Declarative IT and Design for Operations

Was reading a very insightful and interesting blog from @TorstenVolt on Linked.in titled "Digital Transformation Requires Autonomic Computing 3.0" and started to see some commonality in the needed operations processes with a digital future.

This, by the way, is right after @AWS announces their AWS Outposts, a telling example of how coevolution in IT validates concepts and models (Yes, everyone, Hybrid is real, even Microsoft things so) and how terribly important it is to get to the data.

Torsten brings up some very important points about post transformational examples on what direction needs to take place.  It's very much like the conversation that @cpswan brings up almost every time we talk, how does the future solve the problems we see through Design for Operations.

The solution for Design for Operations revolves distinctly around Declarative IT plus a bit of truth and trust in the platform(s) associated with that same delivery.

I've done my best in capturing/mapping this in a Wardley Map as a way to illustrate the idea.

Figure 1.  Wardley Map showing Declarative needs and Design for Operations of IT Infrastructure

In general, IT Infrastructure Design for Operations includes a Platform for management of Declarative states of underlying infrastructure.

With the abstraction of server virtualization and cloud computing, the declarations became very much least common denominator and not very permissible with respect the evolution of application development (the Dev part of DevOps).  It eased operations of the abstracted layer considerably, but it didn't really enhance the Ops side of the infrastructure model.

At this point, we could argue that the Cloud Titans have already achieved a more horizontally evolved Declarative Platform, but now that everyone seems to be reaching back into the Enterprise Datacenter, permissible use and least common denominator may very well come into play again.

So, looking at the problem that now needs to be solved, truly Declarative Storage and Declarative Compute need to be pressed into evolution toward commodity to achieve the rewards of application integrated software definition of the hybrid datacenter.

This should go a long way toward solving the Design for Operations problems as well as matching up with @TorstonVolt's re-conceptualization of the Digital future state.

Wardley Mapping Technical Debt of a System

A friend of mine brought a question, can you map technical debt of a neglected system (edited) with Wardley Mapping.

I'm not sure it's possible in a single slide, mostly because the Wardley Map would require a time axis (or even a worker knowledge axis) to depict it in one slide.

To explain the entire idea, I'm going to use 8 slides such that the entire thought may be followed.  Please keep in mind, that this is entirely theoretical in nature.

For starters, Wardley Maps does have a couple of mechanisms that are useful for this depiction (See Figure 1 upper right).  The first one is inertia, represented as a solid bar and the second is a fat arrow indicating acceleration and deacceleration.

Figure 1 is a starting value chain map, with a system between the electricity anchored in the Commodity (+utility) area and a user need indicated in the most visible position.

Figure 1.  Wardley Map of system with Inertial marker identified

I'm placing the characteristic of Inertia in the middle of the system, with the supposition that there are no other forces acting on the system (like in physics).

Let's suppose then, that a deaccelertion is applied to the system by forgoing a technical upgrade in a part or all of the system.  See Figure 2.

Figure 2.  Stalling Update of System to N-1

This will impose an inertial movement from current position to the left.  See Figure 3.  Also note the "shadow" of Technical Debt in this drawing behind the system's inertial reference.

Figure 3.  System in N-1 State

The system is still within the Product (+rental) space and therefore there is no significant harm to how the system provides for the user need.

Imparting another deacceleration to the center mass of the system by forgoing the next generational needs of upgrading the system again shifts the center of mass of the system to the left.  See Figure 4.

Figure 4.  Forgoing update of system to N-2

See Figure 5, the system now sits on a precarious boundary between Product and Custom.  The possibility that the functions of support and management are greatly increased and the system itself may need some parts of its structure custom maintained.  The "shadow" of Technical Debt has grown with respect to the movement of the system.

Figure 5.  System in precarious boundary between Product and Custom

Forgoing another generational upgrade of the system imparts another deacceleration to the overall system.  See Figure 6.

Figure 6.  Further neglect of System to N-X

Continuous neglect of the system eventually leads to complete failure, where the massive "shadow" of Technical Debt of this neglect leads to system collapse through the technological inability to upgrade or loss of knowledge necessary to upgrade. See Figures 7 and 8.

