The Ineffective OSS Scoreboard Analogy

Imagine for a moment that you’re the coach of a sporting team. You train your team and provide them with a strategy for the game. You send them out onto the court and let them play.

The scoreboard gives you all of the stats about each player. Their points, blocks, tackles, heart-rate, distance covered, errors, etc. But it doesn’t show the total score for each team or the time remaining in the game. 

That’s exactly what most OSS reports and dashboards are like! You receive all of the transactional data (eg alarms, truck-rolls, device performance metrics, etc), but not how you’re collectively tracking towards team objectives (eg growth targets, risk reduction, etc). 

Yes, you could infer whether the team is doing well by reverse engineering the transactional data. Yes, you could then apply strategies against those inferences in the hope that it has a positive impact. But that’s a whole lot of messing around in the chaos of the coach’s box with the scores close (you assume) and the game nearing the end (possibly). You don’t really know when the optimal time is to switch your best players back into the game.

As coach with funding available, would you be asking your support team to give you more transactional tools / data or the objective-based insights?

Does this analogy help articulate the message from the previous two posts (Wed and Thurs)?

PS. What if you wanted to build a coach-bot to replace yourself in the near future? Are you going to build automations that close the feedback loop against transactional data or are you going to be providing feedback that pulls many levers to optimise team objectives?

One big requirement category most OSS can’t meet

We talked yesterday about a range of OSS products that are more outcome-driven than our typically transactional OSS tools. There’s not many of them around at this stage. I refer to them as “data bridge” products.
 
Our typical OSS tools help manage transactions (alarms, activate customers services, etc). They’re generally not so great at (directly) managing objectives such as:
  • Sign up an extra 50,000 customers along the new Southern network corridor this month
  • Optimise allocation of our $10M capital budget to improve average attainable speeds by 20% this financial year
  • Achieve 5% revenue growth in Q3
  • Reduce truck rolls by 10% in the next 6 months
  • Optimal management of the many factors that contribute to churn, thus reducing churn risk by 7% by next March
 
We provide tools to activate the extra 50,000 customers. We also provide reports / dashboards that visualise the numbers of activations. But we don’t tend to include the tools to manage ongoing modelling and option analysis to meet key objectives. Objectives that are generally quantitative and tied to time, cost, etc and possibly locations/regions. 
 
These objectives are often really difficult to model and have multiple inputs. Managing to them requires data that’s changing on a daily basis (or potentially even more often – think of how a single missed truck-roll ripples out through re-calculation of optimal workforce allocation).
 
That requires:
  • Access to data feeds from multiple sources (eg existing OSS, BSS and other sources like data lakes)
  • Near real-time data sets (or at least streaming or regularly updating data feeds)
  • An ability to quickly prepare and compare options (data modelling, possibly using machine-based learning algorithms)
  • Advanced visualisations (by geography, time, budget drawdown and any graph types you can think of)
  • Flexibility in what can be visualised and how it’s presented
  • Methods for delivering closed-loop feedback to optimise towards the objectives (eg RPA)
  • Potentially manage many different transaction-based levers (eg parallel project activities, field workforce allocations, etc) that contribute to rolled-up objectives / targets
 
You can see why I refer to this as a data bridge product right? I figure that it sits above all other data sources and provides the management bridge across them all. 
 
PS. If you want to know the name of the existing products that fit into the “data bridge” category, please leave us a message.

Do you want funding on an OSS project?

OSS tend to be very technical and transactional in nature. For example, a critical alarm happens, so we have to coordinate remedial actions as soon as possible. Or, a new customer has requested service so we have to coordinate the workforce to implement certain tasks in the physical and logical/virtual world. When you spend so much of your time solving transactional / tactical problems, you tend to think in a transactional / tactical way.
 
You can even see that in OSS product designs. They’ve been designed for personas who solve transactional problems (eg alarms, activations, etc). That’s important. It’s the coal-face that gets stuff done.
 
But who funds OSS projects? Are their personas thinking at a tactical level? Perhaps, but I suspect not on a full-time basis. Their thoughts might dive to a tactical level when there are outages or poor performance, but they’ll tend to be thinking more about strategy, risk mitigation and efficiency if/when they can get out of the tactical distractions.
 
Do our OSS meet project sponsor needs? Do our OSS provide functionality that help manage strategy, risk and efficiency? Well, our OSS can help with reports and dashboards that help them. But do reports and dashboards inspire them enough to invest millions? Could sponsors rightly ask, “I’m spending money, but what’s in it for me?”
 
What if we tasked our product teams to think in terms of business objectives instead of transactions? The objectives may include rolled-up transaction-based data and other metrics of course. But traditional metrics and activities are just a means to an end.
 
You’re probably thinking that there’s no way you can retrofit “objective design” into products that were designed years ago with transactions in mind. You’d be completely correct in most cases. So what’s the solution if you don’t have retrofit control over your products?
 
Well, there’s a class of OSS products that I refer to as being “the data bridge.” I’ll dive into more detail on these currently rare products tomorrow.

An OSS checksum

Yesterday’s post discussed two waves of decisions stemming from our increasing obsession with data collection.

“…the first wave had [arisen] because we’d almost all prefer to make data-driven decisions (ie decisions based on “proof”) rather than “gut-feel” decisions.

We’re increasingly seeing a second wave come through – to use data not just to identify trends and guide our decisions, but to drive automated actions.”

