Why “cloud” represents disruptive innovation – and the changes at HP are just the tip of the iceberg

Yesterday, I wrote a post about disruptive innovation, based on a book I’d been reading: The Innovator’s Dilemma, by , by Clayton M Christensen.

In that post, I asked whether cloud computing is sustaining or disruptive – and I said I’d come back and explain my thoughts.

In some ways, it was a trick question: cloud computing is not a technology; it’s a business model for computing. On that basis, cloud cannot be a sustaining technology. Even so, some of the technologies that are encompassed in providing cloud services are sustaining innovations – for example many of the improvements in datacentre and server technologies.

If I consider the fact that cloud is creating a new value network, it’s certainly disruptive (and it’s got almost every established IT player running around trying to find a new angle). What’s different about the cloud is that retrenching and moving up-market will only help so much – the incumbents need to switch tracks successfully (or face oblivion).

Some traditional software companies (e.g. Microsoft) are attempting to move towards the cloud but have struggled to move customers from one-off licensing to a subscription model. Meanwhile, new entrants (e.g. Amazon) have come from nowhere and taken the market for inexpensive infrastructure as a service by storm. As a consequence, the market has defined itself as several strata of infrastructure-, platform- and software- (data- and business process- too) as-a-service. Established IT outsourcers can see the threat that cloud offers, know that they need to be there, and are aggressively restructuring their businesses to achieve the low margins that are required to compete.

We only have to look at what’s happened at HP recently to see evidence of this need for change. Faced with two quarters of disappointing results, their new CEO had little choice but to make sweeping changes. He announced an exit from the device space and an aquisition of a leading UK software company. Crucially, that company will retain its autonomy, and not just in name (sorry, I couldn’t resist the pun) – allowing Autonomy to manage its own customers and grow within its own value network.

Only time will tell if HP’s bet on selling a profitable, market-leading, hardware business in order to turn the company around in the face of cloud computing turns out to be a mistake. I can see why they are getting out of the device market – Lenovo may have announced an increase in profits but we should remember Lenovo is IBM’s divested PC division, thriving in its own market, freed from the shackles of its previous owner and its high margin values. Michael Dell may joke about naming HP’s spin-off “Compaq” but Dell needs to watch out too. PCs are not dying, but the market is not growing either. Apple makes more money from tablets and smartphones than from PCs (Macs). What seems strange to me is that HP didn’t find a buyer for its personal systems group before announcing its intended exit.

[blackbirdpie url=”https://twitter.com/#!/MichaelDell/status/104266609316732928″]

So, back to the point. Cloud computing is disruptive and established players have a right to be scared. Those providing technology for the cloud have less to worry about (notice that HP is retaining its enterprise servers and storage) but those of us in the managed services business could be in for a rough ride…

The theory of disruptive innovation (from The Innovator’s Dilemma)

I always like the idea of reading more business books, but somehow that doesn’t often transition to reality as I tend to use my travel time to listen to podcasts, catch up on email or Twitter and I’m more likely to read a novel or a magazine before I go to sleep at night.

Even so, I have a few business books on the go at the moment and, over the last couple of weeks, I’ve been reading The Innovator’s Dilemma, by Clayton M Christensen.

It’s a bit old now (first published in 1997) and not the easiest read in the world  as it gets a bit repetitive with it’s “tell them what you’re going to say, say it, tell them what you said” approach, nevertheless the author puts forward some interesting theories that are already making me think differently about some of the business decisions I’ve witnessed recently.  In this post, I’ll highlight some of the book’s key points and then I’ll follow up later this week with my thoughts on current IT industry issues and trends.

The book is predicated on the idea that there are two forms of innovation in technology:

  • Sustaining technologies foster improved product performance.
  • Disruptive technologies often worsen performance in the near term, and bring to market a very different value proposition. Typically though, these are cheaper, simpler, smaller and more convenient.

