Discover Kyield on the voyage to CALO (continuously adaptive learning organization)


tidal pool

A tidal pool ecosystem in Half-Moon Bay, CA.

For those involved with the art, science, and mechanics of organizational management, the 1990s was an exciting if humbling decade. The decade was ushered in with new thinking from Peter Senge in The Fifth Discipline, which introduced The Learning Organization to many. The concept sounded absolutely refreshing to those in the trenches—who could disagree with such logic?

I was deeply engaged at the time in a series of turn-arounds in mid-market companies. The particular business case offered for consideration was a union shop that was bankrupt from the day it opened nearly a decade earlier, and had never made a payment on many millions of dollars of debt.

The new owner had acquired the operating company out of bankruptcy with assumptions that were based on experiences that did not apply to the subject or market. Due to a low discounted acquisition value, the buyers had little to lose if the company continued to underperform, but a great deal to gain if the company could be brought up to a competitive market position.

Long-story short, it was a very challenging yearlong engagement during which time we surpassed everyone’s short-term expectations by a significant degree, including my own. Before I share what went wrong, let’s review a few things we did right:

  • The subject called for and we received an unusual level of authority from owners, which required exceptional levels of mutual trust, and a great deal of credibility.

  • We put together a strong team with a mix of experience relevant to the specific case. Each could recognize the opportunity, and even though under-resourced, we were able to create one of the industry’s top performances that year.

  • We were able to gain the trust and support of the union—which enjoyed a double-digit increase in membership, with most members experiencing significant increases in compensation.

  • Though never union members, senior management to include the owners had personal experience with labor, which was demonstrated alongside workers at critical times and helped gain their support (we walked the talk).

  • The few remaining core customers were so desperate for improved product and service that a modest upgrade and competency improved sales as well as the reputation, from which new and larger customers were gained.

  • The communities involved were relieved to see a well-managed company emerge from years of bankruptcy, churn of management, and under-investment, so we gained support of regional governments (important in this case), which also enjoyed increased tax receipts.

  • The pricing of products and services were well below market, so after modest investment with competent management, I was able to raise prices significantly while increasing sales volume, resulting in a substantial, growing profit.

  • We were able to double the value of the company in one year based on cash flow and future orders, which dramatically improved the position of the parent company, allowing refinancing at more attractive rates.

Now allow me to share why I consider this engagement (with others in this era) to be among my greatest mistakes.

Failure to learn, adapt, and seize the more important opportunity

Many of us wrongly assumed that the parent company would learn from the experience and use the success as a platform to seize other opportunities, which would have required a change in the structure, type, and talent of the company.

Despite considerable coaching combined with all that accompanies short-term financial success, the parent company did not learn the most valuable lessons from this intense experience, so they were not able to adapt to rapidly changing markets. Motivation and desire also clearly contributed, so after a final attempt to convince the parent company owners to transform into a competitive model, we moved on after contract.

My final report to owners and lenders concluded that the company had probably hit a ceiling, recommending that they either embrace the transformation strategy or sell the company. We could take them no further in current form. Just one example of why—a colleague who was a key team member had received a far superior offer from a great company in a location he and his family preferred.

The client’s response was way below market at a tiny fraction of what the operations specialist had created for the client. The decision on my colleague was surprising, as he was one of the pillars, so it confirmed the ceiling for me. The owners were left with a rising star in a subsidiary that kept shining for a short period, during which time likely created profits far exceeding investment, but then began to fade. Unlike the other holdings of the parent company, this subsidiary required intensive, sophisticated, and experienced management.

A decade later I read where the subject had fallen back to a similar performance level to when the parent company acquired it. It gives me no pleasure to share that hindsight has demonstrated the period of our engagement to be the peak of the parent company’s 50-year history. We worked very hard to provide the opportunity for an enduring success.

