2013 - 4000km, 47km ascent, 140k calories.

So 2013 all wrapped up with most exercise done on the bike. From somewhere near zero to...well....

2484 miles this year (4000 km exactly) with about 47,807m climbing in that. 

139,837 calories burned, average speed of 16mph (25.5kph), which sounds low but is fine given the climbing done and winter miles that are in there (well, for me anyway). 

Total time on the bike: 166 hours. (Turbo time: another 45 hours.) 

Best ride in terms of overall performance was RideLondon 100, which took me 5 hrs (on the nose). 

Best ride in terms of average speed was Great Manchester Cycle; 37.6 kph average over 52 miles. Lucky to be in with some great riders on that.

To match this level of training in 2014 will be a challenge, given work and family commitments - but the time on the bike is an investment that always pays back. It's worth making the time to do it.

Best kit of 2013? These were all great buys:

• GripGrab Hurricane gloves
• Castelli Gabba long sleeved jersey
• Rapha bib shorts
• Rapha team jersey
• Continental GP4000S and GP 4 Season tyres. Both are superb.

Worst kit of 2013? Not keen on the Endura overshoes; they haven't lasted. On the whole, I think I've chosen kit well.

Bring on 2014.

Killing a social network, Facebook shareholder style

Thought this was a really interesting blog post and clearly with Sabisu in mind, I have an interest in how social networks function - or don't.

In brief, the blog post describes research of the now defunct Friendster network that ascribes its failure to the fact that each user had an insufficient number of friends for it to succeed, meaning the 'cost' of to each user of maintaining their network was too high for the 'benefit' it gave them. The low number of friends per user also meant that when users left the network, it wasn't sufficiently robust, leading to a cascade of leavers.

This fits with some of my own research (Strogatz et al) and seems a decent qualitative theory. And though it's obvious that Facebook/LinkedIn shareholders need to see return on their investment, it should also be a warning for any software vendor interested in social networks to keep the noise down.

Here's my thinking.

No offence to my Facebook & LinkedIn buddies but they're clogging my timeline up with stuff that's less and less relevant. Some of it is their 'fault', some of it is Facebook's.

You could narrow the really valuable (in this case, probably I mean 'interesting') conversations I have on Facebook to a very small group. Maybe only 5% or 10% of my 'friends'. The rest is noise.

Adverts are all over the place - side bars and in the feed, with the latter being particularly intrusive and particularly intrusive on mobile.

Game updates are all over the place and Facebook has not yet realised that certain users are never going to go for games. Again, the feed is clogged with irrelevant nonsense.

Social marketing messages are all over the place; "Share/like this to win X". Not interested.

Chain letters are all over the place. "Like this if your a mother/father/potato lover".

Sure, Facebook is free. The apps can be ignored, as can irrelevant status updates...but that misses a key point...

Filtering out the crap takes effort.

Even though that 'cost' tends to zero, it's there and it's irritating. And if you're only getting a few valuable interactions out of your network for a lot of filtering...well, at some stage you're going to cut back on that effort and not really maintain your network.

Like Clay Shirky's 'mental transaction costs' (see 'penny gap') it's not the financial cost that will drive people away. All that filtering costs brain time.

I understand that Facebook and LinkedIn need to monetise their user-base. I understand that adverts are a really easy way to do that. But with all the other nonsense on my timeline, it's getting to the point where I simply can't be bothered.

(Interestingly, my Twitter feed isn't suffering as badly.)

The first thing you should do with Facebook Graph Search

Just reading this (HT LettersOfNote) and got to thinking: what am I going to do with Graph Search?\

(If you don't know what Facebook's Graph Search is...well, check out the link above and perhaps this. It's interesting and scary in equal measure.)

First, I'm going to show my 5 year old a couple of innocent searches. We're going to talk about privacy. (I know, I'm such a crazy, fun parent.)

I'm beginning to think that privacy education is a serious advantage as they grow up. It may become the most important bit of education kids get as Graph Search proves that anything online with your name on it is a potential threat. One misplaced post could be the end of all opportunities in life.

Then, I'm going to work through some less palatable searches, particularly of my colleagues. Why? So we can pre-emptively address anything that shows up which is a threat. Of course, if it shows up some really unpalatable stuff, we may need to have a chat but it's unlikely that employees of a tech-savvy start-up are going to put up anything that's in that category.

What it will do is force everyone to assess how much slack they cut people. If you have an employee in the EDL, is that a problem for you? If your employees are friends with lots of competitor employees, does that matter?

Then we're going to talk within the company about our attitude to Graph Search. Sales people will see it as a powerful tool to be used by the dark side; those in recruitment will be terrified and excited in equal measure. We need to decide just how far into each other's lives we want to look.

I can't help wonder whether it's a bit like Sauron's ring; even those who would use it for good are ultimately sucked into that which is bad.

Perhaps you start by trying to weed out the racists, but ultimately you end up weeding out everyone you disagree with?

Perhaps Graph Search will increase prejudice? Perhaps it'll play on prejudices you don't know you have?

