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? 😉

Intuitive Reasoning, Effective Analytics, Success: Lessons from Dr. Jonas Salk

Jonas-Salk-MemeApril 14, 2015 marked the 60th anniversary of the Salk Polio Vaccine. On that day in 1955, it was publicly announced that human trials confirmed Dr. Jonas Salk’s vaccine provided effective protection from the polio virus. By 1957, new polio cases fell by 90% from epidemic levels just five years earlier.

A fascinating interview with Dr. Salk on the Academy of Achievement website sheds light on his key personal attributes and values, which are vitally important for success in any line of work. And the best analytic tools will play a leading role in fostering that success.

1. The most successful people practice intuitive reasoning.

Dr. Salk explained how he could identify and solve problems more easily and effectively than others by following his intuition (perceptions, spontaneous creative thought), guided by reason (hard data):

Reason alone will not serve. Intuition alone can be improved by reason, but reason alone without intuition can easily lead the wrong way… both are necessary. For myself, that’s how my mind works, and that’s how I work… It’s this combination that must be recognized and acknowledged and valued.

It was Salk’s intuitive reasoning skills that ultimately led him to his polio vaccine research. Several years prior, as a second year medical student, Salk realized statements from two lectures on immunization techniques contradicted each other. He never got a straight answer as to why, which he (thankfully) could not accept:

It didn’t make sense and that question persisted in my mind… I just questioned the logic of it… I just didn’t accept what appeared to me to be a dogmatic assertion in view of the fact that there was a [medical] reason to think otherwise.

Intuitive reasoning requires not taking “because it is!” as an answer, and “actively pursuing a question and seeing where it leads.”

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When You Have an Analytic Hammer, Every HR Challenge Looks Like a Nail

Back in 2014, IBM announced a new consulting practice offering several new technology services that would apply big data and analytics processes to human resources problems.

From the above-linked article:

One service, predictive hiring, would use large volumes of behavioral assessments and other employee data to better understand the traits that are characteristic of top performers, and then comb through candidates to identify potential hires.

A predictive retention service would analyze workforce data — exit interviews, for instance — to identify those employees most likely to leave.

What struck me when I first read this article is the flawed assumption that job applicants bring high-performance traits with them through the door, or they don’t. And if an employee is looking to leave the company, it’s due to some shortcomings on their part.

It sounds like an example of Maslow’s law: If your only tool is a [analytic] hammer, every [HR] problem will look like a nail.

confirmation-bias-at-workplace

“We’re here today to identify the unique traits of our top performers.”

Since existing leaders will decide what the “traits that are characteristic of top performers” are, they may well end up defining the ideal employee profile in their own image – a clear example of confirmation bias.

Analyzing the traits of perceived top performers who have a long history with the company runs afoul of survivorship biasFrom David McRaney’s blog-turned-book You Are Not So Smart:

You must remind yourself that when you start to pick apart winners and losers, successes and failures, the living and dead, that by paying attention to one side of that equation you are always neglecting the other…

When a company performs a survey about job satisfaction, the only people who can fill out that survey are people who still work at the company. Everyone who might have quit out of dissatisfaction is no longer around to explain why. Such data mining fails to capture the only thing it is designed to measure…

The reality is that workers’ attitudes in the workplace can and do change significantly over time in response to the organization’s own traits, for better or for worse, depending on whether the work environment is proactive or risk averse, collaborative or politically charged, collective or exclusive.

Liz Ryan, former HR VP and founder of Human Workplace, hits the nail right on the head (bad hammering pun; sorry not sorry) in her article:

In order to hit our goals in any organization, we need to build positive energy in the workplace. We need people to be excited about their work…

Can you measure that excitement level? You can’t measure it, but it will show in the results that you do measure, from customer satisfaction to turnover to earnings per share. Anyone in your organization will be able to tell when the excitement level is high, low, or nonexistent. We’d have no trouble reading the energy waves at work if we remembered to stay human on the job.