Figure 7.  Continued neglect until system breaks

Figure 8.  Broken system (fully in Genesis) with Total of Technical Debt

Once the center of mass passes the Genesis boundary, the system is likely incapable of accurate function.  The user needs will no longer be met and that also may need to shift along with the center of system inertial movement or more likely be removed altogether.

As always, if you've any additional thoughts on this topic, I'm active in the twitter-verse @abusedbits

https://threadreaderapp.com/thread/1068822023126827010.html

Evolving a Business Process with a Wardley Map, Part 2

Following on from Evolving a Business Process with a Wardley Map, we were discussing the Cost mechanism.

The question was, if we understand the BOM, "Can we get to ROM Cost quicker?"

5. Cost Calculator

This part of the evolution of the business process is actually more difficult.  It involves having ALL of the partner/vendors willing and able to work with you to create a Cost Calculator.

The good thing, if it is possible to create a BOM Calculator, most of the business logic is understood well enough to introduce the concept of Cost with the same (or similar) business logic.  The reverse is also true, we just needed a starting point.

In Figure 5, we're evolving a Cost Calculator as a product of the BOM Calculator output.  It is most assuredly in the Product category because it requires a product like update on a regular interval with the partner/vendor to maintain currency with their cost changes.

In Figure 6, we've created the new Cost method.

6. New Cost Method

The benefit of this change in process, we've shaved an additional 7-10 days off the Process.  We're also able to discuss pricing with a real BOM and a tolerance based Cost.

The problem, it is unlikely to be as exact as the Quotation step, but fine for ROM review. Providing we can live with small fluctuation between the Cost Calculator and the Quotation, everything should be fine.  Just make absolutely sure you understand completely the cost tolerance requirements.

7. Optimization Method

There's quite a good chance, especially with a mixed product-service opportunity, that there is potentially an unacceptable variation to the required tolerance without looking at Optimization of the BOM to ROM, Figure 7.

The Question for the Cost Calculator, "Is 1 large widget more cost effective than 2 medium widgets or even 4 small widgets?"

Consider the scenario where the product is a combination of labor, hardware and software licensing.  Optimization of the Calculators (a Calculator of Calculators, if you will) provides the means to create a deeper awareness of the product as well as a mechanism for Sizing appropriately for the need with cost in mind.

8. New ROM and BOM tool

We then shift our attention to Customer Pricing.

The Question, "Can we get to Customer Price quicker with better awareness?"

In the mapping exercise, we could very well have started with this question as it would be the most customer centric pursuit.  I'm not entirely sure it would have changed the path we followed, but I'm relatively certain it would have been a more daunting starting point.

9. New Costing and Pricing Method

It's obvious that a Calculator for pricing solves this problem the most expediently.

At this point, we've achieved the knowledge necessary to create calculators for the change in process steps.  We can also use those calculators to feed the Quotation requirements, potentially shortening that process step in Final Pricing.

The big win, we've moved the Sales Agent's view of the process from Custom Build to Product and improved his time to respond to the customer.

The overall process, from start to finish goes like this:

Customer Input -> ROM and BOM Calculator (as many times as Customer Input changes) -> Feed the Sizing from the ROM and BOM Calculator to Quotation -> Run the Quotation(s) through the Pricing Tool -> Sales Agent happiness

This Wardley Mapping exercise ended up being mostly about convenience, a bit about standardization.  How do you execute an On-Demand set of actions that would normally take between 22 and 44 days, in the shortest time possible?

As always, I'm interested in your feedback.  Find me on Twitter @abusedbits

Evolving a Business Process with a Wardley Map

Wardley Mapping (Value Chain Mapping) can be a great asset in describing the evolution of a business process.  In this case, I'm going to illustrate the changes of the Costing and Pricing action to show how improvements may be identified once the current or base activity is mapped.

In this scenario, Costing and Pricing, a sales agent needs to arrive at a Price for the customer.

The process includes engagement of a high skill technical person to create a Bill of Materials (BOM) for the customer.

It requires a Quotation that includes all of the BOM items, this is then used to create a Cost.

The Costs are then all rolled up, margin/cost-of-money and services/labor are included to create Customer Price in such a way that the Sales Agent may have a Rough Order of Magnitude (ROM) conversation with the Customer.

Here's what that map looks like.

