Monday, February 28, 2011
Saturday, February 26, 2011
I was recently at a private conference of CIOs when in the midst of discussing activities such as CRM to ERP, it became clear that not only did everyone understand these applications, but also that every company had them. Almost all of those companies have expensive customization programmes in place, tailoring these systems to fit their needs. This behaviour is puzzling, but to explain why, we'll need to first look at the concept of business evolution.
All business activities share a common evolutionary pathway, a lifecycle; from the innovation of an activity, to custom built systems implementing that activity, to the provision of products, to eventually commodity provision and the appearance of utility services.
It is unusual to find activities that are commonplace, well understood, well defined and accepted as a cost of doing business being treated as though they were in an earlier stage of evolution with heavy customization. But each CIO told how that was exactly what was happening at their company, and the discussion became surreal when we discovered that many of the customizations being made to systems like CRM were common as well.
Surely, these activities are best served by being provided as a standardized commodity ideally through a market of utility services? Isn't that the whole point of cloud computing? So why aren't we all flocking to consume a host of common activities through the utility services of cloud providers? Well, a diverse range of companies already are.
However, several of the CIOs who had looked into the issue said these services didn't work for their company. On further questioning, it became clear that it wasn't IT but the business that was pushing for the customization in order to fit in with their way of working. Several CIOs had even used the standardized services of cloud providers to challenge this - asking the business to justify the additional orders of magnitude costs for a customized system by demonstrating the differential value that 'their way' made.
Whilst we often talk about business and IT alignment, this shouldn't mean IT just delivering what the business asks for. In circumstances where there is no differential value, then it's better to 'fit to the model' rather than 'fit the model to us'. We do the former all the time - from banking to electricity provision; we don't go and build our own customized solutions, we just find a way to work with the dominant market standards.
Will cloud computing help the business to treat common, cost-of-doing-business activities as though they are common and a cost of doing business? Or, will some businesses continue to spend vast sums customizing that which really makes no difference?
Could the cloud help the business re-align itself with the market?
Friday, February 25, 2011
Back in 2009, I proposed that VMware would eventually divest itself of its virtualisation business. The reason for my thinking was as follows :-
- Whilst the majority of VMware's revenue was based upon its virtualisation technology, this was an area that was ripe for disruption through two points of attack - open source systems such as KVM and the formation of marketplaces offering utility based virtual infrastructure. The latter almost certainly requires open source reference models to avoid issues around loss of strategic control and when combined with aggressive service competition around price, this doesn't leave much room for a license based proprietary technology.
- A dominant position in the enterprise is no guarantee for future success - see Novell Netware and the IPX/SPX vs TCP/IP battle. Critical in such battles is the development of wide public ecosystems and inherently open source has a natural advantage. However, given the revenue position of VMware, it could not afford to undertake this route.
- The obvious route for VMware would be to develop a platform play, most likely an open source route with an extensive range of value add services - from assurance to management. The current business would be used to fund the development of this approach until such time as the company could split into two parts - virtualisation & platform.
- Given the likely growth of private clouds as a transitional model in the development of the cloud industry, VMware would position the virtualisation business in this space for a high value sale, benefiting from its strength in the enterprise. This is despite it being unlikely that VMware would become the defacto public standard, that the technology was likely to be disrupted, that hybrid clouds are a transitional strategy superseded by formation of competitive markets and that many "private clouds" would be little more than rebranded virtual data centres.
- During this time, there would be signals of confusion over the VMware strategy precisely because it would be using a time limited cash cow to fund a new venture whilst preparing to jettison the cash cow prior to disruption.
Given the confusion over cloud, the often central (but ultimately misconceived) role that virtualisation is given in the industry, the generally disruptive effects of this change and the wealth of many competitors then in my opinion with luck, timing and good judgement a buyer could be found.
So, in my opinion:
- For VMware, it would mean creating a strong platform business funded by its current revenue stream before jettisoning the virtualisation business at high value prior to its disruption.
- For the buyer, it would mean ... whoops ... well that's capitalism for you. Next time, pay more attention.
Of course, this is just my opinion but I haven't changed my view over the years. I'm expecting to see an increasingly clear division within VMware between platform and virtualisation in the next year or so.
I'm hence curious to know what others think? Do you believe that VMware would sell its virtualisation business?
Following on from my previous post on lifecycle, I thought I'd discuss the question of when to listen to users. I'm going to use my lifecycle and profile graphs to illustrate the points I'll be raising, so if this is not familiar to you then please read my most recent post on lifecycle.
Figure 1 - Lifecycle (click on image for higher resolution)
Figure 2 - Profile (click on image for higher resolution)
Before I start, a word of apology. All of this stuff is blindingly obvious and has been for many many years - hence please don't take offense.
Any new activity starts in the chaotic phase and then as it evolves through its lifecycle it enters a more linear phase. As it moves, its characteristics change from uncertain, deviating and a source of worth to known, standard and a cost of doing business.
Less confusion creeps in, let's just reiterate that a new activity is an innovation. Whilst we tend to abuse the term innovation, a feature differentiation is a feature differentiation, a process improvement is a process improvement and an operational efficiency is an operational efficiency e.g. you can call every process improvement, operational efficiency and feature differentiation an innovation if you want but good luck in trying to make sense of things if you do.
As explained in earlier posts, as an activity's characteristics change then the methods by which you can effectively manage it change as well i.e. we shift from agile to six sigma for example. This is why there is no one size fits all.