Unfortunately, the second wave has an even greater need for data correctness / quality than we’ve experienced before.

The first wave allowed for human intervention after the collection of data. That meant human logic could be applied to any unexpected anomalies that appeared.

With the second wave, we don’t have that luxury. It’s all processed by the automation. Even learning algorithms struggle with “dirty data.” Therefore, the data needs to be perfect and the automation’s algorithm needs to flawlessly cope with all expected and unexpected data sets.

Our OSS have always had a dependence on data quality so we’ve responded with sophisticated ways of reconciling and maintaining data. But the human logic buffer afforded a “less than perfect” starting point, as long as we sought to get ever-closer to the “perfection” asymptote.

Does wave 2 require us to solve the problem from a fundamentally different starting point? We have to assume perfection akin to a checksum of correctness.

Perfection isn’t something I’m very qualified at, so I’m open to hearing your ideas. 😉

 

Riffing with your OSS

Data collection and data science is becoming big business. Not just in telco – our OSS have always been one of the biggest data gatherers around – but across all sectors that are increasingly digitising (should I just say, “all sectors” because they’re all digitising?).

Why do you think we’re so keen to collect so much data?

I’m assuming that the first wave had mainly been because we’d almost all prefer to make data-driven decisions (ie decisions based on “proof”) rather than “gut-feel” decisions.

We’re increasingly seeing a second wave come through – to use data not just to identify trends and guide our decisions, but to drive automated actions.

I wonder whether this has the potential to buffer us from making key insights / observations about the business, especially senior leaders who don’t have the time to “science” their data? Have teams already cleansed, manipulated, aggregated and presented data, thus stripping out all the nuances before senior leaders ever even see your data?

I regretfully don’t get to “play” with data as much as I used to. I say regretfully because looking at raw data sets often gives you the opportunity to identify trends, outliers, anomalies and patterns that might otherwise remain hidden. Raw data also gives you the opportunity to riff off it – to observe and then ask different questions of the data.

How about you? Do you still get the opportunity to observe and hypothesise using raw OSS/BSS data? Or do you make your decisions using data that’s already been sanitised (eg executive dashboards / reports)?

 

Crossing the OSS chasm

Geoff Moore’s seminal book, “Crossing the Chasm,” described the psychological chasm between early buyers and the mainstream market.

Crossing the Chasm

Seth Godin cites Moore’s work, “Moore’s Crossing the Chasm helped marketers see that while innovation was the tool to reach the small group of early adopters and opinion leaders, it was insufficient to reach the masses. Because the masses don’t want something that’s new, they want something that works…

The lesson is simple:

– Early adopters are thrilled by the new. They seek innovation.

– Everyone else is wary of failure. They seek trust.”
 

I’d reason that almost all significant OSS buyer decisions fall into the “mainstream market” section in the diagram above.  Why? Well, an organisation might have the 15% of innovators / early-adopters conceptualising a new OSS project. However, sign-off of that project usually depends on a team of approvers / sponsors. Statistics suggest that 85% of the team is likely to exist in a mindset beyond the chasm and outweigh the 15%. 

The mainstream mindset is seeking something that works and something they can trust.

But OSS / digital transformation projects are hard to trust. They’re all complex and unique. They often fail to deliver on their promises. They’re rarely reliable or repeatable. They almost all require a leap of faith (and/or a burning platform) for the buyer’s team to proceed.

OSS sellers seek to differentiate from the 400+ other vendors (of course). How do they do this? Interestingly, by pitching their innovations and uniqueness mostly.

Do you see the gap here? The seller is pitching the left side of the chasm and the buyer cohort is on the right.

I wonder whether our infuriatingly lengthy sales cycles (often 12-18 months) could be reduced if only we could engineer our products and projects to be more mainstream, repeatable, reliable and trustworthy, whilst being less risky.

This is such a dilemma though. We desperately need to innovate, to take the industry beyond the chasm. Should we innovate by doing new stuff? Or should we do the old, important stuff in new and vastly improved ways? A bit of both??

Do we improve our products and transformations so that they can be used / performed by novices rather than designed for use by all the massive intellects that our industry seems to currently consist of?

 

 

 

 

A billion dollar bid

A few years ago I was lucky enough to be invited to lead a bid. I say lucky because the partner organisations are two of the most iconic firms in the tech industry. The bid was for bleeding-edge work, potentially worth well over a billion dollars. I was a little surprised to be honest. I mean, two tech titans, with many very, very clever people, much cleverer than me. Why would they need to look outside and engage me?

As it turned out, the answer became clear within the first few meetings. And whilst the project had little to do with OSS, it certainly had (has) parallels in the world of OSS.

Both of the organisations were highly siloed. Each product / capability silo had immense talent and immense depth to it. Our combined team had many PhDs who could discuss their own silo for hours, but could only point me in the general direction of what plugged into their products. 

Clearly, I was engaged to figure out the required end-to-end solution for the customer and then how to bolt the two sets of silos into that solution framework.

The same is true when looking for OSS solution gaps, in my experience at least. If you look into a domain or a product, the functionality / capability is usually quite well defined, understood and supported. For example, alarm / event managers are invariably very good at managing alarm / event lists.

If you’re going to find gaps, they’re more likely to be found in the end-to-end solution – in the handoffs, responsibility demarcation points, interfaces and processes that cross between silos. That’s why external consultancies can prove valuable for large organisations. They generally look into the cross-domain solution performance.

