{"id":2290,"date":"2017-08-31T03:10:55","date_gmt":"2017-08-31T07:10:55","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=2290"},"modified":"2017-08-31T15:20:06","modified_gmt":"2017-08-31T19:20:06","slug":"four-key-steps-to-selecting-data-preparation-tools","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/four-key-steps-to-selecting-data-preparation-tools\/","title":{"rendered":"Four Key Steps to Selecting Data Preparation Tools"},"content":{"rendered":"

\"Four<\/p>\n

By Jon Pilkington<\/strong><\/span><\/h5>\n

Navigating today\u2019s big data stores, data warehouses and data lakes to find and access the right information for business analytics projects, and then preparing and exporting it into one streamlined spreadsheet for analysis is still a daunting task for many organizations. Far too many business users and data analysts are spending countless hours manually extracting, blending, reconciling and preparing data from disparate systems. And these manual processes are not only time-consuming and painstaking, but they tax productivity, are susceptible to human error, and take time away from what delivers the most organizational value \u2013 analysis that drives better business decisions.<\/p>\n

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Additionally, in many instances, despite users\u2019 best efforts, valuable data is still locked in unstructured sources, or closely guarded by IT and analytics gatekeepers \u2013 resulting in critical decision-making based on incomplete or outdated information. And the culmination of these data access and preparation challenges can transform even the most common analytics project into a major headache.<\/p>\n

Self-service data preparation<\/strong><\/h5>\n

The good news is that self-service data preparation tools enable business users and analysts to overcome traditional data challenges by simplifying and automating data acquisition, cleansing and blending processes. In other words, the technology empowers all users to easily and rapidly access, manipulate, enrich, blend and reconcile disparate data from virtually any source \u2013 structured or unstructured. Data can then be prepared for analysis in a fraction of the time that it takes using spreadsheets and other manually-intensive measures.<\/p>\n

In terms of ROI, this means that users gain fast access to not only the right data, but all of the data, crucial to getting a holistic view of the business, and the majority of their time can be spent on performing analysis that results in more accurate insights and better business decisions. And, while this technology gives users the autonomy they require to do their jobs, it also satisfies IT\u2019s need for security and compliance by incorporating governance capabilities, such as data masking, data retention, data lineage and role-based permissions.<\/p>\n

This all sounds great so far, right? But, finding the right self-service data preparation technology for your business is no easy task. Not to worry though! Following is a four-step guide to help you choose the solution best suited for your data and analytics needs.<\/p>\n

Step 1: Assess the state of operational and analytical processes<\/strong><\/h5>\n

By identifying your current manual processes for data access and preparation, you can pinpoint labor-intensive, redundant and inefficient tasks, along with areas of risk due to the potential for human error. To complete the assessment phase of the buying process, consider asking yourself questions such as:<\/p>\n