Converting the Enterprise to an Adaptive Neural Network


Those tracking business and financial news may have observed that a little bit of knowledge in the corner office about enterprise architecture, software, and data can cause great harm, including for the occupant, often resulting in a moving van parked under the corner suite of corporate headquarters shortly after headlines on their latest preventable crisis. Exploitation of ignorance in the board room surrounding enterprise computing has become mastered by some, and is therefore among the greatest of many challenges for emerging technology that has the capacity for significant improvement.

The issues surrounding neural networks requires total immersion for extended duration. Since many organizations lack the luxury of time, let’s get to it.

Beware the Foreshadow of the Black Swan

A recent article by Reuters confirms what is perhaps the worst kept secret in the post printing press era: Many Wall Street executives say wrongdoing is necessary: survey. A whopping 25% of those surveyed believe that in order to be personally successful, they must conduct themselves in an unethical manner to include breaking important laws, some of which are intended to defend against contagion; a powerful red flag warning even if only partially true.

This reminds me of a situation almost a decade ago when I had the unpleasant task of engaging the president of a leading university about one of their finance professors who may have been addressing a few respondents to this very survey when he lectured: “if you want to survive in finance, forget ethics”. Unfortunately for everyone else, even if that curriculum served the near-term interests of the students, which is doubtful given what has transpired since, it cannot end well for civilization. Fortunately, in this case the university president responded immediately, and well beyond expectation, after I sent an email stating that I would end my relationship with the university if that philosophy was shared by the institution.

For directors, CEOs, CFOs, and CROs in any sector, this latest story should only confirm that if an individual is willing to risk a felony for his/her success, then experience warns that corporate governance rates very low on their list of priorities. Black Swan events should therefore be expected in such an environment, and so everything possible should be planned and executed to prevent them, which requires mastering neural networks.

Functional Governance: As simple as possible; as complex as necessary

Functional governance and crisis prevention in the modern complex organization requires deep understanding of the organizational dynamics embedded within data architecture found throughout the far more complex environment of enterprise networks and all interconnected networks.

Kyield Enterprise Diagram 2.7 (protected by Copyright and U.S. Patent)

Are you thinking what I think you may be thinking about now? In fact adaptive neural networks in a large enterprise is quite comparable to the complexity found in brain surgery or rocket science, and in some environments even more so. The largest enterprise neural networks today far outnumber comparable nodes, information exchanges, and memory of even the most exceptional human neurological system. Of course biological systems are self-contained with far more embedded intelligence that adapt to an amazing variety of change, which usually enables sustainability throughout a complete lifecycle—our lives, with little or no external effort required. Unfortunately, even the most advanced enterprise neural networks today are still primitive by comparison to biological systems, are not adaptive by design, and are subject to a menagerie of internal and external influences that directly conflict with the future health of the patient, aka the mission of the organization.

So the next question might be, where do we start?

The simple answer is that most organizations started decades ago with the emergence of computer networking and currently manage a very primitive, fragmented neural network that wasn’t planned at all, but rather evolved in an incremental manner where proprietary standards became commoditized and lost the ability to provide competitive differentiation, yet are still very expensive to maintain. Those needing a more competitive architecture have come to the right place at the right time as we are deeply engaged in crafting tailored action plans for several organizations at various stages of our pilot program for Kyield enterprise, which is among the best examples of a state-of-the-art, adaptive enterprise neural network architecture I am aware of. We’ve recently engaged with large to very large organizations in banking, insurance, biotech, government, manufacturing, telecommunications, engineering and pharmaceuticals in the early stages of our pilot process.

Tailored Blueprint

Think of the plan as a combination of a technical paper, a deeply tailored use case for each organization, and a detailed time-line spanning several years. In some ways it serves as sort of a redevelopment blueprint for a neighborhood that has been locked-in to ancient infrastructure with outdated electrical, plumbing, and transportation systems that are no longer compatible or competitive. Most have either suffered a crisis, or wisely intent on prevention, while seeking a significant competitive advantage.