In part 1, the author looks at why great companies fail:

  • Sustaining technology innovations sound great – after all, everybody wants improved performance, don’t they? As it happens, no they don’t: sustaining innovation can lead to companies providing more than customers want or are prepared to pay for. This leaves an opportunity for new market entrants with disruptive innovations.
  • It’s also true that customers may not want disruptive technologies, at least not until a market has been created by others. The problem for established vendors is that, by the time the market is proven, it’s too late (or too difficult) for them to adapt. On the flip side, Clayton Christensen highlights that it’s very rare for new entrants to succeed in marketing sustaining innovations.
  • Another concept the book describes is that of value networks: these may be based on rank order of product characteristics but also on the cost structure required (e.g. margins, etc.). The principle is that technologies with attributes that are only valuable in networks with low gross margins will be ignored by those looking for high margin business. As companies grow, it gets harder to continue the same rate of growth so they start looking for bigger bets.
  • In the book, Clayton Christensen describes a technology “S” curve of performance vs. time or engineering effort. The trick is for companies to switch technologies at the right point on this curve (where a new technology rises to intercept an established technology) but, whilst this works for sustaining innovation, it’s less applicable for disruptive innovation as each new technology has different attributes of performance. Consequently, new entrants get their commercial start in emerging value networks before invading the established networks.
  • Established players can usually create the required technology to match disruptive entrants but it becomes a management decision about resource allocation (known as the “impetus to innovate”) – which would you do, given the choice of sustaining innovations to meet the needs of important customers or investing in disruptive innovations with small markets and unclear needs? And it’s this decision that, all too often, leaves the door open for others – maybe even for others from inside the organsation who leave to create a new venture.
  • Christensen highlights that disruptive technologies don’t match the “S” curve and, typically they improve at a parallel pace with the established value network. Instead of looking for the point where new intercepts old (as with sustaining innovation), the aim is to look for the point where the emerging (disruptive) technology intercepts the market need.Impact of sustaining and disruptive technological change
  • New technologies may well intercept the old ones if the trajectories are different and this allows new entrants to join established value networks (because progress has diminished the differences between technologies). Put differently, once two technologies can both meet a need, the fact that one can do it better ceases to be of competitive relevance.Innovation and value networks
  • For established vendors, Christensen suggests it’s not a problem of being sleepy or of arrogant management – often the disruptive technology simply didn’t make sense (in their value network) – at least not until it was too late. It’s a lot easier to companies to move upmarket into established value networks, but harder to go down. Essentially, middle management will screen innovation projects from deep within organisation by only sponsoring those likely to succeed – i.e. those with a clear market demand. This market demand can be attributed to three factors:
    1. The promise of up-market margins.
    2. Upmarket movement of many customers.
    3. Difficulty cutting costs to move down-market profitably.
  • This creates a vacuum downmarket for new entrants with disruptive technologies and cost structures that are better suited to competition.

Part 2 of the book looks at managing disruptive technology change:

