I will never understand…

I will never understand why Advertising is an investment and customer service is a cost.
Let’s spend millions trying to reach people, but if they try to reach us, make our contact details impossible to find, incentivise call center workers to hang up as fast as possible or ideally outsource it to a bot. It’s absolute lunacy and it absolutely matters
.”
Tom Goodwin
here.

Couldn’t agree more Tom. In fact, we’ve spoken about this exact irony here on PAOSS a few times before (eg herehere and here).

Telcos call it CVR – Call Volume Reduction (ie reduction in the number of customers’ calls that are responded to by a real person who represents the telco). But what CVR really translates to is, “we’re happy for you to reach us on our terms (ie if you want to buy something from us), but not on your terms (ie you have a problem that needs to be resolved).” Unfortunately, customer service is the exact opposite – it has to be on the customer’s terms, not yours.

Even more unfortunately, many of the problems that need to be resolved are being caused in our OSS / BSS (not always “by” our OSS / BSS, but that’s another story). Worse still, the contact centre has no chance of determining where to start understanding the problem due to the complexity of fall-out management and the complicated data flows through our OSS / BSS.

Bill Gates said, “Your most unhappy customers are your greatest source of learning.”

Let me ask you a question – Do you have a direct line of learning from your unhappy customers to your backlog of OSS / BSS enhancements? Or even an indirect line of learning? Nope?? If so, you’re not alone.

Let me ask you another question – You’re an OSS expert. Do you have any idea what problems your customers are raising with your contact centre staff? Or perhaps that should be problems they’re not getting to raise with contact centre staff due to the “success” of CVR measures? Nope?? If so, you’re not alone here either.

Can you think of a really simple and obvious way to start fixing this?

Re-writing the Sales vs Networks cultural divide

Brand, marketing, pricing and sales were seen as sexy. Networks and IT were the geeks no one seemed to speak to or care about. … This isolation and excommunication of our technical team had created an environment of disillusion. If you wanted something done the answer was mostly ‘No – we have no budget and no time for that’. Our marketing team knew more about loyalty points … than about our own key product, the telecommunications network.”
Olaf Swantee
, from his book, “4G Mobile Revolution”

Great note here (picked up by James Crawshaw at Heavy Reading). It talks about the great divide that always seems to exist between Sales / Marketing and Network / Ops business units.

I’m really excited about the potential for next generation OSS / orchestration / NaaS (Network as a Service) architectures to narrow this divide though.

In this case:

  1. The Network is offered as a microservice (let’s abstractly call them Resource Facing Services [RFS]);
  2. Sales / Marketing construct customer offerings (let’s call them Customer Facing Services [CFS]) from those RFS; and
  3. There’s a catalog / orchestration layer that marries the CFS with the cohesive set of RFS

The third layer becomes a meet-in-the-middle solution where Sales / Marketing comes together with Network / Ops – and where they can discuss what customers want and what the network can provide.

The RFS are suitably abstracted that Sales / Marketing doesn’t need to understand the network and complexity that sits behind the veil. Perhaps it’s time for Networks / Ops to shine, where the RFS can be almost as sexy as CFS (am I falling too far into the networks / geeky side of the divide?  🙂  )

The CFS are infinitely composable from RFS (within the constraints of the RFS that are available), allowing Sales / Marketing teams to build whatever they want and the Network / Ops teams don’t have to be constantly reacting to new customer offerings.

I wonder if this revolution will give Olaf cause to re-write this section of his book in a few years, or whether we’ll still have the same cultural divide despite the exciting new tools.

Does the death of ATM bear comparison with telco-grade open-source OSS?

Hands up if you’re old enough to remember ATM here? And I don’t mean the type of ATM that sits on the side of a building dispensing cash – no I mean Asynchronous Transfer Mode.

For those who aren’t familiar with ATM, a little background. ATM was THE telco-grade packet-switching technology of choice for most carriers globally around the turn of the century. Who knows, there might still be some ATM switches/routers out there in the wild today.

ATM was a powerful beast, with enormous configurability and custom-designed with immense scale in mind. It was created by telco-grade standards bodies with the intent of carrying voice, video, data, whatever, over big data pipes.

With such pedigree, you may be wondering then, how it was beaten out by a technology that was designed to cheaply connect small groups of computers clustered within 100 metres of each other (and a theoretical maximum bandwidth of 10Mbps).

