Lessons of Moore's-Law History 3: The Consumer is the Chasm

In the previous two blog posts, we have seen (hopefully!) the importance of betting on technologies and suppliers that most closely resemble the silicon-transistor-chip evolution process, and the need to bet on suppliers adhering to that process that emphasize infrastructure software combining openness to technology evolution with support for existing systems – but we have not dealt with the “chasms” that occur when Moore’s-Law evolution results in new, radically different higher-level technologies. 

Here I am combining two books popular back in the 1990s:  Crossing the Chasm, by Geoffrey Moore (no relation), which talked about how innovative startups bridged the gap between early techie adopters and the bulk of the market; and The Innovator’s Dilemma, by Clayton Christensen, which argued that “disruptive innovation” driven by new technologies would over time change existing markets profoundly, usually to the detriment of larger, well-entrenched companies presently dominant in an industry.  In an industry like the computer industry (including software and services), where Moore’s Law drives a more rapid pace of underlying improvement to platforms, entrenched companies must often react to disruptive innovation by “crossing the chasm” rapidly with their customers to the new technology, else they risk losing their markets and their ability to survive.

When A Company Is Ultimate-Customer-Blind

The 1990s and early 2000s gave us a good set of data on who succeeded and who failed in “crossing the chasm.”  The results were surprising to many.  It seemed reasonable that a Sun or an Oracle would surpass a Microsoft, that a Digital Equipment would beat a Novell, that an IBM would dominate a Compaq or a Dell in the PC market.  And yet, here we are 25 years later, and by no stretch of the imagination can we say that these things happened.

It seems to me that a common theme of this litany of successes and failures at adapting to new Moore’s-Law technologies is that companies that sold to the business market failed far more often than companies that sold to the consumer market.  Sun, which did a superb job jumping from the workstation to the server market, failed among other things to note consumer-driven Linux/Windows open-source software that was undercutting its prices and playing better with the server farms that later led to public clouds.  IBM’s initial great Charlie-Chaplin PC marketing fell before the next-day delivery and Intel allegiance of Dell and Compaq.  On a smaller scale, we saw Informix’s Oracle-protected VAR channel fail fatally when Informix failed to reach through that channel to detect that the servers it was shipping to VARs were no longer being bought at a comparable rate by the ultimate customers. 

On a longer time scale, the proprietary chip sets of IBM, HP, and Sun/Oracle have steadily lost ground compared to Intel markets.  It may seem logical that IBM over the last few years is walking away from the PC, starting with its consumer PCs/laptops; but that approach has steadily given it less and less sense of trends in the consumer market, and that, Moore’s-Law history would suggest, is dangerous.  It was once assumed that one’s employees would buy consumer versions of what a business-focused computer company offered; but, starting in the 1980s, the opposite has appeared true:  Word/Lotus, Excel, presentation software, laptops, smartphones, and social media have all been imported into businesses before and despite corporate standards.  If a computer company wishes to handle a disruptive innovation, Moore’s-Law history suggests, it must immediately sense the consumer movement, and then follow the consumer across the chasm, and quickly.

The Top Line is Now the Bottom Line

From 2008 on until fairly recently, a focus on cutting costs allowed companies like IBM, Oracle, and HP to look good to Wall Street although revenues were slightly down to slightly up.  Now, however, the contrast with consumer-focused companies like Google, Amazon, and Apple that continue to grow revenues by leaps and bounds is becoming all too obvious, and the chasm to cross to support the new mobile and cloud technologies is becoming wider by the day.  Generalizing to other industries, it is not enough to keep in sync with a Moore’s-Law-related process and focus on flexible, forward-compatible software.  One should also focus on growing top-line revenues by crossing the chasm with the customer. 

That means aligning platforms with consumer-successful companies like Intel.  That means aligning strategies with Google's, Amazon's, and Apple’s smartphone and cloud technology changes.  And that means far better coordination with and analytics about ultimate end-user customers – e.g., agile marketing.

Moore’s Law won’t last forever – it probably won’t last more than 5-10 years longer.  But the lessons of its history will last much longer than that.  Caveat seller.

Data Virtualization: Sorry, Forrester, It Seems I Disagree

According to Barry Brunelli of Techtarget, a recent Forrester Research report places IBM and Informatica at the top of the heap, ahead of Composite Software and Denodo – and I disagree.  I do, in fact, have a lot more respect for the data management folks at Forrester than I do for their development folks, who produced a  report a couple of years back with a very poor (imho) understanding of the nature of agile development.  And I do believe that Forrester deserves credit, compared apparently to Gartner, for recognizing both the increasing importance and the ongoing potential for business benefits of data virtualization.  However, I would continue to put Composite Software (now under Cisco) and Denodo ahead of IBM and Informatica in functionality, fit to customer need, and ongoing value-add in the immediate future.  Why?