1.  Costing and Pricing Activity

Keeping in mind that Wardley Map placement is relative, BOM activity is in the Genesis category primarily because "we know very little" about the customer desire until a BOM is generated to discuss and modify to the customer requirements.

Quotation is in Custom Build, because it is generated uniquely based on the BOM for the customer.  It is subject to change, also based on customer requirements.

The same can be said for Cost and Customer Price.  Without a customer qualified BOM, the entire process flow is subject to correction and reset until that action is completed.

The Question to ask, "Can we move this process to Product or Utility?"

2.  Optimize the BOM activity

Any one of the items in the process flow could be looked at for possible improvement.  In determining where to start, sometimes it is just picking a point and mapping out possible evolution steps and see what happens to the map.

In this case, we're looking at the BOM, if for no other reason than it feeds the more visible aspects of the value chain.

The Question for BOM, "Can we improve the BOM by creating a calculator for the BOM?"

There are some implications to this question, the principle one, can we create a standard method.  The standard method would be an idealization of the Bill of Materials in such a way that changes to it became mathematical multiples of or well understood relationships between the elements.  This implies a standard and baseline awareness of a design or architecture that at the very smallest level that consists of units that can be both measured and used as incremental building blocks.

3. New BOM Method

  In the case where that is true, the Bill of Materials Calculator (figure 3) can potentially replace the BOM action in Genesis.  Utilizing well understood units of measure, the calculator makes it possible to manipulate the BOM output to the customer desired goals/end-state much more rapidly.  The output of this tooling is arguably a utility in nature, but because it continues to rely on manual understanding/input, we'll put it squarely on the product/utility line.

The benefit of this change in process, we've shaved 7-14 days off the Process.

The Question for BOM Calculator, "How important is the Quotation?"

As it turns out, the Quotation with the source suppliers is only required when finalizing a deal with the customer.  So, as figure 4 indicates, we're going to skip over Quotation and start to consider the Cost analysis.  Because supply chain / purchasing requires the Quotation, we cannot remove it from the process, but it doesn't have to be done until we're in a position to make a purchase.

4.  Quotation

With Quotation removed (for the moment), and a good awareness of the BOM sizing activity we'll tie the BOM directly to the Costing activity.

The benefit of this change in process, we don't have to Quote each an every time we need to understand a Rough Order of Magnitude (ROM) cost.

The Question for the BOM Calculator mechanism then becomes, "Can we get to ROM cost quicker if we understand the BOM?"

In the next installment, I'll review how Costing evolves to improve the process.

Descriptive Evolution Mapping

Thanks to @swardley and the community for steering me to a better definition of the Wardley Maps I was attempting to produce.

Wardley Maps - Evolution Map

The graphic above introduces the product view of each phase of Design.

As a starting point, it is the output of the workload or application that has value.

The applications, and indeed the early computers, were Designed for Purpose.  They were also Custom built.  This is illustrated by the simple fact that repurposing an early computer may very well mean that the equipment necessary to run a different program may have to be changed in some pretty fundamental ways.

     Evolution of computing lead to computers that were capable of multi-purpose computing.  The applications were no longer specifically tied to hardware that they operated on (I know there's an argument brewing here, but it's the generalization I'm illustrating).

This provided the means for individuals and companies to run a multitude of programs on computing equipment.  This was a generational improvement in application development and lead to an entire industry of boxed software delivery.

     The box software products were developed within the framework of a particular operating system with guidelines about what and how commands were processed.  This is a manufacturing guideline and therefore we can identify the boxed software industry as Design for Manufacture and each "box" contained a Product on some type of media.

     Interestingly, the Product area is not necessarily the valuable output of the workload or application, but a means integrated to produce the valuable output.  This means that, while boxed software may very well be a Product, the overall application may still be Custom build, retroactively putting the entirety of said application back into Design for Purpose.

     Another interesting tidbit, the tight control over box software lead to the creation of competitors and the OpenSource industry.  This happened because the design of the overall application mandated variation that the box software vendors were either unwilling or unable to easily produce or it came at great licensing expense.

We reach another evolutionary state with what are arguably extremely easy to consume application outputs.  Built specifically with the consumer in mind and creating a service category (Utility Service) built upon commodity components. These are more easily offered in a volume consumption model of cost/volume-time.

