Weekly Round-Up: Disruptive Data Science, Data Revenue Models, Moore's Law, and the Facebook Data Team

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from revenue models to Facebook's data science team. In this week's round-up:

  • Transforming Your Company into a Data Science-Driven Enterprise
  • Data Revenue Models
  • For Big Data, Moore’s Law Means Better Decisions
  • Meet the Data Brains Behind the Rise of Facebook

Transforming Your Company into a Data Science-Driven Enterprise

This post on the Greenplum blog goes into quite a bit of depth about the role of data science in organizations. The author covers a variety of important topics in making a company more data-driven, such as where data science functions should reside as well as touching on tools, training, and incorporating data science in operations through process & change management.

Data Revenue Models

Data revenue models were the topic of NYC venture capitalist Fred Wilson's MBA Mondays series this week. In this blog post, Fred mentions a couple of the main revenue models he's seen, gives examples of each, and talks about which model tends to make a better data business and why. Always trying out innovative solutions, Fred also has a Hackpad embedded into the post where readers can contribute to a crowdsourced list of additional data revenue models. If you can think of additional ways to monetize data that aren't already on the list, you can just add them!

For Big Data, Moore’s Law Means Better Decisions

This is an article out of UC Berkeley that provides an interesting take on the growth of data. It points out that the amount of data available is growing faster than Moore's Law and goes on to describe what that means for data processing and data-driven decision-making.

Meet the Data Brains Behind the Rise of Facebook

This Wired article introduces us to some of the members of Facebook responsible for its data infrastructure. The article talks a little about who they are, the tools they use and the products they've built.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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