  • In addressing the challenge of allocating finite resources to innovation projects with unclear returns, one approach is to spin out a new organisation. This new organisation can develop new products without the constraints of the old business, then bring back its values back in house (perhaps replacing most of the old company) once established. It’s also possible to do this with organisational units within a company but resource allocation is a challenge and often fails. The key appears to be embedding independent organisations with different value networks – for example to be able to “get excited about a $50,000 order” when the company is used to $1m orders! Each organisation has to be free to persue its own customers as a separate organisational unit and to compete.
  • Another point that Clayton Christensen makes is that leading in developing and adopting sustaining technologies gives no discernible competitive advantage. On the other hand, leadership in disruptive technologies creates enormous value. Effectively there is a trade-off, exchanging market risk (i.e. the risk that an emerging market might not develop) for competitive risk (entering a market against entrenched competition).  Because markets that do not yet exist cannot be analysed, in order to confront disruptive change, it is necessary to plan for learning and discovery rather than execution.
  • It’s also important to recognise that a failed idea is not the same as a failed business. It’s common for a business to abandon it’s original business strategy after implementation highlights what would/wouldn’t work in the market. Chritensen suggests that guessing the right strategy at the outset is less important than running out of resources or credibility before iterating towards a viable strategy.
  • On the other hand, failed ideas are a different story when it relates to management careers where a failed idea/project can block career progress so we’re generally often unwilling to take on the risk of disruptive technologies. As failure is intrinsic to the process of finding new markets for technologies we have to plan to learn rather than plan to execute. Often this means using discovery-driven planning (identify assumptions upon which business plans/aspirations are based) to test market assumptions before committing.
  • Markets for disruptive technologies often emerge from unanticipated success and Clayton Christensen suggests that discoveries come from sharing how people use a product rather than listening to what they say. In effect, he advises getting out of labs and focus groups, and creating knowledge from discovery-driven expeditions into the marketplace.
  • Even if people have the capabilities to tackle innovation, the organisation in which they work may not. Clayton Christensen suggests looking at organisational capabilities in terms of resources, processes and values. In their startup phase, resources (people) are important to a business; later, the emphasis shifts to process and value. Even with change management, processes are not as flexible as resources (we can train people to be multiskilled) – and values are less so. If an organisation lacks capabilities the options are:
    • Acquire a new organisation.
    • Try to change processes and values.
    • Create a separate, independent, organisation.
  • In the last of these, physical separation is less important than a separate resource allocation process.
  • As we experience performance oversupply, performance attributes change such that, for example, as a product meets market demand for capacity, size, reliability, price etc. become differentiators. When this is completely played out, the product becomes a commodity. Effectively, differentiators lose value when features and functionality exceed market demands.
  • Clayton Christensen attributes a customer buying hierarchy to Windermere Associates’ that identifies four phases of functionality, reliability, convenience and price. Another conception of evolution/technology adoption comes from Geoffrey Moore’s Crossing the Chasm with innovators/early adopters, early majority, late majority [and laggards]. Using this model there is a price premium for early adoption, reliability is important for the early majority, and convenience for the late majority.Technology Adoption Process
  • The same attributes that make disruptive technologies worthless in mainstream markets become strong selling points in emerging markets – as disruptive technologies tend to be simpler, cheaper, more reliable and convenient. Therefore, Christensen suggests three strategies to deal with performance oversupply:
    1. Move upmarket: command a premium for better performance.
    2. Move with the customer, introduce new, disruptive technologies.
    3. Market to convince the customer that they need better performance.

There’s a lot more detail in the book and, although it can be heavy going at times. The writing style, together with notes at the end of each chapter betray the research/academic focus, which provides good accountability but is not easy to skim.

Throughout The Innovator’s Dilemma, Clayton M Christensen uses the disk drive industry as an example (along with other examples from the excavation and steel-making industries) but I can see some parallels with cloud computing too. In my next post, I’ll explain my thinking, but in the meantime I’d like to throw out a question:

Is cloud computing disruptive or is it a sustaining technology?

Finally, if you, like me, find these theories interetsing, you might also be interested in the Disruptive Library Technology Jester, who has produced a pocket-sized graph of the theory of disruptive innovation.

Rumours of the death of IT consumerisation have been greatly exaggerated

If you follow anyone IT-related on Twitter, or even the mainstream media, it’s difficult to have missed Hewlett-Packard (HP)’s news that it is planning to discontinue the production of WebOS devices and is considering a full or partial separation of its personal systems group.

I’m not entirely comfortable with commenting on a competitor’s business strategy on a Fujitsu blog (so I won’t) but I was more than a little surprised this morning when I saw CloudPro’s article suggesting that “HP’s cloud bet could kill consumerisation in IT“. Really?

Yes, all that glitters is not gold and, undoubtedly, there are some challenges for device manufacturers to overcome but, as Joe Baguley (EMEA Chief Cloud Technologist at VMware) has presented on a number of occasions, the consumerisation of IT is nothing to do with iPads, TouchPads (or even Stylistic Q550s…). It’s not about any device!

Put simply, the consumerisation of enterprise IT is about providing IT as-a-service.

