This month, both Apple and Google released significant updates to their operating systems (OS) that will have a big impact on the way location data is shared and collected. It is just one of many ways the tech industry is trying to self-regulate and protect consumers’ information in the absence of federal-level privacy regulations.
These new location-sharing permission changes impact an app’s ability to gather the necessary data they need to build location-based app features, and while it’s too early to understand the significance of the impact, these changes give a clear indication of how the tech industry must evolve to be more transparent with consumers and provide clearer, opt-in consent through any data exchange.
Adapting and adjusting to these changes first and foremost require a high-level understanding of what specifically these updates include, and how they impact the interaction between an app and its users.
Unfortunately, there’s no “silver bullet” for separating good data from bad. Instead, organizations should think of data quality as a habit, with “good” data clearly defined and concrete processes in place to harvest what’s valuable and discard what isn’t.
With that in mind, here are three steps to taking unfiltered data and deciding what to keep — and what to throw out — to achieve optimal data accuracy.