As you’d already know, the end-to-end solution is a combination of people, process and technology. Even so, as the “manager of managers,” I’m not sure our OSS tech is solving this problem as well as it could. Is there even a “glue” product that’s missing from our OSS/BSS stack?

Sure, we have some tools that fit this purpose – workflow engines, messaging buses, orchestration engines, data lakes, etc. Yet I still feel there’s an opportunity to do it far better. And the opportunity probably extends far beyond just OSS and into the broader IT industry.

What have you done to help solve this problem on your OSS suites?

PS. If you’re wondering what happened to the bid. Well, the team was excited to have made the shortlist of 3, but then the behemoths decided to withdraw from the race. Turns out that winning the bid could’ve jeopardised the even bigger supply contracts they already had with the client. Boggles the mind to think there were bigger contracts already in play!!

 

Inventory Management re-states its case

In a post last week we posed the question on whether Inventory Management still retains relevance. There are certainly uses cases where it remains unquestionably needed. But perhaps others that are no longer required, a relic of old-school processes and data flows.
 
If you have an extensive OSP (Outside Plant) network, you have almost no option but to store all this passive infrastructure in an Inventory Management solution. You don’t have the option of having an EMS (Element Management System) console / API to tell you the current design/location/status of the network. 
 
In the modern world of ubiquitous connection and overlay / virtual networks, Inventory Management might be less essential than it once was. For service qualification, provisioning and perhaps even capacity planning, everything you need to know is available on demand from the EMS/s. The network is a more correct version of the network inventory than external repository (ie Inventory Management) can hope to be, even if you have great success with synchronisation.
 
But I have a couple of other new-age use-cases to share with you where Inventory Management still retains relevance.
 
One is for connectivity (okay so this isn’t exactly a new-age use-case, but the scenario I’m about to describe is). If we have a modern overlay / virtual network, anything that stays within a domain is likely to be better served by its EMS equivalent. Especially since connectivity is no longer as simple as physical connections or nearest neighbours with advanced routing protocols. But anything that goes cross-domain and/or off-net needs a mechanism to correlate, coordinate and connect. That’s the role the Inventory Manager is able to do (conceptually).
 
The other is for digital twinning. OSS (including Inventory Management) was the “original twin.” It was an offline mimic of the production network. But I cite Inventory Management as having a new-age requirement for the digital twin. I increasingly foresee the need for predictive scenarios to be modelled outside the production network (ie in the twin!). We want to try failure / degradation scenarios. We want to optimise our allocation of capital. We want to simulate and optimise customer experience under different network states and loads. We’re beginning to see the compute power that’s able to drive these scenarios (and more) at scale.
 
Is it possible to handle these without an Inventory Manager (or equivalent)?

When OSS experts are wrong

When experts are wrong, it’s often because they’re experts on an earlier version of the world.”
Paul Graham.
 
OSS experts are often wrong. Not only because of the “earlier version of the world” paradigm mentioned above, but also the “parallel worlds” paradigm that’s not explicitly mentioned. That is, they may be experts on one organisation’s OSS (possibly from spending years working on it), but have relatively little transferable expertise on other OSS.
 
It would be nice if the OSS world view never changed and we could just get more and more expert at it, approaching an asymptote of expertise. Alas, it’s never going to be like that. Instead, we experience a world that’s changing across some of our most fundamental building blocks.
 
We are the sum total of our experiences.”
B.J. Neblett.
 
My earliest forays into OSS had a heavy focus on inventory. The tie-in between services, logical and physical inventory (and all use-cases around it) was probably core to me becoming passionate about OSS. I might even go as far as saying I’m “an Inventory guy.”
 
Those early forays occurred when there was a scarcity mindset in network resources. You provisioned what you needed and only expanded capacity within tight CAPEX envelopes. Managing inventory and optimising revenue using these scarce resources was important. We did that with the help of Inventory Management (IM) tools. Even end-users had a mindset of resource scarcity. 
 
But the world has changed. We now operate with a cloud-inspired abundance mindset. We over-provision physical resources so that we can just spin up logical / virtual resources whenever we wish. We have meshed, packet-switched networks rather than nailed up circuits. Generally speaking, cost per resource has fallen dramatically so we now buy a much higher port density, compute capacity, dollar per bit, etc. Customers of the cloud generation assume abundance of capacity that is even available in small consumption-based increments. In many parts of the world we can also assume ubiquitous connectivity.
 
So, as “an inventory guy,” I have to question whether the scarcity to abundance transformation might even fundamentally change my world-view on inventory management. Do I even need an inventory management solution or should I just ask the network for resources when I want to turn on new customers and assume the capacity team has ensured there’s surplus to call upon?
 
Is the enormous expense we allocate to building and reconciling a digital twin of the network (ie the data gathered and used by Inventory Management) justified? Could we circumvent many of the fallouts (and a multitude of other problems) that occur because the inventory data doesn’t accurately reflect the real network?
 
For example, in the old days I always loved how much easier it was to provision a customer’s mobile / cellular or IN (Intelligent Network) service than a fixed-line service. It was easier because fixed-line service needed a whole lot more inventory allocation and reservation logic and process. Mobile / IN services didn’t rely on inventory, only an availability of capacity (mostly). Perhaps the day has almost come where all services are that easy to provision?
 
