The OSS dart-board analogy

The dartboard, by contrast, is not remotely logical, but is somehow brilliant. The 20 sector sits between the dismal scores of five and one. Most players aim for the triple-20, because that’s what professionals do. However, for all but the best darts players, this is a mistake. If you are not very good at darts, your best opening approach is not to aim at triple-20 at all. Instead, aim at the south-west quadrant of the board, towards 19 and 16. You won’t get 180 that way, but nor will you score three. It’s a common mistake in darts to assume you should simply aim for the highest possible score. You should also consider the consequences if you miss.”
Rory Sutherland
on Wired.

When aggressive corporate goals and metrics are combined with brilliant solution architects, we tend to aim for triple-20 with our OSS solutions don’t we? The problem is, when it comes to delivery, we don’t tend to have the laser-sharp precision of a professional darts player do we? No matter how experienced we are, there tends to be hidden surprises – some technical, some personal (or should I say inter-personal?), some contractual, etc – that deflect our aim.

The OSS dart-board analogy asks the question about whether we should set the lofty goals of a triple-20 [yellow circle below], with high risk of dismal results if we miss (think too about the OSS stretch-goal rule); or whether we’re better to target the 19/16 corner of the board [blue circle below] that has scaled back objectives, but a corresponding reduction in risk.

OSS Dart-board Analogy

Roland Leners posed the following brilliant question, “What if we built OSS and IT systems around people’s willingness to change instead of against corporate goals and metrics? Would the corporation be worse off at the end?” in response to a recent post called, “Did we forget the OSS operating model?

There are too many facets to count on Roland’s question but I suspect that in many cases the corporate goals / metrics are akin to the triple-20 focus, whilst the team’s willingness to change aligns to the 19/16 corner. And that is bound to reduce delivery risk.

I’d love to hear your thoughts!!

Dematerialisation of OSS

In 1972, the Club of Rome in its report The Limits to Growth predicted a steadily increasing demand for material as both economies and populations grew. The report predicted that continually increasing resource demand would eventually lead to an abrupt economic collapse. Studies on material use and economic growth show instead that society is gaining the same economic growth with much less physical material required. Between 1977 and 2001, the amount of material required to meet all needs of Americans fell from 1.18 trillion pounds to 1.08 trillion pounds, even though the country’s population increased by 55 million people. Al Gore similarly noted in 1999 that since 1949, while the economy tripled, the weight of goods produced did not change.
Wikipedia on the topic of Dematerialisation.

The weight of OSS transaction volumes appears to be increasing year on year as we add more stuff to our OSS. The touchpoint explosion is amplifying this further. Luckily, our platforms / middleware, compute, networks and storage have all been scaling as well so the increased weight has not been as noticeable as it might have been (even though we’ve all worked on OSS that have been buckling under the weight of transaction volumes right?).

Does it also make sense that when there is an incremental cost per transaction (eg via the increasingly prevalent cloud or “as a service” offerings) that we pay closer attention to transaction volumes because there is a great perception of cost to us? But not for “internal” transactions where there is little perceived incremental cost?

But it’s not so much the transaction processing volumes that are the problem directly. It’s more by implication. For each additional transaction there’s the risk of a hand-off being missed or mis-mapped or slowing down overall activity processing times. For each additional transaction type, there’s additional mapping, testing and regression testing effort as well as an increased risk of things going wrong.

Do you measure transaction flow volumes across your entire OSS suite? Does it provide an indication of where efficiency optimisation (ie dematerialisation) could occur and guide your re-factoring investments? Does it guide you on process optimisation efforts?

Getting lost in the flow of OSS

The myth is that people play games because they want to avoid challenging work. The reality is, people play games to engage in well-designed, challenging work. The only thing they are avoiding is poorly designed work. In essence, we are replacing poorly designed work with work that provides a more meaningful challenge and offers a richer sense of progress.
And we should note at this point that just because something is a game, it doesn’t mean it’s good. As we’ll soon see, it can be argued that everything is a game. The difference is in the design.
Really good games have been ruthlessly play-tested and calibrated to the point where achieving a state of flow is almost guaranteed for many. Play-testing is just another word for iterative development, which is essentially the conducting of progressive experiments
.”
Dr Jason Fox
in his book, “The Game Changer.”

