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Too big to ignore
When does big data provide big value?
Much of the language surrounding big data conveys a muddled conception of what data, "big" or otherwise, means to the majority of organizations pursuing analytics strategies. Big data is shrouded in hyperbole and confusion, which can be a breeding ground for strategic errors. Big data is a big deal, but it is time to separate the signal from the noise.
"Beware of false knowledge; it is more dangerous than ignorance."
—George Bernard Shaw
The Signal and the Noise
By now, we have all heard the claims. Data is the new oil. Big data is different.1 Big data is a management revolution.2 Big data is the next frontier for innovation, competition, and productivity.3 Big data makes the scientific method obsolete.4
On the one hand, it is easy so see why big data has generated so much excitement both in the business press and in the larger culture. New business models, scientific breakthroughs, and profound societal transformations are all observable effects of big data.
Familiar examples abound. Google Translate exploits word associations in massive databases of free-form text to yield a tool that can, if you wish, instantly translate Icelandic to Indonesian. Social, political, economic, and professional relationships—and the societal and marketing mores surrounding them—are rapidly evolving along with the evolution of social networking and social media technologies. Political campaigns increasingly harness the power of social networks and social media to raise funds, motivate constituencies, and get out the vote. Companies use detailed databases about their customers' behavior and lifestyles not only to better target them, but to create such innovative "data products" as playlists, newsfeeds, next-best offers, and recommendation engines for items ranging from airplane tickets to romantic partners. The raw material for all of these innovations—and surely many more to come—is large amounts of data.
So the topic is undoubtedly important. Yet at the same time, much of the language that has come to surround big data conveys a muddled conception of what data, "big" or otherwise, means to the majority of organizations pursuing analytics strategies. Big data is indeed a signature issue of our time. But it is also shrouded in hyperbole and confusion, which can be a breeding ground for strategic errors. In short, big data is a big deal, but it is time to separate the signal from the noise.
V is for …
Of course business applications of data analysis and predictive modeling go back decades.5 For example, credit scoring dates back to the ENIAC-era late 1950s, and actuaries have long analyzed industrial-strength data to price insurance contracts and more recently to set aside appropriate loss reserves as well as guide underwriting and claim adjustment decisions. And in the decade since the appearance of Michael Lewis's Moneyball, statistical approaches to improved business decision making have spread to realms as disparate as improving patient safety, making better hiring decisions, and warding off lapses in child support payments. What then is new about big data?
The term is somewhat hard to pin down in part because it is commonly used in at least two distinct senses. In everyday discussions, "big data" is increasingly used as shorthand for the granular and varied data sources that go into the sorts of projects described above.6 Here "big" is used in the sense of "as rich and detailed as practical given the business context." Such data is "big" relative to the small, "clean," easily accessible datasets that can easily be manipulated in spreadsheets and have traditionally been fodder for mainstream academic statistical research. While colloquial, this is actually a useful conception of "big data" for reasons that will become clear.
More officially, "big data" denotes data sources whose very size creates problems for standard data management and analysis tools. Examples include the data continuously emanating in vast quantities from digital sensors, audio and video recording devices, mobile computing devices, Internet searches, social networking and media technologies, and so on. Such examples motivate Doug Laney's widely accepted "three V's" characterization of big data:7
- Volume: Here, "big" is often taken to mean multiple terabyte- or petabyte-class data, motivating the use of such highly parallel next-generation data management technologies as MapReduce, Hadoop, and NoSQL.8
- Variety: Big data goes beyond numbers in databases, a.k.a. "structured data." Such "unstructured" data sources as Internet search log files, tweets, call center transcripts, telecom messages, email, and data from sensor networks, video, and photographs can equally well be considered data. The multi-structured nature of big data in part accounts for its large volume and often high degree of "messiness" and "noisiness."
- Velocity: Because much of it emanates from sensors, web search logs, real-time feeds, or mobile devices, big data is often generated continuously and at a rapid clip.
Big data is said to be different and revolutionary because its size, scope, and fluidity are so great as to enable it to "speak to us," often in real time, in ways not seen before. This point of view is clearly articulated in Chris Anderson's influential essay, "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete":9
There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.
The thinking is that in the pre-big data era, statistical science was necessary to make up for the inherent limitations of incomplete data samples. Statisticians and scientists were forced to cleanse, hypothesize, sample, model, and analyze data to arrive at contingent lessons that are at least consistent with, and at best strongly supported by, limited data. Today, so the thinking goes, organizations increasingly have access to something approaching a complete sample. Therefore the process of "learning from data" becomes akin more to a problem in algorithm and architecture design than one of learning from and quantifying uncertain knowledge using statistical science.
The mode of thought represented by Anderson's essay can be seductive. But for reasons we will explore, most organizations would do well to resist this particular seduction.
Is Big Different?
Fitzgerald: "The rich are different from you and me."
Hemingway: "Yes, they have more money."
Google Translate is a case in point of how big data can be dramatically different. In their widely cited article, "The Unreasonable Effectiveness of Data,"10 Google researchers Alon Halevy, Peter Norvig, and Fernando Pereira describe an approach to translation that hinges on mining the messy patterns from enormous collections of translations that exist "in the wild." The approach is notable in that it bypasses traditional statistical methodology involving laboriously cleansing, sampling, exploring, and modeling data. The very size and completeness of the data now available employ word associations to do the work that linguistic rules and complicated models did in previous, less effective, approaches.
Halevy, Norvig, and Pereira write:
Invariably, simple models and a lot of data trump more elaborate models based on less data. … Currently, statistical translation models consist mostly of large memorized phrase tables that give candidate mappings between specific source- and target-language phrases.
Analogous discussions could be made, for example, about recommendation engines, online marketing experiments performed in rapid succession, and the use of Internet search data to predict flu outbreaks or "nowcast" economic trends. Its near-completeness and real-time nature can make big data an "unreasonably effective" (to use Halevy, Norvig, and Pereira's phrase) force for innovative data products.
In short, why ask why? In many applications, we only care about a good enough next answer, decision, or recommendation. And this is what big data, processed by the right computer algorithms, gives us. Or does it?
How Data Science is a Science
"The whole of science is nothing more than a refinement of everyday thinking."
—Albert Einstein
As important as Google Translate and any number of other big data products are, cursory thinking about their significance can muddle important issues ranging from the epistemological to the economic to the strategic.
First, a fundamental distinction should not be forgotten: Data is not the same thing as information. In itself, data is nothing more than an inert raw material: un-interpreted symbols on a page or in a database. Furthermore, the size of a dataset is often a poor proxy for the amount of useful information it contains. A single Eminem video accounts for more of the world's exabytes of data than do the complete works of Einstein. A typical collection of holiday snapshots from the Galapagos Islands will occupy more disk space than On the Origin of Species.
Second, it is deeply misguided to view the skills needed to convert raw data into usable information in software engineering terms. Contrary to the view articulated by Chris Anderson, these skills are inherently scientific in nature. Indeed, they are increasingly labeled by the helpful umbrella term "data science."11 Data science should be viewed as a synthesis of statistical science, computer science, and domain knowledge appropriate to the problem at hand.12 The term originated from a far-sighted group of statisticians who understood that as data continues to grow in volume and availability, the ability to interact with, visually explore, and generally compute with data becomes an inescapable part of doing serious statistics.13
Business projects with data science at their core—a.k.a. business analytics projects—have three primary phases: design, analysis, and execution. Critical thinking, scientific judgment, creativity, and pragmatism are inherent to each of them. It is, generally speaking, unrealistic to expect that any of these phases could be automated or outsourced to computer algorithms processing big data.
Strategy and design: Perhaps a data scientist's most important skill is in understanding (and often helping to articulate) an organization's questions, problems, or strategic challenges and then translating them into the design of one or more data analysis projects. John Tukey's famous slogan captures the need for such a strategy-led approach: "Better to have an approximate answer to the right question than a precise answer to the wrong question."
To illustrate, consider a hypothetical chief medical officer of a hospital group seeking to improve patient outcomes. Should the focus of data analysis be on preventing medical errors? Identifying physicians at high risk of being sued for malpractice? Identifying patients with potentially high medical utilization? Identifying patients likely to fall off their treatments? Predictive approaches to guiding diagnosis and treatment decisions? Even after the various alternatives have been articulated and prioritized, there remains the design challenge of outlining a sensible series of data analysis and/or predictive modeling steps. Indeed the decision of which issues to tackle and in what order might be partially informed by the opinion of a seasoned data scientist regarding their relative feasibility.
"Better to have an approximate answer to the right question than a precise answer to the wrong question."
Analytical process: The technical aspect of an analytics project itself comprises three distinct phases: data scrubbing and preparation, data analysis, and validation. Each requires judgment, generally making it nonautomatic in character.
The need for statistical programming (computing with data) is rightly emphasized because data comes in such messy and disparate forms: scanned documents, web logs, electronic medical records, billing records and other financial transactions, geospatial coordinates, audio/video streams, and increasingly free-form unstructured text and beyond. But it would be mistaken to conceive of this process purely as a programming challenge.
Analogous to Halevy, Norvig, and Pereira's observation that more data trumps more elaborate models, we have repeatedly found that the creation of innovative data "features" (or "synthetic variables") from raw data elements does more to enhance the effectiveness of analytics projects than does elaborate methodology. Examples of creating explanatory or predictive power where none existed before include calculating the distance between two addresses, calculating social network centrality using one or more shared associations, using behavioral data in a novel way, or creating index variables to serve as proxies for latent traits not directly observable.14 In short, characterizing the feature creation aspect of data analysis as a programming exercise would be akin to characterizing creative writing as word processing.15
After data scrubbing comes data exploration, analysis, and validation. Each of these steps should be viewed as an exercise in creative scientific investigation, aided with the tools of data visualization, statistics, and machine learning. Many datasets used in analytics projects contain considerable amounts of random noise, spurious correlations, ambiguity, and redundancy alongside the abiding "signal" that we wish to capture in a model or analysis.
A particularly diabolical and underappreciated problem is known in the vernacular as "multiple comparisons." Imagine 100 people in a room flipping fair coins. Because so many coins are being repeatedly flipped, it is likely that at least one of the coins will—by chance—produce so many heads or so many tails that the coin will be found to be biased with a high degree of "statistical significance."16 This is despite the fact that the coin thus selected is in reality no different from the other coins and is likely to produce heads in roughly half of the future flips.
A venerable motto of computer science is GIGO: "garbage in, garbage out." Perhaps an analogous motto for the nascent field of data science ought to be NIINO: "no insight in, none out."
In fields such as medicine, drug testing, and psychology, essentially this phenomenon is known as the "file drawer problem": A purely mechanical approach of filing away nonsignificant (or sometimes unwanted) results and publicizing "significant" results systematically yields spurious findings that will likely not hold up over time.17 The xkcd cartoon reproduced here conveys the issue humorously and succinctly.18
While this is a problem for any large-scale analysis involving many possible data dimensions and relationships, it is particularly acute in the age of big data and brute-force algorithms for extracting interesting relationships. Furthermore, as will be discussed below, the cost of such false positives should be taken into account.
The problem of multiple comparisons is but one of many reasons why data science should not, in general, be viewed as the brute-force harvesting of patterns from stores of big data. For others, see the inset, "Putting the science in data science."
Business execution: As the statistician George Box wrote, "All models are wrong, but some are useful." A major implication of this statement is that it is generally advisable to blend model indications with common sense and expert judgment to arrive at a decision. This happens often even in bona fide big data applications. For example, if Jim uses Google Translate to write to a francophone friend he will hopefully know enough to correct various mistakes and change the second person singular from the formal vous to the familiar tu. Similarly, the recommendations of book or movie collaborative filtering algorithms are often useful but other times should be taken with a grain of salt.19
