Showing posts sorted by relevance for query big data. Sort by date Show all posts
Showing posts sorted by relevance for query big data. Sort by date Show all posts

Thursday, January 31, 2013

Big Data Means Big Testing

According to Scott Brinker in Search Engine Land (2013, January 30):
Big data is opening the door to the executive suite for a more hybrid analytical-creative method. The questions big data raises... have an answer.... The answer is big testing.... Big testing is about making a big deal about testing from the top down, fostering a culture of experimentation.... This last point will probably be the most challenging, as culture is not something that changes quickly. Executives need to make a conscious effort to encourage real testing — starting with the acknowledgement that good experiments prove or disprove hypotheses.... Big data is like fuel. Big testing will be the engine that turns it into forward momentum.
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The emergence of "big data" elevates and expands the role of advanced analytics, including hypothesis testing. Information age decision-makers are increasingly beholden to the business intelligence that big data stores can yield, which means acknowledging and fostering an interdisciplinary approach between information technology, advanced analytics, and business processes as synergistic drivers for improved enterprise decision-making in the 21st century. Big data means big testing!

Source: Brinker, S (2013, January 30), Why Big Testing Will Be Bigger Than Big Data, Search Engine Land.

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Wednesday, March 14, 2012

The 4 Biggest Problems with Big Data

The following are the four biggest problems with big data according to Tibco:
  1. A comprehensive approach to using big data.
  2. Getting the right information into the hands of decision makers.
  3. Effective ways of turning “big data” into “big insights.”
  4. Big data skills are in short supply.
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Source: The 4 Biggest Problems with Big Data (2012, March 14), Tibco.

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Saturday, August 03, 2013

Taming Big Data: The Emergence of Self-Service Business Intelligence (BI)

The infographic below created by IBM (2013) seeks to clarify key differences between so-called "small data" and "big data." The migration of data analytics from relational databases to in-memory systems is a vital step toward self-service production of business intelligence (BI)

[Click image to expand]

The migration of data from relational databases to in-memory database systems is good news for business intelligence (BI) analysts. Said another way, the era of self-service BI production has finally arrived.

Source: Taming Big Data: Small Data vs Big Data, Huffington Post.

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Friday, January 10, 2014

Barry Devlin: Business unIntelligence

Argues Dr Barry Devlin in his new book, Business unIntelligence: Insight and Innovation beyond Analytics and Big Data (2013):
The big data affair is coming to an end. The romance is over. Business is looking distraught in its silver Porsche, IT disheveled in the red Ferrari. Of course, it wasn’t just the big data. It started long ago when IT couldn’t deliver the data and business looked elsewhere to PCs and spreadsheets. It’s time for business and IT to renew their vows and start working on renewing their marriage of convenience.

When data warehousing was conceived in the 1980s, the goal was simple: understanding business results across multiple application systems. When BI was born in the 1990s, business needs were straightforward: report results speedily and accurately and allow business to explore possible alternatives. IT struggled to adapt. The 2000s brought demands for real-time freedom: the ability to embed BI in operations and vice versa. The current decade has opened the floodgates to other information, shared with partners and sourced on the Web. Divorce seemed imminent, IT outsourced.

But, almost invisibly, beyond the walls of this troubled marriage, a new world has emerged. A biz-tech ecosystem has evolved where business and IT must learn to practice intimate, ongoing symbiosis. Business visions meet technology limitations. IT possibilities clash with business budgets. And still, new opportunities emerge, realized only when business and IT cooperate in their creation—from conception to maturity. The possibilities seem boundless. But the new limits that do exist are beyond traditional capital and labor. The boundaries are imposed by the realities of life on this small blue planet afloat in an inky vacuum, with its limited and increasingly fragile resources and the tenuous ability of its people to survive and thrive in harmony with nature—within and without.