Reflect with me for a moment – when it comes to your OSS activities, in which situations do you consistently get into a state of flow?

For me, it’s in quite a few different scenarios, but one in particular stands out – building up a network model in an inventory management tool. This activity starts with building models / patterns of devices, services, connections, etc, then using the models to build a replica of the network, either manually or via data migration, within the inventory tool(s). I can lose complete track of time when doing this task. In fact I have almost every single time I’ve performed this task.

Whilst not being much of a gamer, I suspect it’s no coincidence that by far my favourite video game genre is empire-building strategy games like the Civilization series. Back in the old days, I could easily get lost in them for hours too. Could we draw a comparison from getting that same sense of achievement, seeing a network (of devices in OSS, of cities in the empire strategy games) grow rapidly as a result of your actions?

What about fans of first-person shooter games? I wonder whether they get into a state of flow on assurance activities, where they get to hunt down and annihilate every fault in their terrain?

What about fans of horse grooming and riding games? Well…. let’s not go there. 🙂

Anyway, enough of all these reflections and musings. I would like to share three concepts with you that relate to Dr Fox’s quote above:

  1. Gamification – I feel that there is MASSIVE scope for gamification of our OSS, but I’ve yet to hear of any OSS developers using game design principles
  2. Play-testing – How many OSS are you aware of that have been, “ruthlessly play-tested and calibrated?” In almost every OSS situation I’ve seen, as soon as functionality meets requirements, we stop and move on to the next feature. We don’t pause and try a few more variants to see which is most likely to result in a great design, refining the solution, “to the point where achieving a state of flow is almost guaranteed for many
  3. Richer Progress – How many of our end-to-end workflows are designed with, “a richer sense of progress” in mind? Feedback tends to come through retrospective reporting (if at all), rarely through the OSS game-play itself. Chances are that our end-to-end processes actually flow through multiple un-related applications, so it comes back to clever integration design to deliver more compelling feedback. We simply don’t use enough specialist creative designers in OSS

Training network engineers to code, not vice versa

Did any of you read the Light Reading link in yesterday’s post about Google creating automated network operations services? If you haven’t, it’s well worth a read.

If you did, then you may’ve also noticed a reference to Finland’s Elisa selling its automation smarts to other telcos. This is another interesting business model disruption for the OSS market, although I’ll reserve judgement on how disruptive it will be until Elisa sells to a few more operators.

What did catch my eye in the Elisa article (again by Light Reading’s Iain Morris), is this paragraph:
Automation has not been hassle-free for Elisa. Instilling a software culture throughout the organization has been a challenge, acknowledges [Kirsi] Valtari. Rather than recruiting software expertise, Elisa concentrated on retraining the people it already had. During internal training courses, network engineers have been taught to code in Python, a popular programming language, and to write algorithms for a self-optimizing network (or SON). “The idea was to get engineers who were previously doing manual optimization to think about automating it,” says Valtari. “These people understand network problems and so it is a win-win outcome to go down this route.”.

It provides a really interesting perspective on this diagram below (from a 2014 post about the ideal skill-set for the future of networking)

There is an undoubted increase in the level of network / IT overlap (eg SDN). Most operators appear to be taking the path of hiring for IT and hoping they’ll grow to understand networks. Elisa is going the opposite way and training their network engineers to code.

With either path, if they then train their multi-talented engineers to understand the business (the red intersect), then they’ll have OSS experts on their hands right folks?? 😉

A purple cow in our OSS paddock

A few years ago, I read a book that had a big impact on the way I thought about OSS and OSS product development. Funnily enough, the book had nothing to do with OSS or product development. It was a book about marketing – a subject that I wasn’t very familiar with at the time, but am now fascinated with.

And the book? Purple Cow by Seth Godin.
Purple Cow

The premise behind the book is that when we go on a trip into the countryside, we notice the first brown or black cows, but after a while we don’t pay attention to them anymore. The novelty has worn off and we filter them out. But if there was a purple cow, that would be remarkable. It would definitely stand out from all the other cows and be talked about. Seth promoted the concept of building something into your products that make them remarkable, worth talking about.