The Importance Of Paying One’s Dues In Data Virtualization

The understanding of Composite’s and Denodo’s advantages begins with the fact that since the beginning, it has often been confused with a technology originally called EAI, or Enterprise Application Integration.  Both integrate data; but they start from a foundation that aims data integration at very different purposes.  EAI originally aimed (and still does, in some cases), to pass data between two or more enterprise applications, such as SAP and Oracle Apps.  As a result, they created gateways that converted this (usually bulk) data to a common format, and then retranslated as necessary to pass to the target enterprise app.  As it turned out, this conversion to a common format is exactly what is needed to provide a front end to handle data streaming to a data warehouse – and thus, EAI and ETL (extract, transform, load) tools share a fair amount of functionality.  However, there is no sense of urgency about this conversion; it is for populating a database, not for immediately providing an answer to a query.

By contrast, data virtualization from the start aimed to provide querying (and, eventually, updates) across multiple databases and data management tools.  That, in turn, meant leaving most of the data on the device on which it already resided, and converting and combining only those parts of the data needed for a result – and so, high-performance querying became part of the package from the get-go.  Moreover, figuring out how to optimize queries in this way effectively takes quite a while, and new data types (e.g., social media, Hadoop) and data stores (e.g., data from multiple clouds) keep coming along and must be handled.

As I recall, Composite Software have been continually refining their software since at least 2003.  IBM originally had a matching product (now apparently part of InfoSphere).  However, in the mid-2000s, IBM chose to focus on the newly-acquired Ascential (more of an EAI-type product) instead, and only recently have they begun to re-focus on data virtualization technologies, with the acquisition of an unstructured-data virtualization company and with increased (and welcome!) attention paid, notably during the recent Information Management conference.  Based on my last conversations with IBM, I suspect that they have a fair amount of work still to do to upgrade the unstructured-data acquisition’s cross-database querying with many more use cases, from cloud data to object, streaming/sensor, data-warehouse, and IMS/Informix data types – not to mention integrating it with Master Data Management, operational-data querying needs, and features such as information governance.  And, of course, I’ve left out such newer functionality as cross-database updates, cross-database access control, developer support, and administrator support.

Informatica, apparently, is starting from behind what IBM has.  For most of the last decade, it has been playing in the EAI and “data integration” (including ETL) space, but only over the last two or three years has it publicized its “data virtualization” capabilities – nor it is clear where it got its cross-database querying chops.  Certainly, most of the smaller players from 10 years ago are already acquired, and suffering under the negligent hand of their masters – Oracle, for example, acquiring an already-neglected AquaLogic product with its buyout of BEA.  In similar fashion, SAP has wound up with a Sybase-acquired product, and Red Hat with the granddaddy of data virtualization, MetaMatrix.  In any case, large marketing claims do not substitute for a demonstrated pedigree of functional development.

Lessons For Users

So where do I view Forrester as having gone wrong, and how can IT buyers avoid buying less than the needed functionality?  I don’t know for sure, but I suspect that underlying the Forrester take was (a) confusion between EAI-type and data-virtualization-type “data integration” as well as a misunderstanding of what “data virtualization” really means, and (b) a subconscious belief that when a large and a small company say they have something, typically the large company wins because of breadth of features and support.

Let’s take the confusion first.  I am one who wonders if “data virtualization” hasn’t caused as much confusion as attention. Originally, the technology was called Enterprise Information Integration, which at least gets across the idea that the technology delivers value-add (timely, cross-data-type-contexted “information”).  “Data virtualization”, however, suggests that the main value of the technology, like that of storage and server virtualization, is to provide a single view that allows better load balancing.  On the contrary, data virtualization products also provide the basis for global metadata repositories, distributed master data management data-store query optimization, cross-the-hybrid-cloud data discovery, developer data abstraction for longer-lasting code, single-key cross-database administration, and semi-automated data governance, not to mention cross-cloud querying.  Given these additional features, users, unlike Forrester, must carefully probe whether vendors aside from Composite Software and Denodo are really walking the walk.