In these everyday examples, the stakes are low, and it is easy to blend model indications (translations or recommendations) with judgment founded on experience (our prior beliefs about the correct translation or what we will enjoy). In more high-stakes business analytics applications, such as using models to make medical diagnoses, screening potential employees, security rating, fraud investigation, or complex loan or insurance underwriting decisions, the blending of expert judgment with model indications should be viewed as a risk management issue of strategic importance.20 It is important that the data scientist guiding the analytical process play a role in communicating the assumptions and limitations of the analysis or model so that these issues are effectively addressed.21 This is yet another reason why data science should not be framed in purely programming or engineering terms and why big data should not be characterized as an automatic source of reliable predictions regardless of context.
Amassing repositories of big data and purchasing software, therefore, is not sufficient for business analytics. Data scientists—a.k.a. people—are essential to the process. It should also be kept in mind that in many situations, the appropriate data, at least to start, will not be "big in the 3V sense," and much can be done with open-source statistical computing software. Again, domain knowledge and scientific judgment are important factors in such decisions.
A venerable motto of computer science is GIGO: "garbage in, garbage out." Perhaps an analogous motto for the nascent field of data science ought to be NIINO: "no insight in, none out."
What Happens in Vagueness Stays in Vagueness
It is natural to find the discussions of big data in the business press and blogosphere bewildering. The field is inherently multidisciplinary, and terms such as "big data," "business analytics," and "data science" mean different things to different people.22
Confusion also may arise for a more fundamental reason: Concepts relating to statistical uncertainty simply do not come naturally to the human mind. The same body of psychological research that underpins behavioral economics also suggests that we are very poor natural statisticians. We are naturally prone to find spurious information in data where none exists, latch on to causal narratives that are unsupported by sketchy statistical evidence, ignore population base rates when estimating probabilities for individual cases, be overconfident in our judgments, and generally be "fooled by randomness."23 There is little wonder that misleading narratives about big data have multiplied.
Putting the science in data science (Or, Those who ignore statistics are condemned to reinvent it.)24
Not only is information different from data in general, there is no automatic, purely algorithmic way to extract the right islands of information from oceans of raw data. Generally speaking, this process requires a combination of domain knowledge, creativity, critical thinking, an understanding of statistical reasoning, and the ability to visualize and program with data. Theory and sound causal understanding remain important checks on the innate human tendency to be "fooled by randomness." Far from making the scientific method obsolete, increasing volumes of data are making data science a core strategic capability of many organizations. Here are some reasons why.
Data contains too few patterns:
- In many situations, one can fit many models to, and draw a variety of conclusions from, the same data. Examples are all around and include forecasting the profitability of a cohort of insurance policies, estimating Value at Risk (VaR) of a portfolio of securities, evaluating the effectiveness of an ad campaign or human resources policy, analyzing a financial time series, and predicting the outcome of a political election. While more data is often helpful, it is misleading to characterize these activities as "data driven." Rather they are driven by the creative and judgmental application of scientific principles of data analysis. Data—"big" or otherwise—is an input into the process, not the driving force of the process.
- Human creativity and domain knowledge are often necessary to create synthetic data features. Examples include body mass index, measures of social network centrality, distances between relevant physical addresses, composite measures of employee performance, and proxy variables for unobservable latent traits. Once again data is a raw material, not a source of automatic insight.
Data contains too many patterns:
- In many situations, one can fit many models to, and draw a variety of conclusions from, the same data. Examples are all around and include forecasting the profitability of a cohort of insurance policies, estimating Value at Risk (VaR) of a portfolio of securities, evaluating the effectiveness of an ad campaign or human resources policy, analyzing a financial time series, and predicting the outcome of a political election. While more data is often helpful, it is misleading to characterize these activities as "data driven." Rather they are driven by the creative and judgmental application of scientific principles of data analysis. Data—"big" or otherwise—is an input into the process, not the driving force of the process.
- Human creativity and domain knowledge are often necessary to create synthetic data features. Examples include body mass index, measures of social network centrality, distances between relevant physical addresses, composite measures of employee performance, and proxy variables for unobservable latent traits. Once again data is a raw material, not a source of automatic insight.
Data contains too many patterns:
- Datasets contain a mixture of "signal" and "noise," and many big data sources have low signal-to-noise ratios, perhaps earning a fourth "V": "vagueness." While statistical and machine learning techniques continue to improve in their ability to separate signal from noise, they are often best viewed as tools that facilitate an inherently judgmental process. Examples include selecting which variables and variable interactions should be considered for inclusion in a model, which data points should be excluded or down-weighted for various reasons, and whether various apparently linear or nonlinear relationships among variables are real or spurious.
- The problem of "multiple comparisons": It often happens that the more associations you test, the more apparently significant patterns you will detect, even when nothing is actually happening. This is generally regarded as a basic fact of statistical life and becomes more challenging as datasets become larger and messier.
Big datasets are unnecessarily big:
- Summary statistics can be sufficient for the task at hand. An elementary example: Suppose a coin has been repeatedly tossed. Assuming a binomial process, two numbers (the total number of tosses and the number of heads) contain as much information about the probability of heads on the next toss as a complete history of the previous tosses. More bytes do not always translate to more information.
- Scores and composite indices (such as credit scores, social media sentiment scores, or lifestyle clusters) can reduce hundreds of data elements to a handful of numbers.
- Often a small, carefully chosen sample of data contains as much usable information as a large "messy" dataset.
Big datasets are too small:
- Another broadly accepted fact of statistical life is "the curse of dimensionality." In many situations, even the largest conceivable data is "sparse" because of the large number of dimensions involved and/or a rare "outcome" variable (such as fraud or infrequent purchases). Imagine, for example, a marketing researcher confronting a dataset containing few or no purchases for many of the hundreds of different products offered; an actuary confronted with setting professional liability insurance rates for a multitude of specialty/geography combinations; or a geneticist analyzing many thousands of gene combinations for a relatively small patient population.
- Biased sampling frames: In 1936 the Literary Digest conducted a poll that received over 2 million responses predicting that Alfred Landon would prevail over Franklin Roosevelt by a double-digit margin. In fact, Roosevelt won by a landslide. The Literary Digest erred in conducting its poll by telephone, which at the time was disproportionately used by the wealthy. In this sense the huge sample was still too "small." Analogously today, petabytes of social media data aren't guaranteed to accurately reflect the membership or sentiments of the population of interest.25
The patterns in the data are trivial:
While "garbage in, garbage out" is a well-known danger in data science, a less frequently noted GIGO is "gigabytes in, generalities out." Often, an automatic approach to data analysis results in generalities that are obvious, nonactionable, or both. For example, a first attempt at a recommendation engine might suggest the "obvious," most popular movies or songs to most people; similarly a novice analysis of insurance claim severity might uncover obvious facts such as back injuries costing more than fractured arms. Human insight is required to design data analysis approaches that go beyond the obvious.
On the other hand, "obviousness" is sometimes a good thing. Some of the most valuable models insightfully combine a number of fairly obvious variables. The value of such models is due less to incorporating surprise nuggets of insight (although those are always nice) than to outperforming the human mind's ability to quantify their relative importance and interactions. Also important is that such models help enforce consistency on groups of cognitively bounded, and perhaps distracted and tired, human professionals.26
The patterns in the data are diabolically misleading:
- A famous example of "Simpson's Paradox" occurred when the University of California, Berkeley was sued for gender bias in its graduate school admissions decisions: In 1973 Berkeley admitted 44 percent of its male graduate school applicants but only 35 percent of its female applicants. However when the data was broken down by department, the apparent bias disappeared. Why? Women tended to apply to departments (such as humanities) with lower admission rates—doh! Similarly naive analyses can spuriously suggest that higher prices lead to higher demand, or that marketing campaigns can lead to lower sales.
- Studies find that highly intelligent women tend to marry men who are less intelligent than they are.27 Top-performing companies tend to do worse over time. These facts do not require sociological or management theoretic explanations. They are instances of "regression to the mean." The concept was first identified by Francis Galton, a cousin of Charles Darwin and the inventor of regression analysis, when he studied such phenomena as tall parents having shorter offspring (and vice versa). Misunderstanding of this phenomenon can lead to such bad business decisions as paying for past performance of sports players who have experienced winning streaks, or movie genres and franchises whose moment has passed.
Nevertheless, avoiding misconceptions about big data should be regarded as a prerequisite for avoiding analytics projects with negative ROI. First, data should not be confused with useful information. Deriving insight from data, as discussed above, should generally not be viewed as a form of programming or software implementation but as a type of scientific investigation requiring the judgmental evaluation of ambiguous data.
It is equally important to pay attention to the economic and strategic aspects of big data. While the potential benefits of big data get a lot of attention, less attention is given to the costs. Many of these costs are straightforward: It costs money to acquire, store, back up, secure, integrate, manage, audit, document, and make available any data source.28 And while inexpensive multi-terabyte drives are available at the local electronics store, they are not characteristic of the enterprise hardware many organizations use to manage big data.
More subtle economic points can be equally relevant. First, economic decisions should be made at the margins. Therefore, a big data project should not be evaluated in isolation but in terms of how much insight or predictive power it is likely to add over and above a less costly analytics project.
Second, big data projects often carry opportunity costs. Many organizations have a menu of potential analytics projects with limited resources to execute them. For a large retailer wishing to make real-time next-best offers or an Internet company aiming to enjoy the "winner take all" effect of network externalities, big data naturally rises to the top of the list of priorities. For many other organizations, a more likely path to analytics success is paved with a sequence of smaller, well-targeted projects, with the benefits of one helping fund the next. For such organizations, a focus on big data could be an expensive distraction.
Finally, the human capital and organizational costs required to work with, analyze, and act upon big data should not be underestimated. Data science skills are clearly in demand and, therefore, can be difficult and expensive to acquire. While this is subject to change as supply ramps up to meet demand, a deeper and more abiding point was made over 40 years ago by the artificial intelligence pioneer and management theorist Herbert Simon. Simon wrote that:
In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: It consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.29
To be sure, there is ample evidence that when properly analyzed and acted upon, data helps enable organizations to make decisions more accurately, consistently, and economically.30 But this does not imply the cost-effectiveness of big data technology in any particular application.
Moneyball is often held up as a prime example of the sort of transformative innovation enabled by data-driven decision making. In fact, variations on "Big Data: Moneyball for Business" are common names for articles about the topic. But Billy Beane's achievement did not result from using terabyte- or petabyte-class data. As author Michael Lewis recounts, it resulted from an inspired use of the right data (for example, on-base percentage rather than batting average) to address the right business opportunity (an inefficient market for talent due to a widespread culture of intuition-driven decision making). Much the same could be said about the use of statistical analysis and predictive models to help guide medical decisions; loan or insurance underwriting decisions; hiring, admissions, and resource allocation decisions; and so on.
This last point sheds light on the "data is the new oil" metaphor. The metaphor is helpful in driving home the point that data should be treated as a valuable asset that, analogous to oil, can be refined to help power insights and better decisions. But unlike oil, data is not an undifferentiated quantity. More volumes and varieties of data are not necessarily better. Indeed, if not pursued strategically, they can lead to missed opportunities, expensive distractions, and what Simon called "a poverty of attention." Rather than let the data tail wag the strategic dog, it is crucial to begin with a plan within which the organization can judge which data is the right data and which analysis is the right analysis.
Two Cheers for Big data
Is big data a big deal? Yes. Instances of innovative big data-driven products, business models, and scientific breakthroughs are already common and are likely to multiply in the future.
But for a leader seeking an appropriate business analytics strategy specific to his or her organization, the answer to this question is less straightforward. There is indeed abundant evidence that organizations—large and small, public and private—benefit from employing the analysis of data to guide strategic, operational, and tactical decisions. But this does not always entail processing terabyte- or petabyte-class data on Hadoop clusters.
