{"id":36155,"date":"2019-10-31T22:09:53","date_gmt":"2019-10-31T19:09:53","guid":{"rendered":"https:\/\/prohoster.info\/blog\/kachestvo-dannyh-v-hranilishhe\/"},"modified":"2019-10-31T22:09:53","modified_gmt":"2019-10-31T19:09:53","slug":"kachestvo-dannyh-v-hranilishhe","status":"publish","type":"post","link":"https:\/\/prohoster.info\/no\/blog\/administrirovanie\/kachestvo-dannyh-v-hranilishhe","title":{"rendered":"Datakvalitet i lagringen","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Kvaliteten p\u00e5 dataene i lagringen er en viktig forutsetning for \u00e5 oppn\u00e5 verdifull informasjon. D\u00e5rlig kvalitet f\u00f8rer til en negativ kjedereaksjon p\u00e5 lang sikt.<br \/>\nF\u00f8rst mister man tilliten til den informasjonen som tilbys. Folk begynner \u00e5 bruke Business Intelligence-applikasjonene mindre, og potensialet i applikasjonene forblir ubenyttet.<br \/>\nSom et resultat stilles videre investeringer i det analytiske prosjektet sp\u00f8rsm\u00e5l.<\/p>\n<h4>Ansvar for datakvalitet<\/h4>\n<p>\nAspektet knyttet til \u00e5 forbedre datakvalitet er megaviktig i BI-prosjekter. Men det er ikke kun privilegiet til tekniske spesialister.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nDatakvalitet p\u00e5virkes ogs\u00e5 av aspekter som<\/p>\n<p><b>Bedriftskultur<\/b><\/p>\n<ul>\n<li>Er de ansatte selv interessert i \u00e5 produsere god kvalitet?<\/li>\n<li>Hvis ikke, hvorfor? Kan det v\u00e6re interessekonflikter?<\/li>\n<li>Kan det v\u00e6re bedriftsregler som bestemmer hvem som er ansvarlig for kvaliteten?<\/li>\n<\/ul>\n<p>\n<b>Prosesser<\/b><\/p>\n<ul>\n<li>Hvilke data produseres i enden av disse kjedene?<\/li>\n<li>Kanskje er operativsystemene konfigurert slik at man m\u00e5 \"kjempe\" for \u00e5 gjenspeile en bestemt situasjon i virkeligheten.<\/li>\n<li>Utf\u00f8rer operativsystemene selv sjekk og verifisering av dataene?<\/li>\n<\/ul>\n<p>\nAlle i organisasjonen er ansvarlige for datakvaliteten i rapporteringssystemene.<\/p>\n<h4>Definisjon og betydning<\/h4>\n<p>\nKvalitet er bekreftet tilfredsstillelse av kundens forventninger.<\/p>\n<p>Men datakvalitet inneholder ikke en definisjon. Den reflekterer alltid bruken av konteksten. Datavarehuset og BI-systemet har andre m\u00e5l enn operativsystemet hvor dataene er hentet fra.<\/p>\n<p>For eksempel kan kundeattributtet i operativsystemet v\u00e6re et valgfritt felt. I lagringen kan dette attributtet brukes som en dimensjon og dets utfylling er obligatorisk. Dette medf\u00f8rer i sin tur behovet for \u00e5 fylle ut med standardverdier.<\/p>\n<p>Kravene til datalagring endres stadig og de er vanligvis h\u00f8yere enn kravene til operativsystemene. Men det kan ogs\u00e5 v\u00e6re omvendt, n\u00e5r lagringen ikke krever \u00e5 oppbevare detaljert informasjon fra operativsystemet.<\/p>\n<p>For data quality to be measurable, its standards must be described. Those who use information and figures in their work should be involved in the description process. The result of this involvement can be a rule that allows one to determine at a glance whether there is an error in the table or not. This rule needs to be formalized in the form of a script\/code for subsequent verification.<\/p>\n<h4>Data Quality Improvement<\/h4>\n<p>\nIt is impossible to clean and correct all hypothetical errors in the data loading process into the storage. Good data quality can only be achieved through close collaboration among all participants. The people entering data into operational systems need to learn what actions lead to errors.<\/p>\n<p>Data quality is a process. Unfortunately, many organizations lack a strategy for its continuous improvement. Many limit themselves to merely storing data and do not utilize the full potential of analytical systems. Typically, in the development of data warehouses, 70-80% of the budget goes to data integration implementation. The process of control and improvement remains underdeveloped, if it exists at all.<\/p>\n<h4>Tools<\/h4>\n<p>\nThe use of software tools can assist in the automation of improving and monitoring data quality. For example, they can fully automate the technical verification of storage structures: field formats, existence of default values, compliance with table field name requirements.