It's not the fast miles that count

Runners call it building a 'base'...cyclists tend to look a bit morose, or guilty, usually addressing a loved one or cycling companion with the confession; "I'm not doing the miles".

These days there's a constant focus on speed. It seems expected that you'll have a visionary idea on the train, text it to your development team while the Starbucks drone grunts his/her way through making you a latte, and have a prototype awaiting you by the time you've swiped in.

Well, here's the truth:

• Starbucks coffees tend to be mostly sweet milk.
• No lasting change has ever been effected at in a morning.
• If you need a swipe card to get into your office, you're probably not an innovator

See, like the runners, cyclists, triathletes out there that have to get up early and do the miles, innovation is 90% base mileage. It's all about grinding out the 1% improvements in all areas, day after day. (See Team Sky Procycling.)

Over time, those 1% gains accrete into a win - however you define it.

I hear stories cranked out about Twitter/Facebook/<insert favourite> being invented in 10 minutes on a hack day. Yeah? And the infrastructure? In fact, the most crucial bit, the business model isn't yet finished for most of these dominating platforms, some 6/7 years after they hit the market.

Even the initial idea moves on. Facebook now is not Facebook as was. Twitter also. They evolve because they know that you don't conceive a complete vision in a single blinding flash; you refine it over a period.

The code is the least important bit. The infrastructure is the second least important.

The implementation is what gives the software life. The business model is what gives the software a future.

Is the end in sight for bloated MI/BI systems?

There are 2 reasons you do not need a big, monolithic, singular, global datawarehouse.

1. Distributed processing

The techniques and architectures exist to move the aggregation process close to the data - in fact, right on top of it. This makes it quick, efficient and easy to modify without significant overhead - unlike cube -dependent data-warehouses.

Hybrid-cloud architectures make it possible to aggregate on a local basis while making the aggregates available over the cloud to the global enterprise, which is then dealing with relatively small datasets.

So there's no need to spend a fortune on the communications and storage involved with pulling data to a central location, and no need to take the risk of having all your data centralised onto a single point of failure.

So no need for a big datawarehouse.

2. Cloud power

At Sabisu we do much work in the process industry where the perennial question is: do we connect our essential production systems to the cloud?

Sure, you can take advantage of the virtualisation and outsourcing available for risk mitigation and cost reduction, but in fact you're just shifting the risk to the communications provider and you're unlikely to find multi-tenant cost benefits because you're going to want a very private cloud indeed for all your valuable process data.

Cloud computing is valuable to our customers because it gives unlimited, immediately available processing power. This means that all those clever data network modelling techniques that have been the preserve of those with entire datacentres at their disposal are now accessible by anyone with a bit of budget.

So what we have now is an opportunity to try new analysis techniques that do not need a local, on-premise, expensive data-warehouse. All you need is enough communications capability to get the dataset you want to analyse to the cloud, or as described in (1) above, get the right level of aggregate to the cloud.

In fact, you don't need to persist any data in the cloud; you can reconstruct the set of results later if required by supplying the raw/aggregated data.

So, distributed processing and cloud power; an antidote for bloated MIS perhaps?

Future for big data is small, fast and powered by end-users

I was intrigued by this article on the hype around big data: http://venturebeat.com/2012/12/21/big-data-club/

Last year I was invited to speak at a Corporate IT Forum workshop on MIS with lots of big data debate included. Some of the attendees were bemoaning a lack of 'accessible' big data technology, along the lines of 'we have petabytes of data to process and nothing to do it with', whereas others saw this as absolutely irrelevant as their organisations weren't generating this kind of data in the first place.

At Sabisu we do a lot of work with organisations that generate a lot  of data. Some is structured well, lots is structured badly, lots is unstructured. But even these guys don't really have 'big data' issues along the lines of the link above. Virtually everyone we talk to has plain old data issues - the sames ones they had 10 years ago, just on a bigger scale - but not multi-petabyte big data issues.

To put it into perspective, a big enterprise might have 30,000 users, all storing Excel/Word/Ppt docs and emails. Facebook has a billion, all storing video and photos. So chill out. Your organisation probably has an MIS or data management problem but not a big data problem.

That's not to say the technology and techniques pioneered by the Facebooks and Googles of this world don't have value. Every organisation would benefit from working with unstructured, non-relational data in a  distributed, resilient architecture...and that's what I take to mean by big data technology.

As a definition that's pretty sloppy. The fact is that distributed algorithms have been around a while. They've just not been 'accessible', which brings us back to our friends running the IT functions at 'normal' sized enterprises.

Our friends are being sold - and are buying - huge data-warehouses that cost a fortune. It is in the interests of the vendors to push the need for big data capability even if a 'normal' sized enterprise doesn't need it. And I don't believe they do.

I suggest that 99% of enterprises could function magnificently on 5% of the KPIs they currently capture. Most of the KPIs have little operational relevance. Most of the data-warehouse manipulation and exploitation is a waste of time. The reason for this is that the end-users cannot ask the questions they need to ask - there is no interface in place, so they ask a question they can get an answer to instead.

Sure, you have an MIS system. And it's self-service right? And your users love it, right? So how many of those KPIs affect your organisation's bottom-line?

