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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156
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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. data collection”) show the “process” steps that a team performs, while the boxes (e.g.,

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The Definitive Guide To (8) Competitive Intelligence Data Sources!

Occam's Razor

It is simply magnificent what you can do with freely available data on the web about your direct competitors, your industry segment and indeed how people behave on search engines and other websites. Not all sources of CI data are created equal. Typically, data collected is anonymous and not personally identifiable information (PII).

Metrics 123
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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

As mentioned above, one of the great benefits of business intelligence and analytics is the ability to make informed data-based decisions. This benefit goes directly in hand with the fact that analytics provide businesses with technologies to spot trends and patterns that will lead to the optimization of resources and processes.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. PDPs for the bicycle count prediction model (Molnar, 2009). def create_model(): sgd = optimizers.SGD(lr=0.01, decay=0, momentum=0.9,

Modeling 139