The step-by-step process we are tailoring for each customer serves to guide collaborative teams through the conversion process from the ‘current architecture’ to an ‘adaptable neural enterprise network’, starting with the appropriate business unit and extending throughout subsidiaries over weeks, months and years in careful orchestration according to the prioritized needs of each while preventing operational disruptions. Since we embrace independent standards with no lock-in or maintenance fees and offer attractive long-term incentives, the risk for not engaging in our pilot program appears much greater than for those who do. In some cases it looks like we may be able to decrease TCO substantially despite generational improvement in functionality.

Those who are interested and believe they may be a good candidate for our pilot program are welcome to contact me anytime.

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Five Essential Steps For Strategic (adaptive) Enterprise Computing


Given the spin surrounding big data, duopoly deflection campaigns by incumbents, and a culture of entitlement across the enterprise software ecosystem, the following 5 briefs are offered to provide clarity for improving strategic computing outcomes.

1)  Close the Data Competency Gap

Much has been written in recent months about the expanding need for data scientists, which is true at this early stage of automation, yet very little is whispered in public on the prerequisite learning curve for senior executives, boards, and policy makers.

Data increasingly represents all of the assets of the organization, including intellectual capital, intellectual property, physical property, financials, supply chain, inventory, distribution network, customers, communications, legal, creative, and all relationships between entities. It is therefore imperative to understand how data is structured, created, consumed, analyzed, interpreted, stored, and secured. Data management will substantially impact the organization’s ability to achieve and manage the strategic mission.

Fortunately, many options exist for rapid advancement in understanding data management ranging from off-the-shelf published reports to tailored consulting and strategic advisory from individuals, regional firms, and global institutions. A word of caution, however—technology in this area is changing rapidly, and very few analysts have proven able to predict what to expect within 24-48 months.

Understanding Data Competency

    • Data scientists are just as human as computer or any other type of scientist
    • A need exists to avoid exchanging software-enabled silos for ontology-enabled silos
    • Data structure requires linguistics, analytics requires mathematics, human performance requires psychology, predictive requires modeling—success requires a mega-disciplinary perspective

2)  Adopt Adaptive Enterprise Computing

A networked computing workplace environment that continually adapts to changing conditions based on the specific needs of each entity – MM 6.7.12

While computing has achieved a great deal for the world during the previous half-century, the short-term gain became a long-term challenge as ubiquitous computing was largely a one-time, must-have competitive advantage that everyone needed to adopt or be left behind.  It turns out that creating and maintaining a competitive advantage through ubiquitous computing within a global network economy is a much greater challenge than initial adoption.

A deep misalignment of interests now exists between customer entities that need differentiation in the marketplace to survive and much of the IT industry, which needs to maintain scale by replicating the precise same hardware and software at massive scale worldwide.

When competitors all over the world are using the same computing tools for communications, operations, transactions, and learning, yet have a dramatically different cost basis for everything else, the region or organization with a higher cost basis will indeed be flattened with economic consequences that can be catastrophic.

This places an especially high burden on companies located in developed countries like the U.S. that are engaged in hyper-competitive industries globally while paying the highest prices for talent, education and healthcare—highlighting the critical need to achieve a sustainable competitive advantage.

Understanding adaptive enterprise computing:

    • Adaptive computing for strategic advantage must encompass the entire enterprise architecture, which requires a holistic perspective
    • Adaptive computing is strategic; commoditized computing isn’t—rather should be viewed as entry-level infrastructure
    • The goal should be to optimize intellectual and creative capital while tailoring product differentiation for a durable and sustainable competitive advantage
    • Agile computing is largely a software development methodology while adaptive computing is largely a business strategy that employs technology for managing the entire digital work environment
    • The transition to adaptive enterprise computing must be step-by-step to avoid operational disruption, yet bold to escape incumbent lock-in

3)  Extend Analytics to Entire Workforce

Humans represent the largest expense and risk to most organizations, so technologists have had a mandate for decades to automate processes and systems that either reduce or replace humans. This is a greatly misunderstood economics theory, however. The idea is to free up resources for re-investment in more important endeavors, which has historically employed the majority of people, but in practice the theory is dependent upon long-term, disciplined, monetary and fiscal policy that favors investment in new technologies, products, companies and industries. When global automation is combined with an environment that doesn’t favor re-investment in new areas, as we’ve seen in recent decades, capital will sit on the sidelines or be employed in speculation that creates destructive bubbles, the combination of which results in uncertainty with high levels of chronic unemployment.