While we enjoyed deep mutual respect with the parent company, which appeared adaptive in this acquisition and others we brought to them, they weren’t willing to transform their organization. They were opportunistic on a one-off basis, which is best suited for the flipping model, not for an enduring business. Even then technology played a big role through IBM mainframes, inventory management, and transactions over networks. Today of course most companies are facing more dramatic change in an environment that is far more talent and technology dependent.

The learning organization struggles in the 1990s

A few years and dozens of assignments later we had established a tech lab and incubator to explore and test opportunities due to Internet commercialization, which catapulted the economy with significant g-force into the network era. By the mid-1990s a few operational consultants had become critical of the apparent naiveté with the learning organization theory, which like knowledge management had a philosophy that many could embrace, but in practice found difficult to achieve given real-world constraints, including legal and physical, not just cultural or soft issues.

A few researchers pointed out that it was difficult to find actual cases of learning organizations (Kerka 1995). In papers, textbooks, and Ph.D. theses I was invited to review, the same few cases and papers were cited relentlessly, often comparing apples to oranges. One example was a study published by Finger and Brand in 1999, which found that systems needed to be non-threatening. That may have been the case for their subject at the Swiss Postal Service, but would be unrealistic where threats are among few constants, increasingly to include government and academia. Peter Senge apparently took notice of the criticism, reflected in the subtitle of his book The Dance of Change (1999); “The Challenges of Sustaining Momentum in Learning Organizations”.

By the end of the 1990s I had become vocal on the primitive state of systems and tools that could achieve the goals of the learning organization, and more importantly adapt in a sufficient time frame. My perspective was one from operating a live knowledge systems lab that was building, operating, and testing learning networks, which included daily forums that discussed hundreds of real-world cases in real-time over several years. Many were complaining about poor technology and lack of much needed innovation, yet few were focused on improvement from within organizations that would allow innovation to make it to market.

Some researchers have since suggested that in order to achieve the learning organization, it was first necessary for knowledge workers to abandon self-interest, while others claimed that ‘collective accountability’ is the key. In updating myself on this research recently, it sometimes seemed as if organizational management consultants and researchers were attempting to project a vision over actual evidence, denying the historical importance of technology for survival of our species, as well as mathematics, physics, and economics. Consider the message to an AI programmer or pharma scientist today coming from a tenured professor or government employee with life-long security on ‘the need to abandon self-interest’. This has been tested continuously in competitive markets and simply isn’t credible. The evidence is overwhelmingly polar to such advice.

Current state of the CALO

We may have been experiencing devolution and evolution concurrently, yet humans kept working to overcome problems, representing all major disciplines. My company Kyield is among them.

Significant progress has been made across and between all disciplines, including with understanding and engineering the dynamical components of modern organizations.

The network economy

No question that the structure of an organization greatly influences the ability to adapt even if having learned lessons well, including legal, tax, reporting, incentives, physical, and virtual. The network economy has altered the very foundational structure many organizations operate on top of.

While each structure needs to be very carefully crafted, the structural changes vary from the rare ‘no change is needed’ to increasingly common ‘bold change required to survive’. Though it need not be so, it is increasingly more efficient to disrupt and displace than to change from within.

Globalization

While we are experiencing a repatriation and regionalization trend, globalization radically changed the way organizations learn and how they must adapt. Several billion more people are driving the network economy than in 1990, with a significant portion moving from extreme poverty to the middle class.

The global financial crisis (GFC)

Suffice to say for this purpose that the GFC has altered the global operating and regulatory landscape for many businesses and governments—in some cases radically, and is still quite fluid. Currency swings in response to unprecedented monetary policies are the most recent example of this chaotic process, though only one of many organizations must navigate. If the regulatory agencies were CALOs, much of the pain would be mitigated.

Machine learning (ML)

Although quite early in commercialization and most cases still confidential, ML combined with cognitive computing and AI assisted augmentation is rapidly improving. Deep learning is an effective means of achieving a CALO in a pure network environment that interacts with customers such as search and social networking, though is being adopted widely now.

