Healthcare’s Complex Business Models Require Solutions that Address All Business Model Dimensions

Healthcare’s Complex Business Models Require Solutions that Address All Business Model Dimensions

Clay Christensen, Harvard Business School professor and author of Disruptive Business Models in Healthcare (Forbes 2009), discusses the complexity of healthcare’s business models. He states that there are two business models simultaneously operating in the delivery of healthcare in the general hospital: a solutions business model and a process business model.

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Data science and hospital value based purchasing has us excited for healthcare, here’s why you should be too

One doctor explains to another, lab results confirm: The glass is actually not half empty

One doctor explains to another, lab results confirm: The glass is actually not half empty

America’s health care system is neither healthy, caring, nor a system
Walter Cronkite, 1993

You may have missed it, but at this very moment, the innovations sweeping across healthcare offer much to be excited for. Yes, the hyperbole surrounding our broken healthcare system is legitimate. But somewhere between the usual echo chambers in Washington and your local coffee shop — we’re experiencing significant progress within healthcare. And despite what you’ve heard, we now have reason to be optimistic.

Why? Just this week we learned many diseases are declining, rates of age-related illnesses are dropping, and doctors aren’t even sure why.

The fact of the matter is, on the whole, our quality of life has never been better.

While many challenges lie ahead, modern healthcare delivery and administration is turning the corner.

. . .

A May report in the British Medical Journal (BMJ) sparked a firestorm by estimating 250,000 Americans die each year due to medical errors¸ making them the third leading cause of death in the U.S.

It made for great headlines, but masked a bigger problem: Managing today’s health system complexity and communicating scientific (medical) information is a serious challenge, and we’re not doing very well.

Ashish K. Jha, M.D., director of Harvard’s Global Health Institute, recently shared story of a patient he’d cared for who was admitted for pneumonia, put on standard antibiotics, but died 72 hours later. Jha learned days later — after contacting the patient’s daughter — that a different hospital had run labs on this patient two months prior, and discovered his pneumonia was caused by a rare strain of bacterium susceptible to just a few antibiotics, none of which had been administered by Jha’s team.

The previous hospital was on a different electronic health records system. So the admitting physician didn’t know the patient had been treated for pneumonia prior. Which begs the question — Was Dr. Jha’s patient’s death a medical error?

Dr. Jha points out, while individuals make lethal mistakes, the main reason for preventable medical mistakes is due to a health care system inadequate to the complexities of modern medicine. Basically, as simple as it may seem, vital systems simply do not talk to one another. And labeling preventable, yet needless deaths as “errors” is more a matter of convenience than actual gaps in systematic medical treatment.

According to Dr. Jha, the system failed him. “A system designed for complexity would have alerted us that he had gotten care at another institution. It would have allowed us to look up the microbiology results, even in the middle of the night, so we could have made a better antibiotic choice up front — a choice that was customized to him, not to the generic patient.”

These examples bring together several of the biggest problems in healthcare today.

Daily, our system grows in complexity, yet two lingering issues remain unresolved.

1.

We lack common definitions (what is a medical error, what is cost, or value), which amounts to communication.

and

2.

We lack information
Technically, we’re awash in information, just starved for meaning. This boils down to systems not sharing information (interoperability).

 . . .

In his book A Farewell to Alms: A Brief Economic History of the World, historian Gregory Clark demonstrates the typical peasant experienced an even lower standard of living than did his hunter-gatherer ancestors, despite having worked harder. The critical standard of living turning point only surfaced in the past few centuries with the emergence of two new and powerful systems of social institutions: the modern market economy and modern science.

How does this fit in healthcare, and why are we so optimistic?

It’s not as complicated as it sounds. The modern market economy and modern science both rely on a decentralized process of experimentation and feedback, or in plain English, it is what’s become known as the scientific method on one hand. And entrepreneurial risk and return, competitive enterprise and a profit-and-loss system in the other. Both methods utilize quantitative reasoning, driven by evolving degrees of analytical sophistication and rigor.

