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”
Tag Archives: data science
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”
Data Scientist: Your New PartNerd
Inescapable: data science delivers the insights you need in order to be effective with digital marketing. Short-term avoidable: hiring a data scientist. Embraceable: Your new data scientist.Creative people and data science people should work together all day, every day, until they can understand each other, with a shared vision and shared vocabulary. Your data scientist must beContinue reading “Data Scientist: Your New PartNerd”
The Tragedy of Targeted Marketing and Personalization
OK, not really a tragedy. But a source of tremendous pain for many: targeted marketing, and personalization, are the antithesis of one size fits all. And this means you need an assortment for every bit of content that personalizes your customer experience. Not one walnut, but 7 or 8 or dozens. Having lots of contentContinue reading “The Tragedy of Targeted Marketing and Personalization”
The 6 Obstacles to Using Data Science
It would be great if we all had data to drive our decisions and processes — and used it. But the obstacles are so daunting that most of us can’t. Here are the top 6: Leadership from the top is mostly lacking. Data quality issues are omnipresent. Data science in marketing is not established (itContinue reading “The 6 Obstacles to Using Data Science”
On Target 