Recent Read: Internet of things

http://venturebeat.com/2013/04/29/internet-of-things-ibm/

While we’re still figuring out what the Internet of Things will eventually look like, we do know this: It’s going to be very, very busy.

To help make sense of the noise IBM is announcing MessageSight, a new, somewhat mysterious appliance that will help manage the flood of data coming from Internet-connected sensors and devices. To pitch the need for the box, IBM cites data from IMS Research, which says that there will be 22 million web-connected devices by 2020. All these devices, which will generate 2.5 quintillion bytes of data per day, are going to need hardware to process them — or so IBM says.

If the understanding of the “Internet of Things” still eludes you, envision a scenario where every component of you car had its own sensor, and all of these sensors periodically phoned home about what was going on inside them. Leaky tire? Faulty brake light? Empty fuel tank? With sensors, your car could keep track of this information and warn you when bad Things are going down. But getting sensors in devices is easy. The real challenge is processing all of their data.

Recent Read: Data visualization

http://hbr.org/special-collections/insight/visualizing-data
http://blogs.hbr.org/cs/2013/04/tell_better_data_stories_with.html

 

Tell Better Data Stories with Motion and Interactivity

When it comes to making sense of vast amounts of complicated data, time really is on your side. It's a simple concept, one that everyone understands: an action starts, then eventually stops. The distance between those two points conveys information — information about then, about now, and about the differences between the two.

If you apply that simple yet elegant measuring stick to an overwhelming glut of information, you have the beginnings of a powerful data visualization that can simplify the complex, identify trends, and shape your audience's comprehension of the story you want to tell.

However, when time is the canvas for your data, you'll need one, or both, of these techniques: motion and interactivity.

Hans Rosling, who gained popular fame in his 2006 TED Talk on "stats that reshape your worldview" uses the power of motion in the software that runs his Gapminder trend-finding operation.

 

http://blogs.hbr.org/cs/2013/03/when_data_visualization_works--and.html

 

When Data Visualization Works — And When It Doesn't

I am uncomfortable with the growing emphasis on big data and its stylist, visualization. Don't get me wrong — I love info graphic representations of large data sets. The value of representing information concisely and effectively dates back to Florence Nightingale, when she developed a new type of pie chart to clearly show that more soldiers were dying from preventable illnesses than from their wounds. On the other hand, I see beautiful exercises in special effects that show off statistical and technical skills, but do not clearly serve an informing purpose. That's what makes me squirm.

Ultimately, data visualization is about communicating an idea that will drive action. Understanding the criteria for information to provide valuable insights and the reasoning behind constructing data visualizations will help you do that with efficiency and impact.

For information to provide valuable insights, it must be interpretable, relevant, and novel. With so much unstructured data today, it is critical that the data being analyzed generate interpretable information. Collecting lots of data without the associated metadata — such as what is it, where was it collected, when, how and by whom — reduces the opportunity to play with, interpret, and gain insights from the data. It must also be relevant to the persons who are looking to gain insights, and to the purpose for which the information is being examined. Finally, it must be original, or shed new light on an area. If the information fails any one of these criteria, then no visualization can make it valuable. That means that only a tiny slice of the data we can bring to life visually will actually be worth the effort.

utilities: How to mail merge

http://help.thunderbird.edu/content/how-do-i-mail-merge

How Robots and Military-Grade Algorithms Make Same-Day Delivery Possible

http://www.wired.com/business/2013/03/online-retailers-faster-than-overnight/

more comments to follow:

Key themes:

• Store to door
• Predictive stocking
• 7-11 lockers
• Man+ machine integration in picking right items and humans figuring out quality
• City as a warehouse (how ebay has participants from Walgreens to radioshack to THD)
• Couriers (with mobile app tracking)

Forget next-day delivery. The standard in online shopping is rapidly approaching next-hour delivery. Retail giants Walmart, Amazon, and eBay, and a few nimble startups, are testing same-day services, bringing whatever you desire—ice cream, toothpaste, a new TV—to your door, right now. To make it happen, the sellers are revving up supply chains that rely on algorithms of military-grade complexity and workers (human and robot) who roam vast distribution centers 24/7. The trillion-dollar online shopping economy is about to get bigger—and a lot faster.

Recent Read: biological computer

http://www.huffingtonpost.com/2013/03/29/biological-computer_n_2981753.html

Researchers at Stanford University announced this week that they've created genetic receptors that can act as a sort of "biological computer," potentially revolutionizing how diseases are treated.
In a paper published in the journal "Science" on Friday, the team described their system of genetic transistors, which can be inserted into living cells and turned on and off if certain conditions are met. The researchers hope these transistors could eventually be built into microscopic living computers. Said computers would be able to accomplish tasks like telling if a certain toxin is present inside a cell, seeing how many times a cancerous cell has divided or determining precisely how an administered drug interacts with each individual cell.
Once the transistor determines the conditions are met, it could then be used to make the cell, and many other cells around it, do a specific thing--like telling cancerous cells to destroy themselves.

Recent Read: Big data analytics plan

http://www.mckinsey.com/insights/business_technology/big_data_whats_your_plan
A quick snippet

Big data: What’s your plan?

Many companies don’t have one. Here’s how to get started.

March 2013| byStefan Biesdorf, David Court, and Paul Willmott

The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt. The tally of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition.¹ 1. See Dominic Barton and David Court, “Making advanced analytics work for you,” Harvard Business Review, October 2012, Volume 90, Number 10, pp. 78–83. The promised land of new data-driven businesses, greater transparency into how operations actually work, better predictions, and faster testing is alluring indeed.
But that doesn’t make it any easier to get from here to there. The required investment, measured both in money and management commitment, can be large. CIOs stress the need to remake data architectures and applications totally. Outside vendors hawk the power of black-box models to crunch through unstructured data in search of cause-and-effect relationships. Business managers scratch their heads—while insisting that they must know, upfront, the payoff from the spending and from the potentially disruptive organizational changes.
The answer, simply put, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value. The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started.
 

Recent Read: Big data in healthcare

http://www.mckinsey.com/insights/health_systems/the_big-data_revolution_in_us_health_care

Big data could transform the health-care sector, but the industry must undergo fundamental changes before stakeholders can capture its full value.

A big-data revolution is under way in health care. Start with the vastly increased supply of information. Over the last decade, pharmaceutical companies have been aggregating years of research and development data into medical databases, while payors and providers have digitized their patient records. Meanwhile, the US federal government and other public stakeholders have been opening their vast stores of health-care knowledge, including data from clinical trials and information on patients covered under public insurance programs. In parallel, recent technical advances have made it easier to collect and analyze information from multiple sources—a major benefit in health care, since data for a single patient may come from various payors, hospitals, laboratories, and physician offices.

Recent read: Network approach to problem solving

https://www.mckinseyquarterly.com/Strategy/Strategic_Thinking/Five_routes_to_more_innovative_problem_solving_3074

The flexons approach
Finding innovative solutions is hard. Precedent and experience push us toward familiar ways of seeing things, which can be inadequate for the truly tough challenges that confront senior leaders. After all, if a problem can be solved before it escalates to the C-suite, it typically is. Yet we know that teams of smart people from different backgrounds are more likely to come up with fresh ideas more quickly than individuals or like-minded groups do.² When a diverse range of experts—game theorists to economists to psychologists—interact, their approach to problems is different from those that individuals use. The solution space becomes broader, increasing the chance that a more innovative answer will be found.