Until only recently, both of these massive principles have largely skipped the administration of healthcare. That’s all changing with the digitization of healthcare and the expansion of Electronic Health Records. Finally, through new and evolving methods of data analytics, we can apply these powerful market and scientific principles to healthcare.

How? Through a new digital foundation enable by EHR, we can use data science and analytics to generate ideas, insights, and solutions unimaginable even five years ago. 
 
Data analytics is quickly expanding our capabilities through a scientific approach to data driven insights, managerial science and organizational culture.

Technology and modern data science facilitates communication.

Research by athenahealth’s Leadership Forum identifies communication as the most important skill for healthcare management.

Despite an elusive economic payoff to technology, error rates in electronic health records out numbering paper records, and doctors ignoring electronic alerts, because, well they’re overwhelmed by them, research shows investments in healthcare IT does produce value.

According to a detailed McKinsey analysis of the HIMSS Value Suite database, gaps in productivity and efficiency measurement, user satisfaction and return on investment attribution must be addresses before the value from healthcare IT can be fully understood and maximized.

To date, fragmented approaches to data management, competing priorities, poorly defined governance and lack of buy-in from end users has slowed technology and data analytics return on investment. However, data driven insights only now available through big data and data science is helping us uncover and appreciate the tangible benefits to further investment in IT. 
 
Greater emphasis in scientific methods, particularly around data science, simply cannot be overlooked. In many ways, science is expanding our ignorance. Every time we use science to answer one question, invariably we stubble upon even more questions. So while science is increasing our knowledge, it’s increasing our ignorance even faster. To slow our “ignorance”, modern data management and analytics principles are now beginning to catch up with the potential underlying big data.

This leaves us all much to be optimistic about. We used science when transitioning from an agrarian to industrial based economy, this continues today as our knowledge economy continues to take shape. 
 
The digital transformation within healthcare is substantial and in many ways it’s a giant IT project. But on a deeper level, as data increasingly flows from silos to unified platforms, we’re seeing digital transformations are an organization wide process, making communication and collaboration arguably the most important drivers of success.

Value Based Purchasing is moving healthcare closer to a true market based system.

For too long, the productive benefits of creative destruction have eluded healthcare. Instead, we’ve relied on a system that boxes out disruption through a lack of connectedness and transparency. With the transition to value-based reimbursement models — market based principles such as price transparency, competition, and regular innovation are quickly becoming a high priority.

Medicare already ties many payments to quality and plans to further shift reimbursement away from fee-for-service. In a 2015 study, hospital CEOs ranked financial challenges as their top concern, with nearly 65% of surveyed hospital executives ranking volume to value as a top financial challenge.

In healthcare, we’ve been flying blind, but clearly, value and a transition to a more market oriented system now has the attention of the C-suite.

With ever more information and communication available, analytics is upending how consumers and providers engage with healthcare.

At its core, value-based purchasing (VBP) under the Medicare reimbursement model is all about the consumer (or the patient); how the consumer is treated, how well patients respond to treatments, and what the patient thinks of their entire care experience.

This means healthcare is slowly starting to resemble other sectors of the economy, in that, it is starting to recognize that consumers — not just payers — must be satisfied.
 
In 2016, nearly 25% of hospital reimbursement under value-based purchasing will be tied to how well hospitals engage with their patients.

According to the Studer Group, Hospital Consumer Assessment of Healthcare Providers and Systems measures will include:

1. Communication with Nurses

2. Communication with Doctors

3. Responsiveness of Hospital Staff

4. Pain Management

5. Communication about Medicines

6. Hospital Cleanliness and Quietness

7. Discharge information

Notice. Of the 7 measures, 3 clearly identify communication as a vital objective.

 

In healthcare, scaling good ideas has never been easy. Sharing information may never be simple and healthcare will continue to grow in complexity. But advances in data science and market based principles is more than hype, they’re finally making their way into healthcare and its time we start to be more optimistic.

Reflections from Healthcare Analytics 2016

As a co-sponsor, we were privileged to participate in this year’s Healthcare Analytics 2016 conference, held in Chicago.