For the corporate world, Business unIntelligence will succeed when it brings insight into business workings, innovation into business advances, and integration into business and IT organizations. But in the broader context, in the real world in which we all must live, our success in the social enterprise that is business can be measured first and foremost in the survival of the cultures and communities of alleged intelligent man, homo sapiens, as well as all the other creatures of this tiny planet, and finally in our willingness to limit our growth and greediness and embrace the good inherent in each of us. It becomes incumbent on each and every one of us to integrate the rational and the intuitive, the individual and the empathic. To take stock of our personal decision making and reimage it in the vision of the world we want to bequeath to our children.
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We live in a world where information technology (IT) and enterprises (including governments) are landing in unexplored territories, where the rules of technological governance are colliding with the forces of decency across societies in real-time. Indeed, we live in exciting times...

Source: Devlin, B (2013), Business unIntelligence: Insight and Innovation beyond Analytics and Big Data, Perfect Paperback.

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Thursday, February 16, 2012

Deep Analytical Talent: Where Are They Now?

According to James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers of McKinsey (2011, May), the enterprise demand for "deep analytical talent" around the world could reach crisis proportions by 2018:
There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the US alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
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Click to view interactive graphic

Click on the image above to view McKinsey's interactive graphic that shows the employment distribution of America's analytical talent by industry and role.

Source: Manyika, J; Chui, M; Brown, B; Bughin, J; Dobbs, R; Roxburgh, C; & Byers, A H (2011, May 2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey.

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Saturday, March 13, 2010

Why Policymakers Need to Take Note of High-Frequency Finance

by Richard Olsen © VoxEU.org

Why should high-frequency finance be of any interest to policymakers interested in long-term economic issues? This column argues that the discipline can revolutionise economics and finance by turning accepted assumptions on their head and offering novel solutions to today’s issues.

I believe high-frequency finance is turning aspects of economics and finance into a hard science. The discipline was officially inaugurated at a conference in Zurich in 1995 that was attended by over 200 of the world’s top researchers. Since then, there have been a large number of publications including a book with the title Introduction to High-frequency Finance. “High-frequency data” is a term used for tick-by-tick price information that is collected from financial markets. The tick data is valuable, because they represent transaction prices at which assets are bought and sold. The price changes are a footprint of the changing balance of buyers and sellers.

The term “high-frequency finance” has a deeper meaning and is a statement of intent indicating that research is data-driven and agnostic. There are no ex ante theories or hypotheses. We let the data speak for itself. In natural sciences this is how research is often conducted. The first step towards discovery is pure observation and coming up with a description of what has been observed – this may sound easy but is not at all the case. Only in a second step, when the facts are clearly established, do natural scientists start formulating hypotheses that are then verified with experiments.

In high-frequency finance:
  • The first step involves the collecting and scrubbing of data.
  • The second step is to analyse the data and identify its statistical properties.
Here one looks for stylised facts which are significant and not just spurious. Due to the masses of data points available for analysis (for many financial instruments one can collect more than 100,000 data points per day), identification of structures is straightforward, either there is a regularity or there is none.
  • The third step is to formalise observations of specific patterns and seek tentative explanations, theories to explain them.
The abundance of data in high-frequency finance has profound implications for the statistical relevance of its results. Unlike in other fields of economics and finance, where there is not sufficient data to back up the inferences, this is not an issue in high-frequency finance. The results are unambiguous and turn economics and finance into a hard science, just as is the case for natural sciences. This is not a bad thing.

High-frequency data as an answer to singularity of macro events

Today we are all grappling with the global financial crisis and have to make hard decisions. In living memory, we have not seen a crisis of a similar scale, so policymakers are in a vacuum and do not have any comparable historical precedents to validate their policy decisions.

If the global economy had been in existence for 100,000 years, this would be a different matter. We would have had many crises of a similar scale, and we could use these previous events as a benchmark to evaluate the current crisis. The modern economy with financial markets linked together through high speed communication networks trading trillions of dollars on a daily basis is a new phenomenon that did not exist even 20 years ago. People refer to the events of 1929 and subsequent years, but while these events can be used as one possible point of reference, they are not meaningful in the statistical sense. On a macro level, we can make observations but no inferences because we do not have the historical data. There is a void that researchers and policymakers need to acknowledge.

Fractals: Understanding macro structure from micro data

High-frequency finance can fill the void with its huge amounts of data – if we embrace fractal theory that explains how phenomena are the same even if they occur at different scales. Fractal theory suggests that we can search for explanations of the big crisis by moving to another time scale, the short term.