Why does the technology that scaled up to become carrier Ethernet exist in modern telco networks, whereas ATM is largely obsoleted? Others may beg to differ, and there are probably a multitude of factors, but I feel it boils down to operational simplicity. Customers wanted operational simplicity and operators didn’t want to have a degree in ATM just to be able to drive it. By being designed to be all things to all people (carriers), did that make ATM compromised from the start?

Now I’ll state up front that I love the initiative and collaboration being shown by many of the telcos in committing to open-source programs like ONAP. It’s a really exciting time for the industry. It’s a sign that the telcos are wresting control back from the vendors in terms of driving where the collective innovation goes.

Buuuuuuut…..

Just like with ATM, are the big open source programs just too big and too complicated? Do you need a 100% focus on ONAP to be able to make it work, or even to follow all the moving parts? Are these initiatives trying to be all things to all carriers instead of changing needs to more simplified use cases?

Sometimes the ‘right’ way to do it just doesn’t exist yet, but often it does exist but is very expensive. So, the question is whether the ‘cheap, bad’ solution gets better faster than the ‘expensive, good’ solution gets cheap. In the broader tech industry (as described in the ‘disruption’ concept), generally the cheap product gets good. The way that the PC grew and killed specialized professional hardware vendors like Sun and SGi is a good example. However, in mobile it has tended to be the other way around – the expensive good product gets cheaper faster than the cheap bad product can get good.”
Ben Evans
here.

Is there an Ethernet equivalent in the OSS world, something that’s “cheap, bad” but getting better (and getting customer buy-in) rapidly?

Blown away by one innovation. Now to extend on it

Our most recent two posts, from yesterday and Friday, have talked about one stunningly simple idea that helps to overcome one of OSS‘ biggest challenges – data quality. Those posts have stimulated quite a bit of dialogue and it seems there is some consensus about the cleverness of the idea.

I don’t know if the idea will change the OSS landscape (hopefully), or just continue to be a strong selling point for CROSS Network Intelligence, but it has prompted me to think a little longer about innovating around OSS‘ biggest challenges.

Our standard approach of just adding more coats of process around our problems, or building up layers of incremental improvements isn’t going to solve them any time soon (as indicated in our OSS Call for Innovation). So how?

Firstly, we have to be able to articulate the problems! If we know what they are, perhaps we can then take inspiration from the CROSS innovation to spur us into new ways of thinking?

Our biggest problem is complexity. That has infiltrated almost every aspect of our OSS. There are so many posts about identifying and resolving complexity here on PAOSS that we might skip over that one in this post.

I decided to go back to a very old post that used the Toyota 5-whys approach to identify the real cause of the problems we face in OSS [I probably should update that analysis because I have a whole bunch of additional ideas now, as I’m sure you do too… suggested improvements welcomed BTW].

What do you notice about the root-causes in that 5-whys analysis? Most of the biggest causes aren’t related to system design at all (although there are plenty of problems to fix in that space too!). CROSS has tackled the data quality root-cause, but almost all of the others are human-centric factors – change controls, availability of skilled resources, requirement / objective mis-matches, stakeholder management, etc. Yet, we always seem to see OSS as a technical problem.

How do you fix those people challenges? Ken Segal puts it this way, “When process is king, ideas will never be. It takes only common sense to recognize that the more layers you add to a process, the more watered down the final work will become.” Easier said than done, but a worthy objective!

Blown away by one innovation – a follow-up concept

Last Friday’s blog discussed how I’ve just been blown away by the most elegant OSS innovation I’ve seen in decades.

You can read more detail via the link, but the three major factors in this simple, elegant solution to data quality problems (probably OSS‘ biggest kryptonite) are:

  1. Being able to make connections that break standard object hierarchy rules; but
  2. Having the ability to mark that standard rules haven’t been followed; and
  3. Being able to uses the markers to prioritise the fixing of data at a more convenient time

It’s effectively point 2 that has me most excited. So novel, yet so obvious in hindsight. When doing data migrations in the past, I’ve used confidence flags to indicate what I can rely on and what needs further audit / remediation / cleansing. But the recent demo I saw of the CROSS product is the first time I’ve seen it built into the user interface of an OSS.

This one factor, if it spreads, has the ability to change OSS data quality in the same way that Likes (or equivalent) have changed social media by acting as markers of confidence / quality.

