Incorrect values. Product Code: 146

Incorrect values. Product Code: 146, Product Name: Crystal Vase, and Height: 486 inches in the same record point to some sort of data inaccuracy. The values for product name and height are not compatible. Perhaps the product code is also in- correct. Multipurpose fields. Same data value in a field entered by different departments may mean different things. A field could start off as a storage area code to indicate the backroom storage areas in stores. Later, when the company built its own warehouse to store products, it used the same field to indicate the warehouse. This type of problem is perpetuated because store codes and warehouse codes were residing in the same field. Warehouse codes went into the same field by redefining the store code field. This type of data pollution is hard to correct. Erroneous integration. In an auction company, buyers are the customers who bid at auctions and buy the items that are auctioned off. The sellers are the customers who sell their goods through the auction company. The same customer may be a buyer in the auction system and a seller in the property receipting system. Assume that cus- tomer number 12345 in the auction system is the same customer whose number is 34567 in the property receipting system. The data for customer number 12345 in the auction system must be integrated with the data for customer number 34567 in the property receipting system. The reverse side of the data integration problem is this: customer number 55555 in the auction system and customer number 55555 in the property receipting system are not the same customer but are different. These in- tegration problems arise because, typically, each legacy system had been developed in isolation at different times in the past.
DATA QUALITY CHALLENGES
There is an interesting but strange aspect of the whole data cleansing initiative for the data warehouse. We are striving toward having clean data in the data warehouse. We want to ascertain the extent of the pollution. Based on the condition of the data, we plan data cleansing activities. What is strange about this whole set of circumstances is that the pol- lution of data occurs outside the data warehouse. As part of the data warehouse project team, you are taking measures to eliminate the corruption that arises in a place outside your control. All data warehouses need historical data. A substantial part of the historical data comes from antiquated legacy systems. Frequently, the end-users use the historical data in the data warehouse for strategic decision making without knowing exactly what the data really means. In most cases, detailed metadata hardly exists for the old legacy systems. You are expected to fix the data pollution problems that emanate from the old operational systems without the assistance of adequate information about the data there.
Sources of Data Pollution In order to come up with a good strategy for cleansing the data, it will be worthwhile to review a list of common sources of data pollution. Why does data get corrupted in the source systems? Study the following list of data pollution sources against the background of what data quality really is.

Incorrect values. Product Code: 146, Product Name: Crystal Vase, and Height: 486 inches in the same record point to some sort of data inaccuracy. The values for product name and height are not compatible. Perhaps the product code is also in- correct. Multipurpose fields. Same data value in a field entered by different departments may mean different things. A field could start off as a storage area code to indicate the backroom storage areas in stores. Later, when the company built its own warehouse to store products, it used the same field to indicate the warehouse. This type of problem is perpetuated because store codes and warehouse codes were residing in the same field. Warehouse codes went into the same field by redefining the store code field. This type of data pollution is hard to correct. Erroneous integration. In an auction company, buyers are the customers who bid at auctions and buy the items that are auctioned off. The sellers are the customers who sell their goods through the auction company. The same customer may be a buyer in the auction system and a seller in the property receipting system. Assume that cus- tomer number 12345 in the auction system is the same customer whose number is 34567 in the property receipting system. The data for customer number 12345 in the auction system must be integrated with the data for customer number 34567 in the property receipting system. The reverse side of the data integration problem is this: customer number 55555 in the auction system and customer number 55555 in the property receipting system are not the same customer but are different. These in- tegration problems arise because, typically, each legacy system had been developed in isolation at different times in the past. 
DATA QUALITY CHALLENGES
There is an interesting but strange aspect of the whole data cleansing initiative for the data warehouse. We are striving toward having clean data in the data warehouse. We want to ascertain the extent of the pollution. Based on the condition of the data, we plan data cleansing activities. What is strange about this whole set of circumstances is that the pol- lution of data occurs outside the data warehouse. As part of the data warehouse project team, you are taking measures to eliminate the corruption that arises in a place outside your control. All data warehouses need historical data. A substantial part of the historical data comes from antiquated legacy systems. Frequently, the end-users use the historical data in the data warehouse for strategic decision making without knowing exactly what the data really means. In most cases, detailed metadata hardly exists for the old legacy systems. You are expected to fix the data pollution problems that emanate from the old operational systems without the assistance of adequate information about the data there. 
Sources of Data Pollution In order to come up with a good strategy for cleansing the data, it will be worthwhile to review a list of common sources of data pollution. Why does data get corrupted in the source systems? Study the following list of data pollution sources against the background of what data quality really is.