As old friends and some new return home, with the Windy City’s temperate quickly rising, we thought it would be worthwhile to share a few thoughts from the event.

If we had to sum up this year’s message, two words jump out; communication and optimism. Here’s why:

COMMUNICATION

Northwestern University’s J Bryan Bennett and Esther Choy kicked off the event with an interesting spin on leadership models integrated with “storytelling”.

They presented evidence on how healthcare leaders can amplify their message/communication by tapping the power of storytelling.

Given the attention to branding, content marketing, ‘every company is a media company’, storytelling with data, etc… it’s not surprising the idea of storytelling is now working it’s way into healthcare. It’s just the first time we’ve seen it, especially in an analytics setting.

Our take: The benefits to analytics and it’s visualization are well documented. Through ‘storytelling’, we can now share our message more potently by integrating quantitative and qualitative support and adding context in the form of a story. All coming back to better communication.

During the ‘Implementing An Enterprise Analytics Organization at Mayo Clinic’ session, Joseph Dudas, Division Chair — Mayo Clinic caught our attention by sharing some blunt advice: “Infrastructure doesn’t sell to leadership. Value must be proven with use cases”.

This jumps out to us because in many ways, it’s what we’re experiencing when talking with our clients and prospective clients. Everyone gets the technology. Everyone appreciates what analytics can do for them. We’re now far beyond pitching infrastructure and IT. To really sell an idea, management teams need proof positive evidence in the form of rock solid use case examples. Which can be shared best through storytelling and better communication.

The idea of ‘storytelling’ did pop up in several other sessions.

Optimism

The migration of folks from the finance industry to healthcare also caught our attention. In speaking with several participants, we noticed on more than a few occasions folks telling us their backgrounds came from finance.

Purely anecdotal in nature, but it seemed folks expressed more optimism or opportunity by making the shift to the healthcare industry.

June job numbers showed hiring momentum clearly favors healthcare.

Further optimism around analytics and healthcare was seen in the general tone of content. We noticed event content overwhelming centered around opportunities available with big data and analytics, very little emphasis was given to ‘solving challenges’, such has using analytics to save cost, manage risk or handle regulatory burdens.

Surely participants are using data analytics to solve challenges, but we noticed an up beat tone surrounding tapping the potential available in big data.

Radical Transparency or just TMI?

An open letter on culture, client centered design, and connecting at scale.

. . .

TODAY’S LESSON
Going viral in the 18th century

You’ve probably heard the story of Thomas Chippendale, the mid-18th century London-based cabinet maker, furniture designer and interior decorator.

Poor Thomas didn’t have much success until he shared his techniques in his book titled The Gentleman and Cabinet Maker’s Director. Without Snapchat or LinkedIn, for its time, the book went “viral.”

What’s the lesson for us? We find it awfully curious why so many service firms are reluctant to share their so called “secret sauce.”

Chippendale did so almost 300 years ago and his business and influence grew immeasurably as a result. We’re here to do the same.

 

CLIENT CENTERED DESIGN (ORGANIZATIONAL)

We’re quickly finding: The new normal is a client who expects a level of service that only a motivated employee can give.

But like any team, we’re constantly searching for the best ways to recruit talent, get the most out of our talent, then keep them from jumping ship.

Invariably, we come back to one thing as our secret sauce: Our Culture.

Internal efforts to cultivate our culture are on-going, and although we’re not ready for Buffer style radical transparency, we still think it’s time to openly share.

Beginning with this micro-manifesto of sorts, we’re declaring our goal to share an occasional behind-the-scenes look into how we work, what inspires us, and most importantly, how we embrace our shared values.

Our clients come from healthcare and financial services. These organizations operate in some of the most difficult industries.

As their trusted advisor and occasional partner, we, like them, fully appreciate the success of any company going through fundamental digital transformation is understanding that it’s first and foremost a people issue.

Meaning, before we enable our clients to truly enjoy the benefits of data-driven decision making, our team and its people must first be aligned to a common purpose. Our common purpose starts and ends with our culture.