Prior to the emergence of the world-wide web, users did what they were told to – making use of the hardware and software that the IT department provided. Now the dynamic has changed: the boundaries between work and play have eroded and, for many knowledge workers, there is no clear separation between business and personal tasks. Work has become something we do, not a place where we go, and those “users” have become consumers.

Consumers want to feel empowered – they desire flexibility, personalisation and immediate gratification. Our information workers want IT to work for them, in the way that they need it to work. They desire a portable, device independent, always-on (and instant-on) modern working environment that provides access to information from any device (including data synchronisation), with self-service subscriptions to provide access to application stores/portals and personal/professional persona management. If that sounds challenging, they are used to this in the consumer space – now they want it in business and a sizable proportion of employees are circumventing IT policies to self-provision at least a part of their IT toolkit.

Just like our banks, social networks, recreational websites and email, the organisational IT department has become a service provider. Furthermore, if the IT department can’t provide a service, consumers are happy to go elsewhere – leading to the emergence of what has become known as shadow IT.

Sometimes this shadow IT grows out of the need to do something that’s not possible on the corporate infrastructure (like using Dropbox to share a file with a colleague in another part of the world); and sometimes it’s officially sanctioned (for example, a business unit director deciding that salesforce.com is a more appropriate CRM solution than the IT-provided line of business application).

Regardless of the source of the shadow IT, it takes a brave CIO to try and fight it. Whether the approach is to embrace, contain, block or ignore, consumerisation is a trend that’s increasingly difficult to avoid.

[This post originally appeared on the Fujitsu UK and Ireland CTO Blog.]

Adding Twitter’s RSS to Feedburner

I spent some time yesterday afternoon working my way through an article on SEO-ing Twitter profile pages.  Whilst I don’t agree with absolutely every point in the article (e.g. tinyurl.com is too many letters for a URL shortener – I like to use bit.ly with a custom domain), it does contains some good advice (who would have thought of naming their Twitter profile picture to include appropriate keywords?). One point that doesn’t work though, is feeding your Twitter RSS feed to Feedburner.

There is a workaround though. Following Michael Phipps’ advice, I created a page called twitterfeed.php with the following code:

I then fed the URL for this page into Feedburner. I’d prefer to use an address on my own domain though – and it’s simple enough to create an HTML page to redirect to the correct location (and to add information for browsers to recognise the RSS location):



@MarkWilsonIT on Twitter

Redirecting to the @MarkWilsonIT Twitter RSS feed. If you’re not redirected within a couple of seconds,
try this link: @MarkWilsonIT on Twitter


The downside of this is that Outlook doesn’t like an RSS feed that’s redirected from HTML. Google Reader seems happy with the redirection although, because it does actually resolve to the Feedburner address, it won’t help me should I move the feed elsewhere in future…

In the end, I’m not sure what this achieves, but you can now subscribe to my tweets via RSS

Some thoughts on modern communications and the boundary between work and play…

A few months ago, I wrote a post for the Fujitsu CTO Blog about modern communications. In it, I posited the concept of “service level agreements“ for corporate communications:

“[…] regaining productivity has to be about controlling the interruptions. I suggest closing Outlook. Think of it as an email/calendar client – not the place in which to spend one’s day – and the “toast” that pops up each time a message arrives is a distraction. Even having the application open is a distraction. Dip in 3 times a day, 5 times a day, every hour, or however often is appropriate but emails should not require nor expect an immediate response. Then there’s instant messaging: the name “instant” suggests the response time but presence is a valuable indicator – if my presence is “busy”, then I probably am. Try to contact me if you like but don’t be surprised if I ignore it until a better time. Finally, social networking: which is both a great aid to influencing others and to keeping abreast of developments but can also be what my wife would call a “time-Hoover” – so don’t even think that you can read every message – just dip in from time to time and join the conversation, then leave again.”