Equally, in the chaotic stage the approaches taken are about gambling, experimentation, potential future worth and novel practice (i.e. it's highly uncertain), however when that same activity has entered the linear stage it's all about conformity to standards, defined processes, price vs quality of service and best practices (i.e it's predictable). In between these two extremes is where ROI (return on investment) matters because unlike the chaotic stage a market exists to provide some basis for trending and unlike the linear stage, you have a choice over whether to implement it since it is not a cost of doing business.
When it comes to users then :-
- In the chaotic stage the only reason why you would listen to potential users is the same reason why you might collaborate with others - serendipity i.e. the chance encounter of a better idea. Of course, whether the idea is better or not won't actually be known until you "experiment" i.e. you put it out into the market. You have as much chance as identifying the future successful innovation as any user and there are no methods of guaranteeing any innovation will be successful. Like it or not, you have to gamble. The rule of thumb is listen to yourself.
- In the transition phase listening to users is essential because a market has established, users are becoming familiar with the activity, customer feedback and trending is possible and competitors can be analyzed. The rule of thumb is listen to your ecosystem.
- In the linear stage, the activity is a commodity and its all about price vs QoS. Now, assuming you're not going to embark on a disinformation campaign and attempt to persuade users that a commodity is actually some form of innovation (a common tactic) then the only thing you need to really focus on is your position against competitors i.e. faster, more reliable, cheaper etc. Hence the rule of thumb is pricing & quality comparison.
So, should you listen to users? The answer is "yes and no", which one depends upon the stage of lifecycle of the activity in question.
[Added Note 26/02/11] I've just come across this Fred Wilson quote : "Early in a startup, product decisions should be hunch driven. Later on, product decisions should be data driven". It's pretty much spot on.
Friday, February 18, 2011
In figure 1, I've taken part of the lifecycle curve and modeled onto it a differential benefit curve (differential value - cost of implementation). This latter curve shows how the benefit of an activity changes as it evolves from its early innovation (where it is a strain on company resources) to a late product stage where the activity is ubiquitous and of insignificant differential value between competitors.
By the time an activity is implemented, the actual differential benefit may be vastly different. This creates a delta for expectation i.e. a difference from what we thought we would get and what we got. Figure 2 provides a graphical notation of this.
The evolution of an activity from products to utility services invokes its own expectation curve not through differential value (the creation of a new activity) but operational efficiency (a more efficient means of providing an existing activity).
In figure 5, I've provided the later stages of lifecycle including the transition from products to utility services and modeled an operational benefit curve (operational efficiencies over competitors - cost) of a transition to utility services.
The reason why I mention this, is that whilst Cloud Computing is all about volume operations for ubiquitous and well defined activities (i.e. use of computer resources in business) and is hence all about commodities, this transition will create a similar expectation curve around operational efficiency in much the same way that a genuine innovation creates an expectation curve around differential value. This is shown in figure 6, and the result is the same delta in expectation curve shown beforehand.
Hence, in the following hype cycle I've highlighted several activities, including :-
- cloud computing: more efficient provision of the existing activity of "using computer resources in business"
- social network analysis: a relatively new activity and a potential differential
The lesson of this story has been known in military circles for a long time. An imperfect plan executed today is better than a perfect plan executed tomorrow i.e. if you wait until the activity can be easily and effectively implemented (the plateau of productivity), it'll provide little competitive benefit to you.
Fortune favours the brave.
[A final few comments]
To generate the expectation curve I had to create a model over time. This required lots of assumptions because the evolution (lifecycle) curve does not have a time axis (i.e. you can't predict when something will evolve). There are hence a couple of points I'd like to make clear.
- You can't simply overlay the expectation curves of different activities on top of each other - i.e. the axis of time is different (some are stretched, some are shortened). Gartner's curve doesn't define its time axis and we can therefore assume they're referring to a general shape which appears over an undetermined length of time.
- The Gartner curve specifically refers to the technology trigger. We can assume this is when the technology starts to spread and ignores any early stage effects (invention etc).
- If the Gartner curve was based upon the measurement of some physical property, it would be possible to reverse the process i.e. from Gartner curve to expectation curve to evolution lifecyle and accurately state where an activity was along the uncertainty axis. By very definition this is impossible. I can currently only state where something was in the past once it has become a commodity. Hence I have to conclude that Gartner's curve is not based upon some external measurement of physical property but instead it is more likely by a process of expert review (i.e. averaging where forecasters think something is on the curve).
- The expectation curve matches the Gartner curve in certain circumstances. I cannot conclude the Gartner curve is valid in all circumstance but I can approximate the value zones where the curve does match.
Friday, February 11, 2011
As those activities evolve, their properties change from a chaotic to a linear extreme. In the chaotic stage, the activity :-
- deviates from what has existed before and is a novel practice.
- is dynamic and constantly changing.
- is rare and poorly understood.
- has high levels of uncertainty and it is not possible to predict future outcomes.
- has no market data, competitor analysis or well understood trends.
- has characteristics which emerge as we learn about it.
- is strongly affected by serendipity, chance encounters and discovery.
- is a potential source of future worth, differential and hence competitive advantage.
- is a gamble
By the linear stage, that same activity has evolved and:-
- is mature and rarely changes.
- is standardised with a wealth of best practice.
- is commonplace and well understood.
- has a high degree of certainty and known impacts.
- has an abundance of market data, competitor analysis and trends are well known.
- has well defined characteristics.
- has well defined procedures and plans for implementation.
- is a cost of doing business with little or no differential advantage except through operational efficiencies.
- is a known quantity.
Now all businesses consist of a mass of activities, each of which may be at different stages of their lifecycle (stage of evolution). By plotting the frequency of activities at different stages, a profile for an organisation or an industry can be created. This is shown in the figure below, to which the chaotic, linear and in-between stage of transition has been added.
Given all this, here are my questions :-