For the same reason, (b) doesn't apply:  you can’t simply feel comfortable with the large company’s features and support, because the features and support may very well not cover the types of things that data virtualization does well out of the box.  To put it another way, at present, IBM and Informatica have excellent and extensive data-integration and EAI features; but trying to do flexible data management, Web data discovery and ad-hoc querying, near-realtime data warehousing, and global metadata repositories for data governance without a well-optimized data virtualization product is like trying to fight with one hand tied behind one’s back. 

Data virtualization now matters more than ever to you, the IT buyer.  Forrester admits it, IBM admits it, and it seems that folks like Microsoft are now beginning to admit it.  If you don’t get 90% of the potential benefit because someone told you to use flawed criteria, you will therefore be missing out on the things that make companies like Qualcomm achieve real value-add, not just now but well into the future.  Whether I’m right about Forrester or not, the important thing is not to sell data virtualization short. Now, go out and kick those tires – the right way.

A Lousy Job Of Embalming Microsoft

So now that Steve Ballmer has announced he’s leaving, the usual suspects – and some on-high commentators who also seem to be missing the mark – have gathered to criticize Microsoft via Ballmer.  I am no unthinking fan of Microsoft, and I have had my share of strong disagreements with their strategies and solutions over the years.  However, I have found myself in the last decade defending them frequently, simply because their critics seemed to fundamentally misunderstand them.  And now, here we go again.

Let me start by a quick thumb-nail sketch of Microsoft’s history and nature as I see it – not at all the typical view presented these days.  First of all, Microsoft started by finding itself (by virtue of a lucky contract with IBM, then dominant in PC hardware) with an incipient monopoly in operating system software.  The key word here is software – that uniquely flexible foundation for building “meta-solutions” that adapt more rapidly than anything else as technology and customer needs evolve.  Microsoft, even then, had two valuable characteristics:  it could create foundational software like Microsoft Word that was “orthogonal” – it was relatively user-friendly because the commands it offered were relatively compact and powerful – and it could keep at a solution until it got it right.  As a result, Microsoft was able to use its favored position in operating systems to create an effective monopoly in office-system suites, especially when the Windows version of its operating system led to the success of the mouse-using “graphical user interface.”  All of this pretty much happened in the 1980s.

Also in the 1980s, Microsoft took the tack of “rough and ready” versions of the operating system open to developers, and it took great pains to nurture those developers.  Here, I am talking about the ecosystem of developers in their time either outside of their employer, or in small companies dedicated to producing Windows versions of applications.  Apple, by contrast, wanted to control the “quality” of the operating system and sell hardware, so Apple’s share rapidly shrank to the entertainment and education markets, while Microsoft took a larger and larger software share of PCs that were now becoming ubiquitous and the machine of choice as cheap scale-out servers.  And so, by the end of the 1990s, Microsoft had adapted enough to the Internet via Internet Explorer to ensure a strong presence in scale-out server farms (the ancestors of the cloud), in businesses, and in the PCs usually used to access the Internet.

What followed in the decade of the 2000s was not so much the encroachment of competition from the likes of Google and Apple as saturation of existing markets.  Under Ballmer, Microsoft adapted to federal regulation by making a sort of peace with its enemies, from Sun to Apple to IBM, so that Microsoft joined the pervading “coopetition” approach to the market.  Far more importantly, Microsoft got business customers in its “DNA” by hires and investment, so that Microsoft was able to balance its customer markets with a secondary but still very important business market relatively impervious to saturation of the customer market. 

Over the last five years or so, however, Microsoft has finally begun to stop seemingly defying gravity in its rapid revenue growth.  Microsoft could see the handwriting on the wall, and has been trying to establish a strong position – necessarily, via innovation – in new markets.  It has had some success in innovating, and therefore reaping a strong market position, with video-game Kinect, and Windows 8 does represent some needed innovation in its present markets.  However, this is too little to move the revenues dramatically in what is a very-large-revenue company.  And it’s not clear what will change that.

What’s blocking Microsoft is fundamentally the hold of Apple and Google on developers of smartphone apps.  It’s not that Microsoft has lost its own developers, but if it wants a good revenue jump it needs an equal or greater mass of developers in some other market where it is a credible competitor.  And recent moves to establish its own app-development encouragement suggest that it’s not doing enough to create a relatively friendly app-dev environment in mobile smartphones/tablets.

