{"id":1638,"date":"2018-02-16T03:00:10","date_gmt":"2018-02-16T08:00:10","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=1638"},"modified":"2018-03-21T09:25:05","modified_gmt":"2018-03-21T13:25:05","slug":"3-common-data-integration-pitfalls-to-avoid","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/3-common-data-integration-pitfalls-to-avoid\/","title":{"rendered":"3 Common Data Integration Pitfalls to Avoid"},"content":{"rendered":"

\"3<\/p>\n

Data Integration is the stop data makes before its loaded into a business intelligence tool for analysis. The process of integrating data from internal and external sources has grown more complicated in recent years. This has come as a result of \u00a0increasingly large and fast volumes of data, which are now being thrown all over as new data sources continue to emerge.<\/p>\n

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Inside the new big data paradigm, no longer is data migration the only way to organize data in a way that will help companies generate insight. Considering the increasing types of data that are now utilized in the enterprise, many processes must be taken before the movement of data ever actually takes place. Avoid the three common data integration pitfalls:<\/p>\n

Poor Data Quality<\/strong><\/p>\n

In a recent KPMG survey<\/a> in which 400 chief executives were polled, 77 percent of respondents admitted that they had concerns about internal data quality. It’s no secret that this remains one of the biggest challenges in the data-driven organization, with the vast majority of organizations dealing with this problem daily.\u00a0With quickly expanding\u00a0data volumes, properly qualifying data has become an increasingly important job.<\/p>\n

Source and legacy data systems suffer from poor data quality, and any integration job can become compromised from the get-go if “unclean data” is spread cross-company.\u00a0As repositories continue to be inundated with all kinds of new, unstructured data, expect data quality techniques will grow even more\u00a0tightly governed.<\/p>\n

Underestimating Data Velocity<\/strong><\/p>\n

Data is never static. It may have been semi-static in the past when data warehousing was the only kind of repository used in the enterprise and companies could wait for IT to load their data into a legacy reporting tool. No more, however, as a changing landscape filled with all kinds of new data sources that deliver data in near real-time dominate enterprise data architectures. In this way, companies can ill afford to underestimate the frameworks they need to have in place to appropriately capture and migrate increasingly fast data. Data Integration is not a one-time process, it is ongoing.<\/p>\n

Time-Consuming Data Preparation<\/strong><\/p>\n

According to Blue Hill Research, data analysts spend nearly one-third<\/a> of their time on data preparation. As data volumes increasingly grow in size and speed, organizations are having to find the time, resources, and manpower to deal with data preparation challenges. As a result, many companies are investing in new technologies to assist them with data preparation as it becomes vital to driving insights.<\/p>\n

Data preparation has become a burden to organizations who are looking to make the most of their data. In addition, it has forced companies to invest a great portion of resources into human capital even though it is not generally considered to be a direct value-adding activity. Data prep is a necessary process for those looking to gain insights from data. The move to self-service is upon us<\/a>, and the enterprise is jumping with joy.<\/p>\n

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Data Integration is the stop data makes before its loaded into a business intelligence tool for analysis. The process of integrating data from internal and external sources has grown more complicated in recent years. This has come as a result of \u00a0increasingly large and fast volumes of data, which are now being thrown all over […]<\/p>\n","protected":false},"author":23,"featured_media":2640,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[19,343,291],"acf":[],"yoast_head":"\n3 Common Data Integration Pitfalls to Avoid<\/title>\n<meta name=\"description\" content=\"Modern Data Integration is plagued by three avoidable pitfalls.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/data-integration\/3-common-data-integration-pitfalls-to-avoid\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tim King\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta 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