Think about this for a moment – what if everyone who interacts with an OSS GUI had the ability to rank their confidence in any element of data they’re touching, with a mechanism as simple as clicking a like/dislike button (or similar)?

Bad example here but let’s say field techs are given a design pack, and upon arriving at site, find that the design doesn’t match in-situ conditions (eg the fibre pairs they’re expecting to splice a customer lead-in cable to are already carrying live traffic, which they diagnose is due to data problems in an upstream distribution joint). Rather than jeopardising the customer activation window by having to spend hours/days fixing all the trickle-down effects of the distribution joint data, they just mark confidence levels in the vicinity and get the customer connected.

The aggregate of that confidence information is then used to show data quality heat maps and help remediation teams prioritise the areas that they need to work on next. It helps to identify data and process improvements using big circle and/or little circle remediation techniques.

Possibly the most important implication of the in-built ranking system is that everyone in the end-to-end flow, from order takers to designers through to coal-face operators, can better predict whether they need to cater for potential data problems.

Your thoughts?? In what scenarios do you think it could work best, or alternatively, not work?

I’ve just been blown away by the most elegant OSS innovation I’ve seen in decades

Looking back, I now consider myself extremely lucky to have worked with an amazing product on the first OSS project I worked on (all the way back in 2000). And I say amazing because the underlying data models and core product architecture are still better than any other I’ve worked with in the two decades since. The core is the most elegant, simple and powerful I’ve seen to date. Most importantly, the models were designed to cope with any technology, product or service variant that could be modelled as a hierarchy, whether physical or virtual / logical. I never found a technology that couldn’t be modelled into the core product and it required no special overlays to implement a new network model. Sadly, the company no longer exists and the product is languishing on the books of the company that bought out the assets but isn’t leveraging them.

Having been so spoilt on the first assignment, I’ve been slightly underwhelmed by the level of elegant innovation I’ve observed in OSS since. That’s possibly part of the reason for the OSS Call for Innovation published late last year. There have been many exciting innovations introduced since, but many modern tools are still more complex and complicated than they should be, for implementers and operators alike.

But during a product demo last week, I was blown away by an innovation that was so simple in concept, yet so powerful that it is probably the single most impressive innovation I’ve seen since that first OSS. Like any new elegant solution, it left me wondering why it hasn’t been thought of previously. You’re probably wondering what it is. Well first let me start by explaining the problem that it seeks to overcome.

Many inventory-based OSS rely on highly structured and hierarchical data. This is a double-edged sword. Significant inter-relationship of data increases the insight generation opportunities, but the downside is that it can be immensely challenging to get the data right (and to maintain a high-quality data state). Limited data inter-relationships make the project easier to implement, but tend to allow less rich data analyses. In particular, connectivity data (eg circuits, cables, bearers, VPNs, etc) can be a massive challenge because it requires the linking of separate silos of data, often with no linking key. In fact, the data quality problem was probably one of the most significant root-causes of the demise of my first OSS client.

Now getting back to the present. The product feature that blew me away was the first I’ve seen that allows significant inter-relationship of data (yet in a simple data model), but still copes with poor data quality. Let’s say your OSS has a hierarchical data model that comprises Location, Rack, Equipment, Card, Port (or similar) and you have to make a connection from one device’s port to another’s. In most cases, you have to build up the whole pyramid of data perfectly for each device before you can create a customer connection between them. Let’s also say that for one device you have a full pyramid of perfect data, but for the other end, you only know the location.

The simple feature is to connect a port to a location now, or any other point to point on the hierarchy (and clean up the far-end data later on if you wish). It also allows the intermediate hops on the route to be connected at any point in the hierarchy. That’s too simple right, yet most inventory tools don’t allow connections to be made between different levels of their hierarchies. For implementers, data migration / creation / cleansing gets a whole lot simpler with this approach. But what’s even more impressive is that the solution then assigns a data quality ranking to the data that’s just been created. The quality ranking is subsequently considered by tools such as circuit design / routing, impact analysis, etc. However, you’ll have noted that the data quality issue still hasn’t been fixed. That’s correct, so this product then provides the tools that show where quality rankings are lower, thus allowing remediation activities to be prioritised.

If you have an inventory data quality challenge and / or are wondering the name of this product, it’s CROSS, from the team at CROSS Network Intelligence (www.cross-ni.com).

Are your existing data sets actually suited to seeding an AI engine?