Here's where 'accessible' implementations of unstructured, non-relational, distributed data processing will change things. Users would be able to ask questions that directly affect the bottom-line and it won't matter whether the right cube has been built, or batch job run, or ETL function completed, or whatever; the answer will be constructed on the fly by millions of small worker algorithms distributed throughout the IT architecture.

In this way, companies can exploit the data they already have but can't get to - the data in spreadsheets, documents, presentations along with the structured/unstructured line-of-business data. Data Scientists will be roving consultants, building pre-packaged algorithms that users can exploit easily.  

Running kit list

Chatting to the guys in work about getting in shape...not that I am at the moment.

Here's my rough kit list:

http://www.wiggle.co.uk/ronhill-pursuit-short/

http://www.wiggle.co.uk/ronhill-pursuit-tight/

http://www.wiggle.co.uk/ronhill-pursuit-square-cut-short/

http://www.wiggle.co.uk/ronhill-vizion-long-sleeve-crew-top/

http://www.wiggle.co.uk/inov-8-racesoc-16-socks-twin-pack/

I'm running in old model Inov-8 Terraflys at the moment which they seem to have discontinued. These are the nearest equivalent I think and will be my next shoe:

http://www.inov-8.com/New/Global/Product-View-Trailroc-255.html?L=26

(The new model Terraflys have a bigger heel/toe differential.)

Typically what I wear is roughly temp dependent:

• >11C = short sleeve top, shorts
• Between 3C and 11C = long sleeve top, shorts
• <3C = long sleeve top, tights
• <0C = long sleeve top, tights, base layer (gloves, beanie if req'd)

I take into account wind/rain by regarding it as lowering the temp slightly. I've never got it wrong. Nothing I run in is waterproof - I'm only ever 30 mins or so from a warm car/house. Mountain running...well, I'd have more kit.

CAD/CAM as an analogy for data processing algorithms

My last couple of posts (here, here) have been focused on manufacturing automation as an analogy for big data software techniques along with some discussions on the topic.

Here I thought I'd just jot down how CAD/CAM in particular might be an analogy for algorithmic data processing.

CAD/CAM is all about manufacturing physical things required for a physical process and ultimately, a physical product:

Algorithms are all about manufacturing digital things (e.g., datasets, results) required for a business process and ultimately, some sort of product (digital or physical) :

The process is the same, but results in a digital artifact for use in a business process.

I'm sure that there's an argument that what's been done here is to essentially abstract each process to such a degree that it's not representative. But the fact is that production is automated in manufacturing by assigning small, discrete packages of work to many actors, with as much parallelisation as possible, in order to produce a high quality output.

Seems a good analogy to me.

Riding/walking with your iPhone - s/ware, settings, kit

With a bit of care the iPhone can be an asset when you're outdoors - I use it for mapping and tracking runs, walks and bike rides. Strangely, the communications aspect of it is least important; as you'll see later I disable wifi and cellular data to conserve the battery a lot of the time.

Clearly, if you're walking (in particular) you need a proper map and compass. Cross-check with them regularly - you can't be certain that the iPhone hasn't gone quietly nuts.

What I want is:

• A detailed map, available off-line so it's not sucking battery, using data allowance or relying on 3G when out in the great outdoors
• A track of my activity
• Options to conserve battery

There are quite a few integrated mapping & activity solutions out there but I don't want all my eggs in one basket; I've got a suite of software which allows me to chop and change mapping & activity apps.

1. Map it

Personally I like to map the ride using the classic GMap Pedometer:

http://www.gmap-pedometer.com/

I don't have a GMap account, so I simply save the map shortcut as public and save the shortcut for later.

Then I click a shortcut with this URL I got from here. This opens a little JavaScript window which generates a GPX file which I then save with a .GPX extension.

I use Gaia GPS on my iPhone, which means I can email my GPX file to upload@gaiagps.com as described here. It comes back as a link in an email - opening that link imports it into Gaia.

The reason for using Gaia is that it allows you to download all the maps in advance so you don't rely on a mobile connection to download the maps as you go. The maps have been pretty good so far and it's got a lot the OpenCycle routes shown already as a map overlay.

That gives you all the maps and a track ready to follow.

2. Turn everything off

That's the rule; turn everything you need off on the iPhone. I've not tried simply engaging flight mode because that might impact the GPS. Instead I turn off:

• Wifi (no sense in it seeking for a network when you're in the great outdoors)
• Cellular data - all of it (because you don't need it)
• Phone (because hunting for the next cell costs power)
• Auto lock screen (because there's nothing more irritating than it going to the lock screen when you want to know whether it's a right or left turn next)
• Bluetooth - not required.

Ideally there'd be a 'profile' setting which would allow me to do this in one go.

When I move to a Bluetooth cadence sensor clearly that'll have to stay on.

Gaia also allows (in the latest version) you to turn off automatic GPS acquisition, so it'll only acquire and record your position on request. This is a great battery saver.

If you can turn off the screen then doing so will save a lot of battery. With the screen on constantly but all other settings as above, my iPhone 4 burns at most about 10% an hour. Screen off it'll last all day.

