data analytics

When the Right People and the Right Information Come Together, Expect a Masterpiece

“All knowledge is connected to all other knowledge. The fun is in making the connections.”

The remarkable man who said this quote, Arthur Aufderheide M.D. (1922-2013), certainly lived by these wise words.

Dr. Arthur Aufderheide

Dr. Arthur Aufderheide

Dr. Aufderheide was a medical school professor at the University of Minnesota who founded an entirely new area of scientific research: paleopathology – the study of the spread of disease through the forensic analysis of mummies (think of it as CSI: Ancient Civilizations!). He actively pursued his research with true passion for over 30 years, traveling the globe locating mummies, establishing best practices for their proper examination and extracting key specimens.

Dr. Aufderheide’s ground-breaking research was the perfect combination of his medical expertise with his personal passions for archaeology, outdoorsmanship and native world cultures. Simply put, he absolutely loved his work. His excitement and passion for his innovative research inspired his students and earned him widespread recognition from the global scientific community.

Dr. Aufderheide’s life work helps drive home two key points about successful, meaningful work and life:

First: Organizations with genuine passion for their mission will utilize technology and share information far more effectively than other companies.

Dr. Aufderheide’s career as a medical school professor was not his first. He had worked for decades as a hospital pathologist, a job he no longer found fulfilling. Had he opted to just count the days to early retirement, his remaining life work likely would have been mediocre at best. Instead, at the age of 55, he made a career change into academia, resulting in one heck of a “second act”: a highly fulfilling career and life.

Aufderheide’s tremendous passion for his work was key to successfully discover new insights from many far-flung sources of information that had been waiting for centuries to be discovered. Anyone else doing similar work just to blithely earn a paycheck surely would have made very few – if any – meaningful discoveries, much less establish a brand new field of scientific research.

Similarly, organizations with true passion for its mission will uncover more, better and faster business discoveries by collaboratively gaining new insight from big data analytics, enterprise search, enterprise knowledge management, and other silo-busting technologies. While dysfunctional organizations might actively resist sharing information, workers in enlightened companies are actively empowered by leadership to ask new questions about the business, while also being provided the advanced technology resources that enable them to find new answers.

Far from hoarding information, Aufderheide intentionally built a huge referenceable knowledge base of his work, including over 5,000 mummy specimens – the largest database of its kind in the world. And so Dr. Aufderheide’s work lives on today, enabling scientists to reconstruct the ways diseases behaved in antiquity, which can be helpful in controlling those diseases today.

Second: Organizations with a culture of genuine passion for their mission will outperform competitors that don’t.

Leaders with a true passion for their organization’s mission will insist on an open, positive company culture that enables everyone to pursue that mission to the fullest – free from company politics, turf wars or internal arguments.

Passionate leaders will also only hire people who will share their passion. At a recent roundtable event, startup exec John McEleney emphasized the need for start-ups to “have the right people on the bus” and keep mediocre players out of the organization by requiring any new potential hire to be referred by an existing employee.

Without a supportive company culture and proper hiring practices, an organization will reap what they sow, and end up with people who are just working for the money.

This all reminds me of Simon Sinek’s fantastic viral TEDx presentation – a must-watch (and well worth watching again!):

Well, that definitely describes the kind of organization I’d love to work for. How about you? 😉

Advertisement

Using Business Intelligence for Effective Business Storytelling

An appropriately told story has the power to do what rigorous analysis couldn’t: to communicate a strange new idea and move people to enthusiastic action.

~  Steve Denning, “The Leader’s Guide to Storytelling”

Business storytelling, campfire optional

“…And my phone log analysis proved the calls were coming from INSIDE THE HOUSE!!”

The most successful business intelligence professionals are also great storytellers. Regardless of your BI tools of choice, it’s important to note that “business storytelling” is not synonymous with infographics or data visualization. Every analytic tool can slice and dice data in a multitude of ways, but, of course, correlation is not causation. (More on this in a moment…)

Also, effective business storytelling does not necessarily require advanced data visualization tools. Any organization can take a the first step towards better storytelling by following universal best practices when creating even the most simple chart. Data consultant and author Thomas Redman recently wrote: “As Edward Tufte advises, label the axes, don’t distort the data, and keep chart-junk to a minimum.”

Redman’s next recommendation is also very simple: annotate your charts. “While annotations do not replace a well-told story, they do give the reader some inkling of what’s involved.”