In the virtualization domain, the old root cause technology is becoming obsolete because resources and workloads move around dynamically – we no longer have fixed network and compute resources. Existing service assurance systems in the telecommunication network were designed to manage a fixed set of resources and these assurance systems fall short in monitoring dynamic virtualized networks. Code was written using a rule based approach on known problems. Some advances have been made to develop signature patterns to determine the root cause of a problem, but this approach will also fall short in a dynamic virtualized network where autonomous changes will occur continuously.”
Patrick Kelly
here.

This quote is taken from a really interesting article by Patrick Kelly (see link above).

The old models of determining service impact and root-cause certainly struggle to hold up in the transient world of virtualised networks. Artificial Intelligence or Machine Learning or machine-led pattern identification, or whatever the technologies will be called by their developers, have a really important part to play in networks that are not just dynamic, but undergoing a touchpoint explosion.

The fascinating part of this story is that these clever new models will rely on data. Lots of data. We already have lots of data to feed into the new models. Buuuuut…. I’ve long held the reservation that there might be one slight problem… does all of our existing data actually suit the “AI” models available today?

Firstly, our existing data doesn’t include much of a history on dynamically transient networks. But the more important factor is that our networks have been managed by humans – operators who have a tendency of recording the quickest, dirtiest (and not necessarily correct or complete) set of data that allows them to restore service quickly.

Following a recent discussion with someone who’s running an AI assurance PoC for a big telco, it seems this reservation is turning out to be true. Their existing data sets just aren’t suited to the AI models. They’re having to reconsider their whole approach to their data model and how to collect / store it. They’re now starting to get positive results from the custom-built data sets.

It’s coming back to the same story as a post from last week – having connectors that can translate the different languages of ops, data, AI, etc and building a people / process / technology solution that the AI models can cope with.

You might not be ready to start an AI experiment yet, but you may like to start the journey by understanding whether your existing data is suited to AI modelling. If not, you get the chance to change it and have a great repository of data to seed an AI engine when you are ready in future. The first step on an exponential OSS journey.

The answer is soooo obvious…. or is it?

There’s a crowded room of OSS experts, a room filled with serious intellectual horsepower. You might be a virtu-OSS-o, but you surely know that there’s still so much to be learnt from those around you. You have the chance to unlock the experiences and insights of your esteemed colleagues. But how? The answer might seem to be obvious. You do so by asking questions. Lots of questions.

But that obvious answer might have just one little unexpected twist.

Do you ask:

  1. Ego questions – questions that demonstrate how clever you are (and thus prove to the other experts that you too are an expert); OR
  2. Embarrassing questions – questions that could potentially embarrass you (and demonstrate major deficiencies in your knowledge, perhaps suggesting that you’re not as much of as expert as everyone else)

I’ve been in those rooms and heard the questions, as you have too no doubt. What do you think the ratio of ego to embarrassing would typically be? 10 to 1? 20 to 1?

The problem with the ego questions is that they can be so specific to the context of a few that they end up steering the conversations to the depths of technology hell (of course they can also end up inspiring / enlightening too, so I’m generalising here).

But have you observed that the very best in our industry happen to ask a lot of embarrassing  questions?

A quote by Ramit Sethi splices in brilliantly here, “The very best ask lots of questions. 3 questions I almost never hear: (1) “Just a second. If you don’t mind me asking, how did you get to that?” (2) “I’m not sure I understand the conclusion — can you walk me through that?” (3) “How did you see that answer?” Ask these questions and stop worrying about being embarrassed. How else are you going to learn?

Just for laughs, next time you’re at one of these events (and I notice that TM Forum Live is coming up in May), try to guess what the ego to embarrassing ratio might be there and which set of questions are spawning the more interesting / insightful / helpful conversations.

An OSS niche market opportunity?

The survey found that 82 percent of service providers conduct less than half of customer transactions digitally, despite the fact that nearly 80 percent of respondents said they are moving forward with business-wide digital transformation programs of varying size and scale. This underscores a large perception gap in understanding, completing and benefiting from digitalization programs.

The study revealed that more than one-third of service providers have completed some aspect of digital transformation, but challenges persist; nearly three-quarters of service providers identify legacy systems and processes, challenges relating to staff and skillsets and business risk as the greatest obstacles to transforming digital services delivery.