I recently heard an interview with Seth. Despite the book being launched in 2003, apparently he’s still asked on a regular basis whether idea X is a purple cow. His answer is always the same – “I don’t decide whether your idea is a purple cow. The market does.”

That one comment brought a whole new perspective to me. As hard as we might try to build something into our OSS products that create a word-of-mouth buzz, ultimately we don’t decide if it’s a purple cow concept. The market does.

So let me ask you a question. You’ve probably seen plenty of different OSS products over the years (I know I have). How many of them are so remarkable that you want to talk about them with your OSS colleagues, or even have a single feature that’s remarkable enough to discuss?

There are a lot of quite brilliant OSS products out there, but I would still classify almost all of them as brown cows. Brilliant in their own right, but unremarkable for their relative sameness to lots of others.

The two stand-out purple cows for me in recent times have been CROSS’ built-in data quality ranking and Moogsoft’s Incident Room model. But it’s not for me to decide. The market will ultimately decide whether these features are actual purple cows.

I’d love to hear about your most memorable OSS purple cows.

You may also be wondering how to go about developing your own purple OSS cow. Well I start by asking, “What are people complaining about?” or “What are our biggest issues?” That’s where the opportunities lie. Once discovering those issues, the challenge is solving the problem/s in an entirely different, but better, way. I figure that if people care enough to complain about those issues, then they’re sure to talk about any product that solves the problem for them.

An embarrassing experience on an overseas OSS project

The video below has been doing the rounds on LinkedIn lately. What is your key takeaway from the video?

Most would say the perfection, but for me, the perfection was a result of the hand-offs, which were almost immediate and precise, putting team-mates into better position. The final shot didn’t need the brilliance of a Messi or Ronaldo to put the ball into the net.

Whilst based overseas on an OSS project I played in an expat Australian Rules Football team. Aussie Rules, by the way, is completely different from the game of soccer played in the video above (check out this link to spot the differences and to see why I think it’s the greatest game in the world). Whilst training one afternoon, we were on an adjacent pitch to one of that country’s regional soccer teams.

Watching from the sidelines, we could see that each of the regional team’s players had a dazzling array of foot skills. When we challenged them to a game of soccer we were wondering if this was going to be embarrassing. None of us had anywhere near their talent, stamina, knowledge of the game of soccer, etc.

As it turns out, it WAS a bit embarrassing. We won 5-1, having led 5-0 up until the final few minutes. We didn’t deserve to beat such a talented team, so you’re probably wondering how (and how it relates to OSS).

Well, whenever one of their players got the ball, they’d show off their sublime skills by running rings around our players, but ultimately going around in circles and becoming corralled. They’d rarely dish off a pass when a teammate was in space like the team in the video above.

By contrast, our team was too clumsy to control the ball and had to pass it off quickly to teammates in space. It helped us bring our teammates into the game and keep moving forward. Clumsy passing and equally clumsy goals.

The analogy for OSS is that our solutions can be so complex that we get caught up in the details and go around in circles (sometimes through trying to demonstrate our intellectual skills) rather than just finding ways to reduce complexity and keep momentum heading towards the goals. In some cases, the best way-forward solution might not even use the OSS to solve certain problems.

Oh, and by the way, the regional team did score that final goal… by finally realising that they should use more passing to bring their team-mates into the game. It probably looked a little like the goal in the video above.

Assuming the other person can’t come up with the answer

Just a quick word of warning. This blog starts off away from OSS, but please persevere. It ends up back with a couple of key OSS learnings.

Long ago in the technology consulting game, I came to an important realisation. When arriving on a fresh new client site, chances are that many of the “easy technical solutions” that pop into my head to solve the client’s situation have already been tried by the client. After all, the client is almost always staffed with clever people, but they also know the local context far better than me.

Alan Weiss captures the sentiment brilliantly in the quote below.
I’ve found that in many instances a client will solve his or her own problem by talking it through while I simply listen. I may be asked to reaffirm or validate the wisdom of the solution, but the other person has engaged in some nifty self-therapy in the meantime.
I’m often told that I’m an excellent problem solver in these discussions! But all I’ve really done is listen without interrupting or even trying to interpret.
Here are the keys:
• Never feel that you’re not valuable if you’re not actively contributing.
• Practice “active listening”.
• Never cut-off or interrupt the other person.
• Stop trying to prove how smart you are.
• Stop assuming the other person can’t come up with the answer
.”