At a second-by-second level, there are an abundance of crises and systemic shocks; just imagine the occurrence of the many price jumps due to unexpected news releases and political events or large market orders. Albeit on a short-term time scale, we study how regime shifts occur and how human beings react. The large number of occurrences allows for meaningful analysis. We study all facets of a crisis, how traders behave prior to the crisis, how they react to the first onslaught, how they panic, when the going gets hard and finally, how their frame of reference which previously was a kind of anchor and gave them a degree of security breaks down and how later, when the shock has passed, the excitement dies down, there is the aftershock depression and then eventually how gradual recovery to a new state of normality begins.

The everyday events sum up and shape the tomorrow

High-frequency finance has another big selling point, one which policymakers should take note of: the study of market events on a tick-by-tick basis brings to the surface the detailed flows of buying and selling that occur in the market. From this information, it is possible to build maps of how market participants build up positions and how asset bubbles develop over time. By tracking price action on a tick-by-tick basis, it is possible to infer the composition of those bubbles similar to the work of geologists studying rock formations. Researchers can identify, who has been buying and selling, on what time horizons they trade, how resilient they are to price shocks, what makes them turn their position and become net sellers as buyers. Based on this information we can make inferences of the likely collapse of those bubbles.

High-frequency finance opens the way to develop "economic weather maps". Just as in meteorology, where the large scale models rely on the most detailed information of precipitation, air pressure and wind, the same is true for the economic weather map. We have to start collecting data on a tick-by-tick level and then iteratively build large scale models. Today, the development of such a global economic weather map has barely started. The "scale of market quake" (a free Internet service) is a first instalment, but the start of an exciting development.

High-frequency finance holds out the hope of turning aspects economics and finance into a hard science by the sheer volume of data and its ability to set events into their appropriate context by mapping rare events into a short-term time scale with a near infinity of events, albeit at a shorter-term time scale. Second, the tracking of events on a tick-by-tick basis opens the door to identify underlying flows and develop economic weather maps. Surely that’s not a bad thing?

References

Bisig T, Dupuis, A, Impagliazzo, V, and Olsen, R (2009), “The scale of market quakes”, working paper, September.

Gençay, R, Dacorogna, M, Müller, U, Olsen, R, and Pictet, O (2001), An Introduction to High Frequency Finance, Academic Press.

Mandelbrot, B (1997), Fractals and Scaling in Finance, Springer.

Mandelbrot, B, Hudson, R (2004), The (Mis)behavior of Markets, Basic Books.

Republished with permission of VoxEU.org

Tuesday, April 03, 2012

Everything You Wanted to Know About Data Mining but Were Afraid to Ask

According to The Atlantic:
Big data is everywhere we look these days. Businesses are falling all over themselves to hire 'data scientists,' privacy advocates are concerned about personal data and control, and technologists and entrepreneurs scramble to find new ways to collect, control and monetize data. We know that data is powerful and valuable. But how?
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A Modern Business Data Center

Source: Everything You Wanted to Know About Data Mining but Were Afraid to Ask (2012, April 3), Atlantic.

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Saturday, May 18, 2013

On Philosophy, Science, and Data

According to Jim Harris (2013) of the Obsessive-Compulsive Data Quality (OCDQ) Blog:
Some might argue that philosophy only reigns in the absence of data, while science reigns in the analysis of data. Although in the era of big data there seems to be fewer areas truly absent of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise. So, an endless oscillation persists between science and philosophy, which is why science without philosophy is blind, and philosophy without science is empty. Data needs both science and philosophy.
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Jim Harris

Let's face it, the historical relationship between science and philosophy has not always been friendly. Nevertheless, one cannot separate philosophy from science and still make sense of the world.

Source: Harris, J (2013, March 14), On Philosophy, Science, and Data, OCDQ Blog.

