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 gentleman 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 30 years, traveling the globe locating mummies and establishing best practices for their proper examination.

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 recognition from the global scientific community.

Dr. Aufderheide’s life work helps drive home two key points about success, 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. Workers 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.

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 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? 😉

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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:

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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.

How KM & Enterprise Search Help Collective-We Firms Eat Exclusive-We Competitors for Lunch

Poorly managed organizations are likely to function – or, I should say, malfunction – with frequent use of a divisive verbal tactic called the exclusive “we”. I suspect most business people can recall being on the receiving end of remark like this:

We don’t do things that way here.”
“Will you stop asking so many questions? We don’t tolerate ‘fishing expeditions’ around here!”

“I’ve been saying ‘We don’t do things that way’ so long, I’ve forgotten what we DO do.”
Image by HikingArtist.com (CC)

The speaker is clearly excluding the person being addressed from the pronoun “we” to stifle communication. This kind of non-communication is also a sign of a dysfunctional exclusive-we culture, in which information sharing is discouraged in favor of information hoarding. Exclusive-we organizations will struggle to so much as acknowledge business problems before they become undeniable crises, leaving managers in constant ‘fire-fighting’ mode. Hardly a recipe for business success.

Successful companies use the word “we” a lot, too – but in an opposite, winning manner:

“What should we be doing that we aren’t doing now?”
“These questions are important. We need to be able to answer them.”

What a difference! This time the speaker is invoking the collective “we” to equally include the person being addressed along with everyone else in the room, as well as everyone throughout the entire organization.

Leaders in highly successful organizations naturally speak and act from a collective-we perspective. Even better, they build a collective-we culture, actively encouraging and supporting information sharing and collaboration. Doing so transforms a company’s collective-we into a powerful company asset capable not only of quickly solving problems, but also proactively finding them – and, in the process, leaving the exclusive-we competitors in the dust.

Know What You Don't Know by Michael RobertoMichael Roberto, a leading business leadership authority whose excellent book Know What You Don’t Know I have written about previously, strongly urges organizations to develop problem finding skills. Roberto recently commented about new technologies that enable internal crowdsourcing, aka the collective-we:

Crowd sourcing can work inside of a company too, and we’re seeing more and more companies doing that; particularly global companies that have people spread out around the world. They’re using [new] tools to get people sharing [information] across different silos.

Eliminating information silos is a key prerequisite to becoming a collective-we organization capable of effective problem finding. In an interview with management consulting firm Linkage, Michael Roberto shared some valuable insights into the three major ways unified enterprise information management enables the organization’s collective-we:

Organizations must frankly answer, “Why did we fail?”

Take a look at a failure that took place in the organization. Ask yourself, “Could we have seen it coming… were there some signals we missed? Why did we miss them?”

Organizations that have undertaken such “candid self-assessment” have discovered that they had been acting based on an incomplete informational picture that was indeed missing critical business signals. Such signals reside within trends in KPIs and metrics drawn from data warehouses and databases, as well as unstructured content (free-flowing text residing in document repositories, SharePoint, wikis, file servers and external websites).

Boil large quantities of information down to what really matters.

[In the] old-school way, you built a big report, you put it in a binder and it collected dust… the answer is not a big report. The [real] answer is three bullets… the couple of takeaways – and technology can play a role in helping to share those. But the most important thing is boiling it down… If you (have) a 100-page report… no one is going to read it.

Good organizations are already adept at boiling down large volumes of data into KPIs that can be trended over time, but that’s not enough. It is also important to mine “those key takeaways” from every “100-page report no one is going to read” through natural language processing (NLP) and text analytics, including extraction of entities (names, products, places), key phrase extraction, entity normalization, content classification and more.

It’s also important to note a unified knowledge management (KM)/enterprise information management (EIM) system will also utilize advanced enterprise search to present the most relevant information instead of a long laundry list of documents to sort through. As a result, “those key takeaways” from every “100-page report no one is going to read” will be discovered by users whenever they are needed to help directly address any given matter at hand.

In a real world example, a level 1 IT support rep for a leading financial services firm resolved a serious enterprise application failure incident with no known workaround in the first call. The company’s service knowledge management solution surfaced an ideal resolution buried within a 100-plus page application development transitional document, written by one of the original programmers located in India.

Few people probably ever read the entire document, or even knew it existed; and yet, the company’s unified information architecture empowered the company’s collective-we from halfway around the world to solve a serious problem, by presenting that document when it was needed.

“You can’t chase down everything”… so let KM/EIM technology chase it down for you.

You can’t chase down everything [yourself]. I think part of the job of the leader is to be able to prioritize… [and] recognize that you have talent around you that can help you.

The same financial services firm also integrated key information about their own employees, particularly areas of subject matter expertise and current areas of research. Through such “expert finder” capabilities, a worker within a global organization can find and reach out to fellow co-workers for help down the hall or anywhere in the world – once again, empowering the organization’s collective-we to cross international boundaries.

