They will flee from the data wareho

They will flee from the data warehouse in droves and all the effort of the
project team will be down the drain. It will be impossible to get back the trust of the users. Most companies overestimate the quality of the data in their operational systems. Very few have procedures and systems in place to verify the quality of data in their various op- erational systems. As long as the quality of the data is acceptable enough to perform the functions of the operational systems, then the general conclusion is that all of the enter- prise data is good. For some companies building data warehouses, data quality is not a higher priority. These companies suspect that there may be a problem, but that it is not so pressing as to demand immediate attention. Only when companies make an effort to ascertain the quality of their data are they amazed at the extent of data corruption. Even when companies discover a high level of data pollution, they tend to underestimate the effort needed to cleanse the data. They do not allocate sufficient time and resources for the clean-up effort. At best, the problem is addressed partially. If your enterprise has several disparate legacy systems from which your data ware- house must draw its data, start with the assumption that your source data is likely to be corrupt. Then ascertain the level of the data corruption. The project team must allow enough time and effort and have a plan for correcting the polluted data. In this chapter, we will define data quality in the context of the data warehouse. We will consider the com- mon types of data quality problems so that when you analyze your source data, you can identify the types and deal with them. We will explore the methods for data cleansing and also review the features of the tools available to assist the project team in this crucial un- dertaking.

Hasil (Bahasa Indonesia) 1:

Mereka akan melarikan diri dari gudang data di berbondong-bondong dan semua upayaproject team will be down the drain. It will be impossible to get back the trust of the users. Most companies overestimate the quality of the data in their operational systems. Very few have procedures and systems in place to verify the quality of data in their various op- erational systems. As long as the quality of the data is acceptable enough to perform the functions of the operational systems, then the general conclusion is that all of the enter- prise data is good. For some companies building data warehouses, data quality is not a higher priority. These companies suspect that there may be a problem, but that it is not so pressing as to demand immediate attention. Only when companies make an effort to ascertain the quality of their data are they amazed at the extent of data corruption. Even when companies discover a high level of data pollution, they tend to underestimate the effort needed to cleanse the data. They do not allocate sufficient time and resources for the clean-up effort. At best, the problem is addressed partially. If your enterprise has several disparate legacy systems from which your data ware- house must draw its data, start with the assumption that your source data is likely to be corrupt. Then ascertain the level of the data corruption. The project team must allow enough time and effort and have a plan for correcting the polluted data. In this chapter, we will define data quality in the context of the data warehouse. We will consider the com- mon types of data quality problems so that when you analyze your source data, you can identify the types and deal with them. We will explore the methods for data cleansing and also review the features of the tools available to assist the project team in this crucial un- dertaking.

Hasil (Bahasa Indonesia) 2:

Mereka akan lari dari gudang data berbondong-bondong dan semua upaya dari
tim proyek akan sia-sia. Ini akan menjadi mustahil untuk mendapatkan kembali kepercayaan dari pengguna. Sebagian besar perusahaan melebih-lebihkan kualitas data dalam sistem operasional mereka. Sangat sedikit memiliki prosedur dan sistem untuk memverifikasi kualitas data di berbagai sistem erational op- mereka. Selama kualitas data yang cukup dapat diterima untuk melakukan fungsi sistem operasional, maka kesimpulan umumnya adalah bahwa semua data hadiah masukkan-baik. Untuk beberapa perusahaan membangun gudang data, kualitas data tidak prioritas yang lebih tinggi. Perusahaan-perusahaan ini menduga bahwa mungkin ada masalah, tetapi itu tidak begitu mendesak untuk menuntut perhatian segera. Hanya ketika perusahaan melakukan upaya untuk memastikan kualitas data mereka mereka kagum pada tingkat korupsi data. Bahkan ketika perusahaan menemukan tingkat tinggi polusi data, mereka cenderung meremehkan usaha yang diperlukan untuk membersihkan data. Mereka tidak mengalokasikan waktu yang cukup dan sumber daya untuk upaya bersih-bersih. Paling-paling, masalah tersebut tidak ditangani secara parsial. Jika perusahaan Anda memiliki beberapa sistem warisan yang berbeda dari yang rumah data Anda gudang harus menarik data, mulai dengan asumsi bahwa sumber data Anda mungkin akan korup. Kemudian mengetahui tingkat korupsi data. Tim proyek harus memberikan waktu yang cukup dan usaha dan memiliki rencana untuk mengoreksi data tercemar. Dalam bab ini, kita akan mendefinisikan kualitas data dalam konteks data warehouse. Kami akan mempertimbangkan jenis mon com- masalah kualitas data sehingga ketika Anda menganalisis sumber data Anda, Anda dapat mengidentifikasi jenis dan berurusan dengan mereka. Kami akan mengeksplorasi metode untuk pembersihan data dan juga meninjau fitur dari alat yang tersedia untuk membantu tim proyek di dertaking un- penting ini.