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Thursday, May 09, 2013

Quandl Has Arrived

Looking for "big data" sources? Quandl enables searches of over 5,000,000 financial, economic, and social datasets made available by data contributors worldwide. Morover, every Quandl dataset is available for direct download via Python, Stata, Excel, R, as well as embeddable as a graph on any website. According to Quandl's creators:
Quandl has indexed over 5 million time-series datasets from over 400 sources. All of Quandl's datasets are open and free. You can download any Quandl dataset in any format that you want. You can also visualize, save, share, authenticate, validate, upload, index, merge and transform data. Our long-term goal is to make all the numerical data on the internet easy to find and easy to use.
Establishing a Quandl user account is free and easy. I tested downloading Quandl datasets using the Excel add-in and R package accessories readily available for download from the Quandl website -- both worked perfectly.


Quandl has arrived...

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Friday, January 22, 2010

Business Analytics: Questions for Enterprise

Firms today are increasingly seeking competitive advantage through advances in business analytics and decision support systems. According to a white paper published by nGenera (2008) in collaboration with Prof Thomas Davenport:
The next wave of business reengineering is being powered by business analytics, and the potential performance breakthroughs are just as large as they were 15 or so years ago. Many of these breakthroughs will come through the ability to integrate the demand side of the house with the supply side of the house as never before. Even information-rich industries have tended to concentrate on one side or the other. With the power of business analytics, corporations can make and manage the demand-supply connections – a big step closer to the goal of optimizing the performance of the corporation as a whole.

Here are six topical questions (with supporting questions) posed by nGenera for companies seeking to compete analytically:

1. Where should we leverage business analytics?

  • What is our distinctive capability? On what basis do we choose to compete? And how clear and definitive are we about that choice?
  • What performance levels or innovations in this area would blow away the competition?
  • What information, knowledge, and insight would it take to perform that way? What are the biggest unanswered questions and biggest opportunities?
  • How would we act upon that information, knowledge, and insights.

2. Why now?

  • What are our direct competitors doing or attempting with business analytics? Is anyone in our industry jumping ahead in terms of analytical capability?
  • How are analytics changing our competitive landscape? Are we at risk from non-traditional competitors who may use analytics to encroach on our markets?
  • What emerging technologies of information integration and analysis should we be exploring more aggressively?
  • How fast can we launch a serious business analytics initiative? What’s holding us back?

3. What's the payoff?

  • What are our specific performance goals in the area where we choose to compete?
  • How well do we measure them? How might better measurement and analysis of today’s performance reveal tomorrow’s opportunities?
  • How well aligned are the organization, its management, and its stakeholders with these performance goals?
  • What’s our highest ambition? What would it mean in terms of revenue, profit, and market share if we were really to change the basis of competition?

4. What information and technology do we need?

  • Is the information we need at hand? Is the data that support our distinctive capability in one repository, with common definitions of key data elements?
  • Is this data integrated enough not only to be accessible, but also to be manipulated with analytical tools?
  • How completely and accurately does the information measure and represent our distinctive business capability and basis of competition? Is it up-to-date? What are the most glaring gaps and shortfalls?
  • Do we have the technologies in place to support business analytics in this area? Or is technology fragmentation holding us back?

5. What kinds of people do we need?

  • Do we have a critical mass of analytical professionals on staff? Are we prepared to hire them? Do we need to “rent” this talent in the short term to fill gaps?
  • Who can manage analytical professionals? Who has the necessary experience, credibility, and “bridging” skills?
  • Will we be ready to train employees to apply the analytical results and operate differently?
  • Is the organization at large oriented toward analytical decision-making, or is it wedded to yesterday’s procedures and rules of thumb? How quickly can the organization come up to speed analytically?

6. What roles must senior executives play?

  • Are we committed to competing on analytics, starting at the top of the organization? What are the CEO and executive team doing to demonstrate that commitment?
  • Is the leader of the analytical function prepared to act upon the results of the analyses? Are the roles and decision rights of other stakeholders, including the CFO and CIO, clear – especially when their roles are novel or overlap?
  • Do we have a project leader who can span the worlds of strategy, process performance, and analytics?
Reference: Business Analytics: Six Questions To Ask About Information And Competition (2008), Austin, TX: nGenera Corp.