One of the largest continuous learning projects is Orion by UPS, which was kind enough to share some detail in public. Orion provides an excellent case for many to consider as it overlaps the physical world with advanced networks and large numbers of employees worldwide. Unlike Google or Facebook that began as virtual companies running on computer networks, UPS represents a large transformation of the type most organizations need to consider. In the case of UPS of course, they have massive logistical operations with very high volume of semi-automated and automated data management.

Having developed deeply tailored use case scenarios for each sector in Kyield’s customer pipeline, numbering in the dozens, I can offer a few words of advice in public.

  1. While all public cases should be considered, remember that few are shared in public for good reason. Few if any companies have the business model, need, resources, or capacity of Google or UPS, which is why they can share the information.

  2. Rare is the case when enormous custom projects should be copied. The craftwork of planning and design is substantially about taking available lessons, combining with technology, systems, and talent in a carefully tailored manner for the client.

  3. Regardless of whether a large custom project, completely outsourced, or anything in-between, board level supervision is necessary to avoid switching dependencies from one vendor or internal department to another. I see this occurring with new silos popping up in open source models, data science, statistics, and algorithms. The goal for most should be to reduce dependencies, which is nontrivial when dealing with high-level intellectual capital embedded in advanced technology, particularly given talent wars, level of contracting in these functional roles, and churn.

  4. Since few will be able to develop and maintain competitive custom systems, the goal should be to seek an optimal, adaptive balance between the benefits of custom tailored software systems (Orion or Google), with the efficiency of write once and adopt at scale in software development. This is one of several areas where Kyield can really make the difference on the level of ROI realized. Our continuously adaptive data management system (patented) is automatically tailored to each entity with semi-automated functions restricted to regulatory and security parameters at the corporate and unit level, with the option for individual knowledge workers to plan projects and goals.

  5. Plan from inception to expand the system to the entire organization, ecosystem, and Internet of Entities. Otherwise, the organization will be physically restricted from achieving much of the value any such system can offer, particularly in risk management. One of the biggest errors I see being made, including in some of the most sophisticated tech companies, is approaching advanced analytics as ‘only’ a departmental project. Of course it is wise to take the low hanging fruit through use of pre-existing department budgets where authority and ROI are simple, but it is a classic mistake for CIOs and IT to consider such projects strategic, with very rare exception such as for a strategic project.

  6. Optimize relationship management. Just one example is when our adaptive data management is combined with advanced ML algorithms that include predictive capabilities, which among other functions weighs counter party risk. A similar algorithm can be run for identifying business opportunities.

Concluding thoughts

While it is more challenging to achieve buy-in for organization-wide systems, it is physically impossible to achieve critical use cases otherwise, some of which have already proven to be very serious, and can be fatal. Moreover, very few distributed organizations can become a CALO without a holistic system design across the organization’s networks, particularly in the age of distributed network computing.

If this isn’t sufficient motivation to engage, consider that learning algorithms are very likely (or soon will be) improving the intelligence quotient and operational efficiency of your chief competitors at an extremely rapid rate. Lastly, if the subject organization or entire industry is apathetic and slow to change, it is increasingly likely that highly sophisticated, well-financed disrupters are maturing plans to deploy this type of technology to displace the subject, if not the entire industry.

Bottom line: Move towards CALO rapidly or deal with the consequences. 

Mark Montgomery is founder and CEO of http://www.kyield.com, which offers an advanced distributed operating system and related services centered around Montgomery’s AI systems invention.

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Transforming Healthcare With Data Physics


I just completed an in-depth paper on how our work and system can help life science and healthcare companies overcome the great challenges they face, so I wanted to share some thoughts while still fresh. The paper is part of our long-term commitment to healthcare and life sciences, requiring a deep dive over the past several weeks to update myself on the latest research in behavioral psychology, machine learning, deep learning, genetics, chemicals, diagnostics, economics, and particle physics, among others. The review included several hundred papers as well as a few dozen reports.

