Denver Broncos and Olympians Go Digital

The “measured life” of tracking health vital signs, calorie intakes, feces analysis and more is moving past the domain of scientists and onto the professional fields of NFL football and the 2012 Olympics. Data capture, tracking and analysis is taking precedence over “old school” ways of preparing for excellence. Will these new data driven approaches create a whole new breed of champions?

Image courtesy of Denver Broncos. All rights reserved.

For the 2012 NFL football season, the Denver Broncos released their former strength and conditioning coach and hired Luke Richesson.  Mr. Richesson comes to the Broncos from the Jacksonville Jaguars and has a different mentality for getting the best performance from his athletes.

According to an article in the Denver Post, gone are most of the dumbbells and free weights in the Denver Broncos training facility. Now Richesson focuses on resistance training, mixed martial arts, agility drills and heavy conditioning. But Richesson knows that a new approach to strength and conditioning can only take his athletes so far. Data collection and analysis has to be part of the mix.

“These guys are like Ferrari’s”, Richesson says. And Richesson knows that while it’s ultimately the player’s responsibility to be in the best shape possible, he can provide input and guidance along the way towards ensuring maximum output on the football field.

It starts with data collection. Inputs are taken verbally and documented with iPads. Richesson wants to know what athletes eat for breakfast, lunch and dinner. He documents their sleep quality and quantity. And he has outfitted all players with heart monitors during workouts.  Richesson’s staff then uses this data to monitor a player’s workout on a real time basis. And of course, data aren’t discarded afterwards, but stored on a server for future analysis.

“The numbers don’t lie,” Richesson says. He’s constantly looking for peaks, valleys and outliers in the data. When his staff discovers the slightest variance from baseline data, Richesson and his staff start questioning athletes as to changes in diet, sleep schedules or possibly injuries.   In effect, everything is monitored, recorded and analyzed, with the ultimate goal of creating a high performance team.

2012 Olympians have a similar idea. Some are accepting use of high tech gadgets on a trial basis in exchange for providing their personal performance data.  For example, the Daily Mail notes the US track cycling team is “testing a sleep monitor, glucose monitor…and (is receiving) genetic reports on their nutritional needs and muscular capacity.”  While these are “trials” in 2012, it’s highly likely that such data collection and reporting will be commonplace in the near future.

Consistent performance and finishing strong matters for both athletes—and corporate decision makers. It’s good to see that both groups are starting to take analytics seriously in an effort to get a competitive edge.

From Complexity to Simplicity in the Cloud

The inner workings of cloud computing can be quite complex. That’s why the founders of Dropbox are on the right path—make cloud computing as simple as possible with easy to understand user-interfaces to mask “behind the scenes” infrastructure and connections.

Open up the lid of “black box” cloud computing and what you’ll see is anything but simple. Massive and parallel server farms that never sleep, algorithms worming and indexing their way through global websites, large data sets waiting in analytical stores for discovery, message buses that route, control and buffer system requests, and massive processing of images, text and more on a grandiose scale.

That’s why companies that take the complexity out of cloud computing are thriving. Take for instance, Dropbox, a company that allows users to access their personal or corporate files from any internet connected device. A Technology Review article featuring Q&A with CEO Drew Houston cites the efforts of Dropbox to mask behind the scenes efforts of “having your stuff with you, wherever you are.”

With various operating systems, incompatibilities, file formats and more, Dropbox engineers had to wade through mountains of bugs and fixes to make the user experience as seamless as possible. “There are technical hurdles that we had to overcome to provide the illusion that everything is in one place…and that getting it is reliable, fast and secure,” Houston says.

Looking at Dropbox from the outside, a user only sees “visual feedback” via a folder, icon or the like on his/her desktop. But underneath the hood there’s a whole gaggle of technologies and code that makes Dropbox work. And to create a seamless experience, painstaking efforts are involved down to the tiniest components says Houston; “Excellence is the sum of 100 or 1000 of…little details”.

