The Future of Machine Learning

Row of Trees 3 by Charles Plaisted Interview with Tom Mitchell 12/15/16 Having read Tom Mitchell’s great article “Machine learning: Trends, perspectives and prospects” published in Science in July 2015, I wanted an update. He graciously submitted to an interview. Tom M. Mitchell is a computer scientist and E. Fredkin University Professor at Carnegie MellonContinue reading “The Future of Machine Learning”

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Digital Transformation Leader: Suncorp Group

Leaders communicate the path; followers must interpret.  Leaders in digital transformation foster a culture of experimentation, customer experience management supported by mature optimization programs, and measurements relevant to their goals. They demonstrate how an integrated platform, skilled people, and mature optimization capabilities are the keys to success. One such leader is Murray Howe, Executive ManagerContinue reading “Digital Transformation Leader: Suncorp Group”

The Catastrophe of ML Success

It may be unclear that your ML path is a dead end. Cape Palliser, New Zealand So you’re exploring a gnarly problem using MLaaS (machine learning as a service), and you create a model of amazing accuracy and precision. It will be huge for strategic advantage. This is fantastic, way outside expectations. What next? After youContinue reading “The Catastrophe of ML Success”

The Only Big Data Strategy You’ll Ever Need

ETL in the days before data was invented. Keys Ranch, Joshua Tree National Park, CA “What should our Big Data strategy be?” seems about as useful a question as “What should our Telephone strategy be?” Let me suggest that your big data strategy should be to ask, “Where can better information significantly impact achievement of our strategy?” ThenContinue reading “The Only Big Data Strategy You’ll Ever Need”

Data Science CSF: Commitment from the Top

Trolling for powerful backers. Wellington, New Zealand Committment from the top is critical to the success of data science initiatives. Not surprising, since most efforts that have a broad impact in an organization need that commitment. Commitment means that top management places enough priority and value on the initiative that its resources (time, attention, labor, capital,Continue reading “Data Science CSF: Commitment from the Top”

Big Data Bungee Jumping

The Kawarau Bridge Bungy, pictured here, is the earlist commercial bungy operation. Queenstown, New Zealand Applying data science to business, and bungee jumping, have too much in common. There is the committment from the top — in bungee, that’s your brain — that you’re going to make the jump. WIthout this committment, you are going nowhere,Continue reading “Big Data Bungee Jumping”

Predicting the Past

Predictions are guesses about the past, present, or future. Prediction is not reporting: reporting is an organized recap of what is known. Why predict the past? Predicting the past is the starting point for predicting the future. Wait, don’t we know what the past is/was? Not usually. For example, economic events are so hard to identify and measureContinue reading “Predicting the Past”

Yes, Your Data is Crap

Filled with despair because your data is a mess? Welcome to the club, and it is a big one: everyone’s data is a mess. It is always incomplete, inaccuarate, out-dated, redundant, plagued with typos, and unavailable in the desired timeframe. It can’t truly answer the questions you need to ask right now, and the questionsContinue reading “Yes, Your Data is Crap”

Big Data: Buyers Beware

If you are working through your Big Data roadmap and hoping for guidance from the Google, well, hope may be all have. I recently studied the top offerings for my search “big data buyers guide.” These docs clearly state that its complicated, and there are a lot of choices. Good to know. One piece IContinue reading “Big Data: Buyers Beware”

Data Science and Not-So-Big Data

Statisticians joke (yes, they do!) that half the job is wrestling the data into shape, half is performing useful analyses, and half is explaining the findings. The data wrestling too often expands to consume all my time, leaving me too ennervated to find any findings. Big Data is what got everyone so excited about data science. But BiggerContinue reading “Data Science and Not-So-Big Data”