Yes, we continue to need asset management and capacity planning. Yes, we still need inventory management for physical plant that has no programmatic interface (eg cables, patch-panels, joints, etc). Yes, we still need to carefully control the capacity build-out to CAPEX to revenue balance (even more so now in a lower-profitability operator environment). But do many of the other traditional Inventory Management and resource provisioning use cases go away in a world of abundance?
 

 

I’d love to hear your opinions, especially from all you other “inventory guys” (and gals)!! Are your world-views, expertise and experiences changing along these lines too or does the world remain unchanged from your viewing point?
 
Hat tip to Garry for the seed of this post!

Google’s Circular Economy in OSS

OSS wear many hats and help many different functions within an organisation. One function that OSS assists might be surprising to some people – the CFO / Accounting function.

The traditional service provider business model tends to be CAPEX-heavy, with significant investment required on physical infrastructure. Since assets need to be depreciated and life-cycle managed, Accountants have an interest in the infrastructure that our OSS manage via Inventory Management (IM) tools.

I’ve been lucky enough to work with many network operators and see vastly different asset management approaches used by CFOs. These strategies have ranged from fastidious replacement of equipment as soon as depreciation cycles have expired through to building networks using refurbished equipment that has already passed manufacturer End-of-Life dates. These strategies fundamentally effect the business models of these operators.

Given that telecommunications operator revenues are trending lower globally, I feel it’s incumbent on us to use our OSS to deliver positive outcomes to global business models. 

With this in mind, I found this article entitled, “Circular Economy at Work in Google Data Centers,” to be quite interesting. It cites, “Google’s circular approach to optimizing end of life of servers based on Total Cost of Ownership (TCO) principles have resulted in hundreds of millions per year in cost avoidance.”

Google Asset Lifecycle

Asset lifecycle management is not your typical focus area for OSS experts, but an area where we can help add significant value for our customers!

Some operators use dedicated asset management tools such as SAP. Others use OSS IM tools. Others reconcile between both. There’s no single right answer.

For a deeper dive into ideas where our OSS can help in asset lifecycle (which Google describes as its Circular Economy and seems to manage using its ReSOLVE tool), I really recommend reviewing the article link above.

If you need to develop such a tool using machine learning models, reach out to us and we’ll point you towards some tools equivalent to ReSOLVE to augment your OSS.

Another OSS “forehead-slap” moment!

I don’t know about you, but I find this industry of ours has a remarkable ability to keep us humble. Barely a day goes by when I don’t have to slap my forehead and say, “uhhh…. of course!” (or perhaps, “D’oh!!”)

I had one such instance yesterday. I couldn’t figure out why a client’s telemetry / performance-management suite needed an inventory ingestion interface. Can you think of a reason (you probably can)???

My mind had followed the line of thinking that it was for reconciling with traditional inventory systems or perhaps some sort of topology reckoning. It’s far more rudimentary than that. 

Have you figured out what it might be used for yet?

Enrichment!

For example, if device names (hostnames) attached to the metrics aren’t human-readable, simple, just enrich the data with its human-readable alternate name. If you don’t know what device type is generating sub-sets of metrics, no problems, just enrich the data.

I’d heard of enrichment of alarms/event of course, but hadn’t followed that line of thinking for performance management before. Does your performance management stack allow you to enrich its data sets?

Seems obvious in hindsight! Smacked down again!!

I’d love to hear any anecdotes you have where OSS gave you a “forehead slap” moment.

Over 30 Autonomous Networking User Stories

The following is a set of user stories I’ve provided to TM Forum to help with their current Autonomous Networking initiative.

They’re just an initial discussion point for others to riff off. We’d love to get your comments, additions and recommended refinements too.

