There’s nothing sexy about dirty data
Future of Fintech Machine learning and artificial intelligence (AI) can safely be classified as the terms of 2017.
Everyone – from business consultants, to technologists to the media – is talking about machine learning and artificial intelligence, and there’s no wonder why.
These capabilities promise to find ground-breaking answers amidst the chaos of the Findustrial Revolution, where digital transformations are overwhelmed by legacy technology systems, massive amounts of data and emerging innovations that don’t quite hit the spot. However sexy machine learning and AI might seem though, they’re only as powerful as the expertise and content behind them.
After all, machines don’t learn, they’re trained. And this training comes from clean, organised data and experts who know how the data and the technology work.
"However sexy machine learning and AI might seem, they’re only as powerful as the expertise and content behind them."
To teach a machine to learn requires feeding it information in an organised and structured manner. Using a systematic approach is essential in measuring whether the output is strong enough to be used. Ultimately, the success of any machine learning capability directly links back to training the data properly from the start.
What are the challenges?
Get this wrong and you have the classic situation: garbage in, garbage out. Even the smartest capability can’t process a random mass of unverified text strings and log tables to give a meaningful answer. Or even worse, the capabilities do actually process that dirty data and deliver the wrong answer entirely.
"Get it wrong and you have the classic situation: garbage in, garbage out."
Most machine learning business cases fail because they invest in the technology tools, but not in the data management. Machine learning and AI are only as good as the data they learn from, and this requires domain experts that can train systems from the ground up; individuals who can find the answers by connecting the right data to the right models.
The technology solutions that are necessary for any business’ success are the ones that make data digestible, not overwhelming. As every company goes through their digital transformation, what they need is less frivolity and more practicality.
Practicality means clean, structured data managed by trusted experts. After all, machine learning and AI are only as sexy as the clean data and expertise that train them.
For over 100 years we’ve provided leading decision makers with the intelligence, technology and human expertise they need to find trusted answers. We enable organizations in the financial and risk, tax and accounting, legal and media markets to drive innovation and growth.