Driving a successful digital transformation requires companies to transform myriad business and operational domains, including customer journeys, digital product catalogs, partner management platforms and networks via software-defined networking (SDN) and network functions virtualization (NFV).
Survey from Netcracker and ICT Intuition.

Interesting study from Netcracker and ICT Intuition. To re-iterate with some key numbers and take-aways:

  1. 82% of responding service providers can increase digital transactions by at least 50% (in theory).  Digital transactions tend to be significantly cheaper for service providers than manual transactions. However, some customers will work the omni-channel experience to find the channel that they’re most comfortable dealing with. In many cases, this means attempting to avoid digital experiences. As a side note, any attempts to become 100% digital are likely to require social / behavioural engineering of customers and/or an associated churn rate
  2. Nearly 75% of responding service providers identify legacy systems / processes, skillsets and business risk as biggest challenges. This reads as putting a digital interface onto back-end systems like BSS / OSS tools. This is less of a challenge for newer operators that have been designed with digitalised customer interactions in mind. The other challenge for operators is that the digital front-ends are rarely designed to bolt onto the operators’ existing legacy back-end systems and need significant integration
  3. If an operator want to build a digital transaction regime, they should expect an OSS / BSS transformation too.

To overcome these challenges, I’ve noticed that some operators have been building up separate (often low-cost) brands with digital-native front ends, back ends, processes and skills bases. These brands tend to target the ever-expanding digitally native generations and be seen as the stepping stone to obsoleting legacy solutions (and perhaps even legacy business models?).

I wonder whether this is a market niche for smaller OSS players to target and grow into whilst the big OSS brands chase the bigger-brother operator brands?

The challenges in transforming network assurance to network healing

A couple of interesting concepts have the ability to fundamentally change the way networks and services are maintained. If they can be harnessed, we could replace the term “network assurance” with “network healing.”

The first concept is SON, which has been formulated specifically with mobile radio networks in mind, but has the potential to extend into all network types.

A Self-Organizing Network (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster.”
Wikipedia

One of the challenges of creating self-organising, self-optimising, self-healing networks is that every network has physical points of failure – cable cuts, equipment failure, etc. These can’t be fixed with software alone. That’s where the second concept comes in.

The second concept is smart-contract technology (possibly facilitated by Blockchain), which provides the potential for a more automated way of engaging a mini procurement / delivery / test / payment process to fix physical problems (or logical for that matter). Whilst the work might be done in the physical world, it could be done by third-parties, initiated by the OSS via microservice. Network Fix as a Service (NFaaS), with implementation, test, acceptance and payment all done in software as far as the OSS sees it.

To an extent this already happens via the issuance of ToW (Tickets of Work) to third party fault-fix teams, but it’s normally a significantly manual process currently.

However, the bigger challenge of transforming network assurance to network healing is to find a way to self-heal services that span multiple network domains. This could be physical network functions (PNF), virtual network functions (VNF) and the myriad topologies, technologies and protocols that interconnect them.

I can’t help but think that to simplify (self-healing) we first have to simplify (network variant minimisation).

If we can drastically reduce the number of variants, we have a better chance of building self-heal automations… and don’t just tell me that AI engines will solve all these problems! Maybe one day, but perhaps we can start with baby steps first.

Bringing Eminem’s blank canvas to OSS

“When you start out in your career, you have a blank canvas, so you can paint anywhere that you want because the shit ain’t been painted on yet. And then your second album comes out, and you paint a little more and you paint a little more. By the time you get to your seventh and eighth album you’ve already painted all over it. There’s nowhere else to paint.”
Eminem. (on Rick Rubin and Malcolm Gladwell’s Broken Record podcast)

To each their own. Personally, Eminem’s music has never done it for me, whether his first or eighth album, but the quote above did strike a chord (awful pun).

It takes many, many hours to paint in the detail of an OSS painting. By the time a product has been going for a few years, there’s not much room left on the canvas and the detail of the existing parts of the work is so nuanced that it’s hard to contemplate painting over.

But this doesn’t consider that over the years, OSS have been painted on many different canvases. First there were mainframes, then client-server, relational databases, XaaS, virtualisation (of servers and networks), and a whole continuum in between… not to mention the future possibilities of blockchain, AI, IoT, etc. And that’s not even considering the changes in programming languages along the way. In fact, new canvases are now presenting themselves at a rate that’s hard to keep up with.