3. Onto the bike/trail

Hopefully you've got an iPhone holder and a way of keeping it dry on your handlebars, or you're going to fall off trying to retrieve it. If you're on a signed course, you could put it in a saddle bag or triathlon bag and forget about it (see link below).

iPhone 5 users will find limited mounting options at the moment - Topeak have a mount scheduled for spring '13.

Walkers can put it in a pocket or backpack. I've got one of these which is padded and just fits my iPhone 5 (it's fine with a 4/4S) though these would do fine too if it's going in a pocket not surrounded by scratchy things.

If you're out and about for a long time (say, days walking or a few hours on the bike) then an external battery pack is an option. I get 4 full charges out of one of these at a cost of 300g or so. On the bike you can put it in a triathlon bag near the bars and have it plugged in for the duration if required.

I've tried solar chargers - don't bother.

I'll keep running with this and try to build some sort of profile of the likely performance, screen on/off and bluetooth on/off.

Manufacturing automation shows us the future of big data in most companies

Here I wrote about big data software techniques as an analogy to manufacturing automation, and then in practice:
http://onelesscut.blogspot.co.uk/2013/01/big-data-software-techniques-in.html

The analogy is perhaps more interesting than the practice. What do robots bring to manufacturing and how does the analogy with big data software techniques play out in the future?

Regardless of repetition, robots bring :

• Accuracy and quality - they execute repetitive jobs to a high standard with repeatable results
• Speed and efficiency - they're great at crunching through repetitive tasks
• Reliability - they're 'always on'
• Low cost - see Reliability; also they can replace people (hey, it's true) 

They also allow integration up the design and manufacturing process, with the encoding of a physical form into a digital representation with CAD/CAM.

Robots are getting more complex. They're getting smaller, cheaper and more autonomous. They can handle jobs that perhaps required human intervention a few years ago. Most of the advances in robotics appear to be down to advances in software, e.g., signal processing, logic, or whatever.

Big data software techniques are like our robots. They bring:

• Accuracy and quality - algorithms manage distribution and execution of repetitive jobs to a high standard with repeatable results
• Speed and efficiency - they're great at crunching through repetitive tasks
• Reliability - distributed processes give greater resilience but also if you get the algorithm right once, it can be applied to truly massive datasets
• Low cost - you can do an awful lot with less (smaller, cheaper) computing power, and for some tasks they can replace people manually sifting unstructured data.

Where now?

Well, first off I think we may see Data Scientists being moved off the big data frontline, away from the data itself and back towards widely applicable algorithms. Scientists are usually first in to new areas of learning but they're quickly supplanted by engineers. As it was with robotics.

Once the (software) engineers have got these techniques working for industry, their role will move to be supportive, with end-users taking the lead. As CAD/CAM is a way for a subject matter expert to apply their knowledge of the physical domain so as to optimise manufacturing capability, so big data software techniques will allow subject matter experts to apply their algorithms to improve a process - sales, production or whatever.

That sounds a bit like marketing flannel, so here are some examples:

1. "Hey computer, I'm worried about benzene contamination in my product. Should I be?"

[Computer starts complex, distributed log analysis of a few millions lines of real-time data.]

2. "Hey computer, find out how much product we've had to flare off and how much it cost."

[In the future, everyone says 'Hey, computer'. Computer finds all the flaring incidents, exactly how much product was sent to flare from where, the value of each product.]

3. "Hey computer, can you reduce my electricity bill?"

[Computer looks at efficiency of every component in a process, tries to optimise usage taking into account the effect on other parts of the process.]

This is elegant as it doesn't need a multi-petabyte dataset for these techniques to show value; it's about using the existing data, reforming it and translating it into new forms on the fly through algorithms.

Ultimately this feeds right back up to the design process for new facilities, processes and even businesses. As Google extends each of our knowledge and even memories, we'll rely on algorithms chewing through lots of data to be our enterprise memory.

Big data software techniques in automation - as an analogy & in use

I really liked this post (HT @stewarttownsend) :

http://gigaom.com/data/why-big-data-might-be-more-about-automation-than-insights/

As we (@sabisu) do a lot of work in the process industries (oil & gas, chems, manufacturing) the analogy of 'big data' techniques to robotic manufacturing processes really worked for me.

What do robots do in manufacturing? We give them small tasks which require relentless repetition and accuracy. Robots don't do anything you couldn't do by hand but they're many times faster, more reliable and less expensive.

That's a pretty good analogy with many of the big data software tools which involve the distribution of work to many processing nodes. This work is repetitive, requires accuracy, speed, reliability and low cost (in all senses). It's a good fit. For example, the 'reduce' part of MapReduce is in fact a software robot, executing an algorithm. For Hadoop clusters, read any redundant architecture in your manufacturing process.

It's easy to see this translating to the real world. Need to aggregate all the flaring incidents at your petrochems plant? MapReduce will do what your Climate Change Manager might spend hours collating.

Need resilience in data acquisition from real-time manufacturing systems? Plenty of options.

Need someone to check all the logs for incidences of benzene contamination 1.2 standard deviations over the mean? Get an algo to do it.