Above all, business strategy should guide the choice of data and technology, not vice versa. Laying out a clear vision and analytical roadmap helps avoid being side-tracked by narratives in which the phrase "big data" is defined one way and used in another; data is conflated with usable information; the judgment-infused process of data science is mischaracterized as mining patterns and associations from raw data; and the economics of big data are downplayed.
Properly harnessed, the right data can indeed be an organization's new oil. But it is important not to lose sight of two fundamental points. First, analytics initiatives ultimately do not begin with data; they begin with clearly articulated problems to be addressed and opportunities to be pursued. Second, more data does not guarantee better decisions. But the right data—properly analyzed and acted upon—often does. Organizations that lose sight of these principles risk experiencing big data not as the new oil, but as the new turmoil.
EndnotesView all endnotes
- Thomas H. Davenport, Paul Barth, and Randy Bean. "How Big Data is Different," MIT Sloan Management Review, July 30, 2012 <http://sloanreview.mit.edu/the-magazine/2012-fall/54104/how-big-data-is-different/>.
- Andrew McAfee and Erik Brynjolfsson. "Big Data: The Management Revolution", Harvard Business Review, October 2012 <http://hbr.org/2012/10/big-data-the-management-revolution/ar/1>.
- "Big Data: The Next Frontier for Innovation, Competition, and Productivity," McKinsey Global Institute. <http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation>.
- Chris Anderson. "The End of Theory: Big Data Makes the Scientific Method Obsolete," Wired, June 2008. <http://www.wired.com/science/discoveries/magazine/16-07/pb_theory>.
- Or perhaps centuries. The article "Beyond the Numbers: Analytics as a Strategic Capability" pointed out that actuarial science, which dates back 250 years, might reasonably be considered an early instance of what is today called business analytics. <http://www.deloitte.com/view/en_US/us/Insights/Browse-by-Content-Type/deloitte-review/24cda920f718d210VgnVCM2000001b56f00aRCRD.htm>.
- For example, see "The New Boss: Big Data," The Wall Street Journal, September 20, 2012. This article is about the use of predictive models to improve hiring decisions. Here, "big data" is used in the increasingly common colloquial sense of "supporting a business analytics application," not the sense suggesting the involvement of terabyte-or petabyte-class data.
- See Doug Laney's recent blog post for a link to the original research note as well as observations about the conception's widespread adoption: <http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/>.
- A petabyte, which equals 1 million gigabytes and 1,000 terabytes, is approximately the storage space consumed by the movie Avatar, one-twentieth of the storage space consumed by the photographs comprising Google Street View, and the amount of data generated by the CERN Large Hadron Collider (LHC) experiments each second. See: <http://cacm.acm.org/news/110048-cern-experiments-generating-one-petabyte-of-data-every-second/fulltext>.
- See <http://www.wired.com/science/discoveries/magazine/16-07/pb_theory>.
- IEEE Intelligent Systems, March/April 2009. <http://www.csee.wvu.edu/~gidoretto/courses/2011-fall-cp/reading/TheUnreasonable%20EffectivenessofData_IEEE_IS2009.pdf>.
- Unfortunately, like the term "big data," "data science" has been garbled in the popular press to the extent that prominent analysts have come to express frustration with it. In the blog associated with her Columbia University "Introduction to Data Science" course, Rachel Shutt wrote, "I don't call myself a 'data scientist.' I call myself a statistician. I refuse to be called a data scientist because as it's currently used, it's a meaningless, arbitrary marketing term." <http://columbiadatascience.com/2012/10/04/next-gen-data-scientists/>. Similarly, the mathematician/blogger Cathy O'Neil declared, "There are far too many posers out there in the land of data scientists, and it's getting to the point where I'm starting to regret throwing my hat into that ring." <http://mathbabe.org/2012/07/31/statisticians-arent-the-problem-for-data-science-the-real-problem-is-too-many-posers/>. We sympathize with Shutt's and O'Neil's sentiments and attempt to use the term in a way that is consistent with their writings.
- In a blog entry, Drew Conway memorably used a Venn diagram to define data science. According to Conway's characterization, data science is the intersection of three circles representing substantive expertise, math and statistics knowledge, and hacking skills. Conway's excellent definition could perhaps be improved with the addition of the abilities to identify and frame problems as well as communicate effectively. <http://www.drewconway.com/zia/?p=2378>.
- The phrase "data science" in the sense used here originated in a prescient 2001 essay by the Bell Labs statistician and data visualization pioneer William Cleveland: "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics" <http://cm.bell-labs.com/cm/ms/departments/sia/doc/datascience.pdf>. Among other things, Cleveland called for "computing with data" and the evaluation of statistical computing tools to be added to traditional university curricula. It is notable that Cleveland was a collaborator of the other legendary Bell Labs statisticians John Tukey and John Chambers. Tukey is known as the father of exploratory data analysis [EDA] and Chambers was the inventor of the S (as in "statistics") statistical computing language. The original S language, which implemented many of Tukey's and Cleveland's data exploration and data visualization ideas, is the basis for the open-source R statistical computing environment. Today, R is the lingua franca of applied statistics and has played no small role in bringing Cleveland's vision of "applied statistics as data science" to life. Ironically, although Tukey himself never used computers, he coined the terms "bit" and "software," and Chambers discussed him as an inspiration for the development of S. See "John Tukey and 'Software'" by John Chambers: <http://cm.bell-labs.com/cm/ms/departments/sia/tukey/tributes.html>.
- For example, one of the most successful innovations with regard to personal insurance applications of the 1990s was the adoption of credit scoring to select and price personal auto and homeowner policies. Not only is credit score predictive, it is one of the most powerful rating variables in situations where its use is allowed. This is likely because credit behavior is a proxy for non-directly measurable traits that also manifest themselves in risky behavior. It would be misleading to characterize such innovations as mechanical processes of putting numbers into an algorithm and letting computers find the relevant patterns. Rather, deciding to test this novel application of financial stability information was a minor innovative breakthrough.
- Which in turn brings to mind Truman Capote's famous takedown of Jack Kerouac: "That's not writing, that's typing."
- So-called p-values are often used as measures of "significance" which can be thought of as a type of "surprise." A result with a very low p-value (such as 18 heads in 20 tosses) means that we would be very surprised to get such an extreme (or more extreme) result were the coin in fact fair. The problem is that tossing a large number of fair coins—analogous to performing a large number of medical trials or examining a large number of correlations in a big data base—is virtually guaranteed to produce a number of such "significant" results purely due to random chance.
- The epidemiologist John Ioannidis made essentially this point about medical research in a widely cited 2005 article entitled, "Why Most Published Research Findings Are False." <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/>. Ioannidis's point is illustrated by the recent New England Journal of Medicine article "Chocolate Consumption, Cognitive Function, and Nobel Laureates" by Franz Messerli. The article reports "a close, significant linear correlation (r=0.791, p<0.0001) between chocolate consumption per capita and the number of Nobel laureates per 10 million persons in a total of 23 countries." The article speculated on the effects of certain antioxidants found in chocolate on improved thinking ability. <http://www.nejm.org/doi/full/10.1056/NEJMon1211064>. The article, which was intended as a Nobel prize-season parody, was seriously reported in major media outlets, for example: <http://www.forbes.com/sites/larryhusten/2012/10/10/chocolate-and-nobel-prizes-linked-in-study/>. Note that while the article is a parody, the highly "statistically significant" correlation is an actual fact of the dataset studied by Dr. Messerli. This illustrates that studying multiple correlations is bound to lead to results that are "significant" in a certain technical sense, but in substantive terms are either misleading or simply meaningless. It is also interesting to note that the international team of physicists that announced the discovery of the Higgs boson earlier this year, mindful of the problem of multiple comparisons, used an extremely low "five-sigma" p-value (< 0.000001) threshold. See for example: <http://normaldeviate.wordpress.com/2012/07/11/the-higgs-boson-and-the-p-value-police/>.
- <http://www.xkcd.com/882/>.
- Amusing examples are recounted in the Wall Street Journalarticle, "If TiVo Thinks You Are Gay, Here's How to Set It Straight". <http://online.wsj.com/article/SB1038261936872356908.html>.
- Paul Meehl, the quantitative psychologist who pioneered the "Actuarial versus Clinical Prediction" school of research, identified a major reason why models can be wrong and dubbed it "the broken leg problem." Suppose a model predicts with high accuracy someone's attendance at a movie theater every Friday. The model might fit the historical data perfectly, yet provide the wrong prediction if the person suffered a broken leg earlier in the week. Someone who has both this detailed knowledge and also understands the inherent limitations of models will know enough to ignore (or at least down-weight) the model indication in this case. See the 1989 Science survey article "Clinical versus Actuarial Judgment" by Dawes, Faust, and Meehl. <http://apsychoserver.psych.arizona.edu/JJBAReprints/PSYC621/Dawes_Faust_Meehl_Clinical_vs_actuarial_assessments_1989.pdf>.
- This was a major theme of our previous Deloitte Review, Issue 10, article, "A Delicate Balance."
- Even the academic statistics community itself has undergone considerable change in recent decades. Some comments from a recent blog post of the Carnegie-Mellon statistician Cosma Shalizi vividly convey a sense both of this change, as well as the heterogeneity of university-level statistical training: "Suppose you were exposed to that subject as a sub-cabalistic ritual of manipulating sums of squares and magical tables according to rules justified (if at all) only by a transparently false origin myth—that is to say, you had to endure what is still an all-too-common sort of intro. stats. class—or, perhaps worse, a 'research methods' class whose content had fossilized before you were born. Suppose you then looked at the genuinely impressive things done by the best of those who call themselves 'data scientists.' Well then no wonder you think 'This is something new and wonderful;' and I would not blame you in the least for not connecting it with statistics. Perhaps you might find some faint resemblance, but it would be like comparing a child's toy wagon to a Ducati. Modern statistics is not like that, and has not been for decades… the skills of a 'data scientist' are those of a modern statistician." <http://masi.cscs.lsa.umich.edu/~crshalizi/weblog/925.html>.
- See Thinking, Fast and Slow by Daniel Kahneman. Kahneman reported that his landmark research exploring systematic biases in human cognition and leading to behavioral economics was motivated by his experience teaching statistics to university students in Israel. He found what he was teaching to be very unintuitive. This observation ran counter to a then-prominent theory that humans are natural statisticians. In the language of Thinking, Fast and Slow, "System 1" thinking is rapid and biased towards belief and causal narratives rather than skepticism and rational analysis. System 1-style thinking accounts for the bulk of our mental operations and—here's the rub—is very poor at statistical reasoning. "System 2" thinking is slow, effortful, and strives for logical coherence rather than unsupported narrative coherence. The phrase "fooled by randomness" is of course borrowed from the Nassim Nicholas Taleb book of the same name. Regarding Taleb, Kahneman writes, "The trader-philosopher-statistician Nassim Taleb could also be considered a psychologist. … Taleb suggests that we humans constantly fool ourselves by constructing flimsy accounts of the past and believing they are true." See our previous Deloitte Review article "A Delicate Balance" for a discussion of how the Kahnenan school's findings relate to some of the organizational biases that can impede analytics projects.
- This quote is attributed to the Stanford statistician Brad Efron, who is best known as the inventor of the statistical technique known as bootstrapping. Another Efron quote partially inspired this essay: "In some ways I think that scientists have misled themselves into thinking that if you collect enormous amounts of data you are bound to get the right answer … the fallacy that if we could just get it all inside the computer we would get the answer." <http://www-stat.stanford.edu/~ckirby/brad/other/2010Significance.pdf>.
- An intriguing election-year example concerns differing treatments of the presidential candidates in the traditional and social media. The authors thank our colleague Michael Greene for bringing this example to their attention. <http://www.journalism.org/commentary_backgrounder/how_social_and_traditional_media_differ_their_treatment_conventions_and_beyo>.
- A dramatic example of "decision fatigue" (a.k.a. "ego depletion") is judges' parole decisions being strongly influenced by time of day, and presumably blood sugar level. <http://www.nytimes.com/2011/08/21/magazine/do-you-suffer-from-decision-fatigue.html>.
- In Thinking, Fast and Slow (Chapter 17), Daniel Kahneman provided this example to illustrate how regression to the mean tends to be misinterpreted in causal terms.
- It is interesting to speculate whether behavioral economics helps account for an apparent bias toward hoarding data that one often encounters. The tendency of humans to feel losses more keenly than commensurate gains is known in the behavioral economics literature as "loss aversion." As the availability of computer storage power grows exponentially, perhaps there is a tendency to the loss avoidance strategy of storing as much data as possible to ward off the fear of future feelings of loss that would result from having thrown out something useful. This is not to say that organizations should never err on the side of keeping information with no obvious or immediate use; only that the process should be guided by cost/benefit estimates rather than a default strategy of keeping nearly everything.
- H. A. Simon, (1971), "Designing Organizations for an Information-Rich World," in Martin Greenberger, Computers, Communication, and the Public Interest, Baltimore, MD: The Johns Hopkins Press, pp. 40–41.
- Popular books such as Moneyball and Ian Ayres's Super Crunchers have offered considerable anecdotal evidence for this, as have our previous Deloitte Review articles "Irrational Expectations" and "Beyond the Numbers". <http://www.deloitte.com/view/en_US/us/Insights/Browse-by-Content-Type/deloitte-review/24cda920f718d210VgnVCM2000001b56f00aRCRD.htm>. For corroborating academic evidence, see Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? by Erik Brynjolfsson, Lorin Hitt, and Heekyung Kim. The authors find that "data-driven decision making" was associated with 5–6 percent higher productivity in a sample of 179 publicly traded firms studied. <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486>.