However, while strategic computing must consider all areas of cost competitiveness, it’s also true that most organizations have become more skilled at cost containment than human systems and innovation. As we’ve observed consistently in recent years, the result has been that many organizations have failed to prevent serious or fatal crises, failed to seize missed opportunities, and failed to remain innovative at competitive levels.

While hopefully the macro economic conditions will broadly improve with time, the important message for decision makers is that untapped potential in human performance analytics that can be captured with state-of-the-art systems today is several orders of magnitude higher than through traditional supply chain analytics or marketing analytics alone.

Understanding Human Performance Systems:

    • Improved human performance systems improves everything else
    • The highest potential ROI to organizations today hasn’t changed in a millennium: engaging humans in a more competitive manner than the competition
    • The most valuable humans tend to be fiercely protective of their most valuable intellectual capital, which is precisely what organizations need, requiring deep knowledge and experience for system design
    • Loyalty and morale are low in many organizations due to poor compensation incentives, frequent job change, and misaligned motivation with employer products, cultures and business models
    • Motivation can be fickle and fluid, varying a great deal between individuals, groups, places, and times
    • For those who may have been otherwise engaged—the world went mobile

4)  Employ Predictive Analytics

An organization need not grow much beyond the founders in the current environment for our increasingly data rich world to require effective data management designed to achieve a strategic advantage with enterprise computing. Indeed, often has been the case where success or failure depended upon converting an early agile advantage into a more mature adaptive environment and culture. Within those organizations that survive beyond the average life expectancy, many cultures finally change only after a near-death experience triggered by becoming complacent, rigid, or simply entitled to that which the customer was in disagreement—reasons enough for adoption of analytics for almost any company.

While the need for more accurate predictive abilities is obvious for marketers, it is no less important for risk management, investment, science, medicine, government, and most other areas of society.

Key elements that impact predictive outcomes:

    • Quality of data, including integrity, scale, timeliness, access, and interoperability
    • Quality of algorithms, including design, efficiency, and execution
    • Ease of use and interpretation, including visuals, delivery, and devices
    • How predictions are managed, including verification, feed-back loops, accountability, and the decision chain

5)  Embrace Independent Standards

Among the most important decisions impacting the future ability of organizations to adapt their enterprise computing to fast changing external environmental forces, which increasingly influences the ability of the organization to succeed or fail, is whether to embrace independent standards for software development, communications, and data structure.

Key issues to understand about independent standards:

    • Organizational sovereignty—it has proven extremely difficult and often impossible to maintain control of one’s destiny in an economically sustainable manner over the long-term with proprietary computing standards dominating enterprise architecture
    • Trade secrets, IP, IC, and differentiation are very difficult to secure when relying on consultants who represent competitors in large proprietary ecosystems
    • Lock-in and high maintenance fees are enabled primarily by proprietary standards and lack of interoperability
    • Open source is not at all the same as independent standards, nor necessarily improve adaptive computing or TCO
    • Independent standards bodies are voluntary in most of the world, slow to mature, and influenced by ideology and interests within governments, academia, industry, and IT incumbents
    • The commoditization challenge and need for adaptive computing is similar with ubiquitous computing regardless of standards type

Video short on data silos + extending BI to entire workplace


Brief video on adaptive computing, avoiding data silos and extending BI across the enterprise and entire information workplace:

Legacy of the Tōhoku Earthquake: Moral Imperative to Prevent a Future Fukushima Crisis


An article in the New York Times reminds us once again that without a carefully crafted and highly disciplined governance architecture in place, perceived misalignment of personal interests between individuals and organizations across cultural ecosystems can lead to catastrophic decisions and outcomes. The article was written by Martin Fackler and is titled: Nuclear Disaster in Japan Was Avoidable, Critics Contend.