Now, in fact I think most 'big data' technology is needlessly expensive and perhaps sub-optimal for some of these use-cases but the analogy holds.

Top films of 2012

Every year Mark Kermode gives his top 10 films of 2012...well, top 12 in this case...and with a few other honourable mentions thrown in.

Here's a full list to solve those 'what shall we watch tonight' conversations...

You've Been Trumped

Holy Motors

The Raid

(Argo)

Beasts of the Southern Wild

Martha Marcy May Marlene

(Liberal Arts)

Life of Pi

Even the Rain

(Angel's Share)

The Dark Knight Rises

Amour

Skyfall

(The Grey)

(Moonrise Kingdom)

A Royal Affair

(The Hunt)

Berberian Sound Studio

I'm also going to squeeze in Last Shop Standing if I can - about the decline of independent record shops in the UK.

Measured responses via gun control, philosophy and Gary Barlow

There have been few blog posts worth reading in the aftermath of the Sandy Hook horror. Here are some that are linked to each other and other, similar massacres:

http://m.theatlantic.com/technology/archive/2012/07/the-philosophy-of-the-technology-of-the-gun/260220/

http://www.wired.com/wiredscience/2012/07/batman-movies-dont-kill-but-theyre-friendly-to-the-concept/

http://blogs.plos.org/neuroanthropology/2012/07/24/inside-the-minds-of-mass-killers/

Particularly with reference to the first above, my interpretation of these blogs is basically that (you+gun)<>you + gun, i.e., when you pick up the gun, you are a different entity than you were, the gun is a different entity than it was, and the two of you together are a different entity than simply adding you and the gun.

To me, this is beautiful and logical.
Nowhere have I seen this logic applied, where allowance is made for the fact that combining a human with another entity, organic or not, causes them to become non-you, i.e., not the original entity.

Laws certainly don’t account for the fact that (you+gun)<>you + gun. In fact, I'd argue that they effectively disregard the gun altogether, setting parameters only on the basis of your actions. You can imagine the defence, "The gun made me do it", would land you perhaps in a mental hospital instead of prison but otherwise would be pointless.

Similarly, our behaviour changes when we become you+car; it's easy to dehumanise others when they're disguised in a metal box. In fact, when we label another driver based on their car ('typical BMW driver') we effectively negate 'you' out of the equation altogether, leaving just a metal box. And where's the harm in being angry at a metal box?

Everyone drives a car differently to the way they ride a bike. When it's you+bike, the sum total of that entity has a different attitude to risk and pattern of behaviour than you+car, or just plain old you. Regarding ourselves as fluid entities that become changed when combined with different entities (organic or not) allows us to reconsider our actions and reactions. 

The sooner we all realise that our edges aren't lines, that they're blurry boundaries, the better. A jazz pianist friend of mine once talked about being careful of what he listened to, because everything you hear finds it's way into your fingers. He's right - we're porous, badly insulated beings; skin, brains, emotions, everything. We absorb everything and acknowledge consciously a small part of it.

(This is still my argument for not watching Eastenders, listening to Gary Barlow/One Direction.)

Osmosis doesn't judge bad from good. It's a great asset and continual risk. 

I was going to sign off by saying that we all have a responsibility to control what we expose ourselves to but that's palpable nonsense; you can't control everything around you. And perhaps seeking to do so is a mistake, closing doors and limiting options. 

Perhaps all you can do is control how you react to the world, look for the wider implications and be mindful of the impact on others.

Some challenges for 2013

Here are a few sportives I fancy for this year. Some challenging, some not so.

Really they're building up to the Coast to Coast In A Day and Etape Cymru - I think they'll be really tough.

There's a little space in there for this year's Ride With Brad too...

17th February - Cheshire Mini Sportive [booked]

http://cheshireminisportive.blogspot.co.uk/

10th March - Jodrell Bank Classic

https://www.sientries.co.uk/event.php?event_id=859

24th March - Wiggle Cheshire Cat

http://www.kilotogo.com/index.php?option=event_detail&event_id=49

21st April - Manchester-Chester-Manchester

http://manchester-chester-manchester2013.blogspot.co.uk/

18th May - Keswick Sportive

http://www.highterrainevents.co.uk/11.html

29th June - Coast to Coast in a Day [booked]

http://www.opencycling.com/

14th July - Evans Peaks Ride-it

http://www.evanscycles.com/products/evans-cycles/ride-it-peak-district-sportive-road-entry-july-2013-ec043942

4th August - Ride London [in the ballot]

http://www.ridelondon.co.uk/

8th Sept - Etape Cymru [booked]

http://www.etape-cymru.co.uk/

There are a couple of cyclocross events I'm thinking of at the start of 2013. And a couple of other sportives I may yet plump for...I feel I need more hill practice...

Whilst I've got the turbo-trainer...and the bikes...I haven't had the time so far, so I'm at a base level of fitness somewhere around 'lardy'.

Product/Market Fit and Qualitative Value Assessment in Enterprise Software

I'm getting obsessive about product/market fit. That's probably a good thing and there are some great posts out there that help you get started with the concept.

