Dairy breeding is perfect for quantitative analysis. Pedigree records have been assiduously kept; relatively easy artificial insemination has helped centralized genetic information in a small number of key bulls since the 1960s; there are a relatively small and easily measurable number of traits -- milk production, fat in the milk, protein in the milk, longevity, udder quality -- that breeders want to optimize; each cow works for three or four years, which means that farmers invest thousands of dollars into each animal, so it's worth it to get the best semen money can buy. The economics push breeders to use the genetics.

KNIME (Konstanz Information Miner) is a user-friendly and comprehensive open-source data integration, processing, analysis, and exploration platform. From day one, KNIME has been developed using rigorous software engineering practices and is used by professionals in both industry and academia in over 60 countries.

Open Web Analytics (OWA) is open source web analytics software that you can use to track and analyze how people use your web sites and applications. OWA is licensed under GPL and provides web site owners and developers with easy ways to add web analytics to their sites using simple Javascript, PHP, or REST based APIs. OWA also comes with built-in support for tracking web sites made with popular content management frameworks such as WordPress and MediaWiki.

The technology behind inmaps from LinkedIn.

statsd etsy Graphite Bitbucket Dogslow Django Redis PostgreSQL PGFouine gist node2dm

Weave (BETA 1.0) is a new web-based visualization platform designed to enable visualization of any available data by anyone for any purpose. Weave is an application development platform supporting multiple levels of user proficiency — novice to advanced — as well as the ability to integrate, disseminate and visualize data at "nested" levels of geography.

Design Thinking balances analytical thinking and intuitive thinking, enabling an organization to both exploit existing knowledge and create new knowledge. A design-thinking organization is capable of effectively advancing knowledge from mystery to heuristic to algorithm, gaining a cost advantage over its competitors along the way.

Determinism teaches that what will be, will be. Existentialism deals with a humanity in the throes of chaos. Big Data can be seen as either a lens through which determinism is revealed, or a tool for navigating an existential world. As a scenario planner, I take the existential position and see a number of existential threats to the success of Big Data and its applications.

Our work demonstrates the power of social network analysis methods in providing insight into complex social phenomena. Indeed, whereas there are contexts in which simple measures or statistics may provide a very complete picture of an individual's performance —think of golf, baseball, or a track event— for most situations of interest, objectively quantifying individual performances or individual contributions to team performance is far from trivial.

At least in the context of a soccer, where quantification has always been challenging, we are able to demonstrate that flow centrality provides a powerful objective quantification of individual and team performance. While we cannot demonstrate the power of a similar approach in the context of a scientific collaboration, our preliminary results suggest that flow centrality does provide some insight into the variability in the partitioning of responsibilities among co-authors in a project.

Tired of going with the design that will survive the organization's political gauntlet? What if we made decisions based on what actually worked for customers and produced results, not what snaggletoothed solution fit into every stakeholder's personal view of the world?

Here are five startups (in alphabetical order) either in stealth mode or just out of it that could help take Hadoop and its ilk to the promised land.

In the spring of 2011, WCET announced that it had received funding from the Bill & Melinda Gates Foundation for the Predictive Analytics Reporting Framework, to apply predictive analytics in higher education and examine the feasibility of creating a federated database to look for patterns of student loss (e.g., dropping out) and momentum (e.g., achieving academic success).

Rio Salado is now doing the same kind of "predictive analytics" by tracking student behaviors, such as how often they log in, to determine which students are most likely to succeed -- meaning complete the course with a grade of C or better. Those at risk of failure are offered extra help right away.

Many sources of data are coming becoming available in almost every dimension of life and business. Vendors are stepping up to the plate by providing tools to understand big data and any other kind of data. Companies like Splunk and 1010data offer Agile Big Data technology that is simple enough for normal humans to use but powerful enough to handle massive volumes of data. Revolution Analytics aims to make advanced statistics easy to use by enhance the R suite of statistical software. Visualization technologies like QlikView, Tableau, and TIBCO Spotfire, are bringing new analytical power to the edges of the organization.

Explain what decision trees are, how we can construct and analyse them. Finally I will explain how we can use our novel techniques and apply them to financial KYC (know-your-customer) data in order to gain insight in our customers and use that to detect future fraud cases.

Analytics is about corporations learning to drink from the fire hose of cheap data that the modern IT systems wrapped around their operations can generate. So what is new? People have been championing a variety of data-driven approaches to management since Frederick Taylor, so what difference does, say, a modern point-of-sales (POS) or RFID system make? Here’s what’s new, per the authors:

Unlike data mining, analytics is about operational interpretation and visualization, not collecting or reporting. This distinction can range from significant to irrelevant depending on the case you are talking about.
Analytics is to be viewed as a subset of business intelligence (BI), within which it lives next to its older, stupider sibling, reporting. I am not sure this contextualization helps anybody, since BI is a massively overloaded term.
Unlike ideas like lean six sigma and its predecessor, total quality management, analytics (when mature) is i) truly global in scope rather than globally-local, ii) about an enabling information infrastructure for all processes and functions (managed at an enterprise level) and most importantly, iii) about responding opportunistically in real time to some sort of systemic variability via feedback, rather than about reducing process variability via episodic process measurement and re-engineering.
Unlike most management doctrines, analytics needs an enabling technology, since it relies on continuous data flows to support high-frequency operational decisional making, rather than low-frequency interventionist decision-making. Without some sort of technology that provides a breakthrough cost structure for data generation (such as remote diagnostics, RFID, retail POS data aggregation or Web services), the data flows required for competing on analytics would simply be prohibitively expensive. That explains why Web-based businesses lead in best practices.
Unlike related technology paradigms like “Service Oriented Architecture” and “Software as a Service,” analytics is about a business competence that has its locus in people, not (just) software.
Perhaps most important: unlike previous data-driven management paradigms, analytics can be a competitive differentiator. This is such a critical claim that it deserves some probing, so I give it its own section.

So if you are championing analytics-based competition, and someone asks you how it is different from what failed before, your canned answer: “Continuous data flows, cheap, operational measurement systems, enterprise-level presence, differentiating capability.” Keep examples handy for those untalented in the area of business abstractions (a.k.a the “all buzzwords=bullshit” crowd).

There are various ways to visualize connections, but one of the most intuitive and straightforward ways is to actually connect entities or objects with lines. And when it comes to geographic connections, great circles are a nice way to do this.

Packer running back Ahman Green [...] fumbled the ball seven times in the first nine games last year. In his case, the team queried plays in its database by his No. 30 jersey over the last two years. A compilation of plays involving Green took minutes, instead of days�which is what it took three years ago when team staff had to sort through piles of tapes.

Coaches reviewed the compilation of plays and determined that Green fumbled when his elbow wasn't horizontal to the ground as he was hit. When he cradled the ball with his elbow in a horizontal position, Green didn't fumble. Using that business intelligence, the coaches could point out elbow positioning to Green, who made a mental note and made the adjustment. Green only fumbled once (he recovered it) during the team's last seven games.