A collective-we organization fully leverages the power of the collective intelligence, the collective knowledge of the entire organization to find business problems before they become serious issues, as well as seize new business opportunities before the competition even knows they exist.

Big Data Wisdom, Courtesy of Monty Python

Monty Python and the Holy GrailNote: This article was co-written by Mike Urbonas and Rik Tamm-Daniels.

One of our favorite 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…

The knights were either stumped by a surprise trivia question like, “What is the capital of Assyria?” – or responded too indecisively when asked, “What is your favorite color?”

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 “Big Data Holy Grail” 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.’

Read the rest of this article on SmartData Collective.

Big Data Analytics, Business Intelligence and the Mind of Sherlock Holmes

My name is Sherlock Holmes. It is my business to know what other people do not know. — The Adventure of the Blue Carbuncle

Sherlock-Holmes-Big-Data-Analytics-and-BI-133x134Sherlock Holmes turned 125 years old last year, and he’s never been more alive and well. The world seems more captivated by Sir Arthur Conan Doyle’s legendary London detective than ever before. Much of this excitement has been driven recently by the smash BBC One TV series Sherlock, drawing rave reviews for its update of Holmes and Dr. Watson as present-day Londoners fighting 21st-century crime. (Similarly, the U.S. version of the series, Elementary, is also a major new hit.)

Pop culture critic and author John Powers cleverly explains Holmes’ enduring appeal as a literary hero and cultural icon:

Sherlock Holmes “possesses no superpowers — his parents weren’t wizards, no radioactive spider bit him — [and yet] his gifts are cool enough to be superhuman. Playing to our fantasies of being smarter than everyone else, Holmes performs jaw-dropping feats of perception.

It’s no coincidence that heightened interest in Sherlock Holmes coincides with the rapidly accelerating, proliferating sources of information around us: databases, documents/text, big data, social media, web content and more. Like Sherlock Holmes, we all want to make sense of seemingly unrelated information and “be smarter than everyone else” — or at least outsmart the competition, outsmart criminals and fraudsters, outsmart seemingly intractable business problems.

A quick review of Conan Doyle’s novels and short stories reveals Sherlock Holmes shared useful advice on effectively accessing, analyzing, and unifying information. His advice rings truer than ever in today’s increasingly information-rich but insight-deficient world.

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When Performance Metrics Attack! Complete, Agile BI Requires Going Beyond Just the Numbers

I’m reading Howard Schultz’s Onward by Howard SchultzOnward: How Starbucks Fought for its Life Without Losing its Soul (2010). Schultz compellingly conveys his dedication and passion for the company and, of course, great coffee. Returning in January 2008 as Starbucks’ ceo (Starbucks uses lower case for all company titles), Schultz would save the company from its doldrums, rekindle long-lasting success and silence critics who had proclaimed Starbucks’ best days were over.

Just as important as what Howard Schultz did as ceo was what he stopped doing: Soon after returning to the ceo office, Schultz told investment analysts that Starbucks would no longer publicly report its same-store sales, or “comps.” Schultz’s wise decision would prove to be as critical to Starbucks’ revitalization as its new Pike Place coffee blend and Clover brewing machines.

Analysts were predictably annoyed by the move, but Schultz patiently explained that comps did not consider Starbucks’ grocery sales and other revenue beyond its cafes. But Howard Schultz had a far more urgent reason to stop reporting comps: comps had long become “a dangerous enemy in the battle to transform the company.” As Starbucks’ chairman, Schultz had realized the company had, slowly over time, “defaulted” to viewing the health of the company through the singular performance lens of comps; as long as comps were fine, the company was fine – except that it wasn’t.

Comps would eventually prove to be a harmful lagging indicator: as Starbucks persisted with excessive store expansion and a series of missteps that diminished customer experiences, comps remained highly favorable. Only long after “slow, quiet, incremental” damage did comps finally, and very suddenly, trend poorly. Schultz wrote:

Maintaining positive comp growth history drove poor business decisions that veered us away from our core… Once I walked into a store and was appalled by a proliferation of stuffed animals for sale. “What is this?” I asked the store manager in frustration, pointing to a pile of wide-eyed cuddly toys that had nothing to do with coffee. The manager didn’t blink: “They’re great for incremental sales and have a big gross margin.”

This was the type of mentality that had become pervasive. And dangerous…It is difficult to overstate the seductive power that comps had come to have over the organization…overshadowing everything else.

In hindsight, it was very fortunate that Howard Schultz had remained active as Starbucks’ chairman and was willing and able to step back into day-to-day operations as ceo. Having pioneered the company’s signature cafe stores, Schultz had the situational awareness to realize that “something wasn’t right” with the company’s customer experience years before comps finally tanked.