While not unexpected by those who study crises, rather yet another case where brave individuals raised red flags only to be shouted down by the crowd, the article does provide instructive granularity that should guide senior executives, directors, and policy makers in planning organizational models and enterprise systems. In a rare statement by a leading publication, Martin Fackler reports that insiders within “Japan’s tightly knit nuclear industry” attributed the Fukushima plant meltdown to a “culture of collusion in which powerful regulators and compliant academic experts”.  This is a very similar dynamic found in other preventable crises, from the broad systemic financial crisis to narrow product defect cases.

One of the individuals who warned regulators of just such an event was professor Kunihiko Shimizaki, a seismologist on the committee created specifically to manage risk associated with Japan’s off shore earthquakes. Shimizaki’s conservative warnings were not only ignored, but his comments were removed from the final report “pending further research”. Shimizaki is reported to believe that “fault lay not in outright corruption, but rather complicity among like-minded insiders who prospered for decades by scratching one another’s backs.”  This is almost verbatim to events in the U.S. where multi-organizational cultures evolved slowly over time to become among the highest systemic risks to life, property, and economy.

In another commonly found result, the plant operator Tepco failed to act on multiple internal warnings from their own engineers who calculated that a tsunami could reach up to 50 feet in height. This critical information was not revealed to regulators for three years, finally reported just four days before the 9.0 quake occurred causing a 45 foot tsunami, resulting in the meltdown of three reactors at Fukushima.

Three questions for consideration

1) Given that the root cause of the Fukushima meltdown was not the accurately predicted earthquake or tsunami, but rather dysfunctional organizational governance, are leaders not then compelled by moral imperative to seek out and implement organizational systems specifically designed to prevent crises in the future?

2) Given that peer pressure and social dynamics within the academic culture and relationship with regulators and industry are cited as the cause by the most credible witness—from their own community who predicted the event, would not prudence demand that responsible decision makers consider solutions external of the inflicted cultures?

3) With the not-invented-here-syndrome near the core of every major crises in recent history, which have seriously degraded economic capacity, can anyone afford not to?

Steps that must be taken to prevent the next Fukushima

1) Do not return to the same poisoned well for solutions that caused or enabled the crisis

  • The not-invented-here-syndrome combined with bias for institutional solutions perpetuates the myth that humans are incapable of anything but repeating the same errors over again.

  • This phenomenon is evident in the ongoing financial crisis which suffers from similar cultural dynamics between academics, regulators and industry.

  • Researchers have only recently begun to understand the problems associated with deep expertise in isolated disciplines and cultural dynamics. ‘Expertisis’ is a serious problem within disciplines that tend to blind researchers from transdisciplinary patterns and discovery, severely limiting consideration of possible solutions.

  • Systemic crises overlaps too many disciplines for the academic model to execute functional solutions, evidenced by the committee in this case that sidelined their own seismologist’s warnings for further study, which represents a classic enabler of systemic crises.

2) Understand that in the current digital era through the foreseeable future, organizational governance challenges are also data governance challenges, which requires the execution of data governance solutions

    • Traditional organizational governance is rapidly breaking down with the rise of the neural network economy, yet governance solutions are comparably slow to be adopted.

    • Many organizational leaders, policy makers, risk managers, and public safety engineers are not functionally literate with state-of-the-art technology, such as semantic, predictive, and human alignment methodologies.

    • Functional enterprise architecture that has the capacity to prevent the next Fukushima-like event, regardless of location, industry, or sector, will require a holistic design encapsulating a philosophy that proactively considers all variables that have enabled previous events.

      • Any functional architecture for this task cannot be constrained by the not-invented-here-syndrome, defense of guilds, proprietary standards, protection of business models, national pride, institutional pride, branding, culture, or any other factor.