To her credit, Liz Ryan doesn’t pull any punches in rejecting impersonal, technocratic measurement of employee engagement. I doubt the predictive analytics described above would fare any better with Liz than the hollow ritual of the annual employee survey:

If we really care what our employees think, it’s easy enough to find out… We could ask them how they’re doing… We can be human at work…

We don’t have to insult our employees by having them fill out surveys so the people charged with employee engagement can go to the leadership team and say “Look! The employees are 68% engaged. Look how well I’m doing my job!”

Give up the employee engagement survey, drop the junk-science patina on stupid HR practices and learn how to be human at work. You’ll be amazed how the team’s energy will power your success once you let it start flowing.

Analytics, when created and used appropriately, can be a powerful force for success, but there are also many new technologies that help actively engage employees and cultivate employee positivity and productivity. Gamification platforms are just one such example. Here in Boston, for instance, the WeSpire platform engages and energizes employees around company sustainability and social responsibility programs.

“The best way to predict the future is to create it” is an old saw, but it still rings true – especially when leaders choose to seek out genuine, human interactions and build an energetic, collaborative work culture, which should yield much better employee outcomes and improved individual and team performance.

P.S. – On a related note on hiring decisions, what often passes as “common wisdom” within the HR function really isn’t all that wise. Well said, Natasha Bowman!! ⭐️⭐️⭐️⭐️:

HR-Try-Something-New

 

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.

How 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” (sometimes called the royal “we”). I suspect most people have heard a so-called “leader” make a cutting 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 using the pronoun “we” to stifle communication. This kind of behavior is also a sign of a dysfunctional exclusive-we culture, in which information sharing is discouraged in favor of information hoarding. 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.”

That’s more like it, huh? This time the speaker is invoking the collective “we” to equally include everyone in the room to foster open communication.

True leaders are builders of a collective-we culture, actively encouraging and supporting information sharing and collaboration. A collective-we organization is, therefore, much more likely to utilize knowledge management (KM)/enterprise information management (EIM) tools. Doing so enables the organization to not only solve problems more quickly, but also proactively find problems before they turn into a crisis.

Know What You Don't Know by Michael RobertoIn his excellent book Know What You Don’t Know, business school professor Michael Roberto urged organizations to develop problem finding skills. In a recent interview, Roberto discussed four key ways KM/EIM solutions can enable the collective knowledge, the collective-we, of your organization:

Organizations must 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?

Such an unflinching self-assessment after a business failure often reveals misinformed decisions caused by incomplete information that did not include critical business signals. These signals usually do not reside within structured databases and data warehouses; rather, they are often found within unstructured content: text-based information buried within documents, customer notes, wikis, email, news and websites.

A modern KM platform will integrate and harmonize disparate enterprise data sources – structured and unstructured, internal and external – for fast, on-demand access by knowledge workers. This capability is a key prerequisite to becoming a collective-we organization capable of effective problem finding.

Boil large quantities of information down to what really matters.

If you [write] a 100-page report, no one is going to read it. The answer is not a big report… The most important thing is boiling it down into key bullets… and technology can play a role in helping to share the key takeaways.

A unified KM/EIM system will index, find and present the key takeaways from every “100-page report no one is going to read” on demand, so users can utilize them 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 rep used the company’s KM system to search for a possible resolution. Success! The system found some “key takeaways” extracted from a 100-plus page application development transitional document written by one of the original programmers.

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

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

You can’t chase down everything yourself. I think part of the job of the leader is to recognize that you have talent around you that can help you.

The most effective companies, particularly global companies that have people spread out around the world, are using new tools to get people sharing information across different silos.

The same financial services firm also integrated key information about their own employees into their KM system, including each worker’s areas of subject matter expertise and current areas of research. Through such “expert finder” capabilities, a worker within a global organization can find and seek help from co-workers, whether they’re down the hall or anywhere else in the world – once again, empowering the organization’s collective-we to cross international boundaries.

