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Es esta una obra muy oportuna y necesaria que promete reescribir de nuevo las normas de la competitividad. Read more Read less. Product details Paperback: Spanish ISBN Be the first to review this item Amazon Best Sellers Rank: Tell the Publisher! I'd like to read this book on Kindle Don't have a Kindle?

No customer reviews. Share your thoughts with other customers. Write a customer review. There's a problem loading this menu right now. Learn more about Amazon Prime. Get fast, free shipping with Amazon Prime. Back to top. Get to Know Us. Analytic HR strategies are taking hold in European soccer as well.

Milan, uses predictive models from its Milan Lab research center to prevent injuries by analyzing physiological, orthopedic, and psychological data from a variety of sources. Still, sports managers—like business leaders—are rarely fact-or-feeling purists. Louis Cardinals manager Tony La Russa, for example, brilliantly combines analytics with intuition to decide when to substitute a charged-up player in the batting lineup or whether to hire a spark-plug personality to improve morale.

He studied the rows and the columns. But he also knew they could take you only so far in baseball, maybe even confuse you with a fog of overanalysis. As far as he knew, there was no way to quantify desire. And those numbers told him exactly what he needed to know when added to twenty-four years of managing experience. That final sentence is the key.

Of course, not all decisions should be grounded in analytics—at least not wholly so. Personnel matters, in particular, are often well and appropriately informed by instinct and anecdote. But research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations.

For analytics-minded leaders, then, the challenge boils down to knowing when to run with the numbers and when to run with their guts. Analytics competitors are more than simple number-crunching factories.

Certainly, they apply technology—with a mixture of brute force and finesse—to multiple business problems. But they also direct their energies toward finding the right focus, building the right culture, and hiring the right people to make optimal use of the data they constantly churn. In the end, people and strategy, as much as information technology, give such organizations strength.

Although analytics competitors encourage universal fact-based decisions, they must choose where to direct resource-intensive efforts. Generally, they pick several functions or initiatives that together serve an overarching strategy.

UPS has broadened its focus from logistics to customers, in the interest of providing superior service. While such multipronged strategies define analytics competitors, executives we interviewed warned companies against becoming too diffuse in their initiatives or losing clear sight of the business purpose behind each. Another consideration when allocating resources is how amenable certain functions are to deep analysis.

Competing on Analytics

Statistical models and algorithms that dangle the possibility of performance breakthroughs make some prospects especially tempting. Marketing, for example, has always been tough to quantify because it is rooted in psychology.

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But now consumer products companies can hone their market research using multiattribute utility theory—a tool for understanding and predicting consumer behaviors and decisions.

Similarly, the advertising industry is adopting econometrics—statistical techniques for measuring the lift provided by different ads and promotions over time. Wal-Mart, for example, insists that suppliers use its Retail Link system to monitor product movement by store, to plan promotions and layouts within stores, and to reduce stock-outs.

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The distributors, in turn, use that information to help retailers optimize their mixes while persuading them to add shelf space for Gallo products. Culture is a soft concept; analytics is a hard discipline.

Nonetheless, analytics competitors must instill a companywide respect for measuring, testing, and evaluating quantitative evidence. Employees are urged to base decisions on hard facts. And they know that their performance is gauged the same way. Human resource organizations within analytics competitors are rigorous about applying metrics to compensation and rewards.

Senior executives also set a consistent example with their own behavior, exhibiting a hunger for and confidence in fact and analysis. At Yahoo, Progressive, and Capital One, process and product changes are tested on a small scale and implemented as they are validated.

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That approach, well established within various academic and business disciplines including engineering, quality management, and psychology , can be applied to most corporate processes—even to not-so-obvious candidates, like human resources and customer service. It was also expensive to develop, and that increased the risk. In this case, Bezos trusted his instincts and took a flier. And the feature did prove popular when introduced. Analytical firms hire analytical people—and like all companies that compete on talent, they pursue the best.

Since his arrival, Yu and his team have been designing and building sophisticated supply chain systems to optimize those processes. Established analytics competitors such as Capital One employ squadrons of analysts to conduct quantitative experiments and, with the results in hand, design credit card and other financial offers.

These efforts call for a specialized skill set, as you can see from this job description typical for a Capital One analyst:. Ability to quickly learn how to use software applications. Experience with Excel models.

Some graduate work preferred but not required e.

Competing on Analytics

Some experience with project management methodology, process improvement tools Lean, Six Sigma , or statistics preferred. Other firms hire similar kinds of people, but analytics competitors have them in much greater numbers. Capital One is currently seeking three times as many analysts as operations people—hardly the common practice for a bank. Good analysts must also have the ability to express complex ideas in simple terms and have the relationship skills to interact well with decision makers.