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For a lot of people, the new year represents a fresh start and clean slate to start the new year off right. As a college student, the new year is the sign of a fresh semester and the opportunity to take new classes and earn better grades. Sadly though for me, this new semester will also be my last semester, as I will be graduating from the University of Virginia in May. Although I certainly have no regrets (yet!), here are my top new year's resolutions for making my last semester, my best semester.
1. Attend a UVa basketball game and sit in the student section
Although I'm embarrassed to admit, especially because I'm such an avid sports fan, I have never gone to a home UVa basketball game (unbelievable, right?). There's certainly something special about college athletics compared to the pro game, and I crave that last opportunity to root for my alma mater. Plus, there's nothing more fun than chanting "Hoo's your daddy?" at the opposing players and coaches!
2. Cure myself of 4th Year apathy
Everybody knows that seniors, whether it be in high school or college, have a tendency to care less about school and more about having fun. While its certainly fun to be a 2nd semester senior, finishing out school on a high note will be a good boost when god forbid I enter the real world next fall. On the bright side, I'm taking all electives next semester which should certainly make this resolution easier to accomplish.
3. Streak the Lawn
Hey! It might be an archaic, nonsensical UVa tradition but it is a rite of passage for all 4-th years nonetheless. The route from the famed rotunda down toward the statue of Thomas Jefferson might be cold, and snowy but the reward is so worthwhile. What better time to engage in this distinctly UVa tradition then when I'll be walking down this same route maybe a couple weeks later to finally graduate!
So there you have it, my three goals or resolutions for the upcoming spring semester. A perfect mix of pleasure and hard work should make these goals attainable, but hey, isn't the best part about making new year's resolutions is not completing them?
Tell us what your New Year's resolution is in the comments below to be entered into today's coffee giveaway!
Comment on our blog and be entered in to win one of our weekly coffee giveaways. Each winner will receive a $10 gift card, to one of the following: Starbucks, Dunkin' Donuts, or TBD. Winners will be chosen at random and announced each Wednesday.