Collective-we organizations fully leverage the power of KM/EIM to fully leverage the collective intelligence of the entire organization. They find business problems before they become serious issues, as well as seize new business opportunities before the competition even knows they exist. How about you?

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.

Big Data Analytics 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 may be well over 125 years old, but 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.

It’s no coincidence that heightened interest in Sherlock Holmes coincides with the rapidly accelerating, proliferating sources of information around us: databases, documents, social media, web content and much 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.

Sherlock Holmes on Big Data Analytics and Information Management

Now the skillful workman is very careful as to what he takes into his brain-attic. He will have nothing but the tools which may help him in doing his work, but of these he has a large assortment, and all in the most perfect order. — A Study in Scarlet

Holmes draws a wise distinction regarding information of direct, immediate impact that one should remain continuously aware of and be ready to act upon. And today there is indeed “a large assortment”  of information exists across a wide assortment of sources — databases, CMS, email, SharePoint, web and other information silos:

A man should keep his little brain-attic stocked with all the furniture that he is likely to use, and the rest he can put away in the lumber-room of his library, where he can get it if he wants it. — The Five Orange Pips

A “lumber room” in Holmes’ late 19th century Britain stored replaced furniture and related items, particularly in a wealthy Briton’s mansion. As all furniture was custom-made and of possible future use, it would be stored rather than sold or discarded. With the advent of innovations including Hadoop, organizations now have Big Data “lumber rooms” that enable efficient, cost-effective capture and retention of huge volumes of information.

Bringing “perfect order” to these far-flung, siloed information sources by readily combining them for easy access and analysis remains one of today’s most critical challenges. Those organizations that conquer this challenge and eliminate information silos will solve key business problems and identify new business opportunities ahead of the competition.

Sherlock Holmes on Analytic Thinking and Agile Business Intelligence

It is of the highest importance… to be able to recognize, out of a number of facts, which are incidental and which vital. Otherwise your energy and attention must be dissipated instead of being concentrated. — The Reigate Puzzle

For decades, business intelligence (BI) systems have provided managers with reports and dashboards that boil down detailed structured data (databases, data warehouses) into performance metrics trended over time — in an effort to provide quick focus on the vital facts.

However, KPIs alone cannot tell you the whole story about the business; even worse, misguided managers may end up superficially ‘managing to the metric’ instead of managing the business itself:

You see, but you do not observe. The distinction is clear. — A Scandal in Bohemia

As an example I explored in a recent article, Starbucks CEO Howard Schultz wrote in 2008 that Starbucks’ had lost its way in large part due to management overlooking ongoing business missteps in favor of focusing on a single metric which proved to be a poor indicator of the company’s true health:

There is nothing more deceptive than an obvious fact. — The Boscombe Valley Mystery

Simply put, the numbers can tell you what is happening, but the most effective managers of leading organizations will also insist on understanding why.

There are few people able to deduce what the steps were which led up to a given result. This is the power of reasoning backwards, or analytically. — A Study in Scarlet (paraphrased)

The most successful managers are those who think analytically; they refuse to merely accept performance metrics at face value, choosing instead to gain a deep, “root-level” understanding of the company’s operations and customers. Doing so requires asking probing, in-depth “get your hands dirty” business questions. Getting the answers to such vital questions requires the ability to go beyond numbers alone and gain complete agile business intelligence drawn from the entire spectrum of enterprise information — structured and unstructured, internal and external.

On a final related note, one of the most memorable Sherlock Holmes stories featured the detective solving the case of a stolen racehorse and its murdered trainer:

[Police inspector:] “Is there any point to which you would wish to draw my attention?”
[Sherlock Holmes:] “To the curious incident of the dog in the night-time.”
“The dog did nothing in the night-time.”
“That was the curious incident.” — Silver Blaze

Holmes solved the mystery in part by observing the guard dog did not bark, concluding the intruder was not a stranger to the dog. Sherlock Holmes’ brilliance lies in his uncanny ability to carefully observe information and joining together seemingly unrelated facts to assemble a complete picture of a crime.