0/5000

Dari: -

Ke: -

Hasil (Bahasa Indonesia) 1: [Salinan]

Disalin!

Incorrect values. Product Code: 146, Product Name: Crystal Vase, and Height: 486 inches in the same record point to some sort of data inaccuracy. The values for product name and height are not compatible. Perhaps the product code is also in- correct. Multipurpose fields. Same data value in a field entered by different departments may mean different things. A field could start off as a storage area code to indicate the backroom storage areas in stores. Later, when the company built its own warehouse to store products, it used the same field to indicate the warehouse. This type of problem is perpetuated because store codes and warehouse codes were residing in the same field. Warehouse codes went into the same field by redefining the store code field. This type of data pollution is hard to correct. Erroneous integration. In an auction company, buyers are the customers who bid at auctions and buy the items that are auctioned off. The sellers are the customers who sell their goods through the auction company. The same customer may be a buyer in the auction system and a seller in the property receipting system. Assume that cus- tomer number 12345 in the auction system is the same customer whose number is 34567 in the property receipting system. The data for customer number 12345 in the auction system must be integrated with the data for customer number 34567 in the property receipting system. The reverse side of the data integration problem is this: customer number 55555 in the auction system and customer number 55555 in the property receipting system are not the same customer but are different. These in- tegration problems arise because, typically, each legacy system had been developed in isolation at different times in the past. DATA QUALITY CHALLENGESThere is an interesting but strange aspect of the whole data cleansing initiative for the data warehouse. We are striving toward having clean data in the data warehouse. We want to ascertain the extent of the pollution. Based on the condition of the data, we plan data cleansing activities. What is strange about this whole set of circumstances is that the pol- lution of data occurs outside the data warehouse. As part of the data warehouse project team, you are taking measures to eliminate the corruption that arises in a place outside your control. All data warehouses need historical data. A substantial part of the historical data comes from antiquated legacy systems. Frequently, the end-users use the historical data in the data warehouse for strategic decision making without knowing exactly what the data really means. In most cases, detailed metadata hardly exists for the old legacy systems. You are expected to fix the data pollution problems that emanate from the old operational systems without the assistance of adequate information about the data there. Sources of Data Pollution In order to come up with a good strategy for cleansing the data, it will be worthwhile to review a list of common sources of data pollution. Why does data get corrupted in the source systems? Study the following list of data pollution sources against the background of what data quality really is.

Sedang diterjemahkan, harap tunggu..

Hasil (Bahasa Indonesia) 2:[Salinan]

Disalin!

