AI requires an enormous amount of data to learn and train the predictive models. To feed the system with a huge volume of data, business invests in new tools for gathering, storing, and processing the data. Most of us assume the data is of high quality and feed our data into machine learning engines and expect insights and recommendations from the engine that could help the business. Let us take a look at some of the challenges faced by the business involving data accuracy and revisit the process.

As the saying goes “Garbage In, Garbage Out”. If the quality of the data fed into the AI engine is poor, then the resulting predictions are less likely to be reliable and accurate. Data accuracy is a fundamental factor for producing robust models, which could generate reliable predictions. If the data is incomplete, outdated, and inaccurate, the models created from this data source have little chance of being reliable. 

Achieving data accuracy is quite difficult and the reality is quality of data is influenced by various factors such as, how data was captured, where it is sourced, when it was captured, and from whom it was collected. It is extremely important to understand and analysis the key areas including the uniqueness of the data, category and data type, incomplete and missing data, and finally the sample data that represents the larger entity. Understanding these areas will help in figuring out an approach to achieve data accuracy and build reliable predictive models.