What about other leaders who also want true, long term success, but don’t have the same hands-on, ground-floor business awareness of a company founder? How do they acquire similar awareness to avoid overlooking slow, subtle damage to the company and instead make business decisions that promote genuine, long-lasting success? Here are a few essential requirements, based on some insights I drew from Schultz’s book.

Keep score based on how well you achieve your core mission. Howard Schultz had a true passion for revitalizing Starbucks around its core mission – its very reason for existing: delighting customers with its superior coffee and unique cafe experience. Pleasing shareholders was always part of Starbucks’ mission, but doing so slowly eclipsed its core mission, and eventually impaired shareholder value as well. Eliminating the rogue performance metric of comps gave the company “a new way to see” the business based on its core mission and “freed everyone to enthusiastically [re]focus on our coffee and our customers.”

“Get your hands dirty” in the “roots” of the business. Schultz rallied the company around its core mission – freshly updated with his global executive team – and aligned all operations, customer service, and decision making with achieving that mission. He called on everyone in the company to join him in that hard work, urging his executive teams to “get dirty, get in the mud, get back to the roots of the business” – a metaphor that resonated throughout the company. Long term leaders and managers must “get their hands dirty” – fully commit themselves to deeply understanding the key details of the company’s operations and its customers and take action accordingly.

Get a complete informational picture of the business. On his first day as returning ceo, Howard Schultz told employees that “to just go ‘back to the future'” of Starbucks would not be good enough to turn the company around. While the company would “need a piece of its past,” Schultz also believed “many of us at Starbucks had lost our attention to the details” – leading to Schultz’s drive to “get back into the roots of the business.”

By necessity, acquiring a deep, detailed understanding of the business at its “root” level requires a complete picture of the business far beyond numbers alone. Leaders and managers dedicated to long term success will therefore not be content with analytics limited to such superficial questions as, “So how are comps doing?” They will demand answers to far deeper, probing, “get your hands dirty” business questions, such as:

  • How do our sales performance, new product launches, employee retention, etc. correlate with customer sentiment expressed on social media sites, our online surveys, email and chat logs?
  • What complaints, compliments, and/or suggestions keep coming up? Is this customer feedback correlated to specific regions or locations?
  • What other factors we may not yet be fully aware of affect our sales, costs, and customer service: Changes in weather? Changes in local/regional tastes and preferences? And on and on…

Leaders cannot, and will not, wait weeks or months for answers from unresponsive traditional BI processes and legacy IT systems. Answering such vital questions that “dig into the roots of the business” requires a powerful new enterprise information “rototiller”: a new platform capable of providing complete, agile BI – drawn from the widest spectrum of enterprise information: not only structured data (databases), but also unstructured data (social media, knowledge bases, web content and other text-based information).

Take action in person. Make house calls. Howard Schultz used a medical analogy to emphasize the vital need for “root-level” business understanding:

Like a doctor who measures a patient’s height and weight every year without checking blood pressure or heart rate, Starbucks was not monitoring itself at a level of detail that would help ensure its long term health.

Extending Schultz’s leader-as-doctor analogy, the “doctor” must not only prescribe well-informed action for revitalized business health, but also administer it with a lot of house calls.

Once leaders and managers achieve that essential deeper “root level” of business understanding, they must take action based on those insights in a timely – and public – manner. Leaders must be visible to the managers and workers whose daily dedication and effort are critical to achieving the company’s core mission:

I sensed that people inside the company needed to see me… Showing up, listening to and talking with Starbucks’ partners was one way I got my own hands dirty… Whether I was in front of one person or thousands… I strove to be authentic and frank while threading optimism into every communication.

Onward provides substantial insight into authentic leadership. The book is a primer on reigniting internal excitement for the company and its mission, refocusing on the customer experience and growing through innovation with the customer and mission in mind. Leaders driven to achieve these goals and realize long-term success will reject superficial metrics in favor of gaining deep “root-level” business understanding. Doing so requires a cutting-edge, rapidly deployed BI platform capable of eliminating information silos and providing a truly complete business picture, drawing from all information sources – structured and unstructured, internal and external.

Back to the Future of Business Intelligence with H.P. Luhn

When was the term “business intelligence” first coined? You might assume it was first conceived in the late 1980’s; coinciding with the initial emergence of companies offering visual analytic software, but the term was actually first used decades earlier by visionary IBM technology scientist Hans-Peter Luhn in his groundbreaking 1958 research paper, A Business Intelligence System.

Hans-Peter Luhn’s life work at IBM did not include quantifiable (structured) data. Rather, H.P. Luhn’s prolific IBM career focused on documents — letters, research reports, books — the unstructured content of his day.

Reading his paper today, it is clear that Luhn was well ahead of his time, envisioning critical technology components that set the stage for knowledge management and enterprise search today. And now, Luhn’s insights into the effective use of information, such as the vital need to answer three vital overarching questions – what is known, who needs to know, and who knows what – are more relevant to today’s business intelligence than ever before.

Read the entire blog article on the SmartData Collective.