Wednesday, June 10, 2009

Business Intelligence and Spreadsheet Redux

A recent survey released by Nigel Pendse and the Business Application Research Center (2009, “BI Survey 8,” BARC) seems to confirm that business intelligence (BI) is less the domain of information technology (IT) than it is of "disenfranchised" spreadsheet-users. Stephen Swoyer of The Data Warehouse Institute (2009, “Report Debunks BI Myth”) offered this commentary on the BARC survey results:
Business intelligence vendors like to talk up a 20/80 split -- i.e., in any given organization, only 20 percent of users are actually consuming BI technologies; the remaining 80 percent are disenfranchised. According to "BI Survey 8," however, most shops clock in at far below the 20 percent rate. In any given BI-using organization…, just over 8 percent of employees are actually using BI tools. Even in industries that have aggressively adopted BI tools (e.g., wholesale, banking, and retail), usage barely exceeds 11 percent.
James Standon of nModal Solutions (2009, “Business Intelligence Adoption Low and Falling”) concludes that analysts tend to choose BI tools that are best able to get the job done, and more often than not, that tool is the electronic spreadsheet:
Big business intelligence seems to think that BI for the masses is a tool problem - something in how their portal works, or how many rows of data per second their appliance can process. Sure, if the tools are hard to use or learn, it's a factor, but I think more often than not business intelligence isn't used because it's not providing what is required… Often, people use Excel [Microsoft] because last week they didn't know exactly what they needed, and it is a tool that lets them build it themselves this week when the boss wants the answer and there is a decision to make. With all its flaws, it's still the most adopted business intelligence tool in the world.

Monday, August 03, 2009

In Defense of Financial Theories

I recently read a ridiculous critique of Value at Risk (VaR) by Pablo Triana in BusinessWeek (“The Risk Mirage at Goldman,” Aug 10, 2009). His review of this advanced financial technique is scathing:
VaR-based analysis of any firm's riskiness is useless. VaR lies. Big time. As a predictor of risk, it's an impostor. It should be consigned to the dustbin. Firms should stop reporting it. Analysts and regulators should stop using it.
Mr Triana bases his assertion on the observation that VaR is “a mathematical tool that simply reflects what happened to a portfolio of assets during a certain past period,” and that “the person supplying the data to the model can essentially select any dates.” My response to his argument is simply to ask, “Isn’t that true of any model or theory…?” Mr Triana goes on to argue that:
VaR models also tend to plug in weird assumptions that typically deliver unrealistically low risk numbers: the assumption, for instance, that markets follow a normal probability distribution, thus ruling out extreme events. Or that diversification in the portfolio will offset risk exposure.
In essence, Mr Triana seems to be saying that normally distributed results have bounds, and that portfolio diversification does not offset risk. Neither of his assertions are supported by probability theory or the empirical evidence. Yet, Mr Triana goes on to conclude, “it’s time to give up analytics so that real risk can be revealed.”

Mr Triana does a disservice to the financial services industry and public at large with his dramatic commentary. Yes, the discipline of finance has much to learn from the ongoing economic crisis, and of course, financial theory in general will evolve based on these recent lessons. However, just because one gets a bad meal in one restaurant does not mean that one should quit going to restaurants.

Financial theories such as VaR stand as state-of-the-art tools in the business of finance and risk management. These techniques are grounded in the same stochastic methodologies that are used by engineers in virtually every industry. To dismiss VaR so completely without considering its utility for supporting effective financial decisions is tantamount to sending financial theory back to the dark ages. Our knowledge of finance needs to advance as a result of what is happening in the economy, not go backwards.

Wednesday, April 14, 2010

What Do Professors Want?

by Thomas C Reeves © MercatorNet.com

The shady groves of academe have cachet as a home address, but the pay is lousy, the prestige is negligible, and the power is derisory.

Polls and studies have shown consistently that professors, especially in the humanities and social sciences, side with the Left in political and cultural matters. So do public schoolteachers, whose unions are major contributors to the Democratic Party. This bias contrasts sharply, of course, with the dispassionate search for truth that scholars and teachers claim to revere. There are many reasons, no doubt, for the bent shown by professors in the humanities and social sciences, but the most obvious, it seems to me, is envy. A history professor for 40 years, I have felt this prominent member of the Seven Deadly Sins myself, many times. Let us consider three aspects of this thesis.