The head of a customer analytics group at Wachovia Bank describes the rapport with others his group seeks: Of course, a combination of analytical, business, and relationship skills may be difficult to find.

When the software company SAS a sponsor of this research, along with Intel knows it will need an expert in state-of-the-art business applications such as predictive modeling or recursive partitioning a form of decision tree analysis applied to very complex data sets , it begins recruiting up to 18 months before it expects to fill the position.

In fact, analytical talent may be to the early s what programming talent was to the late s. Unfortunately, the U. Some organizations cope by contracting work to countries such as India, home to many statistical experts. That strategy may succeed when offshore analysts work on stand-alone problems. But if an iterative discussion with business decision makers is required, the distance can become a major barrier.

Competing on analytics means competing on technology. And while the most serious competitors investigate the latest statistical algorithms and decision science approaches, they also constantly monitor and push the IT frontier. The analytics group at one consumer products company went so far as to build its own supercomputer because it felt that commercially available models were inadequate for its demands.

Companies have invested many millions of dollars in systems that snatch data from every conceivable source. Enterprise resource planning, customer relationship management, point-of-sale, and other systems ensure that no transaction or other significant exchange occurs without leaving a mark.

But to compete on that information, companies must present it in standard formats, integrate it, store it in a data warehouse, and make it easily accessible to anyone and everyone. And they will need a lot of it. For example, a company may spend several years accumulating data on different marketing approaches before it has gathered enough to reliably analyze the effectiveness of an advertising campaign.

That information allows Dell to fine-tune its promotions for every medium in every region. Business intelligence tools allow employees to extract, transform, and load or ETL, as people in the industry would say data for analysis and then make those analyses available in reports, alerts, and scorecards.

The popularity of analytics competition is partly a response to the emergence of integrated packages of these tools. The volumes of data required for analytics applications may strain the capacity of low-end computers and servers.

Many analytics competitors are converting their hardware to bit processors that churn large amounts of data quickly. Most companies in most industries have excellent reasons to pursue strategies shaped by analytics. Virtually all the organizations we identified as aggressive analytics competitors are clear leaders in their fields, and they attribute much of their success to the masterful exploitation of data.

Rising global competition intensifies the need for this sort of proficiency. Western companies unable to beat their Indian or Chinese competitors on product cost, for example, can seek the upper hand through optimized business processes.

Companies just now embracing such strategies, however, will find that they take several years to come to fruition. The organizations in our study described a long, sometimes arduous journey.

The UK Consumer Cards and Loans business within Barclays Bank, for example, spent five years executing its plan to apply analytics to the marketing of credit cards and other financial products. The company had to make process changes in virtually every aspect of its consumer business: On the technical side, it had to integrate data on 10 million Barclaycard customers, improve the quality of the data, and build systems to step up data collection and analysis.

In addition, the company embarked on a long series of small tests to begin learning how to attract and retain the best customers at the lowest price. And it had to hire new people with top-drawer quantitative skills. Much of the time—and corresponding expense—that any company takes to become an analytics competitor will be devoted to technological tasks: You apply sophisticated information systems and rigorous analysis not only to your core capability but also to a range of functions as varied as marketing and human resources.

Your senior executive team not only recognizes the importance of analytics capabilities but also makes their development and maintenance a primary focus. You hire not only people with analytical skills but a lot of people with the very best analytical skills—and consider them a key to your success.

You not only employ analytics in almost every function and department but also consider it so strategically important that you manage it at the enterprise level. You not only are expert at number crunching but also invent proprietary metrics for use in key business processes. You not only use copious data and in-house analysis but also share them with customers and suppliers.

You not only have committed to competing on analytics but also have been building your capabilities for several years. And, of course, new analytics competitors will have to stock their personnel larders with fresh people.

Existing employees, meanwhile, will require extensive training.

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They need to know what data are available and all the ways the information can be analyzed; and they must learn to recognize such peculiarities and shortcomings as missing data, duplication, and quality problems.

Every day, advances in technology and techniques give companies a better and better handle on the critical minutiae of their operations. Companies of every stripe want to be part of the game. Thomas H. January Issue Explore the Archive. The Idea in Practice To become an analytics competitor: Champion Analytics from the Top Acknowledge and endorse the changes in culture, processes, and skills that analytics competition will mean for much of your workforce.

Create a Single Analytics Initiative Place all data-collection and analysis activities under a common leadership, with common technology and tools. Focus Your Analytics Effort Channel your resources into analytics initiatives that most directly serve your overarching competitive strategy.

Establish an Analytics Culture Instill a companywide respect for measuring, testing, and evaluating quantitative evidence. Hire the Right People Pursue and hire analysts who possess top-notch quantitative-analysis skills, can express complex ideas in simple terms, and can interact productively with decision makers. Analytics 3. Davenport How to compete on the quantitative turf.