3) Adopt a Finely Woven Decision Tapestry with Carefully Crafted Strands of Human, Sensory, and Business Intelligence

Data provenance is foundational to any functioning critical system in the modern organization, providing:

      • Increased accountability

      • Increased security

      • Carefully managed transparency

      • Far more functional automation

      • The possibility of accurate real-time auditing

4) Extend advanced analytics to the entire human workforce

      • incentives for pre-emptive problem solving and innovation

      • Automate information delivery:

        • Record notification

        • Track and verify resolution

        • Extend network to unbiased regulators of regulators

      • Plug-in multiple predictive models:

        • -establish resolution of conflicts with unbiased review.

        • Automatically include results in reporting to prevent obstacles to essential targeted transparency as occurred in the Fukushima incident

5) Include sensory, financial, and supply chain data in real-time enterprise architecture and reporting

      • Until this year, extending advanced analytics to the entire human workforce was considered futuristic (see 1/10/2012 Forrester Research report Future of BI), in part due to scaling limitations in high performance computing. While always evolving, the design has existed for a decade

      • Automated data generated by sensors should be carefully crafted and combined in modeling with human and financial data for predictive applications for use in risk management, planning, regulatory oversight and operations.

        • Near real-time reporting is now possible, so governance structures and enterprise architectural design should reflect that functionality.

 

Conclusion

While obviously not informed by a first-person audit and review, if reports and quotes from witnesses surrounding the Fukushima crisis are accurate, which are generally consistent from dozens of other human caused crises, we can conclude the following:

The dysfunctional socio-economic relationships in this case resulted in an extremely toxic cultural dynamic across academia, regulators and industry that shared tacit intent to protect the nuclear industry. Their collective actions, however, resulted in an outcome that idled the entire industry in Japan with potentially very serious long-term implications for their national economy.

Whether psychological, social, technical, economic, or some combination thereof, it would seem that no justification for not deploying the most advanced crisis prevention systems can be left standing. Indeed, we all have a moral imperative that demands of us to rise above our bias, personal and institutional conflicts, and defensive nature, to explore and embrace the most appropriate solutions, regardless of origin, institutional labeling, media branding, or any other factor. Some crises are indeed too severe not to prevent.

Mark Montgomery
Founder & CEO
Kyield
http://www.kyield.com

New Video: Extending Analytics to the Information Workplace


Press release on our enterprise pilot program


We decided to expand our reach on our enterprise pilot program through the electronic PR system so I issued the following BusinessWire release today:

Kyield Announces Pilot Program for Advanced Analytics and Big Data with New Revolutionary BI Platform 

“We are inviting well-matched organizations to collaborate with us in piloting our break-through system to bring a higher level of performance to the information workplace,” stated Mark Montgomery, Founder and CEO of Kyield. “In addition to the significant competitive advantage exclusive to our pilot program, we are offering attractive long-term incentives free from lock-in, maintenance fees, and high service costs traditionally associated with the enterprise software industry.”

Regards, MM

Strategic IT Alignment in 2012: Leverage Semantics and Avoid the Caretaker


A very interesting development occurred on the way to the neural network economy: The interests of the software vendor and the customer diverged, circled back and then collided, leaving many executives stunned and confused.

The business model in the early years of software was relatively simple. Whether an individual or enterprise, if the customer didn’t adopt the proprietary standard that provided interoperability, the customer was left behind and couldn’t compete—a no brainer—we all adopted. By winning the proprietary standard in any given software segment, market leaders were able to deliver amazing improvements in productivity at relatively low cost while maintaining some of the highest profit margins in the history of business. This model worked remarkably well for a generation, but as is often the case technology evolved more rapidly than business models and incumbent cultures could adapt, so incumbents relied on lock-in tactics to protect the corporation, profit, jobs, and perhaps in some cases national trade.

Imagine the challenge of a CEO today in a mature, publicly traded software company with a suite of products that is generating many billions of dollars in profits annually. In order to continue to grow and keep the job, the CEO would need to either rediscover the level of innovation of the early years—as very few have been able to do, play favorites by providing some customers with competitive advantage and others with commodities—occurring in the enterprise market but risky, or focus on milking the commoditized market power in developed nations while pushing for growth in developing countries. The latter has been the strategy of choice for most mature companies, of course.