By unifying and presenting all related enterprise data and content, your organization gains a complete, 360 degree view of your business that new analytic insights to solve new challenges:

If you have all the details of a thousand [past crimes] at your finger ends, it is odd if you can’t unravel the thousand and first. — A Study in Scarlet

Note: This article was originally written for Attivio, Inc. and also appears on the SmartData Collective.

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 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. That realm of BI would not become practical and cost effective until the advent of mainframe systems later in the 1960’s. Rather, H.P. Luhn focused on the unstructured content of his day: documents, letters, research reports and manuals. So too, the business intelligence system Luhn envisioned focused on computer-automated document processing: auto-abstraction, auto-indexing, selective dissemination of information, and information retrieval.

It’s plain to see 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 are more relevant to today’s business intelligence than ever before.

For example, Luhn demonstrated a keen awareness of the roles communication and collaboration play in the effective use of business information. The system would “channel a given item of information to those who need to know it” and find co-workers “whose interests or activities coincide most closely with a given situation,” using action point profiles of each person’s interests and activities. Luhn’s BI system would therefore quickly answer three vital overarching questions: what is known, who needs to know, and who knows what.

Who knows what - 3 key questions

Now over six decades later, it is remarkable how well Luhn’s insights remain a very solid, results-oriented description of BI. What is also striking to me is how early the silos separating unstructured content and structured data emerged.

Business analytics analyst and consultant Seth Grimes, who has written and presented on Luhn’s contribution to BI, sums up this issue of structured and unstructured silos well:

[For decades,] business intelligence detoured around the estimated 80 percent of enterprise information locked inaccessibly in textual form … So BI thrived crunching numerical, RDBMS-managed data… and delivered findings via tables, charts and dashboards that focus more on numbers than on knowledge.

~ Seth Grimes, BI at 50 Turns Back to the Future

Today it is widely recognized that BI based on structured data alone is not enough. Structured data and unstructured content must be integrated and harmonized together to create a complete analytic picture capable of revealing new breakthrough business insights. Again quoting Grimes, “in the last few years, BI has headed back to the future foreseen by Luhn in 1958.”

What is known? Who needs to know? Who knows what? These three timeless, mission-critical questions Hans-Peter Luhn asked in 1958 require unified information — data and content — to fully answer. And these key questions have become easier than ever to effectively answer and act upon by leveraging new digital transformation technologies capable of integrating and presenting analytic-ready data drawn from all relevant data sources, whether structured or unstructured, internal or external.

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

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Ludwig Bemelmans’ classic picture book Madeline has been enjoyed by generations of children – my own daughters included. The story also has an important lesson on analyzing, and questioning, information, and integrating intuition with data.

Children are perennially attracted to Madeline, the smallest yet most adventurous of twelve little girls in a Paris boarding school. In the first Madeline book, Miss Clavel, the girls’ teacher and caregiver, 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 Madeline 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.

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That scene from Madeline came to mind while reading Know What You Don’t Know: How Great Leaders Prevent Problems Before They Happen by business professor and author Michael Roberto.

One of the most troubling causes of unseen problems mushrooming into catastrophes, Roberto writes, is an organizational culture that dismisses 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 [which] pointed in the opposite direction of my [correct] hunch that we had a problem. I relied on the data and ignored that nagging feeling in my gut.

Roberto drives this point home with a serious real-world medical problem: Many hospitals experience 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 felt a Miss Clavel-like sense that “Something is not right” with a patient who was indeed nearing cardiac arrest, but it may have been based on a subjective observation, such as the patient’s mental condition and level of fatigue or discomfort, with no accompanying significant change in patient monitoring 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 rather 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 warning signs, including concerns not (yet( supported by observed 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.

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 very 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.”