I started to think about this again last week. I was on holiday but that doesn’t mean I stopped communicating with my colleagues. I’ll admit it let me be selective in my responses (i.e. there are a lot of things happening at work right now and I answered the messages that were important or interesting to me, leaving many items for my return – after all, I had set an out of office message) but there were a few times when my wife asked me if I was working, as she saw me tapping away on my iPhone…

I maintain that work is something I do, not a place where I go and that, in this day and age (and at my level of responsibility), there is a grey area between work and play so I was enraged when I read an idiotic post about how telecommuting does not work (hello, 1980 is calling… and it wants you back…). Indeed, my “home-base” is one of the things that attracts me to my current role. Getting me back into a 5-day commute to an office that’s probably at least an hour (and maybe two) from home will require some serious persuasion…

So where is the line? Should we all leave the office and stop checking our devices at the end of “the working day”? What about social networking – part of my job is to build a reputation (and therefore enhance my employer’s) as a thought leader – should I ignore something on Twitter because it’s not “work time”? Or should I ignore Twitter, Foursquare, etc. because it is “work time”? Should I be writing this blog post at 8.30pm? But then again, it is on my personal blog… even if a version of the post might eventually appear on a company-owned website…

In the end, I suggest that the answer is about outputs, not inputs. If I’m producing results, my management team should (and, in fairness, probably will) be comfortable, regardless of how many hours I put in. On the flip-side, there are times when I need to work some very long days just to make sure that I can produce those results – and I’ll get frustrated with organisational challenges, non-functioning IT, pointless meetings and disruptive colleagues, just as everyone else does in a modern office environment.

The days of the 9-5 job are long gone (for knowledge workers at least), but so are the 8-6s and even the 8-8s. We live in a 24 hour society – and the new challenge is finding a balance between “work” and “play”.  I’d be interested to hear your thoughts…

Cloning my Mac’s hard drive to gain some extra space

My MacBook (bought in 2008, unfortunately just before the unibody MacBook Pros were introduced) has always been running with upgraded memory and storage but it was starting to creak.  Performance is okay (it’s not earth-shattering but all I do on this machine is digital photography-related workflow) and it won’t take any more RAM than the 4GB I have installed but I was constantly battling against a full hard disk.

After a recent holiday when I was unable to archive the day’s shots and had to start filling my “spare” (read old and slow) memory cards to avoid deleting unarchived images, I decided to upgrade the disk. I did briefly consider switching to a solid state solution (until I saw the price – enough to buy a new computer), then I looked at a hybrid device, before I realised that I could swap out the 320GB Western Digital SATA HDD for a 750GB model from Seagate. The disk only cost me around £73 but next day shipping bumped it up a bit further (from Misco – other retailers were offering better pricing but had no stock). Even so, it was a worthwhile upgrade because it means all of my pictures are stored on a single disk again, rather than spread all over various media.

Of course, no image really exists until it’s in at least two places (so I do have multiple backups) but the key point is that, when I’m travelling, Lightroom can see all of my images.

I didn’t want to go through the process of reinstalling Mac OS X, Lightroom, Photoshop CS4, etc. so I decided to clone my installation between the two disks.  After giving up on a rather Heath Robinson USB to IDE/SATA cable solution that I have, I dropped another £24.99 on a docking station for SATA disk drives (an emergency purchase from PC World).

I’m used to cloning disks in Windows, using a variety of approaches with both free OS deployment tools from Microsoft and third party applications. As it happens, cloning disks in OS X is pretty straightforward too; indeed it’s how I do my backups, using a utility called Carbon Copy Cloner (some people prefer Super Duper). Using this approach I: created a new partition on the new disk (in Disk Utility), then cloned the contents of my old hard disk to the new partition (with Carbon Copy Cloner); then test boot with both drives in place (holding down the Alt/Option key to select the boot device); before finally swapping the disks over, once I knew that the copy had been successful.  Because it’s a file level copy, it took some time (just under six hours) but I have no issues with partition layouts – the software simply recreated the original file system on the partition that I specified on the new disk.  There’s more details of the cloning process in a blog post from Low End Mac but it certainly saved me a lot of time compared with a complete system rebuild.