CONNECTING AT SCALE

The funny thing about culture is that it’s clearly everywhere, but nowhere to be seen. So if you bump into us at one of the many workshops, meet-ups, or other learning opportunities we frequent, please stop and say hi, because if you don’t, we will.

ON THE TOWN: FOR CHARITY

ON THE TOWN: FOR CHARITY

Empowered by digital, compelled by passion, and enabled by data, we are eager to build meaningful, lasting, and mutually beneficial relationships with our team, clients, and the communities they serve.

. . .

Check back for future behind the scenes looks into life at Gray Matter.

 

 

3 Ways to Build an Analytics Dream Team

Of the original 12 members of the 1992 U.S. men’s basketball team, 11 are in the NBA Hall of Fame (Christian Laettner never made it).

As they say, there is only one Dream Team.

Unsurprisingly, the more companies characterize themselves as data-driven, the better they perform on objective measures of financial and operational results. As the benefits to big data accumulate, the role of the domain expert is changing. Expertise used to be valued for having answers, now it’s about knowing which questions to ask. This is probably what Pablo Picasso had in mind when he famously said, “Computers are useless. They can only give you answers.”

Here are 3 ways to build your Analytics Dream Team of cross-domain experts.

1.

2016 Is The Year Of The Hybrid Job — Hire for a mix of technology and people skills.

To address the depth and breadth of digital transformation, employees must bring deeper and broader competencies. We used to arrange atoms, now we rearrange data. Big data, for example, is quickly becoming a core driver of success in nearly every business, and across every industry. Success in the 21st — century workplace demands staff that can gather, analyze, and apply data driven insights to their role and the company’s goals more broadly.
 
“The ability to compile, analyze, and apply big data to everyday business decisions is driving major change. Regardless of function, employees need to be able to effectively communicate what the data means and apply it to big-picture objectives,” says Susan Brennan, associate vice president of university career services at Bentley University.

“But this can’t be done in a silo; collaboration and teamwork are essential.”

2.

Develop knowledge-transfer programs, before it’s too late.

With large numbers of baby boomers approaching retirement, they’ll be taking with them years of valuable institutional knowledge and skills. Hybrid skills in a younger workforce can ease the generational shift. But formal knowledge-transfer programs can help junior employees step into leadership roles more quickly.

Handling generational and digital culture shock is going to take more than savvy social media. Accenture’s Technology Vision 2016 predicts leading companies that develop a people first approach will win in today’s digital economy. “Companies that embrace digital can empower their workforce to continuously learn new skills to do more with technology and generate bigger and better business results,” says Paul Daugherty, Accenture’s chief technology officer.

Digital success means people too. To create fresh ideas, develop cutting-edge products and services, and disrupt the status quo, organizations that develop efficient knowledge transfer methodologies will enjoy a competitive edge.

3.

Office hierarchy is dead — assemble your Dream Team as a “network”.

It took some time, but the traditional office structure is finally dead. A recent Deloitte survey found only 38% of companies are now “functionally organized.”

To increase industrial production in the 1920s and 30s, the function of a firm was largely to reduce transaction costs, particularly information costs. It made sense for a firm to grow until organizational costs offset efficiency gains, especially when transaction costs were a greater concern than organizational costs. 
 
Today’s information economy turns this model on its head. Technology has minimized transaction costs, while organizational costs have gone up. To fully realize the benefits of investment in digital technologies, businesses can unlock creativity through organizational redesign.

Industrial age top-down management hierarchies are slow to innovate and adapt. The modern workplace includes team members with less defined roles, they move laterally from project to project, taking advantage of new technologies to manage challenges arising from sudden shifts in the world economy.

From Pilot to Enterprise Solution: How Analytics and Innovation Design Pace Digital Transformation

88% of the Fortune 500 companies in 1955 are now gone. Think your company is keeping pace with digital? Around every turn, companies are challenged to transform and adapt. As technology continues to transform the economy, aligning organizational structure to maximize the benefits of digital is going to require a robust innovation strategy.

