Creating a Data-Driven Organization: Practical Advice from by Carl Anderson

By Carl Anderson

What do you want to turn into a data-driven association? way over having great info or a crack crew of unicorn facts scientists, it calls for constructing a good, deeply-ingrained information tradition. This useful booklet exhibits you ways actual data-drivenness comprises techniques that require actual buy-in throughout your organization, from analysts and administration to the C-Suite and the board. via interviews and examples from information scientists and analytics leaders in a number of industries, writer Carl Anderson explains the analytics worth chain you want to undertake while construction predictive enterprise models—from information assortment and research to the insights and management that force concrete activities. you will research what works and what does not, and why making a data-driven tradition all through your company is vital.

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In many cases, especially in medical and social sciences, data is very expen‐ sive to collect, and you might only have one chance to collect it. Imagine collecting blood pressure from a patient on the third day of a clinical trial; you can’t go back and repeat that. A core problem, a catch-22 situation in fact, is that the smaller the sample size, the more precious each record is. However, the less data an imputation algorithm has to work with, the worse its predictions will be. A single missing value within a record can render the whole record useless.

Autocom‐ plete is another alternative. In general, you want users to type as lit‐ tle input as possible: get them to choose from a set of options that you provide—unless, of course, it is an open-ended question with a free-form text field. Ideally, try to remove the human element as much as possible from the data collection process and have as much as possible collected and stored automatically. If you have the time and resources, you can have two people inde‐ pendently transcribe the data, or the same person transcribe the data twice, and then compare results and then recheck where the data doesn’t match.

In his statistics teaching material, Daniel Mintz provides an especially clear example of bias: “Question. ” Who will and will not complete that? The type of missingness is critical. ) You need to investi‐ gate whether the data is: MCAR Missing completely at random, such as the randomly allocated web server traffic. info MAR Missing at random, meaning that the data is missing as a func‐ tion of the observed or nonmissing data, such as the geo-serving web server that resulted in a lower sample size for a subset of ZIP codes.

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