Kyield Distributed OS - Life Science and Healthcare

The good news is that the science is improving rapidly. An important catalyst to accelerated learning over the past 20 years has been embracing the multi-disciplinary approach, which academia resisted for many years despite the obvious benefits, but is now finally mainstream with positive impact everywhere one looks.

The bad news is that the economics of U.S. healthcare has not noticeably improved. For a considerable portion of the population it has deteriorated. The economic trajectory for the country is frankly grim unless we transform the entire healthcare ecosystem.

A common obstacle to vast improvement in healthcare outcomes that transcends all disciplines with enormous economic consequences is data management and analytics, or perhaps more accurately; the lack thereof. There is no doubt that unified networks must play a lead role in the transformation of healthcare. A few clips from the paper:

“By structural we mean the physics of data, including latency, entropy, compression, and security methodology. The Kyield system is intended to define structural integrity in NNs, continually exploring and working to improve upon state-of-the-art techniques.”

“While significant progress has been made with independent standards towards a more sustainable network economy, functionality varies considerably by technology, industry, and geography, with variety of data types and models remaining among the greatest obstacles to discovery, cost efficiency, performance, security, and personalization.”

Life science and healthcare are particularly impacted by heterogeneous data, which is one reason why networked healthcare is primitive, expensive, slow, and alarmingly prone to error.

“Biodiversity presents a unique challenge for data analytics due to its ambiguity, diversity, and specialized language, which then must be integrated with healthcare and data standards as well as a variety of proprietary vendor technology in database management systems, logistics, networking, productivity, and analytics programs.”

“Due to the complexity across LS and healthcare in data types, standards, scale, and regulatory requirements, a functional unified network OS requires specific combinations of the most advanced technology and methods available.”

Among the most difficult challenges facing management in mature life science companies are cultures that have been substantially insulated from economic reality for decades, only recently feeling the brunt of unsustainable economic modeling throughout the ecosystem, typically in the form of restructures, layoffs, and in some cases closure. This uncertainty particularly impacts individuals who are accustomed to career security and relatively high levels of compensation. I observed this often during a decade of consulting. The pain caused by a dysfunctional economic system is similar to the diseases professionals spend their careers fighting; often unjustly targeting individuals in a seemingly random manner, which of course has consequences.

“Among many changes for knowledge workers associated with the digital revolution and macro economics are less security, more free agency, more frequent job changes, much higher levels of global venture funding, less loyalty to corporate brands and mature industry models, and considerably increased motivation and activism towards personal passionate causes.”

Healthcare is a topic where I have personal passion as it cuts to the core of the most important issues to me, including family, friends, colleagues, and economics, which unfortunately in U.S. healthcare represents a highly self-destructive model. My brother was diagnosed with Lou Gehrig’s disease (amyotrophic lateral sclerosis/ALS) in 1997 not long after his only child was born. I’ll never forget that phone call with him or what he and his family endured over the next three years even though his case was a fine example of dedicated people and community. My father passed a decade later after a brutal battle with type 2 diabetes; we had an old friend pass from MS recently, and multiple cancers as well as epilepsy are ongoing within our small group of family and friends. So it would be foolhardy to deny the personal impact and interest. Healthcare affects us all whether we realize it or not, and increasingly, future generations are paying for the current generation’s unwillingness to achieve a sustainable trajectory. Unacceptable doesn’t quite capture the severity of this systemic failure we all own a part of.

The challenge as I see it is to channel our energy in a positive manner to transform the healthcare system with a laser focus on improved health and economic outcomes. This of course requires a focus on prevention, reduced complexity throughout the ecosystem, accelerated science, much improved technology, and last but not least; rational economic modeling to included increased competition. The latter will obviously require entirely new distribution systems and business models more aligned with current science and economic environment. Any significant progress must include highly evolved legislation reflecting far more empowerment of patients and dramatic improvement in fiscal discipline for the ultimate payer we call America while there is still time to manage the disease. If we continue to treat only the symptoms of healthcare in America it may well destroy the quality of life for the patient, if indeed the patient as we know it survives at all. This essentially represents my diagnosis.