The Fourth Industrial Revolution, Second Machine Age, or Age of Disruption, whichever you’re favorite buzzword, one thing is certain — Today’s corporate watchword transformation has no historical precedent.

This year, the World Economic Forum’s annual gathering of world business and political leaders was dedicated to exploring the transformative powers of velocity, scope, and systems impact on business. Programming centered on how the velocity of disruption and innovation are evolving at an exponential rate. Disruption is being experienced in nearly every industry in every country. And the breadth and depth of change is affecting entire systems of production, management, and governance.

Across all industries, computers and digital technologies are doing for mental power — the ability to use our brains to understand and shape our environments — what the steam engine did for muscle power.

Surprisingly, many organizations haven’t fully taken advantage of the technological disruptions available today. Moving from digitization (the Third Industrial Revolution) to the benefits of recombinant innovation (the Fourth Industrial Revolution) requires more than simply raising our digital IQs.

Business leaders and today’s decision-makers must appreciate our rapidly changing environment. Traditional, linear mental models do not appropriately address the forces of disruption and technological change shaping our future.

Contemporary innovation strategies require us to constantly rethink our business models, customer expectations, talent, culture, and organizational forms.

. . .

Connecting Innovation to Strategy

Given our digital, analytics driven world, our natural tendency is to build a strategic priority around big data and the IoT, but the solution isn’t to just build a digital strategy just because everyone else is.
 
Transformation into a digitally-driven organization requires total organizational commitment. Moving past the hype takes a measure of resolve that few companies demonstrate. A 2015 survey by MIT Sloan Management Review and SAS Institute revealed the inconvenient truth about the unglamorous but necessary actions required to improve decision making with analytics.

Gary Pisano, of Harvard Business School, points out, when thinking about innovation opportunities, companies have a choice about how much of their efforts to focus on technological innovation and how much to invest in business model innovation.

As with any good strategy, the process of developing an innovation strategy should start with specific objectives tied to helping the company achieve a sustainable competitive advantage.

This means moving beyond generalities, such as “we must innovative to compete,” “we need innovation to create value,” or my favorite, “innovation is the future.” Providing no sense of which types of innovation matter, these are not strategies.

 

Big Data meets Big Design — 
Meeting customer expectation and product enhancement through creativity and analytics fused innovation

According to Michael Krigsman, founder of cxotalk.com, a weekly web-based talk show on which Krigsman interviews leading tech industry executives. The executive suite has to have a clear plan for the future, and a way to put the company on the road toward delivering on that vision. They can’t hide the innovation team in the basement. They need to inject innovative thinking into every process in the organization and that requires reconsidering every process.
 
To meet customer expectations and product enhancement demands, traditional technology and management consultancies are partnering with design and innovation specialists. The combination proves how central innovation design is to business today.

Innovation is simply too important to left to chance or good fortune. Once limited to pretty packaging, modern design is an incredibly broad discipline, encompassing everything from product & services, branding, and business models to corporate strategy and structure.

To ingrain innovation design as a core competency, the overlap between management consulting and design consulting includes several high profile transactions. Including, McKinsey buying the design firm Lunar in 2015. This February, Deloitte Digital acquired Heat, to become “the world’s first creative digital consultancy.” Accenture acquired Fjord in 2013. And last month. Fahrenheit 212 joined forces with Capgemini Consulting.

Combined technology/management and innovation consultancies can quickly turn big ideas from pilots to scalable, enterprise level solutions.

 

Creating a Culture for Collaborative Innovation and Organizational Form

You’ve probably heard it already. Data is the new oil, bacon, currency, soil, next big thing, and the force behind a new management revolution. Wait. Data is the new bacon? Apparently there are even t-shirts available. Makes me miss Ed Hardy. Matthew Mayo at KDnuggets says it best, Data is the New Everything.

Despite being awash in information, many organizations are still starved of analytics meaning. Michael Schrage, a research follow at MIT Sloan School’s Center for Digital Business shows the real challenge is recognizing that using big data and analytics to better solve problems and/or make decisions obscures the organizational reality that new analytics often requires new behaviors.