Take a look at the “before” and “after” charts cited by Redman in his article. The annotations in the “after” chart tell a story how the company successfully improved customer data quality were successful, all in a very simple line chart:

Image

Image

The chart annotations (above) are not just helpful notes; they also comprise a second set of data (the key milestones of the company’s data quality program – by month), correlated with the monthly data quality measures. As a result of this data correlation, a time series cause-and-effect story emerges, complete with a beginning, middle, and a happy ending.

This leads to a key point: the most compelling business stories present strong correlation-causation relationships using many disparate yet complimentary sets of data.

Perhaps you have seen Charles Joseph Minard’s 1869 data visualization of Napoleon’s army in the Russian campaign of 1812. It was deservingly praised by Edward Tufte in his classic book The Visual Display of Quantitative Information: “It may well be the best statistical graphic ever drawn.”

Charles Josepn Minard's Chart of Napoleon's 1812 March to Russia (1861).

Source: Scimaps.org. Click map to view/enlarge image. See also: http://www.edwardtufte.com/tufte/posters

Minard painstakingly correlated multiple data sources: the movements of Napoleon’s army over time, their geographical location – marching to Moscow and then retreating from it – with the (rapidly narrowing) thickness of the line representing the number of Napoleon’s men, falling in battle as well as from deadly subzero temperatures that hit a low of -30⁰ F/-38⁰ C.

Minard then brought his many datasets together to very effectively tell the story of Napoleon’s futile Russian campaign and the misery of his soldiers, resulting in massive casualties that wiped out the Grande Armee.

Fast forward to today: Big data infrastructures and analytics hold huge potential to not only tell the story of the loss of life from violent conflicts of past history, but also in the future – by piecing together stories that help prevent global violence before it actually happens.

This critical world goal was covered in a Foreign Policy magazine article, Can Big Data Stop Wars Before They Happen? Author Sheldon Himelfarb cites three key trends justifying optimism that the answer will soon become a clear “Yes”.

First, Himelfarb points out the increasing amounts of data being generated by more and more people through digital devices; and second, our expanded capacity to collect and crunch data like never before. But the third trend he notes is the most critical to developing a clear story of human sentiment that can forewarn us of future violence:

When it comes to conflict prevention and peace-building, progress is not simply a question of “more” data, but also different data. For the first time, digital media – user-generated content and online social networks in particular – tell us not just what is going on, but also what people think about the things that are going on.

Excitement in the peace-building field centers on the possibility that we can tap into data sets to understand, and preempt, the human sentiment that underlies violent conflict.

Thankfully, the stories we want and need to tell in our respective organizations don’t fall into this same literal life-or-death category. However, all effective business storytelling requires the same two core elements:

  • Not just “more” data… Different data. Integrate of as many varieties of complimentary data as possible on the backend – structured and unstructured, internal and external. Doing so lets you present what has happened with strong correlation/causation, as well as enabling deeper advanced analytics (e.g., location-based, sentiment, predictive).
  • Clear, annotated, “junk-free” data visualizations. Combine and present your data on the front end as a compelling story that conveys understanding, empathy and a sense of urgency to take action.

Big Data Wisdom, Courtesy of Monty Python

Monty Python and the Holy Grail

One of the best parts of the hilarious 1975 King Arthur parody, Monty Python and the Holy Grail is the “Bridge of Death” scene: If a knight answered the bridge keeper’s three questions, he could safely cross the bridge; if not, he would be catapulted into… the Gorge of Eternal Peril! 

 

Unfortunately, that’s exactly what happened to most of King Arthur’s knights…

Fortunately when King Arthur was asked, “What is the airspeed velocity of an unladen swallow?” he wisely sought further details: “What do you mean – an African or European swallow?” The stunned bridge keeper said, “Uh, I don’t know that… AAAGH!” Breaking his own rule, the bridge keeper was thrown over into the gorge, freeing King Arthur to continue his quest for the Holy Grail.

Many organizations are on Holy Grail Big Data quests of their own, looking to deliver game-changing analytics, only to find themselves in a “boil-the-ocean” Big Data project that “after 24 months of building… has no real value.” Unfortunately, many organizations have rushed into hasty Hadoop implementations, fueled by a need to ‘respond’ to Big Data and ‘not fall behind.’ (1)

The correct response, of course, is to first understand essential details behind the question as King Arthur did. Jim Kaskade, CEO of tech consultancy Infochimps, recently suggested to InformationWeek a simple yet “practical and refreshing” question to ask:

Whether it’s churn, anti-money-laundering, risk analysis, lead-generation, marketing spend optimization, cross-sell, up-sell, or supply chain analysis, ask yourself, ‘How many more data elements can you add with big data that can make your analysis more statistically accurate?’