Doing all of the above simultaneously is nearly impossible. Killer apps by definition must cannibalize cash cows and public company CEOs have a fiduciary responsibility to optimize profits while mitigating risk, so most CEOs in this position choose to remain ‘dairy farmers’ either until retirement or are forced to change from emergent competition. In discussing one such incumbent recently with one of the leading veterans in IT, he described such a CEO as “the caretaker”. For enterprise customers this type of caretaker can be similar to the one we hired a few years ago to protect our interests when we moved to the Bay area, returning to a property that was uninhabitable after messaging ‘all is fine’ (beware of the caretaker wolf in sheep’s clothing).

Now consider that software exports generate large, efficient import engines for currency in headquarter countries, thus placing those national governments in strategic alignment with the incumbents in the short-term (often dictated by short-term politics); and another entire dimension appears that is rarely discussed, yet very strongly influences organizations worldwide. This situation can influence governments in protecting and reinforcing perceived short-term benefits of commoditized market leaders over critical long-term needs of organizations, markets, and economies. It is not inaccurate to suggest that national security is occasionally misunderstood and/or misused in the decision process on related policy.

Monopoly cultures think and act alike, whether in the public or private sector, which is often (eventually) their undoing, unless of course they adopt intentional continuous improvement. This is why creative destruction is so essential, has been embraced internally by most progressive organizations in some form, and why customers should proactively support innovators and farm markets towards sustainable diversity. Despite what may appear to be the case, the interests of incumbents in enterprise software are often directly conflicting with the interests of the customer.

While the theory of creative destruction has roots in Marxism, the practice is a necessity for capitalism (or any other ism) today due to the natural migration of cultures and economies to seek security and protection, which in turn takes us away from the discipline required for continual rejuvenation. We embrace creative destruction in what has become modern global socialism simply because very little innovation would emerge otherwise. Competitive advantage for organizations cannot exist in rigid commoditization of organizational systems as we see in software. Simply put—whether at the individual, organizational, or societal level, we should embrace creative destruction for long-term survival, especially in light of our current unsustainable trajectory.

Which brings us to the present day emergent neural network economy. In our modern network economy we simply must have interoperable software and communications systems. The global economy cannot function properly otherwise, so this is in everyone’s interest, as I have been saying for 15 years now. The overpowering force of the network effect would place any proprietary standard in an extortion position to the entire global economy in short order. The current danger is that functional global standards still do not exist while national interests can align perfectly in the short-term with proprietary standards. That is not to say, however, that proprietary languages and applications should not be encouraged and adopted—quite the contrary—open source suffers similar challenges as standards in terms of competitive differentiation. Rather, it only means that proprietary technologies cannot become the de facto standard in a network economy.

In peering into the future from my perch in our small private lab and incubator in wilds of N AZ more than 15 years ago, the need for standardized structured data becomes evident, as does the need for easily adaptable software systems that manage relationships between entities. Combined with the data explosion that seems infinite, it was also obvious that filters would be required to manage the quality and quantity of data based on the profiles of entities. The platform would need to be secure, not trackable for many applications, reflect the formal relationships between entities, and set the foundation for accountability, the rule of law, and sustainable economics. In addition, in order to allow and incentivize differentiation beyond the software programmer community, thus permitting market economics to function, the neural network economy would require adaptability that is similar to that which takes place in the natural, physical world.

I suggest then while nascent and imperfect, semantics is the preferred method to achieve alignment of interests in the emergent neural network economy, for it represents the building blocks in structured data for meaning in the digital age, resting at the confluence of human and universal languages, and serving as the functional portal to the neural network economy.

Finally, as the humble founder and inventor, permit me then to suggest that Kyield is the optimal system to manage semantics as it intentionally achieves the necessary elements for organizations to align and optimize their digital assets with the mission of the organization, containing adaptable tools to manage the relationships between entities, including with and between each individual and workgroup.

May 2012 deliver more meaning to you, your organization, and by extension our collective future.