subset: commoncrawl


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Knitter's Review
January 3, 2013
Our food has numerous protected designations of origin. Vidalia onions must originate from the area around Vidalia, Georgia; Roquefort, from the Combalou caves of Roquefort-sur-Soulzon.
Yarn? Not so much.
But we do have certain expectations. Say the term "Donegal tweed," for example, and most people think of robust, crunchy, hard-wearing woolens made in County Donegal, Ireland.
This week let's take a look at a new Donegal yarn called Soft Donegal, still spun in County Donegal in the traditional way, but with one big difference: It's made from super-soft imported Australian Merino wool.
Coming up in Boston
In the Forums: Bigger Blankies
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As ever, I thank you for letting me be a part of your knitting life.
I'll talk to you again on January 17th!
P.S. Do you have friends who would enjoy Knitter's Review? If so, don't hesitate to forward this email to them.
Knitter's Review Poll
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Participate in our Knitter's Review Poll and you'll see an instant tally of all the votes.
On the Cover
Soft Donegal on the swift
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Help your kids prepare for the Ladybug Festival with these foldable paper fans.
Watch Ni Hao, Kai-lan only on Nick Jr.
Print out the three page Fan Printable. Cut them out along the dotted lines. (This is a great step for your child to do with a pair of safety scissors.)
Use a glue stick to attach the three pages together, overlapping where indicated. Allow time for the glue to dry.
Place the fan printable with the color side down. Start at one side and fold back a 1-inch section of the paper. Then flip the printable over and fold back another 1/2 inch section. Use the alternative color stripes as a guide. Repeat this process (often called a fan fold) until you reach the other side of the printable.

 Once you get the folds started, your kids can finish up the folding.
Staple the base of the fan together with two staples (just under the yellow dotted line).
Use a glue stick to attach one craft stick to each side of fan where indicated. The craft sticks should extend at least two inches past the end of the fan to form a handle. Allow time for the glue to dry
Extend the fan pulling the craft sticks together at the bottom, and you're ready for the Ladybug Festival!
Show your kids how to tell time with this paper plate peek-a-boo clock!
Make a simple decoration that's SUPER cute
Use words--and make a face--to explore feelings together this no-sew pillow
An imaginative stage for your kid's imaginative mind
Say "ni hao" with the el-egg-ant springtime craft from Kai-lan
We'll shed some light on how to put recyclable jars to good use!
Nothing says "Gong Hay Fat Choy" more than these hand-made money envelopes!
Wear your heart on your CD sleeve this Valentine's Day!
Let sweet holiday wishes bloom with these simple, customized cookie boxes.
It's easy to stay warm when Mr. Sun is overhead
Pick, print, tape -- PRESTO! Enchanting lanterns to put on display!
Face those winter winds with a cheery smile from Kai-lan!
Turn your preschooler into Kai-lan's koala pal, Tolee!
Your kids will roar with delight once you transform them into this friendly tiger!
For the cutest costume at a party, come as Tolee!
A creative paper craft that becomes child's play
Take the game outside or inside and toss to your heart's desire
Your kid's kicks are kickin' with these decorative shoelace covers
Cool off with a little help from Kai-lan
A handy case with a friendly face!
It's perfect for your kids' Pop!
Make a super special card for Mom!
Kai-lan and friends are preparing for spring. Watch their garden grow!
Go, tiger! Step it up with a craft from Kai-lan's best friend
All aboard the Kai-lan Express! Next stop, fun and creativity for you and your child.
Let Tolee's favorite friend give you a hand at bathtime.
Jump in the tub with Hoho!
Make sweethearts super happy with this crafty card
Make your little sweetheart "super happy" with a treats-filled heart box!
Turn a "roarrr!" into a "purrr" with this cuddly pillow


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Lawmakers OK money to repair area schools
Published: Tuesday, May 21, 2013 at 3:30 a.m.
Last Modified: Monday, May 20, 2013 at 11:07 p.m.
MONTGOMERY | The Alabama Legislature on Monday approved selling bonds to borrow $30 million to repair tornado-damaged schools, including Alberta, University Place and Holt elementary schools in the Tuscaloosa area.
The Senate voted 26-6 Monday afternoon to give final passage to the bond legislation. The bill now goes to the governor for his review.
Alberta will receive $3 million and University Place and Holt will receive $2.5 million each. Two other schools damaged in the April 27, 2011, tornado, Phil Campbell High School and Plainview High School, will receive $6.4 million and $604,000, respectively.
The bill provides $15 million for Mobile's Murphy High School, which was damaged by a tornado on Christmas Day.
Reader comments posted to this article may be published in our print edition. All rights reserved. This copyrighted material may not be re-published without permission. Links are encouraged.


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About the Book
"Stunning and stirring."<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" />
—Boston Globe
In The Last of His Kind, renowned adventure writer David Roberts gives readers a spellbinding history of mountain climbing in the twentieth century as told through the biography of Brad Washburn, legendary mountaineering pioneer and photographer. Jon Krakauer, author of Into Thin Air, has praised David Roberts, saying, "Nobody alive writes better about mountaineering"—and nowhere is that truth more evident than in this breathtaking account of the life and exploits of America's greatest mountain climber.
American Brad Washburn's impact on his protégés and imitators was as profound as that of any other adventurer in the twentieth century. Unquestionably regarded as the greatest mountaineer in Alaskan history and as one of the finest mountain photographers of all time, Washburn transformed American attitudes toward wilderness and revolutionized the art of mountaineering and exploration in the great ranges. In The Last of His Kind, National Geographic Adventure contributing editor David Roberts goes beyond conventional biography to reveal the essence of this man through the prism of his extraordinary exploits from New England to Chamonix, and from the Himalayas to the Yukon. An exciting narrative of mountain climbing in the twentieth century, The Last of His Kind brings into focus Washburn's deeds in the context of the history of mountaineering, and provides a fascinating look at an amazing culture and the influential icon who shaped it.
Educator and Librarian Resources
Product Details
- Imprint: William Morrow Paperbacks
- On Sale: 06/15/2010
- Trimsize: 5.313 in (w) x 8 in (h) x 0.836 in (d)
- Pages: 368
- List Price: 14.99 USD
- BISAC1: BIOGRAPHY & AUTOBIOGRAPHY / General
- BISAC2: BIOGRAPHY & AUTOBIOGRAPHY / Artists, Architects, Photographers