The good thing about this is that we have the chance to start over with a blank canvas each time, to create something uniquely suited to that canvas. However, we invariably attempt to bring as much of the old thinking across as possible, immediately leaving little space left to paint something new. Constraints that existed on the old canvas don’t always apply to each new canvas, but we still have a habit of bringing them across anyway.

We don’t always ask enough questions like:

  • Does this existing process still suit the new canvas
  • Can we skip steps
  • Can we obsolete any of the old / unused functionality
  • Are old and new architectures (at all levels) easily transmutable
  • Does the user interface need to be ported or replaced
  • Do we even need a user interface (assuming the rise of machine-to-machine with IoT, etc)
  • Does the old data model have any relevance to the new canvas
  • Do the assurance rules of fixed-network services still apply to virtualised networks
  • Do the fulfillment rules of fixed-network services still apply to virtualised networks
  • Are there too many devices to individually manage or can they be managed as a cohort
  • Does the new model give us access to new data and/or techniques that will allow us to make decisions (or derive insights) differently
  • Does the old billing or revenue model still apply to the new platform
  • Can we increase modularity and abstraction between modules

“The real reason “blockchain” or “AI” may actually change businesses now or in the future, isn’t that the technology can do remarkable things that can’t be done today, it’s that it provides a reason for companies to look at new ways of working, new systems and finally get excited about what can be done when you build around technology.”
Tom Goodwin
.

50 exercises to ignite your OSS innovation sessions

Every project starts with an idea… an idea that someone is excited enough to sponsor.

  1. But where are your ideas being generated from?
  2. How do they get cultivated and given time to grow?
  3. How do they get pitched? and How do they get heard?
  4. How are sponsors persuaded?
  5. How do they then get implemented?
  6. How do we amplify this cycle of innovation and implementation?

I’m fascinated by these questions in OSS for the reasons outlined in The OSS Call for Innovation.

If we look at the levels of innovation (to be honest, it’s probably more a continuum than bands / levels):

  1. Process Improvement
  2. Incremental Improvement (new integrations, feature enhancement, etc)
  3. Derivative Ideas (iPhone = internet + phone + music player)
  4. Quantum Innovation (Tablet computing, network virtualisation, cloud delivery models)
  5. Radical Innovations (transistors, cellular wireless networks, Claude Shannon’s Information Theory)

We have so many immensely clever people working in our industry and we’re collectively really good at the first two levels. Our typical mode of working – which could generally be considered fire-fighting (or dare I say it, Agile) – doesn’t provide the time and headspace to work on anything in the longer life-cycles of levels 3-5. These are the levels that can be more impactful, but it’s these levels where we need to carve out time specifically for innovation planning.

If you’re ever planning to conduct innovation fire-starter sessions, I really recommend reading Richard Brynteson’s, “50 Activities for Building Innovation.” As the title implies, it provides 50 (simple but powerful) exercises to help groups to generate ideas.

Please contact us if you’d like PAOSS to help facilitate your OSS idea firestarter or road-mapping sessions.

Posing a Network Data Synchronisation Protocol (NDSP) concept

Data quality is one of the biggest challenges we face in OSS. A product could be technically perfect, but if the data being pumped into it is poor, then the user experience of the product will be awful – the OSS becomes unusable, and that in itself generates a data quality death spiral.

This becomes even more important for the autonomous, self-healing, programmable, cooperative networks being developed (think IoT, virtualised networks, Self-Organizing Networks). If we look at IoT networks for example, they’ll be expected to operate unattended for long periods, but with code and data auto-propagating between nodes to ensure a level of self-optimisation.

So today I’d like to pose a question. What if we could develop the equivalent of Network Time Protocol (NTP) for data? Just as NTP synchronises clocking across networks, Network Data Synchronisation Protocol (NDSP) would synchronise data across our networks through a feedback-loop / synchronisation algorithm.

Of course there are differences from NTP. NTP only tries to coordinate one data field (time) along a common scale (time as measured along a 64+64 bits continuum). The only parallel for network data is in life-cycle state changes (eg in-service, port up/down, etc).

For NTP, the stratum of the clock is defined (see image below from wikipedia).

This has analogies with data, where some data sources can be seen to be more reliable than others (ie primary sources rather than secondary or tertiary sources). However, there are scenarios where stratum 2 sources (eg OSS) might push state changes down through stratum 1 (eg NMS) and into stratum 0 (the network devices). An example might be renaming of a hostname or pushing a new service into the network.