As a Head of Network Operations, I want to Automatically maintain the health of my network (within expected tolerances if necessary) So that Customer service quality is kept to an optimal level with little or no human intervention
As a Head of Network Operations, I want to Ensure the overall solution is designed with machine-led automations as a guiding principle So that Human intervention can not be easily engineered into the systems/processes
As a Head of Network Operations, I want to Automatically identify any failures of resources or services within the entire network So that All relevant data can be collected, logged, codified and earmarked for effective remedial action without human interaction
As a Head of Network Operations, I want to Automatically identify any degradation of resource or service performance within the network So that All relevant data can be collected, logged, codified and earmarked for effective remedial action without human interaction
As a Head of Network Operations, I want to Map each codified data set (for failure or degradation cases) to a remedial action plan So that Remedial activities can be initiated without human interaction
As a Head of Network Operations, I want to Identify which remedial activities can be initiated via a programmatic interface and which activities require manual involvement such as a truck roll So that Even manual activities can be automatically initiated
As a Head of Network Operations, I want to Ensure that automations are able to resolve all known failure / degradation scenarios So that Activities can be initiated for any failure or degradation and be automatically resolved through to closure (with little or no human intervention)
As a Head of Network Operations, I want to Ensure there is sufficient network resilience So that Any failure or degradation can be automatically bypassed (temporarily or permanently)
As a Head of Network Operations, I want to Ensure there is sufficient resilience within all support systems So that Any failure or degradation can be automatically bypassed (temporarily or permanently) to ensure customer service is maintained
As a Head of Network Operations, I want to Ensure that operator initiated changes (eg planned maintenance, software upgrades, etc) automatically generate change tracking, documentation and logging So that The change can be monitored (by systems and humans where necessary) to ensure there is minimal or no impact to customer services, but also to ensure resolution data is consistently recorded
As a Head of Network Operations, I want to Ensure that customer initiated changes (eg by raising an incident) automatically generate change tracking, documentation and logging So that The change can be monitored (by systems and humans where necessary) to ensure the incident is closed expediently, but also to ensure resolution data is consistently recorded
As a Head of Network Operations, I want to Initiate planned outages with or without triggering automated remedial activities So that The change agents can decide to use automations or not and ensure automations don’t adversely effect the activities that are scheduled for the planned outage window
As a Head of Network Operations, I want to Ensure that if an unplanned outage does occur, impacted customers are automatically notified (on first instance and via a communications sequence if necessary throughout the outage window) So that Customer experience can be managed as best possible
As a Head of Network Operations, I want to Ensure that if an unplanned outage does occur without a remedial action being triggered, a post-mortem analysis is initiated So that Automations can be revised to cope with this previously unhandled outage scenario
As a Head of Network Operations, I want to Ensure that even previously un-seen new fail scenarios can be handled by remedial automations So that Customer service quality is kept to an optimal level with little or no human intervention
As a Head of Network Operations, I want to Automatically monitor the effects of remedial actions So that Remedial automations don’t trigger race conditions that result in further degradation and/or downstream impacts
As a Head of Network Operations, I want to Be able to manually override any automations by following a documented sequence of events So that If a race condition is inadvertently triggered by an automation, it can be negated quickly and effectively before causing further degradation
As a Head of Network Operations, I want to Intentionally trigger network/service outages and/or degradations, including cascaded scenarios on an scheduled and/or randomised basis So that The resilience of the network and systems can be thoroughly tested (and improved if necessary)
As a Head of Network Operations, I want to Intentionally trigger network/service outages and/or degradations, including cascaded scenarios on an ad-hoc basis So that The resilience of the network and systems can be thoroughly tested (and improved if necessary)
As a Head of Network Operations, I want to Perform scheduled compliance checks on the network So that Expected configurations and policies are in place across the network
As a Head of Network Operations, I want to Automatically generate scheduled reports relating to the effectiveness of the network, services and automations So that The overall solution health (including automations) can be monitored
As a Head of Network Operations, I want to Automatically generate dashboards (in near-real-time) relating to the effectiveness of the network, services and automations So that The overall solution health (including automations) can be monitored
As a Head of Network Operations, I want to Ensure that automations are able to extend across all domains within the solution So that Remedial actions aren’t constrained by system hand-offs
As a Head of Network Operations, I want to Ensure configuration backups are performed automatically on all relevant systems (eg EMS, OSS, etc) So that A recent good solution configuration can be stored as protection in case automations fail and corrupt configurations within the system
As a Head of Network Operations, I want to Ensure configuration restores are performed and tested automatically on all relevant systems (eg EMS, OSS, etc) So that A recent good solution configuration can be reverted to in case automations fail and corrupt configurations within the system
As a Head of Network Operations, I want to Ensure automations are able to manage the entire service lifecycle (add, modify/upgrade, suspend, restore, delete) So that Customer services can evolve to meet client expectations with little or no human intervention
As a Head of Network Operations, I want to Have a design and architecture that uses intent-based and/or policy-based actions So that The complexity of automations is minimised (eg automations don’t need to consider custom rules for different device makes/models, etc)
As a Head of Network Operations, I want to Ensure as many components of the solution (eg EMS, OSS, customer portals, etc) have programmatic interfaces (even if manual activities are required in back-end processes) So that Automations can initiate remedial actions in near real time
As a Head of Network Operations, I want to Ensure all components and data flows within the solution are securely hardened (eg encryption of data in motion and at rest) So that The power of the autonomous platform can not be leveraged for nefarious purposes
As a Head of Network Operations, I want to Ensure that all required metrics can be automatically sourced from the network / systems in as near real time as feasible / useful So that Automations have the full set of data they need to initiate remedial actions and it is as up-to-date as possible for precise decision-making
As a Head of Network Operations, I want to Use the power of learning machines So that The sophistication and speed of remedial response is faster, more accurate and more reliable than if manual interaction were used
As a Head of Network Operations, I want to Record actual event patterns and replay scenarios offline So that Event clusters and response patterns can be thoroughly tested as part of the certification process prior to being released into production environments
As a Head of Network Operations, I want to Capture metrics that can be cross-referenced against event patterns and remedial actions So that Regressions and/or refinements can improve existing automations (ie continuous retraining of the model)
As a Head of Network Operations, I want to Be able to seed a knowledge base with relevant event/action data, whether the pattern source is from Production, an offline environment, a digital twin environment or other production-like environments So that The database is able to identify real scenarios, even if  scenarios are intentially initiated, but could potentially cause network degradation to a production environment
As a Head of Network Operations, I want to Ensure that programmatic interfaces also allow for revert / rollback capabilities So that Remedial actions that aren’t beneficial can be rolled back to the previous state; OR other remedial actions are performed, allowing the automation to revert to original configuration / state
As a Head of Network Operations, I want to Be able to initiate circuit breakers to override any automations So that If a race condition is inadvertently triggered by an automation, it can be negated quickly and effectively before causing further degradation
As a Head of Network Operations, I want to Manually or automatically generate response-plans (ie documented sequences of activities) for any remedial actions fed back into the system So that Internal (eg quality control) or external (eg regulatory) bodies can review “best-practice” remedial activities at any point in time
As a Head of Network Operations, I want to Intentionally trigger catastrophic network failures (in non-prod environments) So that We can trial many remedial actions until we find an optimal solution to seed the knowledge base with

New OSS product – Restoration Manager

At Passionate About OSS, we’re lucky enough to count the utilities market as an important part of our client base. This probably puts us in a very small percentage of OSS exponents that work across OSS for telco and utilities.