If information technology leaders plan to bring “BI to the masses”, simplicity will be a necessary requirement to mask the inherent complexity of cloud computing. Ultimately, there are plenty of business users that won’t care how their particular applications are delivered, only that they are carried out with efficiency, reliability and security. Thus, user interfaces designed with clarity, elegance and ease-of-use in mind will ultimately put a “wrapper” on complexity and drive further adoption of cloud computing delivery models.

And it’s also likely that business users will never appreciate the hard work that goes into designing, delivering and sustaining their applications on a 24x7x365 basis, and accessible from any internet enabled device. But then again, perhaps that’s the point. Application availability, security, reliability, simplicity and productivity are now the expectations of business users – it’s best to deliver “in the cloud”exactly what they want.

Brains and Databases: An Obsession with Time Keeping

Courtesy of: Pasieka / Science Photo Library / Corbis

Data management professionals can add a time dimension to data via the use of temporal databases. However, neuroscientists are also interested in the time dimension with their study of the human brain, and in particular a concept called “brain time”.

Ever had an experience where you remember the event like it happened yesterday? How rich and vivid is that event? Can you remember details such as whether it was night or day, various sounds, smells and more?

The April 25, 2011 issue of the New Yorker has a terrific interview with neuroscientist David Eagleman.  As an assistant professor of neuroscience at Baylor College of Medicine, Eagleman is obsessed with mysteries of the brain, and specifically the concept of brain time.

Brain time refers to perceptions that for critical events in our lives, time seems to slow down. Now of course, we know that time really doesn’t slow down, but the brain perceives it that way.  The New Yorker article mentions a sense of time is threaded through everything we perceive. In fact, the article cites there aremultiple parts of the brain such as the suprachiasmatic nucleus, cerebellum, basal ganglia and various sections of the cortex that are all potential timekeepers.

Adding complexity to the issue, not only are different sections of the brain responsible for time keeping, but the “inputs” from our five senses arrive at different times. The New Yorker article points out that our brain can react more quickly to sound as opposed to when we stub our big toe because that sensory pain must travel up the spinal cord.

Even more amazing, Eagleman explains; “The signals reach the brain within a hundred milliseconds of one another, and any differences in processing are erased.” So then, while the brain gets information at different times, it “synchs” all these inputs together to present to us a view of the world.

Neuroscientists aren’t the only ones obsessed with how time is processed and presented. In the analytics space, companies are interested in understanding their business at any point in time and want the ability to take a “state of the business” snapshot for reporting or compliance purposes. These efforts can be managed via use of applications and a temporal database which provides an opportunity for much richer analysis by capturing and understanding historical changes.

There is a common adage that “time marches on” in regular intervals. However neuroscientists are learning the human brain perceives time quite differently, as sometimes slow or fast and starting/stopping “in fits”.  While the concept of time is still a mysterious subject, it appears there is much room for discovery from a careful examination of “in the moment” timekeeping for brains and databases alike.


  • The brain is responsible for “stitching the world together” with inputs from our five senses. How is this similar to the role of BI professionals?
  • What additional insights can you share about how your organization “keeps time” for better decision making?

Five Best Practices in Building Business Intelligence Competency Centers

Successful business intelligence (BI) is much more than purchasing and implementing specialized tools and technology. Organization, process development, goal alignment and people skills are just as important. If your company has seen some success with BI efforts but wants more impact, a Business Intelligence Competency Center (BICC) initiative might be just the remedy.  Read article

Final Marketing Lessons from the Collapse of Lehman Brothers

This final installment of a three part series of marketing lessons learned from the collapse of Lehman Brothers studies the power of deep competitive/market analysis and the dangers of ignoring contrarian voices.

The best seller, “A Colossal Failure of Common Sense; the Inside Story of the Collapse of Lehman Brothers,” by Larry McDonald and Patrick Robinson, chronicles the Lehman Brothers timeline from simple cotton trading in the 1850s to a company selling the most complex of financial instruments in the early twenty-first century.

And while the inner workings of this former investment bank are arguably much different from your own company, there are important lessons that can be applied to modern day marketing decisions. Let’s start with the first.

Perform intense analysis or someone else will

The world’s largest financial institutions have a critical advantage not usually available to most companies; armies of research staff. In order to discern whether they should accumulate a “position” on particular company, research staff deep dive into income, free-cash flow and balance sheet statements.