Andreessen famously made the point that when a start-up has a good product/market fit, the product is virtually 'pulled' out of the start-up by the market. Oh yes, that sounds great doesn't it? Struggling to meet demand is every start-up's dream.

I think that works really well in the B2C sector but not so well in the B2B sector, where a new software product needs to get through numerous layers of shitty bureaucracy at all stages; it's hard to generate awareness in a market cluttered by also rans with big PR budgets; IT people are sometimes not the most accommodating of new ideas; procurement departments enjoy playing with deals; big enterprises enjoy playing with little ones (in every sense).

What trumps all these issues is Value - hence, the often meaningless phrase, 'Value Proposition'. If you can point to a reliable, business (not IT) driven, hard ROI expressed in £/$, then you can jump the hurdles. Hence, I'm interested in the link between Product/Market Fit and Value.

Here's my theory. Let's start by saying that...

Customer Value is proportionate to Usable Product Functionality

Ok, so the more your product can do, the more value a customer can get out of it, with the proviso that it has to be useful to be valuable.

But Usable Product Functionality is basically describing a Product that's a good fit for a particular set of requirements, i.e., a good fit for a particular Market. Andreessen doesn't really talk about the problem that drives the Product in his seminal blog-post, but to me the problem is the key component of the Market. After all, you build a Product in response to a Market need. 

In terms of an individual Customer Value proposition the Market is limited by the size of the enterprise network. It might be equal to the number of users within an enterprise - or, as with Sabisu, it could include users outside the enterprise. The Customer Value is dictated by the Value Potential of the problem the product solves; having a genuine high value problem to solve is the key. It might be a high value problem for a few users, or a low value problem for many users. This 'genuine high value problem' is analogous to the 'must have signal'.

So within a single Customer, Value Potential is simply:

Value Potential = Value of Problem * Number of users 

This means that all the other B2C Product/Market fit characteristics that you'd like to see apply to B2B software; virality drives the 'Number of users', new products can still create new Markets or define problems that have not previously been addressed.   

Let's make this pretty obvious statement:

Customer Value is proportionate to Value Potential

But it isn't just that. That 'good fit for a problem', or 'value potential' needs to be accessible. Every proportional equation needs a constant (remember kids that y=kx) so how about:

Customer Value = Efficiency * Value Potential

Where Efficiency is the ease of extracting value from the solution. You could see it as related to effort required to realise value (so, k=1/effort required) which in enterprise-software-world usually means technical services. Or you could say a solution that's easy to use is easier to extract value from (k=ease of use).

Customer Value = Ease of Use * (1/Services Required) * (Value of Problem * Number of users)

[Here's an interesting game; pick an ERP software implementation of your choice and run it through the above. Qualitatively it's not great, eh?]

Customer Value and Vendor Value really align here. Efficiency is very important to both parties as it limits the Vendor's ability to meet customer needs when the right problem has been identified, e.g., an inefficient platform drives lots of efficiency limiting behaviour such as support calls. 

Note that the contribution of the product itself to the analysis is limited to 'Ease of use'. Everything else is irrelevant so long as it's solving a valuable problem. Just as with the Product/Market Fit concept, the product just has to basically work. It has to be viable - a minimum viable product.

Product/Market Fit is traditionally focused on Vendor Value, i.e., do I pivot because the product/market fit isn't right, or persevere because I believe it is? (cf. Lean Startup). 

The Product/Market Fit calculation still works because Market Value is related to the aggregate of all those individual enterprise problems - that aggregate effectively drives the what the vendor can extract from the current market. If the Value Potential isn't there then a pivot is needed.

So, Vendor Value Potential is proportionate to Sum for customers(Value of Problem * Number of users)

Of course, there's no problem having stacks of Vendor Value Potential if you can't exploit it. If it's easy to exploit, it's efficient, so let's bring that back in again to complete our qualitative equation. 

As a Vendor, Services aren't necessarily negative - in fact they're irrelevant so long as they don't affect your likelihood of a sale. Ease of Use is still relevant as it drives down the cost of maintaining a customer.

What is negative is a high cost of customer acquisition, perhaps because the Market is hard to reach, or it's a new class of product with limited awareness that therefore requires lots of education before the tills can ring. 

Also as we're looking at the Market as a group of Customers, we need to account for the 'Probability of Sale'. Now, I'd be the first to admit that this is hard to determine - there are so many factors that affect whether a sale occurs; 

• Lower price than competitors
• Better marketing/sales collateral
• Better case histories/company history
• Better knowledge of the market

For me the Probability of Sale is just that; a value between 0% and 100% which indicates whether, all other things being equal, your solution would be the one chosen. One thing is for certain; the Product isn't going to affect this - your communications are. :

Vendor Value = Ease of Use * (Probability of Sale/Customer Acquisition Cost) * Sum for all customers(Value of Problem*Number of users)

Of course, the quantitive Vendor Value is impossible to ascertain; you'd need licensing, services and reliable 'Value of Problem' and 'Number of users' terms. However as a qualitative guide...it might have some use.