I’m male and an Engineer, so some might say I’m predisposed to immediately jumping into problem solving mode before fully understanding a situation… I have to admit that I do have to fight really hard to resist this urge (and sometimes don’t succeed). But enough about stereotypes.

One of the techniques that I’ve found to be more successful is to pose investigative questions rather than posing “brilliant” answers. If any gaps are appearing, then provide bridging connections (ie through broader industry trends, ideas, people, process, technology, contract, etc) that supplement the answers the client already has. These bridges might be built in the form of statements, but often it’s just through leading questions that allow the client to resolve / affirm for themselves.

But as promised earlier, this is more an OSS blog than a consulting one, so there is an OSS call-out.

You’ll notice in the first paragraph that I wrote “easy technical solutions,” rather than “easy solutions.” In almost all cases, the client representatives have great coverage of the technical side of the problems. They know their technology well, they’ve already tried (or thought about) many of the technology alternatives.

However, the gaps I’ve found to be surprisingly common aren’t related to technology at all. A Toyota five-why analysis shows they’re factors like organisational change management, executive buy-in, change controls, availability of skilled resources, requirement / objective mis-matches, stakeholder management, etc, as described in this recent post.

It’s not coincidence then that the blog roll here on PAOSS often looks beyond the technology of OSS.

If you’re an OSS problem solver, three messages:
1) Stop assuming the other person (client, colleague, etc) can’t come up with the answer
2) Broaden your vision to see beyond the technology solution
3) Get great at asking questions (if you aren’t already of course)

Does this align or conflict with your experiences?

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?

When your ideas get stolen

When your ideas get stolen.
A few meditations from Seth Godin:
“Good for you. Isn’t it better that your ideas are worth stealing? What would happen if you worked all that time, created that book or that movie or that concept and no one wanted to riff on it, expand it or run with it? Would that be better?
You’re not going to run out of ideas. In fact, the more people grab your ideas and make magic with them, the more of a vacuum is sitting in your outbox, which means you will prompted to come up with even more ideas, right?
Ideas that spread win. They enrich our culture, create connection and improve our lives. Isn’t that why you created your idea in the first place?
The goal isn’t credit. The goal is change.”

A friend of mine has lots of great ideas. Enough to write a really valuable blog. Unfortunately he’s terrified that someone else will steal those ideas. In the meantime, he’s missing out on building a really important personal brand for himself. Do you know anyone like him?

The great thing about writing a daily blog is that it forces you to generate lots of ideas. It forces you to be constantly thinking about your subject matter and how it relates to the world. Putting them out there in the hope that others want to run with them, in the hope that they spread. In the hope that others will expand upon them and make them more powerful, teaching you along the way. At over 2000 posts now, it’s been an immensely enriching experience for me anyway. As Seth states, the goal is definitely change and we can all agree that OSS is in desperate need for change.

It is incumbent on all of us in the OSS industry to come up with a constant stream of ideas – big and small. That’s what we tend to do on a daily basis right? Do yours tend towards the smaller end of the scale, to resolve daily delivery tasks or the larger end of the scale, to solve the industry’s biggest problems?

Of your biggest ideas, how do you get them out into the world for others to riff on? How many of your ideas have been stolen and made a real difference?

If someone rips off your ideas, it’s a badge of honour and you know that you’ll always generate more…unless you don’t let your idea machine run.

Is your data AI-ready (part 2)

Further to yesterday’s post that posed the question about whether your data was AI ready for virtualised network assurance use cases, I thought I’d raise a few more notes.

The two reasons posed were:

  1. Our data sets haven’t had time to collect much elastic / dynamic network data yet
  2. Our data is riddled with human-generated data that is error-prone

On the latter case in particular, I sense that we’re going to have to completely re-architect the way we collect and store assurance data. We’re almost definitely going to have to think in terms of automated assurance actions and related logging to avoid the errors of human data creation / logging. The question becomes whether it’s worthwhile trying to wrangle all of our old data into formats that the AI engines can cope with or do we just start afresh with new models? (This brings to mind the recent “perfect data” discussion).