A few of the 80 references I cited in the paper linked below are good sources to learn more:

Beyond borders: unlocking value. Biotechnology Industry Report 2014, EY
http://www.ey.com/GL/en/Industries/Life-Sciences/EY-beyond-borders-unlocking-value

Dixon-Fyle, S., Ghandi, S., Pellathy, T., Spatharou, A., Changing patient behavior: the new frontier in healthcare value (2012). Health International, McKinsey & Company.
http://healthcare.mckinsey.com/sites/default/files/791750_Changing_Patient_Behavior_the_Next_Frontier_in_Healthcare_Value.pdf

Thessen A., Cui H., Mozzherin D. Applications of Natural Language Processing in Biodiversity Science Adv Bioinformatics.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364545/

Top 10 Clinical Trial Failures of 2013. Genetic Engineering & Biotechnology News.
http://www.genengnews.com/insight-and-intelligence/top-10-clinical-trial-failures-of-2013/77900029/

Begley, C.G., Ellis, L.M. (2012) Drug development: raise standards for preclinical cancer research. Nature 483 http://www.nature.com/nature/journal/v483/n7391/pdf/483531a.pdf

Cambria, E., and White, B. Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9:1–28, 2014.
http://www.krchowdhary.com/ai/lects/nlp-research-com-intlg-ieee.pdf

Montgomery, M. Diabetes and the American Healthcare System. Kyield, Published online May 2010
http://www.kyield.com/images/Kyield_Diabetes_Use_Case_Scenario.pdf

All quotes above are mine from Kyield’s paper of 8-15-2014:

Unified Network Operating System
With Adaptive Data Management Tailored to Each Entity
Biotech, Pharmaceuticals, Healthcare, and Life Sciences

Workplace analytics paper + our new PSN


I wanted to share a couple of quick items. One is an article on workplace analytics I just completed that may be of interest (PDF). While it deals with some of the most complex technical issues, the format is a short non-technical paper intended for senior business managers and boards, most of whom do not have a technical background yet must deal with organizational issues that are increasingly driven by technology.

The second item is that we’ve launched a global professional services network (PSN) surrounding Kyield technology. We currently have a recruitment running on LinkedIn for managing partners.  The PSN is progressing very well, keeping me very busy in discussions with highly qualified consultants worldwide. Our first group of countries have now moved through the initial stage and are engaging with client organizations. -MM

New Year Review 2013: The Importance of Triple Win Ecosystems


Kyield R&D Journey- 2012

Looking Back on 2012

Among the positive trends we observed in 2012 include relatively strong continued adoption of richly structured data across all major industry clusters. A significant portion of senior managers have just recently engaged in an attempt to seriously understand how best to optimize structured data for their organizations and partners, with an exceptional minority now experiencing big aha moments. Until recently semantics was limited primarily to R&D, and analytics was restricted to a very few people in the organization with a fairly limited scope. A growing minority are finally connecting the dots and incorporating semantics into their ‘big data’ analytics strategy.

We changed a few global strategic plans with our Kyield pilot presentations in the past year as organizations began to realize that what seemed futuristic a few years ago is now executable in near real-time. However, what is still missing is a competitive ecosystem that could be called upon to serve the specific needs of customers in a triple win manner.  As is often the case with emerging technologies– individuals, small teams and companies engage due to a combination of motivating factors that includes independence, compensation, and to change the world for the better, but in order to achieve much in the enterprise market we need fully functional ecosystems.

A combination of assets must be offered in a highly efficient and credible manner, including very experienced management, superior technology, deep technical talent, appropriate financial structure and professional services, which is usually far more than even the largest companies can provide on their own. So job one for the emerging semantic enterprise community is to learn how to work together towards building a functional ecosystem, which means creating and offering mutual value while serving customer needs in win/win/win scenarios.