Nilai-nilai yang salah. Kode Produk: 146, Nama Produk: Crystal Vase, dan Tinggi: 486 inci di titik rekor yang sama untuk beberapa jenis data ketidaktepatan. Nilai-nilai untuk nama produk dan tinggi tidak kompatibel. Mungkin kode produk juga di- benar. Bidang serbaguna. Nilai data yang sama dalam bidang dimasukkan oleh departemen yang berbeda dapat berarti hal yang berbeda. Bidang A bisa memulai sebagai kode area penyimpanan untuk menunjukkan tempat penyimpanan backroom di toko-toko. Kemudian, ketika perusahaan membangun gudang sendiri untuk menyimpan produk, dulu bidang yang sama untuk menunjukkan gudang. Jenis masalah diabadikan karena kode toko dan kode gudang yang berada di bidang yang sama. Kode Gudang masuk ke bidang yang sama dengan mendefinisikan kembali bidang kode toko. Jenis polusi data yang sulit untuk memperbaiki. Integrasi salah. Dalam sebuah perusahaan lelang, pembeli adalah pelanggan yang menawar di pelelangan dan membeli barang-barang yang dilelang. Penjual adalah pelanggan yang menjual barang-barang mereka melalui perusahaan lelang. Pelanggan yang sama mungkin pembeli dalam sistem lelang dan penjual dalam sistem pembuatan tanda terima properti. Asumsikan bahwa Tomer cus- nomor 12345 dalam sistem lelang adalah pelanggan yang sama yang jumlahnya 34.567 dalam sistem pembuatan tanda terima properti. Data untuk nomor pelanggan 12345 dalam sistem lelang harus terintegrasi dengan data untuk nomor pelanggan 34.567 dalam sistem pembuatan tanda terima properti. Sisi sebaliknya dari masalah integrasi data adalah ini: nomor pelanggan 55.555 dalam sistem lelang dan pelanggan nomor 55555 dalam sistem pembuatan tanda terima properti tidak pelanggan yang sama namun berbeda. Masalah-masalah ini tegration di- muncul karena, biasanya, setiap sistem warisan telah dikembangkan dalam isolasi pada waktu yang berbeda di masa lalu.
KUALITAS DATA TANTANGAN
Ada aspek menarik tetapi aneh inisiatif seluruh pembersihan data untuk data warehouse. Kami berusaha ke arah memiliki data bersih di gudang data. Kami ingin memastikan sejauh mana polusi. Berdasarkan kondisi data, kami berencana Data kegiatan pembersihan. Apa yang aneh tentang seluruh rangkaian ini keadaan adalah bahwa lution pol- data terjadi di luar gudang data. Sebagai bagian dari tim proyek data warehouse, Anda mengambil langkah-langkah untuk menghilangkan korupsi yang muncul di tempat yang di luar kendali Anda. Semua data warehouse perlu data historis. Sebagian besar dari data historis berasal dari sistem warisan kuno. Sering, pengguna akhir menggunakan data historis dalam gudang data untuk pengambilan keputusan strategis tanpa tahu persis apa data yang benar-benar berarti. Dalam kebanyakan kasus, metadata rinci hampir tidak ada untuk sistem warisan lama. Anda diharapkan untuk memperbaiki masalah pencemaran data yang berasal dari sistem operasional lama tanpa bantuan dari informasi yang memadai tentang data di sana.
Sumber Polusi Data Dalam rangka untuk datang dengan strategi yang baik untuk membersihkan data, maka akan bermanfaat untuk meninjau daftar sumber umum pencemaran data. Mengapa data rusak dalam sistem sumber? Mempelajari daftar berikut sumber pencemaran data terhadap latar belakang dari apa kualitas data sebenarnya.

Sedang diterjemahkan, harap tunggu..

Hasil (Bahasa Indonesia) 3:[Salinan]

Disalin!

Sedang diterjemahkan, harap tunggu..

Bahasa lainnya

Dukungan alat penerjemahan: Afrikans, Albania, Amhara, Arab, Armenia, Azerbaijan, Bahasa Indonesia, Basque, Belanda, Belarussia, Bengali, Bosnia, Bulgaria, Burma, Cebuano, Ceko, Chichewa, China, Cina Tradisional, Denmark, Deteksi bahasa, Esperanto, Estonia, Farsi, Finlandia, Frisia, Gaelig, Gaelik Skotlandia, Galisia, Georgia, Gujarati, Hausa, Hawaii, Hindi, Hmong, Ibrani, Igbo, Inggris, Islan, Italia, Jawa, Jepang, Jerman, Kannada, Katala, Kazak, Khmer, Kinyarwanda, Kirghiz, Klingon, Korea, Korsika, Kreol Haiti, Kroat, Kurdi, Laos, Latin, Latvia, Lituania, Luksemburg, Magyar, Makedonia, Malagasi, Malayalam, Malta, Maori, Marathi, Melayu, Mongol, Nepal, Norsk, Odia (Oriya), Pashto, Polandia, Portugis, Prancis, Punjabi, Rumania, Rusia, Samoa, Serb, Sesotho, Shona, Sindhi, Sinhala, Slovakia, Slovenia, Somali, Spanyol, Sunda, Swahili, Swensk, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Turki, Turkmen, Ukraina, Urdu, Uyghur, Uzbek, Vietnam, Wales, Xhosa, Yiddi, Yoruba, Yunani, Zulu, Bahasa terjemahan.