It will be one thing to identify patterns, but another thing entirely to identify optimum response activities and to automate those.

If we get these steps right, does it become logical that the NOC (network) and SOC (security operations centre) become conjoined… at least much more so than they tend to be today? In other words, does incident management merge network incidents and security incidents onto common analysis and response platforms? If so, does that imply another complete re-architecture? It certainly changes the operations model.

I’d love to hear your thoughts and predictions.

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.

Drinking from the OSS firehose

Most people know what they want, but don’t know how to get it. When you don’t know the next step, you procrastinate or feel lost. But a little research can turn a vague desire into specific actions.
For example: When musicians say, “I need a booking agent”, I ask, “Which one? What’s their name?”
You can’t act on a vague desire. But with an hour of research you could find the names of ten booking agents that work with ten artists you admire. Then you’ve got a list of the next ten people you need to contact.
A life coach told me that most of his job is just helping people get specific. Once they turn a vague goal into a list of specific steps, it’s easy to take action
.”
Derek Sivers
in his blog, “Get Specific!

In a post last week, I spoke about feeling like never before that I’m at an OSS cross-road, looking towards a set of paths. The paths all contribute heavily to the next-generations of OSS, but there’s the feeling of dread that no one person will have the ability to step out each path. The paths I’m talking about include network virtualisation, data-science / artificial-intelligence / machine-learning, open-source deployments like ONAP, cloud infrastructure and delivery models, and so many more. Each represents a life’s work to become a fully-fledged expert.

In the past, a single OSS polymath could potentially scramble along a majority of the paths and understand the terrain within their local OSS environment. But that’s becoming increasingly less likely as we become ever more dependent upon the interconnection of disparate expertise.

This represents a growing risk. If nobody understands the whole terrain, how do we map out Derek’s “list of specific steps” on our complex OSS projects? If we can’t adequately break down the work, we’re at risk of running projects as a set of vague, disjointed activities. So I imagine you’re wondering how we do “Get Specific!”?

Most technology experts appear to me to have a predilection to plan projects from the bottom up (ie building up a solution from their detailed understanding of some parts of the project). However, on projects as complex as OSS, I’ve never seen a bottom-up plan come together efficiently. Nobody knows enough of the details to build up the entire plan.

Instead, I prefer the top-down approach of building a WBS (work breakdown structure), progressively diving deeper into the details and turning the vague goal into a tree of ever more specific steps. I consider the ability to break down complex projects into manageable chunks of work as my only real super-power, but in reality it largely just comes from using the WBS approach.

Okay, it might sound a bit like a waterfall model (depends on how you design the tree really), but it beats the “trying to drink from a firehose” alternative model.

Which approach works best for you?

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.

Dan Pink’s 6 critical OSS senses

I recently wrote an article that spoke about the obsolescence of jobs in OSS, particularly as a result of Artificial Intelligence.

But an article by someone much more knowledgeable about AI than me, Rodney Brooks, had this to say, “We are surrounded by hysteria about the future of artificial intelligence and robotics — hysteria about how powerful they will become, how quickly, and what they will do to jobs.” He then describes The Seven Deadly Sins of AI Predictions here.

Back into my box I go, tail between my legs! Nonetheless, the premise of my article still holds true. The world of OSS is changing quickly and we’re constantly developing new automations, so our roles will inevitably change. My article also proposed some ideas on how to best plan our own adaptation.

That got me thinking… Many people in OSS are “left-brain” dominant right? But left-brained jobs (ie repeatable, predictable, algorithmic) can be more easily out-sourced or automated, thus making them more prone to obsolescence. That concept reminded me of Daniel Pink’s premise in A Whole New Mind where right-brained skills become more valuable so this is where our training should be focused. He argues that we’re on the cusp of a new era that will favor “conceptual” thinkers like artists, inventors and storytellers. [and OSS consultants??]

He also implores us to enhance six critical senses, namely:

  • Design – the ability to create something that’s emotionally and/or visually engaging
  • Story – to create a compelling and persuasive narrative
  • Symphony – the ability to synthesise new insights, particularly from seeing the big picture
  • Empathy – the ability to understand and care for others
  • Play – to create a culture of games, humour and play, and
  • Meaning – to find a purpose that will provide an almost spiritual fulfillment.

