{"id":2990,"date":"2018-09-06T14:57:05","date_gmt":"2018-09-06T18:57:05","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=2990"},"modified":"2018-09-06T16:13:27","modified_gmt":"2018-09-06T20:13:27","slug":"are-you-making-the-most-common-data-preparation-mistake","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/are-you-making-the-most-common-data-preparation-mistake\/","title":{"rendered":"Are You Making the Most Common Data Preparation Mistake?"},"content":{"rendered":"

\"Are<\/p>\n

Data preparation is a pre-processing step that allows for data transformation before analysis to ensure quality and consistency, providing enterprises with maximum potential for business intelligence. A recent article in Information Age<\/a> that highlights the six most common data preparation mistakes<\/a> really got us thinking, and based on conversations we’ve had with end-users and solution providers alike, we can attest to the fact that preparing data without context of use case<\/em> is the single-most common mistake users can make.<\/p>\n

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\"Download<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div>\n

There’s no doubt that IT possesses the technical capability to do data preparation. However, IT often lacks the business context necessary to do it in a way that yields actionable results. This is one of the main reasons data democratization has become so popular in the middle enterprise and up. Without keeping the business context in mind, organizations can waste an untold amount of time and money preparing data for analysis. By keeping the use case in mind throughout a data preparation project, users are much more likely to benefit from the output.<\/p>\n

Data preparation is widely regarded as one of the most time-consuming practices in the analytic processing, and failing to properly identify the context attributes to this. After spending virtually all their time doing data prep as a pre-process to analytics, it\u2019s no surprise that many organizations consider speeding the process up to be a top priority. The best possible way to hasten data preparation is to stay on target and avoid moving to the left and right of an initiative.<\/p>\n

It wouldn’t be an article at Solutions Review if we didn’t at least pint you in the direction of some dedicated data preparation tools to help you avoid the landmines and pitfalls that are befalling others. You can start by perusing our list of vendors to watch<\/a>, or, if you’re in a large organization, the major providers<\/a> in the space.<\/p>\n

We highly encourage you to read the Information Age piece<\/a> and uncover the five other most common data preparation mistakes.<\/p>\n

<\/div>\n
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Data preparation is a pre-processing step that allows for data transformation before analysis to ensure quality and consistency, providing enterprises with maximum potential for business intelligence. A recent article in Information Age that highlights the six most common data preparation mistakes really got us thinking, and based on conversations we’ve had with end-users and solution […]<\/p>\n","protected":false},"author":23,"featured_media":2991,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[343,579],"acf":[],"yoast_head":"\nAre You Making This Common Data Preparation Mistake?<\/title>\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\/are-you-making-the-most-common-data-preparation-mistake\/\" \/>\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 name=\"twitter:data2\" 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