What is Data Quality? As an IT prof

What is Data Quality?
As an IT professional, you have heard of data accuracy quite often. Accuracy is associated with a data element. Consider an entity such as customer. The customer entity has attrib- utes such as customer name, customer address, customer state, customer lifestyle, and so
on. Each occurrence of the customer entity refers to a single customer. Data accuracy, as it relates to the attributes of the customer entity, means that the values of the attributes of a single occurrence accurately describes the particular customer. The value of the customer name for a single occurrence of the customer entity is actually the name of that customer. Data quality implies data accuracy, but it is much more than that. Most cleansing opera- tions concentrate on just data accuracy. You need to go beyond data accuracy. If the data is fit for the purpose for which it is intended, we can then say such data has quality. Therefore, data quality is to be related to the usage for the data item as defined by the users. Does the data item in an entity reflect exactly what the user is expecting to ob- serve? Does the data item possess fitness of purpose as defined by the users? If it does, the data item conforms to the standards of data quality. Please scrutinize Figure 13-1. This figure brings out the distinction between data accuracy and data quality. What is considered to be data quality in operational systems? If the database records conform to the field validation edits, then we generally say that the database records are of good data quality. But such single field edits alone do not constitute data quality. Data quality in a data warehouse is not just the quality of individual data items but the quality of the full, integrated system as a whole. It is more than the data edits on individ- ual fields. For example, while entering data about the customers in an order entry applica- tion, you may also collect the demographics of each customer. The customer demograph- ics are not germane to the order entry application and, therefore, they are not given too much attention. But you run into problems when you try to access the customer demo- graphics in the data warehouse. The customer data as an integrated whole lacks data qual- ity.

Hasil (Bahasa Indonesia) 1:

Apakah kualitas Data? Sebagai profesional IT, Anda telah mendengar akurasi data cukup sering. Akurasi terkait dengan elemen data. Pertimbangkan sebuah entitas seperti pelanggan. Entity pelanggan memiliki attrib-utes alamat pelanggan, pelanggan negara nama pelanggan, pelanggan gaya hidup, dan begituon. Each occurrence of the customer entity refers to a single customer. Data accuracy, as it relates to the attributes of the customer entity, means that the values of the attributes of a single occurrence accurately describes the particular customer. The value of the customer name for a single occurrence of the customer entity is actually the name of that customer. Data quality implies data accuracy, but it is much more than that. Most cleansing opera- tions concentrate on just data accuracy. You need to go beyond data accuracy. If the data is fit for the purpose for which it is intended, we can then say such data has quality. Therefore, data quality is to be related to the usage for the data item as defined by the users. Does the data item in an entity reflect exactly what the user is expecting to ob- serve? Does the data item possess fitness of purpose as defined by the users? If it does, the data item conforms to the standards of data quality. Please scrutinize Figure 13-1. This figure brings out the distinction between data accuracy and data quality. What is considered to be data quality in operational systems? If the database records conform to the field validation edits, then we generally say that the database records are of good data quality. But such single field edits alone do not constitute data quality. Data quality in a data warehouse is not just the quality of individual data items but the quality of the full, integrated system as a whole. It is more than the data edits on individ- ual fields. For example, while entering data about the customers in an order entry applica- tion, you may also collect the demographics of each customer. The customer demograph- ics are not germane to the order entry application and, therefore, they are not given too much attention. But you run into problems when you try to access the customer demo- graphics in the data warehouse. The customer data as an integrated whole lacks data qual- ity.

Hasil (Bahasa Indonesia) 2:

Apa Kualitas Data?
Sebagai seorang IT profesional, Anda telah mendengar tentang akurasi data cukup sering. Akurasi berhubungan dengan elemen data. Pertimbangkan entitas seperti pelanggan. Entitas pelanggan memiliki utes attrib- seperti nama pelanggan, alamat pelanggan, negara konsumen, gaya hidup pelanggan, dan sebagainya
pada. Setiap terjadinya entitas pelanggan mengacu satu pelanggan. Akurasi data, yang berkaitan dengan atribut dari entitas pelanggan, berarti bahwa nilai-nilai atribut dari kejadian tunggal akurat menggambarkan pelanggan tertentu. Nilai dari nama pelanggan untuk kejadian tunggal dari entitas pelanggan adalah sebenarnya nama pelanggan itu. Kualitas data menyiratkan akurasi data, tetapi jauh lebih dari itu. Kebanyakan operasi-operasi pembersihan berkonsentrasi pada akurasi data hanya. Anda perlu melampaui akurasi data. Jika data cocok untuk tujuan yang dimaksudkan, kita kemudian bisa mengatakan data tersebut memiliki kualitas. Oleh karena itu, kualitas data akan berhubungan dengan penggunaan untuk item data seperti yang didefinisikan oleh pengguna. Apakah item data dalam suatu entitas mencerminkan apa yang pengguna mengharapkan untuk di amati? Apakah item data memiliki kebugaran tujuan seperti yang didefinisikan oleh pengguna? Jika tidak, item data sesuai dengan standar kualitas data. Silakan meneliti Gambar 13-1. Angka ini membawa keluar perbedaan antara akurasi data dan kualitas data. Apa yang dianggap kualitas data dalam sistem operasional? Jika catatan database sesuai dengan validasi lapangan suntingan, maka kita umumnya mengatakan bahwa catatan database yang berkualitas data yang baik. Tapi bidang tunggal seperti mengedit sendiri tidak merupakan kualitas data. Kualitas data dalam data warehouse tidak hanya kualitas item data individu tetapi kualitas penuh, sistem yang terintegrasi secara keseluruhan. Hal ini lebih dari suntingan data pada bidang UAL individ-. Misalnya, saat memasukkan data tentang pelanggan dalam order entry aplikasi, Anda mungkin juga mengumpulkan demografi setiap pelanggan. Pelanggan ics demografi tidak erat dengan aplikasi order entry dan, karena itu, mereka tidak diberikan terlalu banyak perhatian. Tapi Anda mengalami masalah ketika Anda mencoba untuk mengakses grafis pelanggan demografis di gudang data. Data pelanggan sebagai suatu keseluruhan yang terintegrasi kekurangan data kualitatif ity.