Lukas Biewald
Lukas Biewald is the co-founder and CEO of CrowdFlower. As a former data scientist, Lukas was frustrated by the amount of time he had to spend cleaning and labeling data instead of actually using it to solve business problems, leading him to co-found CrowdFlower in 2009. Today, the CrowdFlower platform connects over 5 million data enrichers in almost every country who work around the clock to provide companies with clean and actionable data.
Lukas graduated from Stanford University with a BS in Mathematics and an MS in Computer Science. Lukas is also an expert-level Go player.
The opinions expressed in this blog are those of Lukas Biewald and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies.
How machine learning will affect your business
In the past, successful use of machine learning algorithms required bespoke algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Amazon and Alibaba all launched turnkey cloud based machine learning...
Why human-in-the-loop computing is the future of machine learning
Artificial Intelligence is here and it’s changing every aspect of how business functions. But it’s not replacing people one job function at a time. It’s making people in every job function more efficient by handling the easy cases and...
The simple way to make data science effective
Data scientists are trained to make more efficient algorithms, but the simple way to make an algorithm work better is to feed it more data.
It's time for data science to be part of your hiring process
Nearly every CEO will tell you human talent is the reason their business is successful: A great sales hire can change the direction of your entire company, while a bad engineering hire could result in your product falling flat on its...
How to hire data engineers
In order to build a great data science practice, you need great data engineers. Here's how to hire them.
You're hiring the wrong data scientists
Companies are building data scientist teams which is great. But they are not giving them the support they need and they're incurring a ton of unnecessary overhead.
The data science ecosystem, part 3: Data applications
The third part in a series on the data science ecosystem looks at the applications that turn data into insights or models.
The data science ecosystem part 2: Data wrangling
Data scientists spend 80% of their time convert data into a usable form. There are many tools out there to help and I will go over some of the most interesting.
The data science ecosystem
Data science isn't new, but the demand for quality data has exploded recently. This isn't a fad or a rebranding, it's an evolution.
We need open data to become the new open source
Data is becoming more and more critical to businesses, but almost all data is siloed inside corporations. The lack of open data sets today holds innovation back and that needs to change.
Why more data isn't always better
In the past 10 years, the focus of data has been on amassing and storing: the more data collected, the better. But while we all became expert data gatherers, what we actually ended up with was a glut of data, a shred of the insights...
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