big data

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

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

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

Dr. Arthur Aufderheide

Dr. Arthur Aufderheide

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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 bias. From 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.

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)

Artwork by: BTimony (click to see original)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?

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.