     Utility specifically targets ease of consumption, foremost the usage mechanisms or operation.  It therefore represents a Design for Operations method of delivery.  Constantly being updated and firmly rooted in the idea that the easier it is to use, the more difficult it will be to replace.

     An interesting consequence of Design for Operations is that competitive pressures in Utility drive new business practices like Business Agility/Agile DevOps.

What's next you might ask?  google serverless, then google codeless

Enterprise Virtualization vs Public Cloud 2018 (Mapping, Wardley)

Enterprise virtualization prediction for 2018, tl:dr - no drastic changes from 2017.

There are some interesting possibilities, though, and I've used Simon Wardley mapping to diagram them.

Enterprise Virtualization 2018 prediction vs Public Cloud

As shown in the map on the left, we can now argue that the consumption of virtual machines has trended all the way into commodity consumption (with standardized tooling, automation and operating environment).  If it hasn't in your company, you may want to start asking why.

One of the more interesting possibilities for 2018, if the equipment vendors do this correctly, is composable infrastructure. This could completely displace traditional compute and push it into commodity consumption.  I'm going to leave it as a dotted line in the figure for now, as the business impact of technical accounting for corporations might make this a non-starter. That said, I have to imagine that a true utility in any premises would be good for the industry and the consumers.

In the public cloud map on the right, we may need to incorporate some changes based on enterprise use of public cloud to include the difference between “cloud native” capability vs enterprise hosting requirements.

Cloud native capability is the service consumption of the public cloud that relies only on the tools and capabilities built into the public cloud platform element.  Using it for new application development, including things like serverless application design, is growing as AWS and Azure partners and consumers learn to take advantage of those cloud native features.

     Cloud Native platforms are not particularly well placed for traditional enterprise workloads, though, which often require more specific (as well as traditional) care and feeding.  Furthermore, refactoring enterprise applications to take advantage of Cloud Native features may not be a worthwhile endeavor considering the cost to do transformation of applications.  The general thought is to give them a safe place to run until they are no longer needed. 

The enterprise hosting data center exodus from 2017 provides some of the highlights of why workloads will move out of the data center.  It may not be obvious, but the unifying element of both of the diagrams is how Hybrid Computing will be handled between enterprise virtualization and public cloud.  This integration still looks very much like early product (see diagram above).

One of the possible next steps is being taken by both Microsoft Azure and AWS / VMware who have announced methods to move non-cloud native workloads to their IaaS:  Microsoft Azure Stack and VMware Cloud on AWS.  Over time, both of these services should drive workloads more than "smooth out the peaks".  This is a major movement in what I’d titled my prediction last year, and why I say “Public Cloud Continues to Win Workload Love.”

If you've followed the mapping threads on my blog, here's the historical links on these predictions:

2017

2016

And if you want to know more about my Wardley mapping mis-adventures, follow this link.

updated 19 Dec 2017

Next IT Shift, back to Distributed

Figure 1.  We're about to swing to Distributed Computing again

There's a lot to be said for the ever changing IT industry and 2017 should be a banner year in new things happening.

As a recap, in 2016 we saw the Digital Shift take hold of IT and give it a really good shake.  Starting 2016 everyone was asking if you had a plan for Cloud Computing.  I now hope that you do and if not......

There's a huge number of predictions for what the focus and industry will be looking at in 2017.  Of these, #Artifical #Intelligence or #AI and its many brethren (#MachineLearning #Robots, etc) are likely to impose a new Transformation Shift in the industry.

The way I think about it is #IoT with some #smarts.  At a most basic level, anything that can have a sensor applied to it is likely to be considered for an IT upgrade in the framework of an #IoT style device.  These upgrades will be sensors that record some type of data, maybe multiple types of data.

The result is a tiny sensor recording something that people find of value and would like to act upon.

This means data.  And when I say data I mean #LotsOfData.  Big Data levels of lots.

Because there will be so much data, it is almost certain that the data will require some culling and curating at the source.  So, there will be a tiny little computer attached to the tiny sensor.

The data from the sensor is going to have to go somewhere and I think this will have an effect on Cloud Computing.  We're about to have another pendulum swing in computing back to distributed processing of a sort.

Just like other swings, it will not eliminate the previous technology area, but it will force it to change.

Ideally, valuable data that needs to be acted on rapidly will need to have most of the computation done very close to the source.  The entire idea behind the #OODA #Loop is needed to take advantage of real time or near real time data.