However, this analysis is periphery, and in fact there’s something much deeper going on. In addition to potential interviews of senior management and/or possibly sending teams on site to delve into operations, some analyst firms also review quantitative measures they believe will correlate and/or predict future performance. And this deep analysis often leads to some pretty confident decisions.

In his book, McDonald tells the story of one particular analyst at Lehman Brothers responsible for research on a major US airline. McDonald takes pains to note the decision to purchase the debt (bonds) of this airline was based on a level of analysis that most companies don’t bother to attempt. “Jane can tell you what (this airline) is serving for lunch on their flight from JFK to Berlin and what it cost them,” he says. “There is nothing she doesn’t know about that company.”

And this fanatical level of analysis pays off in spades as Lehman buys bonds of this particular airline from frenzied sellers for sixteen to eighteen cents on the dollar. Meanwhile, “Jane knew exactly what the bonds were worth; .52 cents on the dollar,” McDonald writes. And while Lehman traders had to hold this airline’s debt for an entire year waiting for the right opportunity, Lehman was able to eventually sell the bonds and make a $250 million profit in one day!

Bringing this back to the marketing discipline, it seems that competitive and market analysis is often an after thought for many companies. And it shouldn’t be.

Deep competitive intelligence is getting to the heart of the matter. It’s synthesizing all the learnings found in the annual reports, 10-K’s and other information sources and coming to solid and verifiable conclusions about your where your competition is strong and where/if they have a weak underbelly.

Going a step further, deep intelligence gathering is also about using the assembled information to get such a compelling picture of the competition that one can predict competitive intent with a high degree of confidence.

Extreme analysis was important in many of Lehman’s decision making processes. Are you investing enough in competitive and market analysis? Is your marketing team providing this “insight” into senior management decision making?

Listen to your best people and seek contrarian opinions

Employees from the top to the bottom of the corporate ladder often bring unique perspectives and experience to the table—if only someone would bother listening.

McDonald cites an example of one senior manager at Lehman Brothers—Mike Gelband—going against the grain and warning about the potential of a housing bubble. And while there is nothing intrinsically wrong with riding an asset bubble, one needs a solid exit strategy to get out before the bubble implodes.

Mike Gelband saw the writing on the wall and warned Lehman Brothers CEO Dick Fuld, and COO Joe Gregory to get out of the housing market while there was still an opportunity.

However, Lehman senior management didn’t listen. In fact, McDonald writes, “The general drift upstairs was that Mike Gelband had developed some kind of attitude problem and it needed to be changed real fast.”

Some contrarian views rub us the wrong way. We know what we know, and we like our positions. And sometimes we think that anyone who sees differently is a trouble maker with an attitude problem.

Do you have a “Mike Gelband” in your company right now? On your marketing team? Does he or she have a reasoned position on a competitor, market trend, or even a critique of your marketing campaign? Is his/her opinion worth another look—just as a sanity check?

• Should competitive and/or market analysis be a larger portion of the marketing budget? Why/why not?
• Voicing a contrarian opinion can sometimes punch a ticket for the unemployment line. If you have an alternate view to a commonly held belief in your company, how should it be expressed?

Keeping Your Eye on the Prize: Business Value

eye on the prizeRightfully so, sometimes data warehouse directors and database administrators get caught up in sourcing, modeling, integrating and managing data in the data warehouse. Don’t get me wrong—these are all important activities.

However, let us not forget to keep our eyes on the prize—the business value of distributing intelligence throughout the enterprise and unlocking the ivory tower of decision making.

In a recent article, Stephen Brobst reminds us:

Far too often, the focus of enterprise data warehouse (EDW) construction is on getting data into the relational database management system (RDBMS) repository rather than getting it out.

Certainly, acquiring, cleansing and integrating data into the EDW is a critical set of activities. However, the true success of an EDW will be measured by its ability to deliver decisioning capabilities. In the end, a high-value data warehouse is more about exporting decisions throughout the organization than it is about aggregating all of its data into a big bucket.