For many businesses today, decision making may be more data-driven, but the organizational culture feels the same. Mr. Schrage’s research proves the quality of big data and analytics, ironically, mattered less than the purpose to which they were used. The most interesting tensions and arguments consistently revolve around whether the organization would reap the greatest returns from using analytics to better optimize existing process behaviors or get people to behave differently. But the rough consensus is that the most productive conversations centered on how analytics changed behaviors rather than solved problems.

Getting the right answer, or even asking the right question, turns out not to be the dominant concern of high ROA enterprises. But how those questions, answers and analytics align, or conflict, with individual and institutional behaviors matters more.

Accenture’s 2016 Technology Vision Report claims the success of any company going through fundamental digital transformation is understanding that it’s first and foremost a people issue. Meaning, before a business can truly enjoy the benefits of data-driven decision making, an organization and its people must first be aligned to a common purpose. Common purpose starts and ends with culture.

In 2014 Microsoft CEO Satya Nadella, offered an interesting blog post: A data culture for everyone. According to Nadella, with the right tools, insights can come from anyone, anywhere, at any time. When that happens, organizations develop what can be described as a “data culture.”

“However, a data culture isn’t just about deploying technology alone, it’s about changing culture so that every organization, every team and every individual is empowered to do great things because of the data at their fingertips. This means bringing together people, IT and developers to create a cultural shift that is just as important as systems and infrastructure. In a data culture, everyone benefits when more people can ask questions and get answers. In a data culture, the entire effectiveness of an organization can elevate. This is especially true when every employee can harness the power of data once only reserved for data scientists and tap into the power of natural language, self-service business insights and visualization capabilities.”

. . .

Addressing digital disruption with innovation design isn’t easy. But with the right culture, enabled by data and analytics, it can be a reality.

 

Photo: Altimeter Group. www.altimetergroupdigitaltransformation.com

A Bicycle for the Mind: Beyond Big Data hype, making sense of digital disruption, innovation and the analytics of everything

Recently, at work, we found ourselves wondering if a particular company was a competitor. So we dove into some competitive analysis. I’m certain this is a natural reaction at companies large and small.

Folks had compelling arguments for both sides of the debate and as I took it all in I couldn’t help but wonder — who cares, what’s the point?

Clearly, analysis is absolutely necessary, but scratch-the-surface, first-order comparisons passing as deep dives simply don’t meet the analytical demands of today’s modern business landscape.

In short, we need true, strategic thinking to explain the forces of disruption and innovation currently shaping our future. Each day, crisis to crisis, we put increasing demands on our attentions — traditional, linear forms of analysis are proving deeply inadequate in our rapidly changing business, cultural, and technological environments.

Might the sort of analysis in use today be like monitoring the gauges in a car; telling a great deal about where things are at currently, but not much in the way of where things are heading. (Unless of course you have GPS)

This really gets me thinking: while performing competitive analysis, did traditional taxi providers see the growing competition coming from Uber?

Does Uber see the growing competition coming from Google’s self-driving cars? Imagine a fleet of driverless cars, GPS guided, delivering packages, people, or even puppies, on demand.
(It’s a thought exercise, I have no idea if Google is actually exploring, but they probably are.)

I suspect not. And that’s my point. I’m not saying competitive, fundamental, business or whatever analysis you prefer is useless. 
But your competition isn’t your competition, until they are.

 

YOUR COMPETITION IS OFTEN IN THE LAST PLACE YOU LOOK

If you sell medical equipment, then your competition is other people pushing medical equipment. If you’re a financial advisor, your competition is other advisors.

However, today that’s no longer true. Not only are your industry peers no longer your main competition, they’re not even your most dangerous competition.

Your bigger, more dangerous, business competitor is very simply digital disruption.

According to Yale professor Richard N. Foster, co-author of Creative Destruction. Business disruption is so pronounced, at the current churn rate of the Standard & Poor’s 500 Composite Index, nearly 75% of the S&P 500 will be replaced by 2027.

