A new facility at Virginia Tech uses large-scale visuals and sound to immerse users in vast amounts of data. Very good presentation.
Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.
Good data visualization helps us see the meaning in data, hence it has always been an important tool. Today computers crunch numbers and design programs help us visualize data in many ways, from infographics to interactives. But in a time before those tools, designers were creating truly beautiful work that should be an inspiration to anyone working today.
These maps are pulled from the 1870 edition of the Statistical Atlas of the United States, visualizing data from the 1870 census. Each cover a different aspect of the rapidly changing America. While the book is full of detailed visualizations, these maps are particularly notable. The choice of visual presentation and vibrant color palettes make them enticing (and a little surprising) seeing as they’re from the staunch Victorian era.
These just prove that no matter how tedious the subject, you can always inject a little creativity.
This is a very nice addition to Kanban board.
Reference: The Arrow – An advanced Kanban board
Here is another listing of free Microsoft eBooks.
Reference: I’m giving away MILLIONS of FREE Microsoft eBooks again! Including: Windows 10, Windows 8.1, Windows 8, Windows 7, Office 2013, Office 365, SharePoint 2013, Dynamics CRM, PowerShell, Exchange Server, Lync 2013, System Center, Azure, Cloud, SQL Server, and
This is so far the best article that I have been reading about the Big Data. It is what I have been advocating to people.
1. They talk about “bigness” and “data,” rather than “new questions”
… It seems most of the tech industry is completely drunk on “Big Data.”
… most companies are spending vast amounts of money on more hardware and software yet they are getting little, if any, positive business value.
… “Big Data” is a terrible name for the revolution going on all around us. It’s not about Bigness, and it’s not about the Data. Rather, it’s about “new questions,” being facilitated by ubiquitous access to massive amounts of data.
… If all you’re doing is asking the same old questions of bigger amounts of the same old data, you’re not doing “Big Data,” you’re doing “Big Business Intelligence,” which is itself becoming an oxymoron.
2. They talk about technology, rather than business
… You may end up with the world’s largest server cluster, but other than bragging rights, who cares? START with a business issue, figure out how to better-characterize that issue with data, THEN start working on a technical solution.
3. They focus on insights, rather than actions
Most of the organizations that I work with are so focused upon analytics as an end-result they completely miss the whole point of this Big Data exercise: better actions. … If, after all of this effort, you haven’t changed how your organization acts, what your product or service does for your customers, or how you subsequently respond to the world around you, you’ve failed, utterly.
… Insight is great, but action is what brings home the bacon. If your “Big Data Expert” is focused on gaining insight rather than generating new business outcomes, you’re running a science experiment.
4. They talk about conclusions, rather than correlations
… Many of this new wave of Big Data experts don’t understand the nuance between correlation and causation. … Correlation means that there is the appearance of a relationship between things. Such relationships may indicate that certain inputs MAY lead to certain outputs. But, with correlation, there is no certainty.
… This is sort of a bummer to business people, who like to work with absolutes, or at least the appearance of absolutes. Well, there’s no such thing in data analytics. Your data may represent a vast collection of facts, but analytics and statistics are theater. What you see isn’t always what you get. Indeed, many “data scientists” are more “data manipulators,” generating politically acceptable outputs that support a given agenda.
… Correlation does not guarantee causation. Any Big Data expert who tells you they found causation should be immediately suspect until proven otherwise.
5. They talk about data quality, rather than data validity
… While data quality matters, it’s far more important to focus on data validity: Do I even have the right data to answer the questions I’m asking? … New analyses require VALID data, but determining whether or not data is clean before asking questions of it makes no sense whatsoever.
6. They sound like everyone else who is talking Big Data
… We are being drowned in all of the noise surrounding Big Data. … If your “Big Data Experts” don’t get this, then they’re not getting it. And neither are you.
Reference: Six signs that your Big Data expert, isn’t
It’s interesting that some people come up new ideas of creating infographics in Excel. I wonder how data navigation is going to be supported!?
Reference: Info Graphics with Excel