text:
Criminal Jury
Definition 1: "Sexual intercourse" means
vaginal intercourse, anal intercourse, fellatio or cunnilingus between
persons regardless of sex. Its meaning is limited to persons not married
to each other. Penetration, however slight, is sufficient to complete
vaginal intercourse, anal intercourse or fellatio and does not require
emission of semen. Penetration may be committed by an object
manipulated by the actor into the genital or anal opening of the
complainant's body.
Source: General Statutes § 53a-65
(2) (applies to Part VI: Sex Offenses, §§ 53a-67 -- 53a-90a).
Commentary: The definition of
sexual intercourse includes four alternative means of performing sexual
intercourse that are not conceptually distinct. See State v.
Anderson, 211 Conn. 18, 35 (1989) ("[t]he several ways in which
sexual intercourse may be committed under General Statutes § 53a-65 (2)
are only one conceptual offense"); see also State v. Griffin, 97
Conn. App. 169, 181-85, cert. denied, 280 Conn. 925 (2006) (the court's
instruction that sexual intercourse included vaginal intercourse or
cunnilingus did not constitute a non-unanimous instruction of two
conceptually distinct alternatives).
"[T]he phrase 'penetration, however slight,'
evinced an intent to incorporate, into our statutory law, the common-law
least penetration doctrine." State v. Albert, 252 Conn. 795, 804
(2000) (concluding that penetration, however slight, of the external
genitalia is sufficient to constitute vaginal intercourse). Albert
has a lengthy discussion of the legislative history of this definition. See also
State v. David N.J., 301 Conn. 122, 154-60 (2011) (supplemental instruction on the meaning of penetration).
In State v. Scott, 256 Conn. 517, 529
(2001), the Supreme Court held that the trial court's failure to
instruct the jury that penetration is an element of the crime of sexual
assault in the first degree by fellatio deprived the defendant of a fair
trial. The court concluded that "the penetration requirement is met,
for purposes of first degree sexual assault by fellatio, when a
perpetrator forcibly inserts his penis into the victim's mouth. . . .
The act of licking a penis, by contrast, does not satisfy the
penetration element of §§ 53a-70 and 53a-65 (2)." Id., 534-35.
The Court concluded in State v. Kish, 186
Conn. 757, 762-66 (1982), that the definition of sexual intercourse
specifically omits cunnilingus when discussing penetration because
penetration is not an element of the crime when cunnilingus is charged.
The limitation specified in the definition that
it only applies to "persons not married to each other" incorporates the
common-law marriage exemption to a charge of rape. See State v.
Scott, 11 Conn. App. 102, 112-19, cert. denied, 204 Conn. 811
(1987). Note that General Statutes § 53a-70b, Sexual assault in spousal
or cohabiting relationship, contains its own definition of sexual
intercourse, which omits this limitation.
Definition 2: "Sexual intercourse" means
intercourse, real or simulated, whether genital-genital, oral-genital,
anal-genital or oral-anal, whether between persons of the same or
opposite sex or between a human and an animal, or with an artificial
Source: General Statutes §
53a-193 (9) (applies to Part XX: Obscenity and Related Offenses, §§
53a-194 -- 53a-210).
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text:
Georgia's Gentle Path in the SEC
Georgia is one win away from playing for the national championship. To its critics, if Georgia gets that victory against Alabama on Saturday, it would double the team's number of quality wins for the season.
The Bulldogs are the only team to beat Florida, now the No. 5 team in the country. They lost by four touchdowns to South Carolina, now the No. 11 team in the country. They beat Vanderbilt by 45 points, and while the Commodores are 8-4, they haven't been ranked in the top 25 by AP voters at any time this year. And that's about it for tough opponents. Georgia has played five other SEC teams, which combined for six wins in 35 conference games against everyone other than Georgia. And none of Georgia's three nonconference FBS opponents has a winning record this season.
That doesn't necessarily mean that Georgia doesn't deserve the shot at the title berth. It tied Florida atop the top-heavy SEC East, and it has to beat No. 2 Alabama in the SEC championship game to advance to the BCS title game against Notre Dame. Two wins over top-five opponents out of two games would be impressive.
But it does show how, even within the same conference, two teams can have very different strengths of schedule. Georgia's schedule strength, according to Sports Reference, is 2.06 points per game above average — tougher than the average schedule nationwide but the easiest in either division of the SEC. Florida's, though, is 6.81 points per game above average, and Missouri, which had the hardest schedule in the nation, faced opponents that were 8.78 points per game better than the average team. Each team plays in the SEC East with Georgia.
That spread between Missouri and Georgia is the largest between any pair of teams in the same BCS conference. It's the second largest overall behind the eight-point gulf in schedule strength between the WAC's Idaho, which played tough nonconference foes LSU, BYU and North Carolina, and Texas-San Antonio, which played three non-major opponents and two ranked in the bottom third of the FBS by Sports Reference.
Some of the gap in schedule strength between 5-7 Missouri and 11-1 Georgia can be explained by the simple point that Georgia got to play Missouri and Missouri had to play Georgia. (Neither team attempted the difficult feat of playing itself.) For similar reasons, the best teams in each conference typically have easier schedules than the worst teams. In the SEC and in eight of the other 10 FBS conferences, there was a negative correlation between strength of schedule and strength of teams, as measured by Sports Reference's Simple Rating System.
But teams also have some control over their schedule strength. Last year, Georgia also won the SEC East, with a 7-1 conference record identical to that of this year's Bulldogs team, while playing the second toughest schedule among the division's six teams. Partly that was the result of factors outside Georgia's control: Its annual rivalry game against Georgia Tech represented a tougher test last year than this year. But Georgia also opened last season against a very tough Boise State team that finished No. 8 in the AP rankings.
Florida this year has a much tougher schedule than Georgia's in part because its annual rivalry game, against Florida State, was particularly difficult this year. Meanwhile, Alabama's only strong nonconference opponent, Michigan, left the Crimson Tide with the fourth-weakest schedule among the 12 SEC teams. That doesn't mean that either contestant of the SEC title game had an easy ride there: Alabama's and Georgia's schedule strength is higher than every conference's average mark besides the Pac-12 and Big 12. But relative to most of their SEC peers, they have had gentle paths to the Georgia Dome this Saturday.


text:
Good morning and welcome to Summit Up, the world's only daily column that is looking for some ice fishing buddies.Dan Williams of Dillon dropped us a line recently asking for a bit of assistance. He wrote thusly:"I am a paraplegic and just recently moved back to Summit County and looking for anyone that would go with me ice fishing at Green Mountain Reservoir. I have most of the gear but need some assistance. If anyone has the time it would be greatly appreciated."So there you have it. If you like to ice fish, give Dan a call. We're sure you'll both have a great time. You can reach him at (970) 527-5429.***Changing gears, it look like it's almost time for spring training to kick off in the sunny parts of the ol' U.S. of A. (Arizona & Florida) and we're starting to get an itch to kick back with a cold one on a warm afternoon and cheer on the boys of summer. But until then, we hear that snow is coming our way, so get out there and enjoy it.
Back to: News
February 6, 2013
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text:
NEW: For a prettier blog interface, see the Wordpress version!
|Merge fixes for planner-id : E-Mail from Jim Ottaway {{Tasks:12}} (bug)
|Fix errors in remember-el package : E-Mail from Romain Francoise (debian)
|Reply about project ideas and Vocal Village : E-Mail from Mark Chignell (2005.02.16 FurtherStudies grad gradschool)
|Reply about business model : E-Mail from Mark Chignell {{Tasks:15}} (2005.02.16 gradschool grad)
|Check out Slashdot from 2005.02.15
1. Managing my mail
Mail splitting
Yes, yes, the Gmail way is to keep everything in one folder and then use searches to filter your messages. Still, I like being able to glance at my screen and see 2 personal messages and 3 planner-related messages.
Topics and group hiding
I use Gnus topics to divide my mail into folders and subfolders. Mail groups are hidden unless they have mail. Some groups like mail.misc and mail.planner are generally useful, so I keep them visible even if they don't have unread mail.
Gnus allows you to automatically score threads and messages up and down based on various criteria. You can set it to completely hide boring messages, show them in a different color, show interesting messages in a different color, etc.
On most mailing lists and newsgroups, I don't bother reading message bodies. I just scan through subjects, hitting k to kill entire threads I don't find interesting. Gnus remembers what threads I've killed, marks them as read, and scores them down automatically. It also scores up messages containing certain keywords, replies to my posts, and threads I found interesting.
Integration with my contacts
I put interesting people in my BBDB contact database. Gnus indicates messages from them with a little + beside their name in the message summary. If someone I know is interested in a thread, I might find it interesting as well.
Hiding and article washing
I've set Gnus up to hide quoted text. This makes browsing through threads much easier because I can concentrate only on the the new parts. I can hit a few keys to expose sections of the quoted text if the replies aren't immediately obvious from the context.
I can also set it up to remove ads at the bottom of messages, particularly long signatures, To: lines with more than N recipients, that sort of thing. I can tell it to strip out HTML, too.
Displaying parent article
Sometimes I'll jump into the middle of a thread. I can use ^ to get to the parent message.
I use swish++ to index and search through my personal and planner-related mail.
Planner hyperlinks
Most of my tasks come in through e-mail. Planner lets me keep track of my TODOs easily by automatically hyperlinking to the mail message I'm looking at when I create a task. Dealing with a few items on my TODO list is much easier than going through a large inbox! =)
On Technorati: productivity, emacs, mail
I'd love to hear about any questions, comments, suggestions or links that you might have. Your comments will not be posted on this website immediately, but will be e-mailed to me first. You can use this form to get in touch with me, or e-mail me at [email protected] .


