasi data wajib menjamin unsur kelengkapan dan kebenaran data. Berdasarkan observasi data dan wawancara menunjukan bahwa kualitas data belum memenuhi sasaran strategis organisasi. Untuk mengatasi hal tersebut, perlu dibangun perancangan manajemen kualitas data yang sesuai dengan kebutuhan PDSPK dalam mengelola data pendidikan. Perancangan manajemen kualitas data mengacu pada panduan dari Data Management Body of Knowledge (DMBOK) dalam kategori plan dan development yang dikeluarkan DAMA Internasional. Penelitian berhasil merumuskan rancangan manajemen kualitas data yang terdiri dari mendefinisikan kebutuhan, menilai kondisi kualitas data saat ini, menyusun metrik, menetapkan aturan bisnis kualitas data, menetapkan tingkat layanan kualitas data, dan menyusun prosedur operasional manajemen kualitas data.

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Data Center and Statistic of Education and Culture (PDSPK) is a unit in the Ministry of Education and Culture which is tasked with supporting the Ministry's duties in the fields of education and culture data and statistics. PDSPK conducts a verification and validation process of data from the results of DAPODIK data that are reference data. This verification and validation process is carried out to ensure data quality that meets the requirements of a single identity to be able to integrate educational data. Data integration must guarantee the elements of data completeness and correctness. Based on data observations and interviews shows that the quality of data has not met the organization's strategic objectives.

To overcome this problem, it is necessary to build a data quality management planning that is in line with the PDSPK requirements in managing education data. Data quality management planning refers to the guidelines of the Data Management Body of Knowledge (DMBOK) in the plan and development category issued by DAMA International. The research succeeded in formulating a data quality management design consisting of defining requirements, assessing current data quality conditions, define metrics, define data quality business rules, establishing data quality service levels, and developing data quality management operational procedures."

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