Repeatability: The Key to Scaling Data Science -

Repeatability: The Key to Scaling Data Science –

Repeatability: The Key to Scaling Data Science –

Like most organizations, you want to embed analytics insights in your operational processes and promote a culture of analytical decision making. You want to use machine learning, deep learning, and related technologies to automate decision making when and where it makes sense.

These goals might seem both realistic and attainable. After all, software and cloud vendors are pitching you easy-to-use, quasi-automated, self-service tools and consultants promise to help you bridge the gap between the skills you have and the skills they say you'll need. Piece of cake, right?

Far from it, says Mark Madsen, a research analyst with information management consultancy Third Nature. Between the idea and the reality of embedded analytics falls the shadow of significant people, process, and methodological issues. "Technology is the easy part. Figuring out what to do and how to do it is a lot harder. In spite of this, there are lots of shiny new tools that promise to make all of these problems go away. Not surprisingly, they're attracting a lot of attention," he says.

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The use and abuse of data science is a topic that's near and dear to Madsen's heart. He'll be speaking about this issue at TDWI's upcoming Accelerate conference, held in Boston April 3-5, 2017, which TDWI describes as "the leading conference for analytics and data science training." It features deep-dive tutorials, networking opportunities, and presentations from Michael Li, Claudia Perlich, Eduardo de la Rubia, and other luminaries. It will also be a forum for insightful and provocative content, including presentations such as Madsen's, which addresses the issues vendors, consultants, and would-be adopters are keen to minimize.

"People and data are the truly hard parts. People can be problematic because many believe data is absolute rather than relative, that analytics models produce a single, definitive 'answer' rather than a range of answers with varying degrees of truth, accuracy, and applicability," Madsen says.

"Data is a problem because managing data for analytics is a nuanced, detail-oriented, seemingly dull task left to back-office IT.

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