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Right out of the gate Maze just rips open the first set. Page rocks! Right into a cool sounding Fee, complete with megaphone verses. Fishman continues drumming right into a nice, tight ATR. Trey flubs the very begining notes, but gets right back on. A little funk comes along with a solid, dynamic Melt. Bouncing is standard, but also well done. Pen is the bluegrass call tonight and is pretty tight. Holy crap! Another heavy hitter. Chalk Dust rocks, as usual. Mellowing out, the Horse is pretty well played by Trey. Silent is getting better, but still is not quite tight. Reba also has it's problems tonight. Great jam at the end, but Mike struggles early and then Trey and Page don't quite match up in the middle parts. The crowd quiets down at Trey's request for a nice Adeline. Really good Antelope to close the set. Fun intro, nice jam, very dynamic and tight.
The recording cuts into the intro of a rough sounding Rift. Not just the recording, but Trey also has problems throughout the solo section. Guelah is well done and Trey explains the balls before the Fly. Fluffhead gets cut on the recording from the "Asking me for change", right to the "Powerful pills!" line. Fluff's Travels hits a few bumps in the road tonight. Not too bad. Mike's Song is well played with great dynamic vocals from Mike. Great jam. Hydrogen was better than average, but still has it's misses and bumps. Weekapaug was great! Mike really shines tonight. Time to mellow out again, this time with a smooth Lawn Boy. Page gets the nod for the solo tonight. Ice was good, but still needs more polishing. My Sweet One was so frantic that they almost lost the handle coming out of the solo section. Otherwise good with a bonus Big Ball Jam. This one is the first one I've heard that sounds like what would eventually become the "real" Big Ball Jam, with Fish playing a bluegrass style roll, and Trey tags the standard ending on this one. Full on HYHU for Fish as he comes out and does a pretty mellow Rosie. Like he wasn't quite "all in" tonight. Well, I was thinking about only giving this show four stars because of Reba and Fluff, but A-Train changed my mind. Absolutely gorgeous jazz. Right into a great Cavern closer.
Many, many calls for Harpua throughout the night fall on deaf ears as we have a really nice Bold as Love for the encore tonight. Page does the vocal job well and Trey wails away like his idol Jimmy.
Really good show! Like I said, maybe four to four and a half stars, but the A-Train was so freaking good! Five stars, definitely worth the listen. Mike had great moments, but Page might be the hero of this one in my opinion.


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Welcome to St. Angelo's Pizza!
The Award Winning St. Angelo's in Atlanta has Pizza, Wings, and More! 35 years of family history has gone into this local treat! Come in and enjoy delicious Pizza, Salads, Wings, Gelatos, a great Beer and Wine selection, and much more!
Click here to see what Best Self Magazine has to say about us!


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Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).
Criteria for Performance Excellence
The Baldrige Criteria for Performance Excellence provide a systems perspective for understanding performance management. They reflect validated, leading-edge management practices against which an organization can measure itself. With their acceptance nationally and internationally as the model for performance excellence the Criteria represent a common language for communication among organizations for sharing best practices. The Criteria are also the basis for the Baldrige Award process.Optional Self-Analysis Worksheet-- MS Word Version
This optional worksheet can be used in conjunction with the Criteria for Performance Excellence to accelerate your improvement journey by doing simple self-analysis, after responding to the Criteria questions. Use this worksheet to identify your own strengths and opportunities for improvement and to establish goals and action plans.
You need to have Acrobat Reader installed on your computer to view the PDF file. If you do not have Acrobat Reader installed on your computer, download the program at http://get.adobe.com/reader. People with visual disabilities can download tools and information at http://access.adobe.com to help make Adobe PDF files accessible.
Optional Self-Analysis Worksheet - Microsoft Word Version
Baldrige Customer Service:


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Harper Chapel United Methodist Church will hold their First Annual Charity Golf Tournament on Sunday, Sept. 22, 1 p.m. at The Oaks Golf Course at Tan-Tar-A. The event will be a four-person scramble, shotgun start. Proceeds benefit the local community through the many ministries of Harper Chapel UMC, with a portion of the proceeds donated to Medical Missions for Christ.
Join us for the fun! $75 per golfer, $300 per team; sponsor a hole for only $200. Prizes will be awarded for closest to pin, longest drive, low score and high score. Lunch will be provided. Contact the church office at (573) 348-2617 to register.
Location, ST | website.com


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Welcome to Ag 1 Source - The Leaders in Agricultural Talent
Ag 1 Source is the most trusted and reliable recruiting firm in the agriculture industry. Our recruiters come directly from the specialties in which they recruit, leading to highly credible and unparalleled knowledge to identify and attract the best talent. As your trusted advisors, we address your ag recruiting needs confidentially.
Destination Employer
Employees are your most important asset. A company recognized as an employer of choice will have a significant competitive advantage in the market.
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Referral Bonus
Our referral bonus program maximizes our outreach and ability to network with the very best candidates and industry experts.
We have a track record of finding the best talent for the most challenging searches. Read what our clients say about our work.


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Hydrogen and Fuel Cells Interagency Working Group Launches Web Portal
August 31, 2010
The Hydrogen and Fuel Cells Interagency Working Group (IWG) recently launched a new portal Web site: www.hydrogen.gov. Hosted by the U.S. Department of Energy (DOE), the site is a resource for participating agencies and group stakeholders. The IWG consists of several federal agencies, including DOE's Fuel Cell Technologies Program, that exchange information about hydrogen and fuel cell research, development, demonstration projects, and related activities. Representatives from the agencies meet regularly to share research results, technical expertise, and lessons learned about program implementation, technology development, and deployment. They also coordinate interagency programs and activities related to safe, economical, and environmentally sound hydrogen and fuel cell technologies.
The new site serves as both an information resource on hydrogen and fuel cells and a portal to participating agency activities.


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Climate Change
Did you know?
Fish & Fishing in Wisconsin
Great Lakes Ships
Green Gardening
Invasive species
Native Americans
Wastewater Treatment
Water Conservation
Water Gardening
Water Supply
Water Tourism & Exploration
Students of University of Wisconsin should refer to the Water Research Guide to get scholarly sources and help in using MadCat. Those not affliated with the University should start with these Web sites:
USGS Water Resources (Wisconsin Water Science Center)
The United States Geological Survey (USGS) Water Resources Division is the first place to look for online water data. NWISWeb contains selected water-resources data for approximately 1.5 million sites across the United States, selected territories and border locations, from 1857 to present. Site also proves water quality, water use, acid rain, and other types of water data as well as program information, full-text publications, links to state USGS programs and other sources of information.
Great Lakes Information Network (GLIN)
GLIN offers an abundance of data and information about the Great Lakes region's environment, economy, tourism, education and more. Plus, this well-organized and easy-to-use site provides many links to additional sources of reliable information.
NAL Water Quality Information Center
The National Agricultural Library's (NAL) Water Quality Information Center Web site emphasizes water quality and agriculture. Examples of water quality information available include bibliographies, announcements of upcoming meetings, a listing of Internet discussion groups covering water issues, water-related databases, and annotated links to other Internet sites providing water quality information.
US EPA Office of Water
The Office of Water Web site provides EPA program information, water laws and regulations, links to regional offices, online publications, training information.
US Fish and Wildlife Service
Information about US Fish and Wildlife Service programs, but also has many links to other sites particularly for wetlands and invasive species.The topics in the left navigation have specific links on their page so be sure to click on them if interested in a specific topic listed. If you have a topic of interest that is not listed here, please be sure to askwater and we'll include it on our list. Good luck in your search!
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North Country Farmers Cooperative celebrates its incorporation
WHITEFIELD — After five years of working together farmers and bakers celebrated their incorporation as a cooperative business, bringing together their unique talents and experiences at a gala occasion held on Sunday afternoon at the Mountain View Grand.
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|Saving levels fall as inflation increases - Fri, 31 Dec, 2010
Too few people are using their savings accounts, according to new figures from the NS&I.
Only half of Britons are actually putting money into savings and the average amount they are putting asid [more]
|Santander sends wrong statements to customers - Wed, 29 Dec, 2010
Current account holders at Santander branches may have received the wrong statement this week after a printing error on 18 December.
The Spanish chain admitted the fault after 35,000 statements were [more]
|Small firms divided on how to deal with VAT rise - Tue, 28 Dec, 2010
Small businesses are divided on the best path to take in order to deal with January's VAT rise.
The Institute of Chartered Accountants in England and Wales (ICAEW) has advised SMEs to start pre [more]
|Car insurance claims soar in snowy weather - Fri, 24 Dec, 2010
Car insurance firms have revealed that the recent deluge of snow and ice has resulted in a huge increase in car insurance claims.
The AA reported that claims made on Friday 17 December were 10 per ce [more]
|Interest rates could start rising next year - Thu, 23 Dec, 2010
The Bank of England has indicated that interest rates will rise next year, potentially reaching 5 per cent.
Monetary Policy Committee member, Paul Fisher, stated that the base rate would 'normal [more]
|Savings protection to be upped in response to EU directive - Wed, 22 Dec, 2010
As of 31 December this year, consumers' savings will be protected up to the value of £85,000, according to changes to EU regulations.
The changes mean that more savings will be protected [more]
|Personal loans may facilitate pre-VAT rise purchases - Tue, 21 Dec, 2010
Increasing numbers of consumers are expected to start taking out personal loans to pay for large purchases, before the VAT rate rises to 20 per cent in January.
Sainsbury's Finance says it expec [more]
|Lloyds chief hopes to increase transparency - Mon, 20 Dec, 2010
Lloyds Banking Group's outgoing chief executive has promised to increase the transparency of the firm's accounts.
Eric Daniels spoke to the Sunday Telegraph about his plans to try to impro [more]
|Brits more in control of personal finances - Fri, 17 Dec, 2010
Following the recession, British people are more financially aware, according to analysis by Lloyds TSB.
Savings accounts, fixed rate savings bonds and ISAs are set to become more popular with the Br [more]
|Northern Rock offers new online savings account - Thu, 16 Dec, 2010
Northern Rock Savings has launched a new online account in the form of a new e-saver package.
The gross interest rate per annum is three per cent on the new online account, which Northern Rock feels [more]
|Consumers set to increase spending this Christmas - Wed, 15 Dec, 2010
Consumers are expected to spend more on Christmas this year, according to research carried out by Barclays bank.
Its current account holders alone claim they will spend a total of £48.9 billion [more]
|Households still under pressure from debt - Tue, 14 Dec, 2010
British households are still feeling the pinch of the recession despite continuing low interest rates, according to research carried out for the Bank of England.
The NMG Consulting study discovered t [more]
|Current account switchers should keep account numbers - Mon, 13 Dec, 2010
Co-operative Financial Services (CFS) boss, Neville Richardson, says that consumers switching their current accounts should be able to keep their account number.
This would entice more people to cons [more]
|Personal loan interest rates finally coming down - Fri, 10 Dec, 2010
Research published in late November shows that interest rates on personal loans are coming down.
Consumers have been hit with increasing interest rates on loans over the past year or more. These have [more]
|Not enough time spent researching current accounts - Thu, 9 Dec, 2010
Over 51 per cent of consumers claim they spend less than an hour researching bank accounts before signing up to a current account.
The Santander survey found that just 35 per cent spend more than an [more]
|Reality TV hotel up for sale - Wed, 8 Dec, 2010
A Brighton-based hotel which featured in reality TV series The Real Swiss Family Robinson, has been put up for sale.
Blanch House on Atlingworth Street in the fashionable seaside city has been put o [more]
|Last chance to secure £100 incentive for switching to Santander - Tue, 7 Dec, 2010
Current account holders who want to take advantage of Santander's five per cent interest rate account and the £100 switching incentive being offered, need to ensure they apply before midni [more]
|Firm fined for breaching Data Protection Act - Mon, 6 Dec, 2010
A Leicester-based employment services company has been fined following the theft of a laptop on which was stored personal details about 24,000 people.
A4e had stored the names, postcodes, income lev [more]
|Insurers poised for burst pipe claims - Mon, 6 Dec, 2010
The Association of British Insurers (ABI) says that this time of year is particularly busy for home insurers as people claim for burst water pipes caused by the freezing weather.
Homeowners are still [more]
|Making the most of your savings - Fri, 3 Dec, 2010
Recent research from Mintel has shown that 43 per cent of consumers in the UK plan to save this year but how can people to make the most of their funds?
Last year only 15 per cent of UK consumers pla [more]
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Business to offer designer items
Published: Friday, February 1, 2013 at 11:30 a.m.
Last Modified: Friday, February 1, 2013 at 11:30 a.m.
The grand opening of the Barrow Street Marketplace in Houma is scheduled for Saturday.
The new business at 1244 Barrow St. Suite 101 will open at 9 a.m. and feature five different vendors offering custom, handcrafted designer quality items.
Customers will have the chance to win a free gift during the opening if they like Barrow Street Marketplace on Facebook.
For future vendor opportunities, contact email@example.com or stop by the market.
Reader comments posted to this article may be published in our print edition. All rights reserved. This copyrighted material may not be re-published without permission. Links are encouraged.


