Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become.  Of course there are also some interesting winner and loser stories.

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020.  The really big news is how many excellent choices are now available.  In a remarkable move, the whole field of…

Summary:  The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists.  Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.

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Summary:  Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models.  This is what prescriptive analytics was meant to be.

Just when I thought we’d figured it all out, something comes along to make…

Summary:  A major pain point is standing in the way of many companies’ ability to maximize the value of their ML/AI initiatives.  The competing goals of data flexibility versus single version of the truth can only be solved with an effective data governance strategy.

There are many reasons why companies…

Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications.  Here are five strategies for getting around the data problem including the latest in One Shot Learning.

For at least the last two years we’ve been in an…

Summary:  Here are our 5 predictions for data science, machine learning, and AI for 2019.  We also take a look back at last year’s predictions to see how we did.

It’s that time of year again when we do a look back in order to offer a look forward.  What trends will speed up, what things will actually happen,…

Summary:  Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations.  We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.

If you’re a CEO or any other flavor of top executive leading a…

Summary:  If you’re still writing code to clean and prep your data you're missing big opportunities for efficiency and consistency with modern data prep platforms.

Two things are true. 

• Data prep still occupies about 80% of our model building time – the least enjoyable part.
• If you’re still writing code…

Summary:  Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies.

It appears that data science has exited its age of exploration and entered into its…

Summary: Now that we’ve detailed the four main AI-first strategies:  Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick.  Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.

In our last several articles we’ve taken a tour of the four major…

Summary:  The fourth and final AI strategy we’ll review is Systems of Intelligence (SOI).  This is getting nearly as much attention as the Vertical strategy we previously reviewed.  It’s appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning.

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Summary:  A defensible data strategy increasingly defines those AI businesses that will be successful.  VCs know this and are steering the funding to this strategy.  Read here about what a defensible data strategy is and how to identify your next AI opportunity using this technique.

The most recent issue of MIT Technology…

Summary:  Data Science is the secret sauce that turns the dumb internet into the smart internet driving changes in society as fast as we drive changes in the internet.  The best place to find data on this is Mary Meeker’s Internet Trend Report 2018.  Here we use that data to look back at the last year and forward a bit in time to see what impact data science is having.

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Summary: This is the second in our “Off the Beaten Path” series looking at innovators in machine learning who have elected strategies and methods outside of the mainstream.  In this article we look at Numenta’s unique approach to scalar prediction and anomaly detection based on their own brain research.

Numenta, the…

guest blog by Jin Kim, VP Product Development for…

Summary:  Once you’ve decided to become a data scientist, where should you get your education?

This is a continuation of the ‘how to become a data scientist conversation’ (see “So You Want to be a Data Scientist” at…

Summary: Gartner says that predictive analytics is a mature technology yet only one company in eight is currently utilizing this ability to predict the future of sales, finance, production, and virtually every other area of the enterprise.  What’s holding them back?

In an earlier posting we argued that much of what is holding companies back from…