The Importance Of Getting Data Right Before Using AI In Your Business

AI relies on accurate and unbiased data to deliver strategic business insights, emphasizing the importance of addressing data quality before implementation.

Businesses across a plethora of sectors—including healthcare, finances, e-commerce and even manufacturing—are rapidly embracing artificial intelligence (AI). AI has a knack for automating repetitious work, interpreting voluminous data and forecasting potential ramifications.

However, there’s a catch. Before you rush into integrating AI into business practices, you must first ensure the data is accurate.

AI systems are data-hungry. They use what is referred to as “training data” because these datasets act as educational content for the AI model. If the data is inadequate, incomplete or incorrect, the developed AI will yield poor and/or erroneous outcomes. Having optimum performing AI models within your business all stems from having the correct and appropriate data—organized, clean and comprehensive.

Why Good Data Is So Important For AI

1. AI lacks the capability or willingness to set objectives.

The tools encapsulated by AI processes are not natively innovative. AI is a trained performance model that looks for guidance from the data to create objectives and maximize functionalities. For instance, before it can forecast customer actions or enhance a certain procedure, AI has to study historical data to obtain such frameworks. For AI to provide any valid results, it must draw on accurate and relevant data.

2. Don’t throw garbage in if you expect to get something clear.

A popular adage in the tech industry is “garbage in, garbage out.” Bad results are the consequences of putting unnecessary or dirty data into an AI system. It doesn’t matter how sophisticated your AI is, the bad data is going to let you down.

If your data is incomplete or has bias or errors, the AI output will mirror all of these issues.[…]