The Usual Misunderstandings

You see very little of this in today’s commentary on Steve Ballmer.  There’s notes about how he didn’t understand Microsoft’s friction with US and EU governments about its monopolies – actually, that has died down, precisely because he did enough to defang much of it.  There’s the “dominance of iOS on mobile” – granted, Apple’s laptops have also made major inroads in the PC/laptop market, but that still leaves Windows with the overwhelming majority of sales and existing boxes, and, as noted, the non-smartphone/tablet market is saturated but not in a major decline, especially if you look at IBM Windows-PC sales compared to Unix/Linux.   There’s “it’s all about Apple design”, while a cursory inspection reveals that Samsung Galaxy based on Google Android is more than competitive with Apple design these days, and Samsung is seeing the results in its sales.  I’ve already noted the exceptions to the “Microsoft can’t innovate” mantra.

Then there are the more global economic commentators – now that they’ve finally realized that computing software matters, although they’re still underestimating the importance of software compared to hardware.  Prof. Krugman posited that Microsoft might do OK despite Apple’s seemingly obvious and eternal dominance, because it is now focused on business.  Um, no, Microsoft’s revenues and employees are still not primarily focused on business.  Microsoft will do OK because its present markets are saturated, not defunct, because Apple’s relative “innovative” status is going away where it counts, in the actual products, and because Apple is not doing enough for its own developer ecosystem.  Hence, as the need for larger-form-factor products for the enduring popularity of longer blog posts (like this!) persists, Microsoft will get its share via not-dead-yet laptops and their hybrid variants.

Or, we can cite Alex Tabarrok as simply noting that Microsoft’s stock jumped when Ballmer announced his resignation.  Sorry, the stock market is notorious for predicting 9 out of the last 3 recessions, as the old saying goes.

Above all, commentators continue to misunderstand the ongoing global economic contribution of software to such factors as productivity and “technological” advances.  Microsoft’s commitment to a developer ecosystem and its “orthogonal” designs (in the best cases) are simply examples of how the software industry enables a welter of new applications that actually do, once the dust finally settles, produce fundamental innovation that leads to productivity improvement as measured by our economic statistics. 

Resignation?  Who cares?  I don’t.  Microsoft?  Who cares any more?  I do; and you should.  For all the reasons no one seems to be talking about.

We Have Blamed the Vendors and IT; Is It Now Time to Blame the Business Types?

Reflecting on the recent financial reports of the giants of computing infrastructure – companies like IBM, HP, Oracle, and Microsoft – I am struck by the continuation of a troubling trend:  The technology and its usefulness is moving ahead as fast as, if not faster than, ever before; IT has made some striking adjustments; but the revenues to vendors from this, and therefore IT spend, are flat if not falling. Why?

Let’s see.  In databases, Oracle and IBM are almost flat, despite (at least in IBM’s case) two technologies in two years that potentially deliver performance and price-performance gains well ahead of Moore’s Law.  I find plenty to criticize in Windows 8, but its implementation with regard to business needs, in Windows and Office usage, should deliver benefits comparable to that of the introduction of the mouse-based user interface – and Microsoft enterprise revenues are flat.  Cloud technology is not replacing, but rather complementing, in-house architectures, and therefore should require, initially, more rather than less total spending – and yet, increases in cloud spending are instead paired with overall flat or lower user computing spend.  No, I don’t buy the “public cloud saves money” rationale – the benefits come later rather than sooner, and involve mainly flexibility and to a much lesser extent multi-tenancy and grid savings, especially when many users are employing multiple cloud providers that must be coordinated.  And the IBM and Oracle/SPARC data suggest the biggest hardware decline is in Unix/Linux hardware, the darling of the cloud set, not in mainframes or PCs. 

In the past, a typical explanation for this has been the need for the business to cut back IT spend in response to decreasing profits.  Of course, that did not prevent IT and computing from becoming a larger and larger part of overall business spending during the 1980s and 1990s.  In the early 2000s, IT spend actually decreased proportionally to the cost-cutting of the rest of the business – and yet, despite an initially tepid overall business recovery, computing spending from about 2003 to 2008 rebounded and grew quite briskly. So this pattern of user computing spending is unprecedented in three ways:  its length, its unremitting focus on cost-cutting apparently comparable in size to the rest of the business, and its seeming inability to reflect major technology advances with a good claim to deliver major cost or other benefits to the rest of the business.

The Blame Game

In the past, I have found, when things go wrong in computing spending the first reaction is to blame the vendors.  In this case, the obvious critique is that they have failed to communicate the benefits of the technology, cost and otherwise.  Except that, as I can attest from my experience as an analyst, both the technology advances and the communications from the vendors are as good as, if not in many cases better than, those of most or all of the past.  Big Data, for example, is not pure hyperbole, and vendors have done a reasonable job by traditional standards of highlighting the ability of targeted Big-Data analytics to explain and bind the customer as never before, in a cost-effective way. 