Continued executive sponsorship of a business intelligence initiative is usually predicated on showing and mapping “value” back to the top priorities of your company.  To do this, it is important to keep your eyes on the prize—the business value from creating a single repository of fresh, relevant and clean data to help your company enhance revenues, keep customers longer, lower operating costs and improve employee productivity and job satisfaction.

Have you revisited your original “BI” business case? If not, dust it off and see what “value” was promised to your sponsors. Spend time validating your performance. You might be surprised to discover what was promised—and more surprised to see today’s variance from your original goals.

The Moneyball-itzation of Marketing

moneyball_3Oakland A’s General Manager Billy Beane started the “Moneyball Revolution,” where analytics replaced intuition as the primary method of evaluating talent and assembling a professional baseball team. And while Beane’s critics entertain some self-satisfaction from the recent mediocrity of the A’s, there’s no doubt that quantitative analysis has changed baseball forever.

Similarly in the marketing discipline, while practitioners often debate whether marketing is more “art than science”—a trend towards analytics is afoot.

Tradition and convention are certainly hallmarks of Major League Baseball. And for many years, the status quo reigned—especially in the processes used to construct a baseball team.

Using knowledge, intuition and experience to evaluate talent, field managers and scouts would scour high schools, practice fields and colleges looking for the missing pieces that could potentially elevate them to a championship. Gut decision making ruled—until Billy Beane and the Moneyball analytics revolution started.

An ESPN Magazine article shows how based on geographical location, Oakland was forced to compete in a smaller market with revenues far lower than teams like Boston or New York. Attempting to level the playing field, Billy Beane took a different approach to baseball resourcing. Instead of trying to sign big name players with the best batting average, Beane used statistical analysis to discover indicators that he believed would have a better correlation with offensive success.

Michael Lewis, author of Moneyball—a book on Billy Beane’s methods writes,

“By analyzing baseball statistics you could see through a lot of baseball nonsense. For instance, when baseball managers talked about scoring runs, they tended to focus on team batting average, but if you ran the analysis you could see that the number of runs a team scored bore little relation to that team’s batting average. It correlated much more exactly with a team’s on-base and slugging percentage.”

And for awhile, Moneyball worked. In the early years of Moneyball, the Oakland A’s were competitive with payrolls in the $50 million range whereas larger market teams were spending $100 million plus. It wasn’t that Oakland was choosing to pocket the $50 million annual difference—they simply didn’t have that kind of money to spend. Oakland needed a way to compete and they chose analytics.

Unfortunately for Billy Beane, his competitive advantage didn’t last very long. Other baseball teams adopted statistical analysis and General Managers like Boston’s Theo Epstein quickly combined analytical prowess with the advantage of a major revenue market to assemble a perennial powerhouse. Like it or not (and some GMs still don’t), the adoption of analytics drastically changed baseball and now the use of analytics to help build a ball club is a standard process.

Similar to the adoption of Moneyball, marketing is in the throes of an analytical revolution.

Specifically, practitioners of marketing know they need fresh and accurate data for advanced marketing functions such as better segmentation, devising more effective campaigns and offers, and creating relevant interactions with the customer across multiple touch points. This data must be clean, modeled and managed—a large undertaking that involves marketers working closely with IT.

Marketers also are realizing that some understanding of analytical applications and business intelligence know-how is necessary to help analyze and translate data into actionable information that can be used to create better customer experiences. Hundreds of case studies in business publications and books have emerged over the past five to seven years as a testimony to these trends.

Analytics helped a small market team like the Oakland A’s compete with clubs that had much larger budgets. Indeed, Oakland enjoyed a period of success before larger teams “caught on” to Beane’s analytical approach.

In the same vein, the window of opportunity for marketers to adopt business analytics—before their competitors—is closing rapidly.

  • With the early success of Moneyball, Billy Beane parlayed himself an ownership stake in the Oakland A’s. For marketers, how valuable will analytical skills be in the near future?
  • Are you competing with companies that have much larger budgets and personnel resources? If so, what strategies are you using to win?
  • Critics of Moneyball say that one cannot run a major league baseball team with a computer. Going forward—in marketing—will knowledge and intuition win out over analytics?