{"id":12670,"date":"2014-01-13T20:00:56","date_gmt":"2014-01-13T22:00:56","guid":{"rendered":"https:\/\/www.mjvinnovation.com\/blog\/data-quality-processos-pessoas-e-ferramentas\/"},"modified":"2014-01-13T20:00:56","modified_gmt":"2014-01-13T22:00:56","slug":"data-quality-processos-pessoas-e-ferramentas","status":"publish","type":"post","link":"https:\/\/www.mjvinnovation.com\/pt-br\/blog\/data-quality-processos-pessoas-e-ferramentas\/","title":{"rendered":"Data Quality: processos, pessoas e ferramentas"},"content":{"rendered":"

Essa tr\u00edade \u00e9 inerente a ele, e exige cada vez mais profissionais capacitados para trabalhar todo o conjunto a que ele est\u00e1 envolvido. Tal conjunto visa, sobretudo, garantir que os dados armazenados sejam:<\/p>\n

– Padronizados;
\n– Completos;
\n– \u00cdntegros;
\n– Aderentes \u00e0s regras de neg\u00f3cios;
\n– \u00danicos.<\/p>\n

E como medir a Qualidade dos dados?<\/strong><\/p>\n

Geralmente, ela possui 6 dimens\u00f5es:<\/p>\n

1) Completude – Medir se o dado est\u00e1 completo. Um endere\u00e7o sem n\u00famero, por exemplo, pode gerar um grande impacto financeiro \u00e0s empresas, quando do envio de correspond\u00eancias aos seus clientes.<\/p>\n

2) Conformidade – Dados armazenados em formato padr\u00e3o. Existem v\u00e1rios padr\u00f5es de data de nascimento, por exemplo. \u00c9 preciso, ent\u00e3o, colocar todos esses dados de forma igual.<\/p>\n

3) Consist\u00eancia – Dados<\/a> que d\u00e3o informa\u00e7\u00f5es conflitantes como, por exemplo, a quest\u00e3o de g\u00eanero: masculino e feminino. Se a empresa \u00e9 uma seguradora de autom\u00f3veis, certamente o fato de o nome do cliente ser masculino e o sexo feminino ir\u00e1 impactar no valor do seguro.<\/p>\n

4) Precis\u00e3o – Dados que est\u00e3o incorretos ou desatualizados. S\u00e3o exemplos dessa dimens\u00e3o produtos e pre\u00e7os que estejam com valores muito abaixo do real ou muito acima.<\/p>\n

5) Duplicidade – Dados que est\u00e3o repetidos. H\u00e1 empresas que chegam a ter mais de cinco registros do mesmo cliente.<\/p>\n

6) Integridade – Quando h\u00e1 dado de refer\u00eancia \u00edntegro ou faltando. Se todos os dados de cadastro, de endere\u00e7o e de produto de um determinado cliente est\u00e3o \u00edntegros, essa dimens\u00e3o est\u00e1 completa.<\/p>\n

Como falamos, o Data Quality envolve processos e n\u00e3o somente uma ferramenta. Eles precisam ser implementados para uma correta implanta\u00e7\u00e3o de Data Quality numa empresa.<\/p>\n

Por onde come\u00e7am os processos de Data Quality?<\/strong><\/p>\n

Geralmente, por meio da identifica\u00e7\u00e3o de padr\u00f5es e problemas na Qualidade de dados. A simples implementa\u00e7\u00e3o de um profile em uma base pode trazer muitos ganhos para qualquer integra\u00e7\u00e3o de dados. O profile, que \u00e9 uma das t\u00e9cnicas do Data Quality, faz a descri\u00e7\u00e3o real do estado do seu dado.<\/p>\n

Para saber mais sobre o assunto, consulte um de nossos consultores de Data Quality aqui<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Embora muitas organiza\u00e7\u00f5es ainda associem o Data Quality ou Qualidade dos dados a uma ferramenta poderosa da Tecnologia da Informa\u00e7\u00e3o, o fato \u00e9 que ele n\u00e3o existe sem processos, pessoas e ferramentas.","protected":false},"author":6,"featured_media":11146,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[156],"tags":[],"class_list":["post-12670","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data-analytics"],"acf":[],"yoast_head":"\nData Quality: processos, pessoas e ferramentas<\/title>\n<meta name=\"description\" content=\"Entenda o que \u00e9 Data Quality e a import\u00e2ncia dos processos, pessoas e ferramentas nesse processo!\" \/>\n<meta name=\"robots\" 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