The answer to this key question will lead to additional important questions:

  • “What variety of data sources are needed to fulfill my business case – structured data, unstructured data and/or unstructured content?”
  • “How do I correlate structured and unstructured information together?”
  • “How do I integrate data and content so our users can analyze it on demand, using our existing data visualization tools?”

There is no one-size-fits-all “Holy Grail” Big Data technology out there. In reality, a successful Big Data architecture consists of multiple components to address the unique aspects of all your disparate data sources, structured and unstructured, internal and external. Keep that in mind and show the wisdom of a king by taking pause and asking a few basic business questions to stay on the right path to Big Data business success.

 

(1) Source: InformationWeek article by Doug Henschen, Vague Goals Seed Big Data Failures.

“Something is not Right!” Don’t Ignore Your Gut When Analyzing Information

Image

Ludwig Bemelmans’ classic children’s book Madeline has been enjoyed by generations of kids — my daughters included. The story also has an important lesson on “knowing what you don’t know.”

In Madeline, Miss Clavel, the teacher and caregiver of twelve little girls in a Paris boarding school, suddenly awoke one night sensing trouble:

In the middle of the night
Miss Clavel turned on her light
and said, “Something is not right!”

Sure enough, she found little Madeline crying in her bed, in pain from appendicitis. Of course, all turns out well, thanks to Miss Clavel listening to her personal sense that something was not right.

Image

Having frequently read Madeline to my daughters years ago, that story came to mind while reading Know What You Don’t Know: How Great Leaders Prevent Problems Before They Happen, the first of many excellent books by business professor Michael Roberto.

Perhaps one of the most troubling causes of unseen problems mushrooming into catastrophes noted in Roberto’s book is an organizational culture that pooh-poohs intuition in favor of hard data:

Some organizations exhibit a highly analytical culture [to the point that] employees may self-censor their concerns.

In one case, a manager told me, “I was trained to rely on data that pointed in the opposite direction of my hunch that we had a problem.”

The manager’s hunch was correct; there was indeed a serious problem. And yet, the manager, “relied on the data and ignored that nagging feeling in my gut.”

Roberto drives this point home with a medical crisis spanning hospitals across the US: Troublingly high levels of cardiac arrest among admitted patients. One study found hospital personnel who observed some advance warning sign(s) of cardiac arrest alerted a doctor only 25% of the time! Why? Nurses and other staff often felt a Miss Clavel-like sense that “Something is not right” with a patient who was indeed nearing cardiac arrest, based on a personal observation, such as a change in the patient’s mental condition, or a higher level of fatigue or discomfort — but with no accompanying change in patient monitoring levels — so the concern is effectively ignored in favor of the patient’s quantifiable data.

The consequences of a hospital culture that unwittingly encourages caregivers to ignore their intuition are high. Once the window of opportunity to avert cardiac arrest closes, a life or death “Code Blue” crisis is at hand.

As Roberto’s hospital case study illustrates, a gnawing sense that “Something is not right” should not be ignored, but rather recognized as an alert that you probably do not have all the facts, but just some of the facts — that is, you don’t know what you don’t know.

Recognizing this issue, many hospitals have implemented new Rapid Response Teams that have sharply reduced Code Blue incidents. Nurses and staff are actively encouraged to report observed changes in patient affect, reported symptoms and other concerns, even if they are not supported by patient data. Once notified, the Rapid Response Team will arrive at an affected patient’s bedside within minutes and actively diagnose whether further testing or treatment to prevent a cardiac arrest is warranted. Unlike a Code Blue team that “fights the fire” of a full-on heart attack, Roberto writes, a Rapid Response Team “detects the smoke” of a potential heart attack.

Traditional data warehousing and data analytics vendors often present their solutions as a way to make decisions ‘based on objective facts’ rather than relying on ‘emotional gut feel.’ The problem is, however, the known ‘objective facts’, the known ‘hard data’, may not provide a complete — or even accurate — picture of what’s really going on. For example, structured data sources generally cannot on their own integrate vital additional business signals often buried within such text-based information sources as field reports, knowledge bases, wikis and other documents. 

So, listen to your gut, your intuition, as a signal that you need to dig deeper into the matter at hand. Actively seek out further information beyond the hard data available to you. Compare that information with your hard data and “connect the dots” for a far more complete picture, which may well yield surprising new insights.

What I find exciting is that unified information access is playing a vital role in empowering managers and leaders to connect those dots between data and other silos of information to realize those critical new insights.

Unified information access integrates, joins and presents all related information — structured data and unstructured content alike — to complete the informational picture and significantly expand what organizations “know” to determine with confidence whether “Something is not right.”