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Spare 130 rd M4/M16 Series Mid Cap Magazine Tan by MAG
$ 9.99
Spare 130 rd M4/M16 Series Mid Cap Magazine Tan by MAG - Quantity:
Registered User Login:
- Great mid capI bought this along with the Echo 1 SCAR l tan because I like mid caps and the one that the gun comes with is garbage. Buy 4 and along with what ever high caps you have, ammo will be no issue. There is only one thing to do when you receive this mag. Take some silicon spray and but it down the shaft where the BBs go. Then fire through a couple clips and you are good to go. As the title says, it is a great mid cap.


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Hottest ticket: Justin Timberlake in Raleigh
Published: Thursday, May 9, 2013 at 3:15 a.m.
Last Modified: Thursday, May 9, 2013 at 1:43 p.m.
Justin Timberlake: The 20/20 Experience World Tour
PNC Arena
1400 Edwards Mill Road, Raleigh, N.C.
www.livenation.com or 919-861-2323
On sale: 9 a.m. Monday
Show date: 8 p.m. Nov. 13
Tickets: $90-$175
Reader comments posted to this article may be published in our print edition. All rights reserved. This copyrighted material may not be re-published without permission. Links are encouraged.


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REVIEW SNAPSHOT®by PowerReviews
Best Uses
Reviewed by 3 customers
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Best Uses
I was thrilled to open the Duluth package on it's arrival. I Absolutely Loved and have wanted an English Waxed Cotton Walking Jacket for such long time!!!! This had all the simple details I thought necessary; a rain placket w/ snaps, double quilted elbow patches, adjustable waist cinch(snaps), and although the double-headed zipper(a nice convenience) & shoulder epaulets(purely cosmetic) are not present, the styling looked so smart. Alas, I ultimately- and unfortunately- had to return my new "E.W.C" jacket due to the fit. I ordered an XL(the largest available), and even though the bodice was quite generous(almost too), the shoulders were too narrow, and sleeves were too short( approx. 2")... For those who are curious, I am 5' 9", 160lbs., & athletic/slim build...
---For Your Information--- only because it was an important deciding factor in My purchasing of this particular jacket: English made/cut clothing have always been true to a particular size/style, and suited me somewhat comfortably. So having misread the description for the "E.W.C".jacket's "Imported" as being made in England was disappointing---It was not---the jacket was made in China--- I just wanted the nexy buyer to be aware. Thank-you for allowing me the opportunity to save any one else the cost of return postage-
Best Uses
It's my daily wear. I don't agree with the temp rating; better for above freezing.
I haven't yet worn it in a down pour, but have no reason to doubt it being waterproof.
Below freezing the sleeves feel cold if you aren't wearing long sleeves. It's not got any insulation, just cotton canvas. And being canvas, it feels heavy; but that also reflects the quality and durability of the canvas.
The zipper is nice, easy to zip but if you don't zip it up expect to hear the zipper pulls clap against each other.
Overall, I feel it's still worth the money. I would give it 4.5 if I could as the noisy zipper and the temp rating shouldn't take it down a whole point.
Best Uses
I got this for an old never-worn small-game field coat given to me by my grandfather. The canvas field coat was still water-repellent so I decided to try this on a Levis Denim Jacket instead and it works great. It takes some time to work it in to the jacket, but the effort is well worth it. I can now run my jacket arm under a faucet and the water just rolls off. To use I just melted the wax down a little by setting the tin in hot water. This makes it easier to spread into the fabric with my fingers. Every 5 minutes I would then have to re-heat the water to get the wax warm again. Worked great.
Well, shoot.
Sorry to disappoint you, but this item sold out faster than we ever expected.
But if we can be so bold, there's plenty more great stuff available:
Shop Duluth's Top Picks for Men
Shop Duluth's Top Picks for Women
Original Wax Dressing
Item: 67522


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- Aug 19:
- Limo fire: Deadly flames sparked by drive shaft; no charges to be filed
- Aug 3:
- Limo fire: Inside the model that burned on San Mateo Bridge
- Jul 21:
- Limo driver's wife says they argued on phone shortly before deadly blaze on bridge
- May 8:
- Joyous wedding plans become tearful funeral preparations for family of limo fire victim
- Limo fire: No safety inspections for smaller California limos
- May 7:
- Strong bonds connected bride, friends killed in limo fire
- Limo fire: Survivor, driver recount horrific blaze that killed bride, four others
- May 6:
- Limo fire: Bride's driver was licensed, but maximum capacity exceeded
- Limo fire: Bride's ride to shower turns into deadly inferno
- California limo fire victims: List of dead and injured includes bride, friends
- May 5:
- Bride among five killed when limo bursts into flames on San Mateo-Hayward bridge
- Limo fire kills five women on San Mateo-Hayward Bridge
REDWOOD CITY -- The San Mateo County Coroner this morning released the names of the five women who were killed in the limousine fire last weekend on the San Mateo bridge.
They are: Michelle Estrera, 35, Fresno; Anna Alcantara, 46, San Lorenzo; Felomina Geronga, 43, Alameda; Neriza Fojas, 31, Monterey; and Jennifer Balon, Dublin.
Saturday night, the five, along with four other women who survived, were on their way to celebrate Fojas' recent wedding Saturday night when a fire erupted in the stretch 1999 Lincoln Town Car on the bridge.
The four survivors are: Jasmin Deguia, 34, of San Jose; Amalia Loyola, 48, of San Leandro; Mary G. Guardiano, 42, of Alameda; and Nelia Arrellano, 36, of Oakland.
Deguia and Loyola remain at Valley Medical Center, where they have been upgraded to fair condition. Guardiano and Arrellano were taken to Stanford Medical Center. Arrellano has been released, and it is not known if Guardiano is still hospitalized.
Check this website for more on this story.