{"id":144,"date":"2021-02-22T17:54:59","date_gmt":"2021-02-22T22:54:59","guid":{"rendered":"https:\/\/www.aerv.us\/?p=144"},"modified":"2021-02-23T14:00:07","modified_gmt":"2021-02-23T19:00:07","slug":"data-strategy","status":"publish","type":"post","link":"https:\/\/www.aerv.us\/index.php\/202102\/data-strategy\/","title":{"rendered":"Data Strategy for the Win"},"content":{"rendered":"\n

In this post we explain our view on Data Strategy purpose and implementation. As the topic is quite broad, we break the narrative down to multiple sub-posts.<\/p>\n\n\n\n

Proponents of Artificial Intelligence implementation solutions usually clash on 2 separate starting points:
a)<\/strong> Immediate roll-out of some highly touted package that has generated the latest buzz, and
b)<\/strong> Implementing a solid Data Program.<\/p>\n\n\n\n

Without question, we are a staunch proponent of the latter option (b)<\/strong>, as good data will always trump any latest magic algorithm. This brings to question, \u201cwhat is good data, and how do you build it\u201d?<\/p>\n\n\n\n

Although good data has several easily recognizable properties, achieving good data requires a comprehensive and effective Data Strategy program to assess, augment, and monetize<\/strong>. In a series of posts, we will try to clarify how many inter-related issues come to play in producing good data from which Enterprises can then extract revenue and value. Below is a broadly described list of components to an effective Data Strategy. Follow the links on the list items for an more detailed explanation.<\/p>\n\n\n\n