{"id":87,"date":"2020-03-09T11:17:58","date_gmt":"2020-03-09T11:17:58","guid":{"rendered":"http:\/\/demo.sidekick.be\/yellowground\/?p=87"},"modified":"2020-05-13T08:56:47","modified_gmt":"2020-05-13T08:56:47","slug":"7-steps-to-better-data","status":"publish","type":"post","link":"https:\/\/yellowground.eu\/en\/7-steps-to-better-data\/","title":{"rendered":"7 steps to better data"},"content":{"rendered":"
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Back to inspirations<\/span><\/a><\/div><\/div><\/div><\/div>
Expert talk<\/span><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>

7 steps to better data<\/h2>
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In our personal live we all are worried about our health, our job and how to manage things at home. At work, we should be worried about the data. Data is the source on which organizations take decisions. Complete and reliable information is crucial to justify these decisions. In this article you’ll read how to organise your data better and improve the quality of it.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>

Why is data that important?<\/h3>\n

Without a\u00a0 solid fundament of information it is difficult to gain trustworthy insights and to act on those. Masterdata are the source of information that is essential for your organization. These data support important company processes and transactions. If they are not reliable, there will be compliance and performance issues. When you can optimize data at the source, you will prevent a lot of recovery and reparation work later on in the chain.<\/p>\n

Therefore it is of high importance that you have your data in order before you are running analysis on them. Data<\/em> will determine the truth and analytics<\/em> create the meaning behind them. Compare it to creating a nice meal; even if you recipe is top-notch without the best ingredients you’ll never cook on a michelin-star level.<\/p>\n<\/div><\/div>

The 7 steps to better data<\/h3>\n
    \n
  1. Involve the right stakeholders and set priorities<\/li>\n
  2. Identify the biggest pain-points and opportunities for improvement<\/li>\n
  3. Identify the data sources<\/li>\n
  4. Judge the quality of your current data<\/li>\n
  5. Define standards for your datafields<\/li>\n
  6. Validate your data<\/li>\n
  7. The fun part, optimize your data!<\/li>\n<\/ol>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>
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