Take the issue of money -- always a good place to begin with things American. Academics outside business and the sciences often labor for many long years in college and graduate school in order to obtain a doctorate. More than a few collect their diplomas sporting some gray in their hair along with a briefcase full of debts. If we are lucky enough to land a tenure-track position in higher education, a large "if" over the last four decades, we frequently start at a salary that a skilled blue collar worker might expect a few years out of high school. Don't think about salaries at Harvard; consult the data on most academics published in the Chronicle of Higher Education. A friend's son, a brand new pharmacist, recently started work at a local drug store with a salary that exceeded my University of Wisconsin System salary when I retired as a full professor.

Serious economic problems face the glowing, self-confident scholar with little money. How, for example, is he able to find adequate housing? Even US$300,000, well beyond the reach of most young and many senior professors, won't buy much in Boston, New York, Los Angeles, New Orleans, Atlanta or Chicago, not to mention Madison, Sarasota, Ann Arbor, Palo Alto or Santa Barbara. The affluent suburbs, where the successful in other fields gather, are out of the question, of course. And so many of us move into older, deteriorating, often dangerous areas, telling all who listen that we made the choice deliberately and that we, being humanists, have a natural desire to live among the poor and oppressed. In my experience, some English and anthropology professors actually believe this nonsense, and enjoy dressing as factory workers and displaying furniture obviously purchased at a rummage sale.

Many academic families have two incomes, and some have other sources of private income. These professors can and often do enter the less exclusive suburbs, only to find that they have very little in common with their neighbors. They aren't invited to join the country club, as everyone understands that professors lack the necessary funds. They aren't invited to join the yacht club for the same reason. It's difficult to join a cocktail party discussion on the joys of owning a Lexus when you've just driven up in an older Corolla.

At public gatherings of all sorts, the professor might receive many awkward occupational questions. I was once asked how much professors are paid by the hour. I once gave a talk before a group of Rotarians as a favor for a dentist friend, and was introduced as a writer. The businessman sitting next to me during lunch asked, "What do you do all day beside write?"

Neighbors often assume that professors spend their summers in indolence and revelry. Thus they conclude that such people are not actually professionals and shouldn't make much money. Tell them you're writing a book and you might be asked what its chances are of being approved by Oprah. If it's a university press sort of topic, you might face such questions as "Who would read that?" and "How much could that make?" These inquiries are often followed by a wan smile or patronizing chuckle.

The education of the professor's children is another sticky point. Good private schools are out of reach financially, and religious schools are, well, religious. That leaves the public schools, which all good humanists officially champion. Those who know better feel obligated to remind colleagues and neighbors that young people learn a lot about "real life" while evading bullies, drug dealers, and gangs, and being instructed by teachers whose true calling in life was employment at Wal-Mart.

As for higher education, the low income professor faces an even greater obstacle to happiness. Tuition and expenses in even the mediocre private institutions are absurdly high, and public colleges and universities have been steadily raising their tuition for years. Few if any want to send their young people to the open-admissions College for Dummies across town, even if that would save some money. One wants to boast to a sniffy neighbor at a cocktail party that junior attends Brown, not Damp Valley State. Scholarships, grants, and federal student jobs are hoped for. Large loans increase the frustration.

Many academics not only envy people with money, but also those who enjoy political authority. Professors are more confident than most that they have the truth and are convinced that, if given the opportunity, they would rule with intelligence, justice, and compassion. The trouble is that few Americans, at least since the time of Andrew Jackson, will vote for intellectuals. (The widespread assumption that Presidents who have Ivy League degrees are intellectuals is highly debatable. The Left declared consistently that George W. Bush, who had diplomas from Yale and Harvard, was mentally challenged. Barak Obama, who was not really a professor, has sealed his academic records.) How many professors run City Hall anywhere? How many would like to? How many humanities and social science professors are consulted when great civic issues are discussed and decided? Who would even invite them to join the Elks?