One challenge would be the vast different data sets and how to disseminate / reconcile across the network without overloading it with management / communications packets. The other would be that format consistency. I once had a device type that had four different port naming conventions, and that was just within its own NMS! Imagine how many port name variations (and translations) might have existed across the multiple inventories that exist in our networks. The good thing about the NDSP concept is that it might force greater consistency across different vendor platforms.

Another would be that NDSP would become a huge security target as it would have the power to change configurations and because of its reach through the network.

So what do you think? Has the NDSP concept already been developed? Have you implemented something similar in your OSS? What are the scenarios in which it could succeed? Or fail?

A summary of RPA uses in an OSS suite

This is the sixth and final post in a series about the four styles of RPA (Robotic Process Automation) in OSS.

Over the last few days, we’ve looked into the following styles of RPA used in OSS, their implementation approaches, pros / cons and the types of automation they’re best suited to:

  1. Automating repeatable tasks – using an algorithmic approach to completing regular, mundane tasks
  2. Streamlining processes / tasks – using an algorithmic approach to assist an operator during a process or as an alternate integration technique
  3. Predefined decision support – guiding operators through a complex decision process
  4. As part of a closed-loop system – that provides a learning, improving solution

RPA tools can significantly improve the usability of an OSS suite, especially for end-to-end processes that jump between different applications (in the many ways mentioned in the above links).

However, there can be a tendency to use the power of RPAs to “solve all problems” (see this article about automating bad processes). That can introduce a life-cycle of pain for operators and RPA admins alike. Like any OSS integration, we should look to keep the design as simple and streamlined as possible before embarking on implementation (subtraction projects).

RPA in OSS feedback loops

This is the fifth in a series about the four styles of RPA (Robotic Process Automation) in OSS.

The fourth of those styles is as part of a closed-loop system such as the one described here. Here’s a diagram from that link:
OSS / DSS feedback loop

This is the most valuable style of RPA because it represents a learning and improving system.

Note though that RPA tools only represent the DSS (Decision Support System) component of the closed-loop so they need to be supplemented with the other components. Also note that an RPA tool can only perform the DSS role in this loop if it can accept feedback (eg via an API) and modify its output in response. The RPA tool could then perform fully automated tasks (ie machine-to-machine) or semi-automated (Decision support for humans).

Setting up this type of solution can be far more challenging than the earlier styles of RPA use, but the results are potentially the most powerful too.

Almost any OSS process could be enhanced by this closed-loop model. It’s just a case of whether the benefits justify the effort. Broad examples include assurance (network health / performance), fulfilment / activations, operations, strategy, etc.

The OSS / RPA parrot on the shoulder analogy

This is the fourth in a series about the four styles of RPA (Robotic Process Automation) in OSS.

The third style is Decision Support. I refer to this style as the parrot on the shoulder because the parrot (RPA) guides the operator through their daily activities. It isn’t true automation but it can provide one of the best cost-benefit ratios of the different RPA styles. It can be a great blend of human-computer decision making.

OSS processes tend to have complex decision trees and need different actions performed depending on the information being presented. An example might be a customer on-boarding, which includes credit and identity check sub-processes, followed by the customer service order entry.

The RPA can guide the operator to perform each of the steps along the process including the mandatory fields to populate for regulatory purposes. It can also recommend the correct pull-down options to select so that the operator traverses the correct branch of the decision tree of each sub-process.

This functionality can allow organisations to deliver less training than they would without decision support. It can be highly cost-effective in situations where:

  • There are many inexperienced operators, especially if there is high staff turnover such as in NOCs, contact centres, etc
  • It is essential to have high process / data quality
  • The solution isn’t intuitive and it is easy to miss steps, such as a process that requires an operator to swivel-chair between multiple applications
  • There are many branches on the decision tree, especially when some of the branches are rarely traversed, even by experienced operators

In these situations the cost of training can far outweigh the cost of building an OSS (RPA) parrot on each operator’s shoulder.

Using RPA as an alternate OSS integration

This is the third in a series about the four styles of RPA (Robotic Process Automation) in OSS.

The second of those styles is Streamlining processes / tasks by following an algorithmic approach to simplify processes for operators.

These can be particularly helpful during swivel-chair processes where multiple disparate systems are partially integrated but each needs the same data (ie reducing the amount of duplicated data entry between systems). As well as streamlining the process it also improves data consistency rates.

