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:

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.

Why the Question “Is Your Product a Vitamin or a Painkiller?” is a False Choice

I recently read an article posing the well-known sales question, Is Your Product a Vitamin or a Painkiller? by George Deeb. It’s a good reminder that it’s better to be selling a “painkiller” technology product that relieves acutely-felt, pervasive business problems, rather than a “vitamin” product that offers some lesser, more specialized value.

I agree with Deeb that it’s much harder to build a large, scalable business around vitamin products than painkiller products, but a product-as-painkiller is not the ultimate or best product offering either.

In other words, the question “Is your product a vitamin or a painkiller?” is a false choice – and businesses that rely on painkiller product revenue are at more risk than they might realize.

The issues of trying to sell a vitamin product are described quite well in Deeb’s article. But painkiller products have their own issues. For example, one of the most frequent and frustrating “competitors” to a painkiller product sale is “none of the above”. Much to many a sales manager’s chagrin, prospects often decide that while the business pain is real, alleviating it simply isn’t worth the effort, like Norm in this classic scene from Cheers:

Meanwhile, new enabling technologies march on: painkiller products that once upon a time required a huge capex for on-premise enterprise software, servers and services (CRM, marketing automation, legacy BI) are now offered inexpensively on a SaaS basis (SFDC, Marketo, GoodData). More and more painkiller products are becoming available at lower “vitamin-level” cost and simplicity!

Another issue with painkiller products is they implicitly assume a business status quo. Consider Polaroid in the mid 90’s. Like so many other large companies, Polaroid jumped in with both feet into ERP, the ultimate painkiller technology of its time. Polaroid even won major awards for its SAP implementation. While Polaroid’s ERP no doubt lightened many operational pains by optimizing inventory, purchasing, quality control and such, meanwhile the company was failing miserably with new products and all but ignoring the deterioration of its instant photography market to digital cameras.

I recall reading a Polaroid executive praising the company’s new operational efficiency of its instant photography “core business.” Not long after, in 2001, Polaroid filed for bankruptcy, with most of that “core business” long gone.

Clearly, while reducing business “pain” is important, such efforts are no substitute for the ultimate purpose of a business, as memorably described by Peter Drucker:

There is only one valid definition of a business purpose: to create a customer… Because the purpose of business is to create a customer, the business enterprise has two – and only two – basic functions: marketing and innovation.

And for decades, business technology has focused on operational efficiencies instead of serving as new platforms for innovation. Again, quoting Peter Drucker:

For top management, information technology has been a producer of data [for operational tasks]… Business success is based on something totally different: the creation of value and wealth.

This requires risk-taking decisions… on business strategy, on abandoning the old and innovating the new… the balance between the short term and the long term… These decisions are the true top management tasks.

The technology products that will reap the greatest financial rewards will be those that address those “true top management tasks”: innovation that creates new business value and wealth; such as

Advanced analytic platforms that reveal all-new insights into markets, products, customers and competitors
Gamefication platforms that motivate employees, customers and partners to want to take actions that mutually benefit the organization, themselves and other stakeholders
Customer/prospect engagement technologies that personalize and optimize every experience with your organization, whether online or in-person, across all channels (particularly mobile)

These and other new technologies designed to enable innovation make up a third category of products that go far beyond painkiller or vitamin products.

So what should we call this third product category? Maybe… “steroids”? Nah, don’t think so…

Perhaps “miracle drug”? No…

What about… “Popeye’s Spinach”?!

What do you think?

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”). When someone uses the pronoun “we” to refer to everyone – except the person being spoken to – they are using the exclusive we, typically to single out that person and stifle communication.

collective-we-cartoon For example, I’m willing to bet 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!”

This kind of behavior is also a sign of a dysfunctional company 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 a much better way:

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

Now that’s more like it! 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 effectively. Doing so enables the organization to not only solve problems more quickly, but also proactively find problems before they turn into a crisis.

In his excellent book Know What You Don’t Know, business school professor Michael Roberto urged organizations to develop problem finding skills.

Michael Roberto recently discussed three key ways KM/EIM solutions can enable the collective knowledge, the collective-we, of your organization:

1. Organizations must answer, “Why did we fail?”

Take a hard look at a failure that took place in the organization. Ask yourself… How could we have seen this coming? Were there some telltale signals we missed? Why did we miss them?

Such an unflinching self-assessment after a business failure will often reveal misinformed decisions caused by incomplete information that did not include critical business signals. These signals usually do not reside within structured data sources such as 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.

2. 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 to effectively share those 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, in the first call, a critical stop enterprise application failure incident with no known workaround. The rep used the company’s KM system to search for a possible resolution. Success! The system found the answer, extracted from a 100-plus page application development transitional document written by one of the original programmers.

Few people had probably ever read that 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 the key points from that document precisely when it was needed.

3. You can’t chase down every piece of information yourself… so ask for help!

Part of the job of the leader is to recognize that you have talent around you that can help you. But you have to actively seek out that help.

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

The same financial services firm mentioned above also added to their KM system useful information about their own employees, including each worker’s areas of subject matter expertise. 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 well 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

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-133x134 Sherlock 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: 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.