Now all I need to do is sort out those images…

Cloud adoption “notes from the field”: people, politics and price

I’ve written about a few of the talks from the recent Unvirtual unconference, including Tim Moreton’s look at big data in the cloud; and Justin McCormack’s ideas for re-architecting cloud applications. I’ve also written previously about a previous Joe Baguley talk on why the consumerisation of IT is nothing to do with iPads.  The last lightning talk that I want to plagiarise (actually, I did ask all of the authors before writing about their talks!) is Simon Gallagher (@vinf_net)’s “notes from the field” talk on his experiences of implementing cloud infrastructure.

Understanding cloud isn’t about Amazon or Google

There is a lot happening in the private cloud space and hybrid clouds are a useful model too because not everybody is a web 2.0 start-up with a green-field approach and enterprises still want some of the capabilities offered by cloud technologies.

Simon suggests that private cloud is really just traditional infrastructure, with added automation/chargeback… for now. He sees technology from the public cloud filtering down to the private space (and hybrid is the reality for the short/medium term for any sizeable organisation with a legacy application set).

The real cloud wins for the enterprise are self-service, automation and chargeback, not burst/flex models.

There are three types of people that want cloud… and the one who doesn’t

Simon’s three types are:

  1. The boss from the Dilbert cartoons who has read a few too many analyst reports (…and is it done yet?)
  2. Smart techies who see cloud as a springboard to the nest stage of their career (something new and interesting)
  3. Those who want to make an inherited mess somebody else’s problem

There is another type of person who doesn’t welcome cloud computing – people whose jobs become commoditised.  I’ve been there – most of us have – but commodisiation is a fact of life and it’s important to act like the smart guys in the paragraph above, embracing change, learning and developing new skills, rather than viewing the end of the world as nigh.

Then there are the politics

The first way to cast doubt on a cloud project is to tell everyone it’s insecure, right?

But:

  • We trust our WAN provider’s MPLS cloud.
  • We trust our mail malware gateways (e.g. MessageLabs).
  • We trust our managed service provders staff.
  • We trust the third party tape collection services.
  • We trust out VPNs over the Internet.
  • We may already share datacentres with competitors.

We trust these services because we have technical and audit controls… the same goes for cloud services.

So, I just buy cloud products and start using them, yeah?

Cloud infrastructure is not about boxed products.  There is no “one-size fits all” Next, Next, Next, Finish wizard but there are complex issues of people, process, technology, integration and operations.

It’s about applications, not infrastructure

Applications will evolve to leverage PaaS models and next-generation cloud architectures. Legacy applications will remain legacy – they can be contained by the cloud but not improved. Simple provisioning needs automation, coding, APIs. Meanwhile, we can allow self-service but it’s important to maintain control (we need to make sure that services are de-provisioned too).

Amazon is really inexpensive – and you want how much?

If you think you can build a private cloud (or get someone else to build you a bespoke cloud) for the prices charged by Amazon et al, you’re in for a shock. Off the shelf means economised of scale and, conversely, bespoke does not come cheap. Ultimately, large cloud providers diversify their risks (not everyone is using the service fully at any one time) and somebody is paying.

Opexification

There’s a lot of talk about the move from capital expenditure to operating expenditure (OpEx-ification) but accounts don’t like variable costs. And cloud pricing is a rate card – it’s certainly not open book!

Meanwhile, the sales model is based on purchase of commercial software (enterprises still don’t use open source extensively) and, whilst the public cloud implies ability to flex up/down, private cloud can’t do this (you can’t take your servers back and say “we don’t need them this month”). It’s the same for software with sales teams concentrating on license sales, rather than subscriptions.

In summary

Simon wrapped up by highlighting that, whilst the public cloud has its advantages, private and hybrid clouds are big opportunities today.

Successful implementation relies on:

  • Motivated people
  • A pragmatic approach to politics
  • Understand what you want (and how much you can pay for it)

Above all, Simon’s conclusion was that your mileage my vary!

Can we process “Big Data” in the cloud?

I wrote last week about one of the presentations I saw at the recent Unvirtual conference and this post highlights another one of the lightning talks – this time on a subject that was truly new to me: Big Data.