Utilities have a number of interesting and unique nuances compared with other OSS markets. Starting at the top, the network is core business for a telco, whereas the comms network only supports the core business of other utilities.

Despite having vastly different functions, there are still many similarities between operational support tools at telcos and other utilities. Similarities include:

  • Network inventory is made up of nodes and arcs (nodes are routers vs pumps vs sub-stations; arcs are comms cables vs power cables vs pipes)
  • All are CAPEX-heavy industries, so asset management is important from a financial (ie depreciation and “useful life remaining” modelling) as well as physical perspective
  • Assets need to be systematically life-cycle managed (ie commissioned, repaired, replaced, modified, maintained, decommissioned, etc)
  • A field workforce needs to be coordinated to keep the network in a healthy operational state
  • The network either provides (or supports) essential services so rapid remediation of failed / degraded services is expected by customers

Anyway, enough of the preamble. I find it interesting to observe the tools used by the different utilities to prompt alternate ways of thinking about our OSS.

Last week I observed a tool called a Restoration Manager. It is used widely in the power industry to handle fault restoration on comms networks. It has little direct equivalent in comms network management.

Some ticket managers allow task templates to be developed and defined. Similarly, the Restoration Manager also retains restoration plans, which are sequences of responses, but it also goes further by:

  • Coordinating implementation of restoration plans in real-time 
  • Looking ahead of each step in the  restoration plan to determine whether it is still useful or potentially harmful
  • Providing an indicator of whether the current network state is suited to being handled by each of the stored restoration plan/s
  • Coordinating restoration of planned or unplanned outages and even degradation events
  • Facilitates use AI or past restorations to create an optimal restoration plan
  • Documenting a proposed plan of action/s that can be audited by internal groups (eg engineering, QC, etc) or external groups (eg regulators)

The restoration plans could be made to tie in with DRP (Disaster Recovery Plans), CRB (Change Request Boards), outage window sequencing/management and even security incident response.

What are your thoughts? Would a Restoration Manager be useful for our OSS stack (ie solves an existing, unsolved problem) or do we already have suitable ways of solving / avoiding the outage problem?

H-OSS-ton, we have a problem

You’ve all probably seen this scene from the Tom Hanks movie, Apollo 13 right? But you’re probably wondering what it has to do with OSS?

Well, this scene came to mind when I was preparing a list of user stories required to facilitate Autonomous Networking.

More specifically, to the use-case where we want the Autonomous Network to quickly recover (as best it can) from unplanned catastrophic network failures.

Of course we don’t want catastrophic network failures in production environments, but if one does occur, we’d prefer that our learning machines already have some idea on how to respond to any unlikely situation. We don’t want them to be learning response mechanisms after a production event.

But similarly, we don’t want to trigger massive outages on production just to build up a knowledge base of possible cause-effect groupings. That would be ridiculous.

That’s where the Apollo 13 analogy comes into play:

  • The engineers on the ground (ie the non-prod environment) were tasked with finding a solution to the problem (as they said, “fitting a square peg in a round hole”)
  • The parts the Engineers were given matched the parts available in the spacecraft (ie non-prod and prod weren’t an exact match, but enough of a replica to be useful)
  • The Engineers were able to trial many combinations using the available parts until they found a workable resolution to the problem (even if it relied heavily on duct tape!)
  • Once the workable solution was found, it was codified (as a procedure manual) and transferred to the spacecraft (ie migrating seed data from non-prod to prod)

If I were responsible for building an Autonomous Network, I’d want to dream up as many failure scenarios as I could, initiate them in non-prod and then duct-tape* solutions together for them all… and then attempt to pre-seed those learnings into production.

* By “duct-tape” I mean letting the learning machine attempt to find optimal solutions by trialing different combinations of automated / programmatic and manual interventions.

I was wrong. Forget about investing in your OSS UI

I must’ve written dozens of posts about us needing to collectively invest a lot more effort into UI / UX. I’ve written quite a few over the last few months especially. This one in particular springs to mind.

As an industry, we typically don’t do user experience journeys (UX) or user interfaces (UI) very well at all yet. I know thousands of OSS experts, but only 2 who specialise in UI / UX! That ratio is far too small….

… but then something dawned on me when writing the Autonomous Networking post earlier this week – All effort invested into UI (and most effort on UX) is pointless if we succeed in building autonomous networks. You get the implication don’t you? Truly autonomous networks are machine-driven, so you don’t need users, UI or UX.

Oh, I should make one last point though. If you don’t expect to get all of your network operations activities to midday on the Autonomous Networks Clock, within the next couple of years, then you probably should still invest in your UI / UX!!

We use time-stamping in OSS, but what about geo-stamping?

A slightly left-field thought dawned on me the other day and I’d like to hear your thoughts on it.

We all know that almost all telemetry coming out of our networks is time-stamped. Events, syslogs, metrics, etc. That makes perfect sense because we look for time-based ripple-out effects when trying to diagnose issues.