Instead of steering the machinery of local, state, and national politics, academics are relegated to writing angry articles in journals and websites read by the already converted and pouring their well-considered opinions into the ears of young people who are mostly eager to get drunk, listen to rap, watch ESPN, and find a suitable, or at least willing, bed partner for the night.

On the Left and Right money means power, and we "pointy heads" and "eggheads" are on the outside looking in. One thinks of Arthur Schlesinger Jr swooning over the Kennedys for the rest of his life because they gave him a title and a silent seat in some White House deliberations. Those making as much money as, say, an experienced furnace repairman account for little in this world, despite the PhD. How many academics even sit on the governing board that sets policies for their campus? It is all most humiliating. (To see how intelligently and objectively academics use the authority they have, examine the political correctness the suffocates the employment practices and intellectual lives of almost all American campuses. Aberlour's Fifth Law: "Political correctness is totalitarianism with a diploma.")

Thirdly, there is the issue of occupational mobility and professional advancement. High income neighborhoods have constant turnover because of promotions and advancement. Professors, on the other hand, are more often than not (especially the white males) stuck on a campus for many years without a prayer of moving up or out. They have little or no control over their annual salary increases, if any, and having attained the rank of full professor have only "more of the same" and retirement to look forward to. Watching their former students scale the heights of prosperity and power can cause considerable chagrin.

A few professors will attempt to become campus administrators. Chancellors and top level bureaucrats often have very high incomes and command real authority. But most faculty choose not to become politicians. Many lack the necessary cynicism.

One way to compensate for this bleak and futureless existence is to become involved in left-wing causes. They give us a sense of identity in a world seemingly owned and operated by Rotarians. And they provide us with hope. In big government we trust, for with the election of sufficiently enlightened officials, we might gain full medical coverage, employment for our children, and good pensions. These same leftist leaders might redistribute income "fairly," by taking wealth from the "greedy" and giving it to those of us who want more of everything. A "just" world might be created in which sociologists, political scientists, botanists, and romance language professors would achieve the greatness that should be theirs. It's all a matter of educating the public. And hurling anathemas at people of position and affluence we deeply envy.

Thomas C Reeves writes from Wisconsin. Among his dozen books are Twentieth Century America: A Brief History, and biographies of John F Kennedy, Joseph R McCarthy, Fulton Sheen, Walter J Kohler, Jr and Chester A Arthur.

Republished with permission of MercatorNet.com

Tuesday, August 17, 2010

US Inflation-Adjusted Pay Increases 2000-2009

According to a report in USA Today (2010, August 17), “After adjusting for inflation, military compensation rose 84% from 2000 through 2009. Compensation grew 37% for federal civilian workers and 9% for private-sector employees...” USA Today based the report on data provided by the Bureau of Economic Analysis (BEA). The chart below compares the inflation-adjusted pay increases for military personnel, Federal civilians, and private sector employees for the period 2000-2009.

[Click image to enlarge]

Source: Cauchon, D (2010, August 17), Military Towns Enjoy Big Booms, USA Today.

Monday, March 15, 2010

Advanced Analytics Not Information Technology

I recently fielded a forum question about the cost-creation versus value-adding capabilities of information technology (IT) and advanced (i.e., bespoke) analytics in enterprise. Here is how I responded:

Regarding the linkages between information technology (IT), advanced analytics, and value, I would gently suggest that IT is a cost center, and advanced analytics are the value-adding proposition. In other words, don't go to the IT department if you are seeking to activate value-adding analytics (though I will concede that IT does have an effective role in business intelligence [BI] production, which is very different from advanced analytics in my view).

Unfortunately, IT solution providers know full well that advanced analytics is what creates value, and so IT firms will typically "bundle" various analytic offerings with a proposed IT solution in an effort to bamboozle the client into believing that scarce IT dollars can buy both transaction management and advanced analytical services together in one "big" IT installation deal. Buyers of IT solutions should therefore beware.

What is needed today is for IT managers to yield the analytics space to subject matter experts with analytical solutions that stand separate from the data warehousing infrastructure, while seeking to reduce costs in IT by exploiting the economies of scale that IT solutions typically contribute to the cost analysis.

Again, IT is a cost center, while advanced analytics (separate from BI) are the value-adding activity.