Something will be needed to jumpstart the #AI training to make it all work.  This may be the resulting use of Cloud Computing, or it may create the need for a "Near Edge" Cloud computing capability.

Once the AI is trained, the tiny computer should be self sufficient enough to handle everything else it needs to do other than store the sensor data, making the "Near Edge" Cloud as much a storage system as anything else.

This means that the next transformation is going to make computing at the #Edge a reality again.

It's also likely to change how we deal with programming languages.  Logic programming will give way to data flow programming.  (Might be time to brush the dust off some old data flow language or build a new one).

Why Public Cloud wins Workload Love

Enterprise Virtualization 2017 prediction vs Public Cloud

The graphic on the right is by @swardley (http://blog.gardeviance.org/) consumer cloud value chain and the drawing on the left is the 2017 update of the Enterprise Virtualization Value Chain @abusedbits.

I'm using this representation to provide a basis of understanding for the growth in workload volume in the Public Cloud space.

IDC is reporting that World Wide Public Cloud Services Spending Forecast will reach $195B by 2020 and I needed a way to show WHY this was happening.

This shift in workload delivery is not about cost, although the argument that workload execution relative to the coverage of the gap in the value chain is becoming extremely important.  The barrier of entry in a public cloud is extremely low.  So low that we have to pay attention to it.

Its also not about the technology currently available and operating at volume in each space, as both private and public cloud do nearly the same things.  Although, this is changing for Public Cloud with the addition of Lambda functions to market players like Amazon Web Services.

Containers are following a similar path in this space, making the locality of execution of a workload subsequently less "sticky" to the environment.  Where host virtualization has stalled, containers are positioning for least common denominator execution in multiple cloud environments.

What it does show is that the Public Cloud is operating with nearly all aspects of their service delivery in or near the Commodity or Utility area of the value chain.  Moreover, anything added to the Public Cloud capability is more rapidly pushed toward Commodity delivery.

Update:What's more, the pace of change in Private Cloud seems to be slowing in magnitude toward Utility.  If you consider the previous 3 years of change in the Private Cloud space, not even compute has made it to a Utility service.

This doesn't mean that everything is fit for Public Cloud.  Applications will need to be revisited with the idea that application execution has to evolve to reduce the effects of technical debt.  Realistically there is a crossroads in choice that needs to be made with older software that doesn't fit very well.

As @jpmorganthal states on migration to cloud .... “It’s complicated” http://jpmorgenthal.com/2016/08/  .

Update:  http://www.businesswire.com/news/home/20161130006375/en/Worldwide-Server-Market-Revenue-Declines-7.0-Quarter


Part 1:  There is a shift afoot, that is changing the business.
     http://www.abusedbits.com/2016/11/public-cloud-echo-in-data-center.html

Part 2: Moving time for the Enterprise Data Center?
     http://www.abusedbits.com/2016/11/diminution-of-enterprise-data-center.html

Part 3: The Enterprise Data Center is getting smaller.  Time to add that to the roadmap.
     http://www.abusedbits.com/2016/11/smallering-of-enterprise-data-center.html

2016 - Mapping Exercise - Enterprise Virtualization

Healthcare Value Chain 2016

Value Chain for healthcare IT systems in 2016

Femi and I created these to describe the Healthcare value chain for 2016.

The first figure indicates the high level value chain and the area where the most value may be derived from an increase in technical capability.

The supporting requirements, functions and interactions are all indicated as subsequent capabilities.

Possible Technology Step Function for Healthcare

The second figure shows a possible future step function that causes a reset in the value chain and what those potential Healthcare technology areas would become.

In the event that this future becomes reality, where Population Health Management resides in the top figure today, Targeted or Precision (Personalized) Medicine will become the operational mode in the future. #mapping

Network Function Virtualization - Value Chain 2016

Value Chain for NFV in 2016

I'm using this to describe the relative position of functions in the value chain for Network Function Virtualization (NFV).

Where the customer is consuming Virtual Network Functions (VNFs) there are a large number of supporting functions underlying the delivery of the VNFs.

This is intended to show the relationship between those elements that would perform part of a MANO Functional Block in the VIM (Virtualized Infrastructure Manager). #mapping