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.