The First 3 Weeks of the 2012 NFL Season Were Brought to You by Schlitz Beer-Early 1970s Formula

The National Football League’s (NFL) petty 2012 lockout dispute with the NFL referees union has officially ended. Not a moment too soon: The “real” referees will return to officiate all Week 4 games – just in time for the early Thursday Night game – rescuing the rest of the football season from further tarnish, embarrassment and harm at the hands of incompetent replacement referees.

Source: Bleacher Report

Before the 2012 season began, players and analysts warned NFL Commissioner Roger Goodell that the poorly qualified fill-in referees would lose control of football games. They did. The rightful outcome of games would be altered by poor calls and non-calls. Oh, were they ever. And repeated failure to call serious personal fouls would put players at needless heightened risk of serious injury. Sadly, and most unacceptably, that happened as well.

I thought, what other organization, #1 in its field, has ever made such a major non-forced mistake, causing highly visible, self-inflicted harm? My answer rewinds back to my freshman year, Intro to Business, first case study: Schlitz Beer in the 1970s. Once the #1 beer in America, as equally an iconic brand as Budweiser, Schlitz proceeded to lose nearly all of its value by that decade’s end, thanks to inexplicable, self-induced sabotage of its own product.

The NFL’s decision to install replacement referees was a “Schlitz Mistake.”

Continue reading →

Catching People Doing Things Right is Smart, but Less Fun than Catching People Doing Things Wrong

With a new football season well under way, I thought people might enjoy this blast from the past:

We all know that “catching people doing things right” and recognizing their good work is a tactic of a winning manager. Doing so boosts morale and helps motivate more people to do more things right. It also avoids the potentially serious pitfalls of setting financial incentives for certain worker behavior.

And then there’s another school of thought focused instead on catching workers doing the wrong things, led by the legendary Terrible Terry Tate.

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.

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.

“I’d Like to Have an Argument, Please” – An Innovation Message from Monty Python

A while back, Jeff Hayden wrote a clever article for Inc.: The Monty Python Guide to Running a Business, spinning business lessons from classic sketches by Monty Python’s Flying Circus, the legendary British comedy team.

I was surprised that Jeff Hayden’s top ten “business advice by Monty Python” selections didn’t include my #1 choice: The Argument Clinic, one of Monty Python’s most popular comedy sketches ever. I believe this skit also does a great job portraying the ideal business environment… for killing innovation and creativity, that is! Have a look:

Monty Python’s Michael Palin actually pays for the ‘privilege’ of having an argument in this sketch. Meanwhile, too many real-world businesses with poor company cultures are basically paying their managers to routinely engage in arguments (turf battles, verbal sparring, flame mail wars, etc.) with others in the company!

Such misguided organizations will also have plenty of de facto Verbal Abuse departments (“Stupid git!”) to go along with the endless arguments – all of which are sure to give workers plenty of headaches (though hopefully not from a literal wooden mallet to the head).

Simply put, constant arguing is the opposite of collaborating.

As noted in Walter Isaacson’s Steve Jobs, Sony lost the personal music market to Apple and iTunes, because its divisions were too busy constantly arguing with each other:

[A music executive told Isaacson he] “had spent two years working with Sony, and it hadn’t gone anywhere… Steve would fire people if the divisions didn’t work together, but Sony’s divisions were at war with one another.”

Indeed, Sony provided a clear counterexample to Apple. It had a consumer electronics division that made sleek products and a music division with beloved artists (including Bob Dylan). But because each division tried to protect its own interests, the company as a whole never got its act together to produce an end-to-end service.

Arguments can also be induced when workers feel compelled to look over their shoulders instead of working and collaborating openly with others. For one very big example, it is hard to comprehend why ex-CEO Marissa Mayer inexplicably enacted the long-discredited HR practice of “stack ranking” (aka “rank and yank”) at Yahoo, given the well-documented havoc it had already wreaked at Microsoft.

rank-and-yank-tweet-yahoo-2016

About the same time Yahoo was ramping up its stack ranking agenda, Microsoft finally eliminated it, shortly after the departure of Steve Ballmer as Microsoft CEO. For years under Ballmer, Microsoft had been using stack ranking, which “forces every unit to declare a certain percentage of employees as top performers, good performers, average, and poor,” as explained by Kurt Eichenwald in his landmark article, Microsoft’s Lost Decade. Stack ranking “crippled Microsoft’s ability to innovate”:

Every current and former Microsoft employee I interviewed – every one – cited stack ranking as the most destructive process inside of Microsoft, something that drove out untold numbers of employees.

“If you were on a team of 10 people, you walked in the first day knowing that, no matter how good everyone was, 2 people were going to get a great review, 7 were going to get mediocre reviews, and 1 was going to get a terrible review,” says a former software developer. “It leads to employees focusing on competing with each other rather than competing with other companies.”

Time spent by workers, managers, departments, business divisions, etc. on internal arguments and other infighting is time not spent battling competitors outside the company. Time wasted developing “innovations” in self-preservation, co-worker character assassination and other manipulative office politics is time not spent innovating new products, effective marketing messages and other creative solutions. The business costs and negative personal consequences of such dysfunctional behavior are massive.

Hmm, I think this article has gotten a bit too serious. Let’s close, then, with one more Monty Python “business lesson,” this time regarding customer service gone very wrong!

Mike Urbonas

Product marketing, innovation, content creation, business intelligence and intelligent business.