Imagine what the business models of the S&P 500 constituents will look like in 2027. By appreciating that facts change (and how they change), we can better cope with a world of constant uncertainty, particularly in such important areas as business and commerce.

 

THE RISE OF DATA-DRIVEN DECISION MAKING

Digital economy experts Kristina McElheran and Erik Brynjolfsson point out: growing opportunities to collect and leverage digital information have led many managers to change how they make decisions — relying less on intuition and more on data.

Their work highlights an important fact, “at their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery”.

Not surprisingly, in the digital age companies are increasingly turning to advanced analytics to improve decision making, execute business strategy and fine tune their managerial approaches.

 

A BICYCLE FOR THE MIND

In the early 1980s, few people were accustomed to using a computer, much less ready to buy one.

Apple’s late leader Steve Jobs loved to liken the computer to a bicycle. Check out this wonderful clip where he cites a study by Scientific American.

The study calculated the locomotive efficiency of various animals to determine which could travel from most efficiently. Turns out, the condor used the least amount of energy to move a kilometer, humans didn’t fare so well, with an unimpressive showing about a third down the list.

Fortunately enough, someone at Scientific American was insightful enough to test man with a bicycle. With a bicycle humans excelled — coming in nearly twice as efficient as the condor.

Here’s where Job’s genius shines. By using the Scientific American study as analogy, he helped make people more comfortable with adopting a computer. As Jobs described it, Apple was building a “bicycle for the mind” — a tool that could take our minds anywhere through imagination and the multiplication of power.

“Man as a toolmaker has the ability to make a tool to amplify an inherit ability that he has. And that’s exactly what we are doing here at Apple. We’re not making bicycles to be ridden between Palo Alto and San Francisco…in general what we’re doing is building tools to amplify a human ability. You could say that the Industrial Revolution was basically an amplification of a human ability: sweat. We amplified human sweat. What we’re working towards now is the ability to amplify another human ability, and we’re just starting to get a glimmering of where it’s going to go.”

. . .

That glimmering, however faint in the early 1980s is shining bright as ever today.

Smart devices, artificial intelligence, networked communications and the digitization of just about everything is rendering the analog ten-speed bike a collector’s item.

The hype behind big data is very real, but how do we cut through the noise and actually create value through the benefits of advanced analytics?

Quite simply, we need a better tool-set to manage the growing complexities brought on by abstract thinking and our “knowledge economy”.

Enhanced, data-driven decision making is the perfect tool for the job.

If Steve Job’s early Macintoch computer was a ‘bicycle for our minds’, through data science, we now have free, open access to Italian racing bikes for modern management.

To help business leaders create new tools and tap the potential in big data, analytics, and data science, follow-on research in this series will explore:

• Applying scientific methods to technological problems. How big data fits into the ‘Second Machine Age’ and how it will impact business?

• Creating a data driven strategy for sustainable growth. How do we embrace a “data culture?”

• Data networks and the flow of information. In today’s modern economy, might the structure of our relationships be more important than organizational structure?

• Use Cases: who’s using data science as a bicycle for the mind.

 

Slowing Big Data Confusion Starts In the C-Suite

Confusion:

What’s the confusion? As it should be, the targeted business audience for Big Data, Business Intelligence and data analytics is business executives and its owners. Each business executive must use data to improve operational efficiencies, comply with regulatory requirements and use data to assess how to improve their products and services.

This all leads to growing revenues. Where’s the confusion in all this? It comes from multiple places.

First, there’s no off the shelf solution to meeting a business executives’ requirements for hardware, software and professional services. Making matters worse. Many business executives’ professional roots do not come from IT. Having lived in a world of readily available apps, business owners are often confused on how to purchase and implement the right solution for their respective business requirements for data analytics. Seeing the right solution comes from three sources, hardware, software, and professional services companies, only compounds the confusion. Good solutions must be customized for particular environments and business strategies.