The most valuable aspect of this style of RPA is that it can minimise the amount of integration between systems, thus potentially reducing solution maintenance into the future. The RPA can even act as the integration technique where an API isn’t available or documentation isn’t available (think legacy systems here).

Onboarding outsiders as a new OSS business model

The majority of these new services [such as healthcare, content and media, autonomous vehicles, smart homes etc.] require partnerships and will be based on a platform business model where the customer is not aware of who is providing which part of the service and to be quite frankly honest, wont care. All as they will care about is the customer experience and the end-to-end delivery of their service that they have paid for. This is where the opportunity for the telco comes and we need to think beyond data!
Aaron Boasman-Patel
here on TM Forum Inform.

Are your OSS tools already integrating with third-party services?

Do your catalog / orchestration engines already call upon microservices from outside your organisation? Perhaps it’s something as simple as providing a content service bundled with a service provider’s standard bitpipe service. Perhaps it’s also bundled with an internal-facing analytics service or an outward-facing shopping cart service.

A telco isn’t going to want to (or be able to) provide all of these services but can use partnerships and catalog items to allow each unique customer to build the bundled offer they want.

This is where catalogs and microservices potentially represent a type of small-grid model. There are already many APIs from third-party providers and the catalog / orchestration tools already exist to support the model. For many telcos, it will take a slight mindset shift – to embrace partnerships (ie to discard the “not invented here” thinking); to allowing their many existing bit-pipe subscribers to sell and bill through the telco platform (embrace sell-through); to build platforms and processes to allow for simple certification and onboarding of third-parties.

If your current OSS isn’t already integrating with third-party services, is it on your roadmap? Then again, does it suit your proposed future business models?

Will it take open source to unlock OSS potential?

I have this sense that the other OSS, open source software, holds the key to the next wave of OSS (Operational Support Systems) innovation.

Why? Well, as yesterday’s post indicated (through Nic Brisbourne), “it’s hard to do big things in a small way.” I’d like to put a slight twist on that concept by saying, “it’s hard to do big things in a fragmented way.” [OSS is short for fragmented after all]

The skilled resources in OSS are so widely spread across many organisations (doing a lot of duplicated work) that we can’t reach a critical mass of innovation. Open source projects like ONAP represent a possible path to critical mass through sharing and augmentating code. They provide the foundation upon which bigger things can be built. If we don’t uplift the foundations across the whole industry quickly, we risk losing relevance (just ask our customers for their gripes list!).

BTW. Did you notice the news that Six Linux Foundation open source networking projects have just merged into one? The six initial projects are ONAP, OPNFV, OpenDaylight, FD.io, PDNA, and SNAS. The new project is called the LF Networking Fund (LFN).

But you may ask how organisations can protect their trade secrets whilst embracing open source innovation. Derek Sivers provides a fascinating story and line of thinking in “Why my code and ideas are public.” I really recommend having a read about Valerie.

Alternatively, if you’re equating open source with free / unprofitable, this link provides a list of highly successful organisations with significant open source contributions. There are plenty of creative ways to be rewarded for open source effort.

Comment below if you agree or disagree about whether we need OSS to unlock the potential of OSS innovation.

It’s hard to do big things in a small way

it’s hard to do big things in a small way, so I suspect incumbents have more of an advantage than they do in most industries.”
Nic Brisbourne
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The quote above came from a piece about the rise of ConstructTech (ie building houses via means such as 3D printing). However, it is equally true of the OSS industry.

Our OSS tend to be behemoths, or at least the ones I work on seem to be. They’ve been developed over many years and have millions of sunk person-hours invested in them. And they’ve been customised to each client’s business like vines wrapped around a pillar. This gives enormous incumbency power and acts as a barrier to smaller innovators having a big impact in the world of OSS.

Want an example of it being hard to do big things in a small way? Ever heard of ONAP? AT&T is a massive telco with revenues to match, committed to a more software-centric future, and has developed millions of lines of code yet it still needs the broader industry to help flesh out its vision for ONAP.

There are occasionally niche products developed but it’s definitely hard to do big things in a small way. The small grid analogy proposed earlier gives more room for the long tail of innovation, allowing smaller innovators to impact the larger ecosystem.

Write a comment below if you’d like to point out an outlier to this trend.