Why this “thankless chore” is central to your AI implementation

The postcard view of artificial intelligence, or AI, in business is to sail into the sunset, free of time-consuming tasks as you soak up the benefits of data-driven insights.

Data scrubbing is not in that postcard.

Cleaning or scrubbing your data sounds like a thankless chore – and in some ways, it is – but it’s a crucial step to ensure your AI ship doesn’t sink long before it gets to your burden-free paradise.

Dirty data is information that is invalid, unchecked, or captured incorrectly, and it can damage the health and accuracy of your database, which is costly in terms of inaccurate forecasts and missed opportunities.

As many as 85% of businesses are affected by poor data quality, in wasted resources and additional costs (40%), damaged reliability of analytics (36%) and negative effects on customer relationships and trust (32%), according to data analytics firm Experian.

Poor data hygiene can also lead to serious compliance problems, such as violating data privacy laws, which can easily happen if documents are fed ad hoc into AI tools, exposing sensitive information such as names, bank details and personal records.

Why you need to clean your data

So why is it so important? 

Data hygiene underpins the architecture used for AI....