Tim Moreton (@timmoreton), from Acunu, spoke about using big data in the cloud: making it “elastic and sticky” and I’m going to try and get the key points across in this post. Let’s hope I get it right!

Essentially, “big data” is about collecting, analysing and servicing massive volumes of data.  As the Internet of things becomes a reality, we’ll hear more and more about big data (being generated by all those sensors) but Tim made the point that it often arrives suddenly: all of a sudden you have a lot of users, generating a lot of data.

Tim explained that key ingredients for managing big data are storage and compute resources but it’s actually about more than that: it’s not just any storage or compute resource because we need high scalability, high performance, and low unit costs.

Compute needs to be elastic so that we can fire up (virtual) cloud instances at will to provide additional resources for the underlying platform (e.g. Hadoop). Spot pricing, such as that provided by Amazon, allows a maximum price to be set, to process the data at times when there is surplus capacity.

The trouble with big data and the cloud is virtualisation. Virtualisation is about splitting units of hardware to increase utilisation, with some overhead incurred (generally CPU or IO) – essentially multiple compute resources are combined/consolidated.  Processing big data necessitates combining machines for massive parallelisation – and that doesn’t sit too well with cloud computing: at least I’m not aware of too many non-virtualised elastic clouds!

Then, there’s the fact that data is decidedly sticky.  It’s fairly simple to change compute providers but how do you pull large data sets out of one cloud and into another? Amazon’s import/export involves shipping disks in the post!

Tim concluded by saying that there is a balance to be struck.  Cloud computing and big data are not mutually exclusive but it is necessary to account for the costs of storing, processing and moving the data.  His advice was to consider the value (and the lock-in) associated with historical data, to process data close to its source, and to look for solutions that a built to span multiple datacentres.

[Update: for more information on “Big Data”, see Acunu’s Big Data Insights microsite]

IT and the law – is misalignment an inevitable consequence of progress?

Yesterday evening, I had the pleasure of presenting on behalf of the Office of the CTO to the Society for Computers and Law (SCL)‘s Junior Lawyers Group. It was a slightly unsual presentation in that David [Smith] often speaks to CIOs and business leaders, or to aspiring young people who will become the next generation of IT leaders.  Meanwhile I was given the, somewhat daunting, challenge of pitching a presentation to a room full of practising lawyers – all of whom work in the field of IT law but who had signed up for the event because they wanted to know more about the technology terms that they come across in their legal work.  Because this was the SCL’s Junior Lawyers group, I considered that most of the people in the room have grown up in a world of IT and so finding a level which was neither too technical nor too basic was my biggest issue.

My approach was to spend some time talking about the way we design solutions: introducing the basic concepts of business, application and technology architectures; talking about the need for clear and stated requirements (particularly non-functionals); the role of service management; and introducing concepts such as cloud computing and virtualisation.

Part way through, I dumped the PowerPoint (Dilbert fans may be aware of the danger that is “PowerPoint poisoning”) and went back to a flip chart to sketch out a view of why we have redundancy in our servers, networks, datacentres, etc. and to talk about thin clients, virtual desktops and other such terms that may come up in IT conversations.

Then, back to the deck to talk about where we see things heading in the near future before my slot ended and the event switched to an exercise in prioritising legal terms in an IT context.

I’m not sure how it went (it will be interesting to see the consolidated feedback from the society) but there was plenty of verbal feedback to suggest the talk was well received, I received some questions (always good to get some audience participation) and from the frantic scribbling on notes at one table I must have been saying something that someone found useful!

The main reason for this blog post is to highlight some of the additional material in the deck that I didn’t present last night.  There are many places where IT and the law are not as closely aligned as we might expect. Examples include:

These items could have been a whole presentation in themselves but I’m interested to hear what the readers of this blog think – are these really as significant as I suggest they are? Or is this just an inevitable consequence of  fast-paced business and technology change rubbing up against a legal profession that’s steeped in tradition and takes time to evolve?

[This post originally appeared on the Fujitsu UK and Ireland CTO Blog.]