Questions:

• What other negative/positive factors need to be taken into consideration, particularly around the product?
• What else might inflate Customer Acquisition cost?
• Could the Vendor Value calculation be applied quantitatively to a particular prospect, defining whether it was worth pursuing?

Why Social Media has put Albert Camus & me off football

Football is, at best, a trivial game. This is obvious to me but it wasn't always so.

Until my first football game I had little appreciation for team sports. Perhaps that's why the team ethic at work is important to me...perhaps like Albert Camus, French philosopher, substantial parts of my education is owed to football - or, as he put it:

"what I know most surely about morality and the duty of man I owe to sport"

As Wikipedia states, "Camus was referring to a sort of simplistic morality he wrote about in his early essays, the principle of sticking up for your friends, of valuing bravery and fair-play."

Yes, precisely that. 

However, for me football is dying. It has been for a while but it's taken Facebook and Twitter to demonstrate it.

Let's start with Twitter. Everyone knows that Match of the Day is terrible. The pundits are overpaid and  incapable of basic communication. Their insights are insipid and unremarkable to start with, perhaps due to a preoccupation with employing ex-footballers, so they suffer terribly from being reheated every week. Sky is a bit better but not a great deal. As a result the focus shifts away from analysis to comment - at which point, football is lost.

Because that's where Twitter takes off; comments drive more comments but very little analysis or insight. The coverage often gets an almighty kicking on Twitter. As do the players. And the officials. (Amusingly, when referee Chris Foy was confused with cyclist Chris Hoy.)

The Twitter action demonstrates that the football itself isn't the focus anymore. It could be any sport. In fact it could be anything. Wound up fans wind each other up more, as they do on non-football or non-sport messageboards the world over. Fans even wind up their own clubs - which demonstrates the sport's obsession with the media, but also shows how the unique qualities of football that made it important to Camus have been lost in the barrage of irrelevant commentary. Sure, there are pearls of wisdom in there somewhere but they're cast before swine.

Perhaps more serious is what I see in Facebook. Inevitably feelings run high after a match and everyone piles in with vitriolic comments which always look more serious in print, without facial expressions to mitigate delivery, or the chance to buy a reconciliatory pint. People defend the indefensible (cf. recent high profile racist abuse cases) and it all gets a bit nasty. 

These days I watch football at home. Really, there's nothing at the match I need to see, though there's much to be missed. Most importantly, the TV goes off when the half-cocked opinions come out. I avoid the Facebook threads and trending Twitter topics where football is driving people mad. 

Perhaps social media is just exposing what had been there all along. Idiot footballers with little to say have historically been in luck as they weren't required and had limited platforms for saying what little they did. Now we all get to listen to them. 

And everybody else.

100,000 calories later...

September 10th, 2011, I decided it was time I went for a run.

Here I am, 95,000 calories, 190 activities and 916 miles (304 on the bike) later.

Back then, running 3.03 miles took about half an hour - at 10:32 a mile, mixing a bit of walking in there to keep me going, it was not fast. Now, on a good day, I'd do that same route in about 22 minutes.

Right now I'm running nowhere; last week I needed to have a bit of my abdomen hacked out, so it's all recovery for at least another week.

However, I can look back on some highlights;

• An early morning run out of Portwrinkle in Cornwall. Sunday sun, no-one else on the road.
  
• Great Manchester Run; a 50 minute 10k.

• Running 4 miles in 30 mins; my objective when I started, broken in July - averaged 7' 20"

And on the bike:

• Ride With Brad; thoroughly enjoyable, opened my eyes to sportives and 'proper' road cycling. Might well be hooked.
  
• Grinding my way over Kirkstone Pass in the driving wind and rain.
  

Not counting a couple of days here and there, this is the first real break. In March my achilles were complaining a bit, so I took a couple of weeks where I concentrated on the bike. Then, in preparation for the Ride With Brad sportive, the cycling came before the running again in August. Outside of that, it's been mainly running.

What now? Assuming recovery goes ok, there are some targets worth thinking about for the next 12 months:

• Average 100 miles/wk on the bike - with a few sportives helping to make that up

• The 7 min/mile 10k
  
• The 8 min/mile half-marathon (1hr 44 mins)
  
• Some running in the mountains
  
• A quick (but not sub-24 hr) Bob Graham Round

Obviously the wheels could come off any second. But life's too short not to try.

Publisher + Advertiser + Health Advice = Cover up?

This is pretty much the text of an email I sent to trailrunning@bauermedia.co.uk on 22nd July. I thought I'd leave it until they had time to respond but they chose not to.


A friend lent me a copy of Trail Running magazine (@TrailRunningMag). I liked the mag - otherwise I wouldn't be writing this - but there were a couple of articles that I felt were of low quality. The most serious of these was one on Dehydration, on page 26 of issue 8.

The alarm bells started to ring when I read the line, 'thirst is not a good sign of dehydration', which is point of view often trotted out by sports drinks manufacturers. The article also gives potentially dangerous advice on quantities which could lead to drinking too much, and it's ultimate conclusion ("even death a possibility") is completely unproven. It struck me as scare tactics.

