The saying 'Garbage In, Garbage Out' (GIGO), has been in existence since the invention of computers. But since the invention of machine learning and artificial intelligence (with their strong need for data quality), the saying is more relevant than ever. Machine learning is highly data-sensitive. For this reason, the quality of data being used on any machine learning process has a significant impact on its success. Let us have a look at how bad data impacts machine learning.
Significant advancements in how we collect, archive, and analyze data have progressed AI (artificial intelligence), and more specifically, ML (machine learning) efforts. Solving complex problems demands a lot of rich, quality data and fortunately users can now gather it easily, store it cheaply, and process it much faster.
From robotic process automation that enhances efficiency to predictive analytics that solve complex problems, technology has fastened the pace for companies looking to be ahead of their competitors. But technology aside, the sophisticated tools that drive these innovations are useless if the data is bad!
Thomas C. Redman denotes that wrong data is the leading enemy to the profitable and widespread use of machine learning. Training data governs the performance of machine learning systems. Bad data return bad results. Worse, it flows via ML systems, feeding into the models, and giving out incorrect information.
How Bad Data Ruins Machine Learning
Data is incomplete or missing. In some cases, data is combined, and fields are left blank since the info is missing, resulting in several values being omitted. Nevertheless, the learning, translation, and the prediction of machine learning is difficult to attain with missing or incomplete info.
The data is incorrect. When deploying an ML project, it is advisable to clean data before training the predictive model. However, cleaning doesn't always correct or identify all errors, and data can be compromised. Data experts warn that even an insignificant error possesses a substantial negative impact.
The data is biased. When bias gets into the data used by machine learning for training, data integrity is compromised, and predictions become inaccurate. For instance, LinkedIn's search engine encountered gender bias when a search for a female contact name returned male names.
Establishing a culture of quality data into ML projects is possible. Comprehensive testing, cleaning, and auditing guarantees accuracy. Finally, if you take more time to understand your data, where it originates and what you need to attain with it, you will be much more successful with your ML projects. Read more blogs about artificial intelligence and machine learning on our website and Twitter Account.