Many of you are scratching your head and saying, CUSTOMIZATION! You have to be kidding me; we’re way past custom solutions. Because we now live in the world of open source, you’re right if you agree we’re past hard coded custom software solutions. In today’s environment, customization includes the right combination of tools, software, and implementation with the best business processes and governance practices. This delivers the optimal Business Intelligence solution for business owners.

Because of confusion, organizations have purchased a flavor of every new tool developed by Silicon Valley, but have been disappointed by having not solved and/or met their business requirements. In search of a competitive edge, companies have actually made finding their best solutions more challenging by adding ever more technology, business platforms, tools, and software products.

To unwind the confusion, we simply must lead with the business problem first, not with technology! Sure, embrace your technology partners as part of your solution, but business executives and owners must learn to lead in the customization and design of their business objectives.

The good news. Those businesses truly leading in the design and customization of their Business Intelligence and Big Data capabilities will enjoy real enterprise differentiation; setting the pace for how businesses compete and win in their respective markets.

Why to Hire Women for STEM Roles

The tech industry is continuously criticized for the lack of women in leadership roles. According to the U.S. Bureau of Labor Statistics, women hold 47 percent of all jobs, but just 25.6 percent of computer and mathematical occupations. There is a vast shortage of women in big data analytics, but a surfeit of qualified women who are up for the job.

Diversity is a crucial component within any business, as company leaders tend to get more out of their employees when all ages, genders, and nationalities work together. In a tech company or startup, diversity is required for maximizing innovation. For example, when it comes to gender, men and women often bring different viewpoints and ideas to the table, which results in more efficient problem solving for companies. Qualified women have the collaborative skills and presentation skills that are crucial in the tech world. Tech is an industry wherein the talented are chosen and moved ahead based on their perceived intellectual capabilities. That being the case, women have the opportunity to be selected for these leadership roles on the basis of their ability. It’s a matter of opportunity – ensuring tech leaders are aware of qualified female candidates and that these candidates are given equal consideration.

In my experience, the shortage of talent in the industry allows for exposure and the ability to contribute in a lot of different roles in a technology company. When I was coming out of college and choosing an industry to work in, I chose tech because there was a shortage of talent and I knew I would have the opportunity to work in several different roles. That has been the case in my career, but you need to position yourself and convince leadership to give you a shot.

Breaking into the Tech Industry

According to the Economics and Statistics Administration at the U.S. Department of Commerce, women hold a disproportionately low share of science, technology, education, and math (STEM) undergraduate degrees, and women with STEM a degree are less likely than their male counterparts to work in a STEM occupation. The thought of jumping into a male-dominated field can be very intimidating for some women. Experience can help alleviate this. Remember: It’s not necessary to come into technology from engineering. The move into tech can be made with a business degree, for example, as technology companies have accountants, sales and marketing professionals, customer service, etc.

Today, many universities offer certificate programs in data analytics, which women can take advantage of as a way into the data analytics arena. And once women get into the industry, it is important to stay in the industry, as retention, experience, and seniority play a large role in determining which candidates are considered for leadership positions.

One way to increase the number of women in technology is to increase the number of women in senior leadership roles. Women in senior management roles attract other women to join the company at all levels. Let’s face it, when candidates interview at a company, they want to know where they can see themselves within the company. If there are no role models for them to emulate, they will gravitate to a company that does have role models.

Arguably the greatest advantage to working in the tech field is that it’s a growth industry. This allows anyone to begin at the bottom and maintain longevity in the field. I have been in this industry for 30 years and, even when there’s a downturn, people who have great skills can always reinvent themselves – the technology is always changing, so keeping current is critical. A growth industry affords you the ability to work at a mature company or a startup, and you can even float throughout your career.

The tech industry is currently male dominated, but the number of women entering the industry can grow if companies begin considering the recruitment of women as part of their overall performance evaluation. Women are equal to men in capabilities and often bring a management style that is complementary to that of their male counterparts. That diversity of management approaches can benefit any organization, tech or otherwise. Because of the shortage of technology talent and the global competiveness in business, companies that hire women will have a decided advantage over those that fail to consider the entire universe of qualified tech candidates.