We held discussions with a few companies interested in partnering who underestimate the risk they face and overestimate their power, some of whom apparently don’t understand how to form mutually beneficial partnerships. In Kyield’s partner program we will only consider triple wins. And BTW, don’t expect anything but obstacles thrown in the way from the trillion dollar global integration machine, which is a mature, highly sophisticated ecosystem with unlimited funds that is threatened by independent standards and lower TCO. Customers have a special responsibility to assist the emerging semantic ecosystem as they are the primary beneficiaries of the technology and have a very important strategic interest in helping to ensure that the semantic ecosystem becomes more viable and sustainable.

Expectations for 2013

An interesting event recently occurred at a leading bank that could be telling for the near future with respect to semantics and data standards in 2013 and beyond. The bank recently announced large layoffs that included 25% within IT. The bank previously invested heavily in internal proprietary systems and are now moving towards standards where they expect to see a significant ROI with speculation that other leading banks will follow. Banks were late to semantics and obviously some proved that their proprietary risk management and governance systems were systemically dangerous, but we’re seeing a positive trend now. These layoffs may sound like bad news, but we should remember that the primary role of leading banks is to lend modestly leveraged capital to others, which creates far more jobs in a more diverse manner than when employed internally at a bank for proprietary systems and integration work. This is an important trend toward higher efficiency in a more economically sustainable manner thanks largely to adoption of independent standards. When combined with proper governance and advanced analytics throughout the financial system, this trend can become powerful indeed.

The big question in 2013 more broadly is to what degree the EU and U.S. can get their long-term fiscal house in order. The macro economic environment can easily dominate technology adoption and business creation. There is a direct relationship between balanced federal budgets, job creation, and sustainable economic progress. Clearly the U.S. culture is in a deep state of denial relative to sustainable economics reflected in a severely dysfunctional political system, and it’s having a strong negative impact even for those who offer an exceptional ROI.  In the case of Kyield, for example, the macro environment has resulted in some customer prospects in otherwise strong organizations freezing new projects until political and fiscal uncertainty is resolved, which in turn of course deters any prudent entrepreneur, investor or other decision maker from taking risk that may have seemed rational in the past in a much different macro economic environment. Since those in the health management business of elephants don’t have the luxury of being trampled more than once, Kyield plans to continue with our lean organic hybrid approach in partnership with customers and partners and leave speculative build outs to those who enjoy rolling the dice. 

I am confident that in 2013 we’ll see the competitive gap continue to widen between those who adopt more advanced, efficient, intelligent systems, and those who don’t, especially new systems that reduce lock-in and improve interoperability resulting in a sharply lower TCO. There is a strong compounding effect that occurs with adoption of rich data standards containing more integrity with advanced knowledge systems and real-time analytics, and it’s becoming increasingly more difficult to deny as organizations that master this technology pull ahead of competitors. Smart adaptive people working with smart adaptive computing is a powerful combination not to be denied.

To good health in 2013 for you, family and organization. – MM

Yield Management of Knowledge for Industry + FAQs


Industrial Yield Management of Knowledge from www.kyield.com

I decided to share this slightly edited version of a diagram that was part of a presentation we recently completed for an industry leading organization. Based on feedback this may be the most easily understandable graphic we’ve produced to date in communicating the Kyield enterprise system. As part of the same project we published a new FAQs page on our web site that may be of interest.  Most of my writing over the past several months has been in private tailored papers and presentations related to our pilot and partner programs.

I may include a version of this diagram in a public white paper soon if I can carve out some writing time. If you don’t hear from me before then I wish you and your family a happy holiday season.

Kind regards, MM

New Video on Kyield Enterprise – Data Tailored to Each Entity


White board video presentation on Kyield Enterprise


Kyield founder Mark Montgomery provides a 14 minute white board presentation on Kyield Enterprise and the Kyield Enterprise Pilot