In fact, there is not a single instance of a dehydration related fatality at a distance running race. On the other hand hyponatraemia, where runners drink too much liquid leading to a critical dilution of electrolyte has killed quite a few and hurt many more. Fully 13% of finishers at the 2002 Boston Marathon were clinically hyponatraemic...and I just had to go to Wikipedia for that information.

Even on a subject of this seriousness getting the facts wrong is perhaps understandable; perhaps the journalist's research has taken them down paths that differ to mine. Yet this article was written by Professor John Brewer (@sportprofbrewer), presented as an expert in the field.

Why would John Brewer want me to drink lots of liquid on a run? Is his advice independent, if misguided?

Perhaps I could have made a decision on that if I'd been presented with the fact that he worked for GlaxoSmithKline for 5 years and now 'evaluates the efficacy of sports nutrition products'.
Interestingly the link I used to find this out has been hidden behind a password. How interesting.

Clearly someone cared about my email...Possibly an editor with an advertiser to protect?

Happily we have the internet so he's easy to track down along with information that his research projects are funded by Maxinutrition - part of GlaxoSmithKline and that he used to be Director of Sports Science at the Lucozade Sports Science Academy.

Surely, we have a right to full disclosure when an article may affect our health?

What do you do if you can't be 'on it' every day?

We live in a world where the ambitious, dedicated and determined are expected to be 'on it' every day. Anything less is a dereliction of duty, regardless of what's going on in your life outside the office. 

Here's the truth; you can't be 'on it' every day. Some days you're recovering after an illness. Other days you're recovering after the kids have been up all night. Party animals might have the odd day here or there where they're feeling worse for wear.

Those days of sub-optimal performance are what I call 'sludge' days. Classic symptoms are being easily distracted, finding non-core activities intriguing and exciting, and worrying about your clothes too much. It's a day you feel like you're trekking through mud. It might be signalled by feeling tired, fidgety or demotivated.

The best thing to do is to recognise that you're having a sludge day, accept it and work out a coping strategy. 

Buzz disperses sludge

Only one thing can actually disperse sludge; an inspiring conversation that shifts the sludge with a big or stimulating idea. Buzz beats sludge. Sometimes sludge days are actually good for generating those ideas if you accept the downshift.

If you have buzz deficiency then you can't win. That's why you need to identify some 'sludge work'.

Sludge work

Sludge work is work that you don't need all of your brain to execute. It's work which is perhaps repetitive and low risk. 

What constitutes sludge work depends on what you do when you're whizzing along on an optimal day and the kind of person you are. Here's some that work for me: 

• Read all the updates on tickets executed by the team; gets me closer to what's going on day today and updates that are notable will disperse sludge. 
• Invoice related work; any that gets to me is largely repetitive.
• Research papers or blog posts; things you need to just get into your brain for use later.

Too much sludge

I think the odd isolated sludge day is fine. Two a week, or two consecutive sludge days indicates a problem, either with your personal life or your job. 

There's a problem if the culture of the company indulges sludge days as they tend to proliferate. Some enterprise have a culture that permits bits of sludge (see below) to creep into a typical working day. 

Bits of sludge

It may be appropriate to do some sludge work in an otherwise busy, buzzy day. So perhaps you have a circadian dip at 1400 to 1500 every day, just after lunch. This might be a great time to get through ticket updates done by the team in the last 24 hrs (see above). Then it's coffee time and off you go, back into the zone of optimal performance.

So that's how I handle those times of sub-optimal performance. In time, you could start to respect these times as opportunities to rest the brain whilst staying productive.

Press releases suck; go live or go home

When a company makes great claims, it's a natural reaction to want to see those claims proven. Today we were discussing a press release from IBM which talked about a 'new category of business intelligence' and made reference to 'hyper-intelligence'.

I can take a little hyperbole, but it has to be backed up by something tangible. If you're going to make claims let's see something in action. Get it centre stage. Ship it. Put it out there on the internet for us all to use to its fullest and give feedback on.

Of course, in the world of enterprise software most vendors can't, don't and won't.

They can't because they're scared; of exposing their ideas to the market, of what users and commentators will say, of the fact that these much-vaunted products don't actually do anything new. Even if there was a genuinely good product, a live, public platform would need sign off from so many different levels of hierarchy that a public site that really flexes its muscles is never going to happen. Large enterprise software vendors are just not agile enough.

They don't because they don't think they need to win your business; they think they've already won it just by being who they are. Their FUD strategy is so institutionalised even they believe it. Why should they risk their reputation by actually putting a live version of their solution out there, in the public domain? They don't need to...so...

They won't because there's too much to lose. They know that under the microscope their key value propositions break down, that the total cost of ownership for a big vendor solution is a big number; that if you went to a smaller vendor with a similar budget you'd get an awesome solution, not an awful one. (Or at least a useable one, eh, SAP users?). Under the microscope relying on FUD is unsustainable, as numerous dictators have found.

But times are changing. Users want to engage honestly and are willing to engage  immediately. So if it works, ship it. Get it on the cloud so users can honestly evaluate it.

If the big enterprise vendors don't, their agile competitors surely will.