Overcoming Data Paralysis in AI Applied Intelligence Accenture

It doesn't have to be perfect, but it needs to have enough quality and consistency for useful patterns to emerge. However, many companies are overwhelmed by the volume, velocity and variety of their data and find themselves unable to access data's fourth V: value. So how should we think about data preparation strategies to avoid potential data paralysis or over-ambition with your AI projects? The better the data, the better the AI. But for many companies, there's a problem: 85 percent of their data is either dark (whereby its value is unknown), redundant, obsolete or trivial.