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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

datapine

Data analysis and interpretation have now taken center stage with the advent of the digital ageā€¦ and the sheer amount of data can be frightening. In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 This helps businesses to develop responsive, practical business strategies.

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Global Multichannel Consumer Behaviour (Research/Purchase) Analysis

Occam's Razor

My goal is to get them excited about creating an amazing digital acquisition strategy that delivers noteworthy experiences through their owned digital platforms, and which ultimately delivers bigger profits (usually offline, but online as well). But they are just scratching the surface of what's possible. " Summary: Just do it.

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Using Geographic Data To Create A Perfect Google Maps Radius

Smart Data Collective

The highly intuitive data interface provided by Google Maps can be very helpful. In 2012, Google boasted about its capabilities of using big data to create storytelling via interactive maps. Using a custom mapping tool will enable you to create visually informative and functional maps for both personal and business use.

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Mobile Marketing 2015: Rethink Customer Acquisition, Intent Targeting

Occam's Razor

… But I was thrilled to see my utility marketing strategy in action! So, what is your company's mobile strategy? If you don't, it is time to future-proof your marketing strategy, because the world is most definitely moving to utility marketing. Re-think Customer Data Acquisition, Intent Targeting/Monetization.

Marketing 144
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Top 24 RPA tools available today

CIO Business Intelligence

RPA benefits RPA is also a relatively simple way to integrate AI algorithms into old applications. The biggest benefit, however, may be how RPA tools are ā€œprogrammed,ā€ or ā€œtrainedā€ ā€” a process by which the platformsā€™ robots ā€œlearnā€ watching business users click away. What is RPA?

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Unintentional data

The Unofficial Google Data Science Blog

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. Consider your loss function.