But therefore does it also make sense to geo-stamp telemetry data too? Just as time-based ripple-out is common, so too are geographic / topological (eg nearest neighbour and/or power source) ripple-out effects.

If you want to present telemetry data as a geo/topo overlay, you currently have to enrich the telemetry data set first. Typically that means identifying the device name that’s generating the data and then doing a query on huge inventory databases to find the location and connectivity that corresponds to that device.

It’s usually not a complex query, but consider how much processing power must go into enriching at the enormous scale of telemetry records.

For stationary devices (eg core routers), it might seem a bit absurd adding a fixed geo-code (which has to be manually entered into the device once) to every telemetry record, but it seems computationally far more efficient than data lookups (please correct me if I’m wrong here!). For devices that move around (eg routers on planes), hopefully they already have GPS sensors to provide geo-stamp data.

What do you think? Am I stating a problem that has already been solved and/or is not worth solving? Or does it have merit?

As a network owner….

….I want to make my network so observable, reliable, predictable and repeatable that I don’t need anyone to operate it.

That’s clearly a highly ambitious goal. Probably even unachievable if we say it doesn’t need anyone to run it. But I wonder whether this has to be the starting point we take on behalf of our network operator customers?

If we look at most networks, OSS, BSS, NOC, SOC, etc (I’ll call this whole stack “the black box” in this article), they’ve been designed from the ground up to be human-driven. We’re now looking at ways to automate as many steps of operations as possible.

If we were to instead design the black-box to be machine-driven, how different would it look?

In fact, before we do that, perhaps we have to take two unique perspectives on this question:

  1. Retro-fitting existing black-boxes to increase their autonomy
  2. Designing brand new autonomous black-boxes

I suspect our approaches / architectures will be vastly different.

The first will require a incredibly complex measure, command and control engine to sit over top of the existing black box. It will probably also need to reach into many of the components that make up the black box and exert control over them. This approach has many similarities with what we already do in the OSS world. The only exception would be that we’d need to be a lot more “closed-loop” in our thinking. I should also re-iterate that this is incredibly complex because it inherits an existing “decision tree” of enormous complexity and adds further convolution.

The second approach holds a great deal more promise. However, it will require a vastly different approach on many levels:

  1. We have to take a chainsaw to the decision tree inside the black box. For example:
    • We start by removing as much variability from the network as possible. Think of this like other utilities such as water or power. Our electricity service only has one feed-type for almost all residential and business customers. Yet it still allows us great flexibility in what we plug into it. What if a network operator were to simply offer a “broadband dial-tone” service and end users decide what they overlay on that bit-stream
    • This reduces the “protocol stack” in the network (think of this in terms of the long list of features / tick-boxes on any router’s brochure)
    • As well as reducing network complexity, it drastically reduces the variables an end-user needs to decide from. The operator no longer needs 50 grandfathered, legacy products 
    • This also reduces the decision tree in BSS-related functionality like billing, rating, charging, clearing-house
    • We achieve a (globally?) standardised network services catalog that’s completely independent of vendor offerings
    • We achieve a more standardised set of telemetry data coming from the network
    • In turn, this drives a more standardised and minimal set of service-impact and root-cause analyses
  2. We design data input/output methods and interfaces (to the black box and to any of its constituent components) to have closed-loop immediacy in mind. At the moment we tend to have interfaces that allow us to interrogate the network and push changes into the network separately rather than tasking the network to keep itself within expected operational thresholds
  3. We allow networks to self-regulate and self-heal, not just within a node, but between neighbours without necessarily having to revert to centralised control mechanisms like OSS
  4. All components within the black-box, down to device level, are programmable. [As an aside, we need to consider how to make the physical network more programmable or reconcilable, considering that cables, (most) patch panels, joints, etc don’t have APIs. That’s why the physical network tends to give us the biggest data quality challenges, which ripples out into our ability to automate networks]
  5. End-to-end data flows (ie controls) are to be near-real-time, not constrained by processing lags (eg 15 minute poll cycles, hourly log processing cycles, etc) 
  6. Data minimalism engineering. It’s currently not uncommon for network devices to produce dozens, if not hundreds, of different metrics. Most are never used by operators manually, nor are likely to be used by learning machines. This increases data processing, distribution and storage overheads. If we only produce what is useful, then it should improve data flow times (point 5 above). Therefore learning machines should be able to control which data sets they need from network devices and at what cadence. The learning engine can start off collecting all metrics, then progressively turning them off as they deem metrics unnecessary. This could also extend to controlling log-levels (ie how much granularity of data is generated for a particular log, event, performance counter)
  7. Perhaps we even offer AI-as-a-service, whereby any of the components within the black-box can call upon a centralised AI service (and the common data lake that underpins it) to assist with localised self-healing, self-regulation, etc. This facilitates closed-loop decisions throughout the stack rather than just an over-arching command and control mechanism

I’m barely exposing the tip of the iceberg here. I’d love to get your thoughts on what else it will take to bring fully autonomous network to reality.

For those starting out in OSS product, here’s a tip

For those starting out in product, here’s a tip: Design, Defaults*, Documentation, Details and Delivery really matter in software.”
Jeetu Patel here.

* Note that you can interpret “Defaults” to be Out-Of-The-Box functionality offered by the product.

Let’s break those 5 D-words down and describe why they really matter to the OSS industry shall we?