<\/p>\n<p>It can be more challenging to verify the content. As storage requirements change, so may the interpretation of data. The tool itself can turn into a massive project requiring support.<\/p>\n<h4>Advice<\/h4>\n<p>\nRelational databases, in which warehouses are typically designed, have the remarkable ability to create views. These can be used for quick data verification if the specifics of the content are known. Each instance of finding an error or problem in the data can be recorded as a query to the database.<\/p>\n<p>Slik vil det dannes en kunnskapsbase om innholdet. Selvf\u00f8lgelig m\u00e5 slike foresp\u00f8rsel v\u00e6re raske. Vanligvis tar det mindre menneskelig tid \u00e5 vedlikeholde visninger enn verkt\u00f8y organisert i tabeller. Visningen er alltid klar til \u00e5 vise resultatet av kontrollen.<br \/>\nI tilfellet med viktige rapporter kan visningen inneholde en kolonne med mottakeren. Det gir mening \u00e5 bruke de samme BI-verkt\u00f8yene til \u00e5 lage rapporter om datakvaliteten i lagringen.<\/p>\n<h4>Eksempel<\/h4>\n<p>\nForesp\u00f8rselen er skrevet for Oracle-databasen. I dette eksemplet returnerer testene et numerisk verdien som kan tolkes p\u00e5 \u00f8nsket m\u00e5te. Verdiene T_MIN og T_MAX kan justere graden av varsling. Feltet REPORT ble en gang brukt som melding i et kommersielt ETL-produkt som ikke kunne sende e-poster p\u00e5 en ordentlig m\u00e5te, s\u00e5 rpad er en 'l\u00f8sning'.<\/p>\n<p>I tilfelle av en stor tabell kan man legge til for eksempel AND ROWNUM &lt;= 10, dvs. hvis 10 feil har blitt samlet, er dette nok for en varsling.<\/p>\n<pre><code class=\"sql\">CREATE OR REPLACE VIEW V_QC_DIM_PRODUCT_01 AS\nSELECT\n  CASE WHEN OUTPUT&gt;=T_MIN AND OUTPUT&lt;=T_MAX\n  THEN &#039;OK&#039; ELSE &#039;ERROR&#039; END AS RESULT,\n  DESCRIPTION,\n  TABLE_NAME, \n  OUTPUT, \n  T_MIN,\n  T_MAX,\n  rpad(DESCRIPTION,60,&#039; &#039;) || rpad(OUTPUT,8,&#039; &#039;) || rpad(T_MIN,8,&#039; &#039;) || rpad(T_MAX,8,&#039; &#039;) AS REPORT\nFROM (-- Testen selv\n  SELECT\n    &#039;DIM_PRODUCT&#039; AS TABLE_NAME,\n    &#039;Antall tomrom&#039; AS DESCRIPTION,\n    COUNT(*) AS OUTPUT,\n    0 AS T_MIN,\n    10 AS T_MAX\n  FROM DIM_PRODUCT\n  WHERE DIM_PRODUCT_ID != -1 -- ikke standardverdi\n  AND ATTRIBUTE IS NULL ); -- tell tomrom\n<\/code><\/pre>\n<p>\n<i>Artikkelen bruker materiale fra boken<br \/>\nRonald Bachmann, Dr. Guido Kemper<br \/>\nUt av BI-fellen<br \/>\nSlik blir Business Intelligence en suksess<\/i><br \/>\n<br \/>Kilde: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/459682\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0430\u0436\u043d\u043e\u0439 \u043f\u0440\u0435\u0434\u043f\u043e\u0441\u044b\u043b\u043a\u043e\u0439 \u043a \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044e \u0446\u0435\u043d\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438. \u041f\u043b\u043e\u0445\u043e\u0435 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e 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\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0439 \u043e\u0441\u0442\u0430\u0451\u0442\u0441\u044f \u043d\u0435 \u0432\u043e\u0441\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u043d\u044b\u043c. \u0412 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435, \u043f\u043e\u0434 \u0432\u043e\u043f\u0440\u043e\u0441 \u0441\u0442\u0430\u0432\u044f\u0442\u0441\u044f \u0434\u0430\u043b\u044c\u043d\u0435\u0439\u0448\u0438\u0435 \u0438\u043d\u0432\u0435\u0441\u0442\u0438\u0446\u0438\u0438 \u0432 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043f\u0440\u043e\u0435\u043a\u0442. \u041e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0441\u0442\u044c \u0437\u0430 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0434\u0430\u043d\u043d\u044b\u0445 \u0410\u0441\u043f\u0435\u043a\u0442, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0439 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-36155","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.5 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0430\u0436\u043d\u043e\u0439 \u043f\u0440\u0435\u0434\u043f\u043e\u0441\u044b\u043b\u043a\u043e\u0439 \u043a \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044e \u0446\u0435\u043d\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438.\" \/>\n\t<meta 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