An edges’ matrix

We know that teams do not start out to create technical debt.  They do the best they can with what they have to work with.  For the sake of this context, we can refer to the ongoing processes funded in an annual budget as ‘to be built’ throughout the year.  These comprise a microeconomics in the manner of a theory of the firm analysis.

There are three types of technical debt (imho, most analyses identify just two types).   Annual budgets are set based on the assumption that requirements for which solutions are to be required are allocated resources sufficient to meet those requirements.  If, during a fiscal year, a key staff member, partner, or ecosystem member opts out of a funded project, the investment in generating a solution for that requirement to that point, and going forward until the project is restarted, is Type I technical debt.  This would be a micro-economic form of that type of debt.

When, say in the middle of a year, a business unit is forced to realign with a market, a segment, or a customer base, the ‘to be built’ category of solutions changes.  This is along the lines of a meso-economic change that causes whatever has been spent on assigned tasks to that point in the year which will not be completed, forms Type II technical debt.  Abandoned projects which will not be restarted, along with any previous year’s investment is a form of Type II technical debt.

Type III technical debt comes from macro-economic trends changing the markets within which the organization operates.  Often now occurring at the start of a fiscal year, business requirements evolve in a step-wise, or jump-wise manner due to new market conditions or new market competitors.  Since digital techniques allow for organizations to transfer existing capabilities from one market to another more seamlessly than in the past, Type III technical debt is created at an ever-increasing rate.  Assuming that is, that an organization has a digitizable net future value offer set.

Starting our journey to the edge from legacy circumstances can be demonstrated in the graphic below.  Ideally, organizations want to follow the diagonal from the lower left to the upper right as it is the shortest line between two points.  Namely, the as-built and the as-envisioned organization.  Since organizations cannot simply drop all the things everyone is doing and proceed along a 100% fit-to-future program, the paths from as-built to as-envisioned will take a path that balances data-driven and digital twin-driven development.

Generally speaking, organizations offering direct to consumer services will focus more on the data-driven path.  To these organizations, digital twins of the consumer consist of behavioral models that trace an acquisition lifecycle.  This is represented in the graphic above by the upper arc.

Again, generally speaking, organizations offering machining capabilities for creating assets and systems of value will use a digital twin-driven adoption model that is informed, step by step, with data from previous stages and expectations of future capability.  This is represented in the graphic above by the lower arc. 

For our purposes, we are experimenting with a process that links data engineering and digital twin engineering in the form of a supply chain factory. That is, a supply chain that ‘factors’ supply chains for the creation of development environments. The analyses generated from the experiments will be published in the Works-in-Progress section of this website.

Those with some recollection of past digital transformation techniques will note the resemblance of this type of analysis to the use of the four types of organizations and how they develop.  Though there were variations, this would be the insights gained from classifying organizations as laggards (lower left) and leaders (upper right).   Up until the pandemic was recognized as having an impact on commerce of all types, the main differences between laggards and leaders were the urgency dedicated to the technology, process change, and cultural change necessary for digital transformation.

Fast forward to a period of endemic pandemic where businesses are re-assessing the degree to which hiring was done to support first stage pandemic impacts.  Depending on how you count the stages, we are in something like the fourth stage of a pandemic economic winter.  Venture capital levels are decidedly lower than they have been, and interest rates no longer favor long-term borrowing to support innovations of any kind.

Fewer staff, fewer resources, and are we still at 75% digital transformation adoption rates?  While the adoption rate may be as high as pandemic stage 1 (December 2019 to September 2020), the level of maturity in digital transformation programs has certainly not much improved since then.  Or rather, interruptions in business processes and commercial investment models changed from future next to now next.  And then changed again. Every six to nine months, or so.

The current state of digitalization in terms of a high-level perspective as of the endemic pandemic stage is nicely summarized in the four statistics, below.

37 Incredible Digital Transformation Statistics [2023]: Need-To-Know Facts on The Future Of Business - Zippia; accessed 19 april 2023

Accelerators of digital adoption from here forward look to be artificial intelligence, re-tasking work to include increased digital content and context, making cloud more omnipresent in technology plans and to continue on with digital-first strategies.

These trends can be found within the NIST Cyber Physical Systems framework.  Using the extended digital journey matrix, repeated below, we can build a path to a digital economy model.

Along the traces drawn on the inner matrix, we have placed the nine aspects of the NIST CPS framework.  Each phase of development along the Business line contains its own CPS model set.  This creates a five-phase development model for use in a platform engineering use case, beginning with the Meaningful Use phase at the lower left, and ending with the Population Expectations for digital catalogs at the upper right.

In using procedural terms from the healthcare market, we get access to a wide range of existing policy, program, practice, protocol, and process tools to enable the adoption of the Smart Grid Maturity model onto our journey.

The use of the NIST CPS framework as the representation of digital asset characteristics, we also gain a list of 27 concerns which every industry faces during its development.  Since our goal is to develop a transferable platform engineering model which is vertical market, horizontal market, legacy market and emerging market agnostic, we need a mechanism to track the relationship between the adoption of the CPS-as-market twin and the field of econometrics.

This comes from the following representation, based on the Cobb-Douglas Production frontier.

In the graphic, above, the common form of the Cobb-Douglas function is shown as a purple arc.  Instead of labor and capital, we have data under management and digital twin adoption as the axes.  The dotted line represents the production function sustainable by an organization operating within a market.  The degree to which the organization’s sustainability is enhanced by digital transformation is determined by how effectively it can transform any of the technical debt and directed investment along that dotted arc into digital-first (or digital next, in the case of advanced practitioners) solutions representing cost savings or revenue growth.

In the final graphic for this Note, we have mentioned the Smart Grid Maturity Model.  This work, conducted by the Carnegie Mellon Software Engineering Institute, provides eight domains for the operations management of a practice.  Here, the practice is developing digital capabilities within organizations.  Mapping the SGMM Domains to the matrix used here, we get the following perspective.

There are seven SGMM Domains on this representation.  The eighth Domain consists of Strategy, Management, and Regulatory components and these will be used to develop an assessment program which maps the intention of an organization relative to its digitalization effort and the results gain as determined by market expression.  The multi-layered approach, from the matrix to the SGMM view, and then out to the market (the Cobb-Douglas study) reflect the same three level architecture found in the analysis of technical debt which began this Note.