  • Design – The power of OSS product development tends to lie with engineering, ie the developers. I have huge admiration for the very clever and very talented engineers who create amazing products for us to use, buuutttttt……. I just have one reservation – is there a single OSS company that is design-driven? A single one that’s making intuitive, effective, beautiful experiences for their users? The obvious answer is of course engineering teams hold sway over design teams in OSS – how many OSS vendors even have a dedicated design department??? See this article for more.
  • Defaults – Almost every OSS I know of has an enormous amount of “out-of-the-box” functionality baked in. You could even say that most have too much functionality. There’s functionality that might be really important for one customer but never even used by any of the vendor’s other customers. It just represents bloat for all the other customers, and potentially a distraction for their operators. I’m still bemused to see vendors trying to differentiate by adding obscure new default features rather than optimising for “must-have” functions. See this article for more. However, I must add that I’m starting to see a shift in some OSS. They’re moving away from having baked-in functionality and are moving to more data-repository-driven architectures. Interesting!!
  • Documentation – This is a really interesting factor! Some vendors make almost no documentation available until a prospect becomes a paying customer. Other vendors make their documentation available for the general public online and dedicate significant effort to maintaining their information library. The low-doc approach espoused by Agile could be argued to be reducing document quality. However, it also reduces the chance of producing documentation that nobody will read ever! Personally, I believe vendors like Cisco have earnt a huge competitive advantage (in the networking space moreso than OSS) because of their training / certification (ie CCNA, etc) and self-learning (ie online documentation). See this article for more. As such, I’d tend to err on over-documenting for customer-facing collateral. And perhaps under-documenting for internal-facing collateral unless it’s likely to be used regularly and by many.
  • Details – This is another item where there are two ends to the spectrum. That might surprise some people who would claim that attention to detail is paramount. Well, yes…. in many cases, but certainly not all on OSS projects. Let me share a story on attention to detail on a past OSS project. And another story on seeking perfection. Sometimes we just need to find the right balance, and knowing when to prioritise resilience and when to favour precision becomes an art.
  • Delivery – I have two perspectives on this D-word. Firstly, the Steve Jobs inspired quote of “Real artists ship!” In other words, to laud the skill of shipping a product that provides value to the customer rather than holding off on a not-yet-perfected solution. But the second case is probably more important. OSS projects tend to be massive and complex transformation efforts. Our OSS are rarely self-installed like office software, so they require big delivery teams. Some products are easy to deliver/deploy. Others are a *&$%#! If you’re a product developer, please get out in the trenches with your delivery teams and find ways to make their job easier and/or more repeatable.

Exactly what is an OSS’s “intuition age”

I’m currently reading a book entitled, “Jony Ive. The genius behind Apple’s greatest products.”

I’d like to share a paragraph with you from it (and probably expect a few more in coming days):

“…Apple’s internal culture heavily favored the engineers within the product groups. The design process was engineering driven. In the early days of Frog Design, the engineers had bent over backward to help implement the design team’s ambitions, but now the power had shifted. The different engineering groups gave their products in development to Brunner’s group, who were expected to merely “skin” them.

Brunner wanted to shift the power from engineering to design. He started thinking strategically… The idea was to get ahead of the engineering groups and start to make Apple more of a design-driven company rather than a marketing or engineering one.”

That’s an unbelievably insightful conclusion Robert Brunner made. If he wanted to turn Apple into a design-driven company, then he’d have to prepare design concepts that looked further into the future than where the engineers were up to. Products like the iPod and iPad are testimony that Brunner’s strategy worked.

We face the same situation in OSS today. The power of product development tends to lie with engineering, ie the developers. I have huge admiration for the very clever and very talented engineers who create amazing products for us to use, buuutttttt…….

I just have one reservation – is there a single OSS company that is design-driven? A single one that’s making intuitive, effective, beautiful experiences for their users? Of course engineering holds power over design in OSS – how many OSS vendors even have a dedicated design department???

Let me give a comparison (albeit a slightly unfair one). Both of my children were reasonably adept at navigating their way around our iPad (for multiple use cases) by the age of three. What would the equivalent “intuition age” be for navigating our OSS?

If you’re a product manager, have you ever tried it? Have you ever considered benchmarking it (or an equivalent usability metric) and seeing what you could do to improve it for your OSS products?

Interesting metrics from The Blue Book launch

When I first started the Passionate About OSS site / blog many years ago, I was lucky to get a handful of views per day. It’s grown by many multiples since then, fortunately.

The launch of The Blue Book OSS/BSS Vendor Directory generated some exciting metrics yesterday. The directory alone came within 5 pageviews of the highest count we’ve ever seen on PassionateAboutOSS.com (and PAOSS is up to nearly 2,500 posts now). That total appeared in only a 14-hour window because we didn’t go live with The Directory or metric collection until ~10am local time! The graphs are indicating that we should easily exceed PAOSS’s best ever count today.

If you were one of the many viewers who popped in from all around the world to look at The Directory, thank you! If you have any suggested improvements, we’d love to hear from you as we’re sure to be making many further tweaks in coming days/months.

But the most interesting fact about the launch yesterday was that a job posting appeared on UpWork to scrape all the data we’ve presented. On our very first day!! In fact a gentleman in the US reviewed bids and awarded the UpWork job all within about 14 hours of go-live.

That’s positive news because it means that at least one person must’ve thought the data was useful. 🙂