Wednesday, April 23, 2014

Where is the Hybrid Cloud Adoption?

Since the early days of the enterprise cloud adoption discussion the concept of Hybrid Cloud, combining the control and security of the private cloud with the breadth and economics of the public cloud, has been a topic.  Cloudbursting emerged as one of the early, highly touted use cases for cloud, and I was one of those consultants adding to the hype.  Yet today with valid reasons for adopting Hybrid Cloud, where are the adopters?

I believe there are several reasons Hybrid Cloud has yet to take off including:
  • Offerings - the offerings in the market fall well short of what companies need because none provide any orchestration, policy management or governance tools.  Buyers need a way to specify the environment blueprint to automate the provisioning at the provider.  The environment needs to be  configured precisely as needed in an automated fashion and this must include the ability to specify the location based on the location of other virtual machines and storage.  Policy management across providers, applications and data are necessary to ensure data is protected, business rules are enforced, and costs are effectively managed.  And without governance tools in place administration and change management become ad-hoc to a large degree.  There are tools available to help, but without strong provider integration the tools are merely best efforts.
  • Critical Mass - there are companies who are using Hybrid Cloud successfully today, but they rarely talk about it.  Whether its to maintain a low profile to the public and shareholders in case the bet doesn't pay off, to protect competitive advantage, or simply because they don't feel others should benefit from their investment we have very few stories to learn from and retell.  Most of IT progress is predicated on following the leaders.  CRM, SFA, ERP, MDM, eCommerce, Web 2.0, Mobility, M2M, Analytics.  All of these technologies are pioneered by visionaries and derided by the status quo until a tipping point is reached after which the critical mass of thinking drives widespread adoption.  However in Hybrid Cloud the benefits of sharing the stories in the form of larger, more robust Public Clouds at lower costs points will benefit the early adopters.  Telling the stories will yield dividends.  There is very little competitive threat of the followers suddenly catching on and accelerating into a pioneer.
  • Risk vs Reward - we know many CIO's have no interest in doing cloud let alone leveraging public clouds because they don't see the reward for taking the risk. Risk is nothing more than a euphemism for fear: fear of failure, fear of losing control, and fear of personal risk. Although all the experts say it's the only way to go and a fundamental part of transforming IT into a business services organization, cloud isn't easy.  And anything that isn't easy is rife with risk.  Therefore it remains easier to follow along at a snails pace talking up the desire to leverage Public Cloud resources in a hybrid model than actually take any steps.  Often these organizations are characterized by having a less than robust private cloud and have done no organized research into making a Hybrid Cloud reality. 
  • Learning Curve - learning anything new takes time, and with an expansive domain such as cloud becoming an expert seems impossible.  Focusing on private cloud enables organizations to build skills related to cloud and move up the learning curve at reduced risk because there is no threat of losing control.  However Private Clouds provide a false sense of accomplishment because the end of their road is rarely much further than mass virtualization, well short of the value propositions of cloud.
  • Business Value - the concept of cheaper, better, faster has made inroads at the virtual machine level, but few in the business understand how Hybrid Cloud can drive real business value and thus are allowing IT more leeway than necessary.  The last thing many IT leaders want is a business to expect the deployment of entire environments within minutes, scale up and scale down within minutes, and frankly deployment of entire new capabilities in minutes.  It makes IT look slow and incapable.  Slowly but surely IT executives are getting their arms wrapped around the idea that Hybrid Cloud is an enabler, but at the same time they have to gain comfort with giving up control.  
Just as cloud isn't easy, neither is transforming IT or being an IT executive at this point in time.  Control has been the hallmark risk mitigation technique of IT for 40+ years and is unlikely to change soon.  And why is this the case?  Because the business wanted it that way.  Consider it the unintended consequence.

Wednesday, April 2, 2014

The Blinding Light of Big Data

I have held back from publishing this article because I'm not sure I'm using the right terminology.  I am not a data wonk.  However over the past six months my interactions with those who are heavy weight experts have convinced me I'm on the right path and need to start the dialog.  So my apologies if my point isn't crisp and please ask me to explain what's not clear.

I feel the bright light of interest in Big Data is casting a long shadow over the convergence of two significant challenges: depth of executive experience with analytics and their understanding of the limits on predicting the future.  Good executives want data to drive their decisions and big data promises to expand the range of leaders using data to draw conclusions.  However I see nothing being done to protect us from a group of leaders thrust into a dependence on data analytics without insight into how to compose analytic groups, an appreciation for the limits of predictive models, and knowledge of the law of unintended consequences.

One glaring reality today is the lack of available analytics talent; we simply don't have enough people with the requisite skills.  The subset of people with the requisite background and experience who can turn the torrent of new data into value is significantly smaller than the need.  Equally important, that subset is not exclusively statisticians and modelers.  We need diverse teams to protect us from weighting conclusions too heavily based on natural biases of one group of professionals.  Picking on one of those groups of people, my favorite quote  "A statistician is a person who can draw a straight line from an unwarranted assumption to a forgone conclusion" reminds me every professional has their own set of biases.  A diverse steam will improve the quality of data used, models developed and conclusions drawn.

Second, leaders need to understand the limits of predicting the future.  We often find data which purports to predict the future through the application of some algorithm which was likely developed through the use of historical data.  But how well does the algorithm's predictions predict the future?  It depends on how much the future repeats the past.  As long as things stay within tight bounds, the future is reasonably predictable.  Yet few leaders understand the stress points of predictive models, the data elements where small changes can generate a wide variance in results.  Our financial meltdown in 2008 was precipitated by an untested boundary condition; what happens to the value of Mortgage Backed Securities (MBS) when the default rate goes outside of historical trends.  Unfortunately we all experienced the result.  Whereas in statistics we were taught such testing is imperative, for some reason it was entirely ignored.

Predictability is the byproduct of repetition without variance.  However today we live in a world rife with innovation.  When innovation happens, analytics are of little help to comprehend the impact until the population size is significant enough, by which time the opportunity door is closing fast.  Once a model seems to predict the future it seems all consideration for its continued accuracy evaporates; worse when it directly correlates to making money.

Finally we cannot overlook the impact of unintended consequences.  There is a well known story of a retailer using analytics on purchases to identify a woman was pregnant, only to inadvertently notify her family of her secret by sending her targeted marketing products.  This is one of many unintended consequences of making decisions purely based on data.  I am convinced concern about unintended consequences needs to be part of the thinking.  As William Gibson said "the future is already here but unevenly distributed".  Having a diverse team is a key element of limiting this negative side of analytics, however executives need to be sensitive to it as well.  One angle I use is to consider the motivation of participants versus the actions being considered based on the conclusion.  If the motives don't match the actions we open the door for an unintended consequence.  Returning to the retail example, the action to communicate marketing information did not align with the motivation of the buyer; the conclusion that all buyers want to save money was false.  Where the retailer failed was in not considering the buyer's motivation which was not possible given their limited data set.

My greatest fear is the widespread movement evolving which attempts to replace intuition with modelling.  I've already seen the seeds of this being sowed in books and articles from highly respected authors casting doubt on the value of intuition.  Intuition helps one to see the future whereas data can only help one to project the past into the future.  The difference is significant.