I must admit that I hadn’t previously thought about adding these factors to my development plan. Had you?
Do you agree with Dan Pink or will you continue to opt for left-brain skills / knowledge enhancement?

Finding the most important problems to solve

The problem with OSS is that there are too many problems. We don’t have to look too hard to find a problem that needs solving.

An inter-related issue is that we’re (almost always) constrained by resources and aren’t able to solve every problem we find. I have a theory – As much as you are skilled at solving OSS problems, it’s actually your skill at deciding which problem to solve that’s more important.

With continuous release methodologies gaining favour, it’s easy to prioritise on the most urgent or easiest problems to solve. But what if we were to apply the Warren Buffett 20 punch-card approach to tackling OSS problems?

I could improve your ultimate financial welfare by giving you a ticket with only twenty slots in it so that you had twenty punches – representing all the investments that you got to make in a lifetime. And once you’d punched through the card, you couldn’t make any more investments at all. Under those rules, you’d really think carefully about what you did, and you’d be forced to load up on what you’d really thought about. So you’d do so much better.”
Warren Buffett
.

I’m going through this exact dilemma at the moment – am I so busy giving attention to the obvious problems that I’m not allowing enough time to discover the most important ones? I figure that anyone can see and get caught up in the noise of the obvious problems, but only a rare few can listen through it…

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
.

I’m predicting the demise of the OSS horse

“What will telcos do about the 30% of workers AI is going to displace?”
Dawn Bushaus

That question, which is the headline of Dawn’s article on TM Forum’s Inform platform, struck me as being quite profound.

As an aside, I’m not interested in the number – the 30% – because I concur with Tom Goodwin’s sentiments on LinkedIn, “There is a lot of nonsense about AI.
Next time someone says “x% of businesses will be using AI by 2020” or “AI will be worth $xxxBn by 2025” or any of those other generic crapspeak comments, know that this means nothing.
AI is a VERY broad area within computer science that includes about 6-8 very different strands of work. It spans robotics, image recognition, machine learning, natural language processing, speech recognition and far more. Nobody agrees on what is and isn’t in this.
This means it covers everything from superintelligence to artificial creativity to chatbots
.”

For the purpose of this article, let’s just say that in 5 years AI will replace a percentage of jobs that we in tech / telco / OSS are currently doing. I know at least a few telcos that have created updated operating plans built around a headcount reduction much greater than the 30% mentioned in Dawn’s article. This is despite the touchpoint explosion and increased complexity that is already beginning to crash down onto us and will continue apace over the next 5 years.

Now, assuming you expect to still be working in 5 years time and are worried that your role might be in the disappearing 30% (or whatever percentage), what do you do now?

First, figure out what the modern equivalents of the horse are in the context of Warren Buffett’s quote below:

“What you really should have done in 1905 or so, when you saw what was going to happen with the auto is you should have gone short horses. There were 20 million horses in 1900 and there’s about 4 million now. So it’s easy to figure out the losers, the loser is the horse. But the winner is the auto overall. [Yet] 2000 companies (carmakers) just about failed.”

It seems impossible to predict how AI (all strands) might disrupt tech / telco / OSS in the next 5 years – and like the auto industry, more impossible to predict the winners (the technologies, the companies, the roles). However, it’s almost definitely easier to predict the losers.

Massive amounts are being invested into automation (by carriers, product vendors and integrators), so if the investments succeed, operational roles are likely to be net losers. OSS are typically built to make operational roles more efficient – but if swathes of operator roles are automated, then does operational support also become a net loser? In its current form, probably yes.

Second, if you are a modern-day horse, ponder which of your skills are transferable into the future (eg chassis building, brakes, steering, etc) and which are not (eg buggy-whip making, horse-manure collecting, horse grooming, etc). Assuming operator-driven OSS activities will diminish, but automation (with or without AI) will increase, can you take your current networks / operations knowledge and combine that with up-skilling in data, software and automation tools?

Even if OSS user interfaces are made redundant by automation and AI, we’ll still need to feed the new technologies with operations-style data, seed their learning algorithms and build new operational processes around them.

The next question is double-edged – for both individuals and telcos alike – how are you up-skilling for a future without horses?

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.