Data Quality for Machine Learning

Posted by Katie Mowery on September 27, 2018

You can’t open a website these days without seeing the words machine learning or AI front and center. Often they’re used without context or explanation of how they relate to what you’re reading. We wrote a while back on the Utopia blog about IoT and other hot buzzwords of our technology-driven society, so let’s dive into machine learning for those who want to know some of the biggest ways it affects them.

In the simplest form, machine learning technology teaches systems to perform tasks by learning from stored and collected data – instead of someone needing to explicitly take those learnings and program them into usable code or instructions.

This “learning” occurs through sophisticated algorithms analyzing and looking for things like patterns, inconsistencies, traits, in massive volumes of big data – both structured (clear fields in a spreadsheet or system database) or even unstructured (PDF documents, manuals, emails) – without explicitly being programmed to do so, absorbing new behaviors and functions over time. The more data available for the algorithms to access, the more there is to learn.

Machine learning has developed in the real-world (none of that science fiction stuff in the movies) to the point where many organizations are already leveraging it to deal with increasingly large volumes of data that exceed the realm of possibility for manual analysis.

However, there’s one major barrier facing machine learning – the quality of the data being used. (Obviously putting aside the fears of a robot uprising.) The vast amounts of data being leveraged in smart technology could be wreaking havoc on output and data analysis for decision-making.  

This is why organizations who are thinking of, or even those who are already actively using machine learning to predict consumer behavior, identify trends or leverage for more efficient operations, need to take the necessary steps to ensure data accuracy. Bad data in means unreliable analytics out. How can you trust that your machine learning algorithms are delivering the right results if what it is learning from is nonsense data. 

For the last 15 years, as Utopia’s portfolio of data solutions has grown, we’ve also been actively developing machine learning algorithms to address complex unstructured data with web-scraping and long-text passing to assign class characteristics, values and automated creation of material masters and taxonomies.

By leveraging machine learning to address some of the biggest challenges that come with a lot of data in all different formats, we’re ready to help you get the most out of all your valuable data assets.

Want to know more about machine learning? Let’s talk!

Topics: Machine Learning