6 data preparation best practices for analytics applications
Amid issues of data sources, data silos and data quality, the process of collecting and prepping data for analytics applications requires a practical and effective approach.
Enterprise software applications save data in a form most suitable for their own purpose, not for your analytics needs. Even within a single data source, some of the information is irregular, out of date or in different shapes. That makes the process of analysing data far more complex and frustrating than anyone could imagine.
These kinds of issues underline why data preparation best practices are critical. In this infographic, we put together 6 data preparation best practices for analytics applications to help enterprises master their data.