So you’re collecting lots of data with the intention to automate decision-making through the strategic use of .
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So you’re collecting lots of data with the intention to automate decision-making through the strategic use of
At a high level, the anatomy of
One person who’s familiar with the complexities of data workflows for
According to Nexla, which has just published a white paper titled “Managing Data for
1. Data discovery: What data do you need, and how do you find it?
2. Data analysis: Is the data useful for
3. Data preparation: What format is the data in? How do you transform it into the correct format?
4. Data modeling: What algorithm can you use to model the date
5. Time spent in different tasks in
Some of these tasks might be longer or shorter depending upon the type of data you’re working with and the type of problem you’re solving, Shahdadpuri says. “If your data is very segmented, you might have a bigger discovery phase,” he says. “If you’re very intimate with the data, you might have shorter discovery phase, but you’ll still be doing these things on and off.”
Data catalog and discovery tools can be very useful for finding relevant data sets, Shahdadpuri says. But many enterprises have millions of different data sets, many of which have not been cataloged via metadata analysis. That could lessen the usefulness of cataloging tools when workign with dark data sets. Now you’re back to fishing in the data lake with a fishing pole and hoping to get lucky. […]
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