OK, then the next line of analysis is to blame IT, typically for failing to align themselves with the business’ strategy, or (when times are tough) identify ways of lifting the dead hand of older systems and other seemingly outdated costs.  To this, I have one response:  agile IT.  As never before, IT is aligning itself with agile development, by supporting a software lifecycle that includes operational feedback integrated with development, and by encouraging automation of functions that provides a basis for rapid response and identification of architectural problems and opportunities at the administrator level.  As my studies have shown, these plus the increasingly agile nature of the software that IT makes available to the business translate into major cost-cutting and major benefits beyond what traditional IT approaches can deliver.  And as for aligning itself with business strategy, IT is well aware of Big Data as a hot topic, hence the sudden and somewhat odd demand for Hadoop experts.  No, whatever IT may have done in the past, it seems clear that it has upped its game.

OK, then, who does that leave?  And yet, how can we blame the business types?  Aren’t these the same folk who drove those increases in IT percentage of spending over the 1980s and 1990s?  Aren’t these the most receptive listeners when we talk about the power of embedded analytics to improve business processes and the importance of analyzing the “customer of one”?

There’s Something Not Going On Here …

To understand why there might be cause for concern and, yes, for blame in business strategy – driven by the CEO, the CFO, and their staff – let’s look in very broad-brush terms at what has been going on, not just for the last five years, but for the last three decades.  Here are a few of the highlights, as suggested by various studies:

1.       Advances in productivity have been accruing 90% to the CEO – ½ in his income as CEO, and ½ in his increases in wealth as an investor.  While this still represents a fairly small percentage of overall expense, it does unnecessarily increase the focus on cutting costs for all except the CEO.

2.       Hedge funds that sometimes act as turnaround artists or “shadow banks” have taken a good 90% or so of the funds’ investment profits for themselves, and thus receive compensation not just in the tens of millions of dollars but, at the top, in billions of dollars, despite the fact that they deliver less to the investor than an index fund in most if not all cases.  While it is difficult to see direct effects of this “fleecing of the rich” on corporate strategies, one significant effect is to increase the fleeced CEO’s focus on growing profits uber alles, to at least get some return from their investments – with the Googles of the world the few happy exceptions.

3.       The present long-lasting recession/stagnation has effectively removed perhaps 10% of the workforce from work over a long period of time.  This has the effect, since businesses often discriminate against the long-term underemployed, of creating a permanent gap between “potential” GNP and real GNP.  To put it another way, even if the economy grows at a reasonable pace, it will still be much smaller it should be.  That means smaller markets and, again, more relative emphasis on cost-cutting to achieve profit growth rather than growing revenues to grow profits.

There are three concerns about this excessive focus on cost-cutting.  The first, which probably is not serious right now but is far more troubling than five years ago, is that cost-cutting eventually runs out as a strategy if revenues continue to be flat (and, don’t forget, revenues are pretty flat for the bulk of businesses, hence the 1% growth in GNP over the first part of this year).  The second concern is that this cost-cutting, applied economy-wide (as it seems to be doing), reinforces and cements smaller markets by eliminating the part of the market funded by the now underemployed.  Ordinarily, recessions are too short for this to happen; but it seems to be happening now, as new jobs continue to make no dent in the workforce/working-age-population ratio.

The third concern, which I for one find the most troubling, is the possibility that reflexive cost-cutting becomes the answer to every situation, the reflex of the business.  If that is true, it would suggest that businesses are blowing opportunities for savvy increases in computing technology spend because it’s all about cost-cutting, not strategic investment. 

Blame or Not, What Might Business Strategists Do Better?

First, I would suggest that businesses set up a long-term plan and process for using analytics to better understand and improve the relationship with the customer.  This means acquiring data virtualization software and the like, to allow aggressive searching out and combination of all the new customer information constantly being created.  It also means buying the new infrastructure tools (database and information architecture) that can scale to handle aggregation of social-media data on the Web and in-house customer-experience data. 

Second, I would argue (not that I expect many businesses to listen) that in order to grow revenues as well as profits (and probably grow profits faster), business types need to acquire and foster use of agile tools and processes – such as agile marketing, with its emphasis on data-driven understanding of the customer and prospect rather than the anecdotal, top-down opinion that has been all too common in the past.  As I noted in a recent blog post, an agile business is a possibility, if the CEO and his lieutenants really commit to it – and it typically means not only the CEO using a Scrum-type planning process, but also constant modification and constant feedback up and down the organization about plans.  I should also note that an agile process specifically builds in slack for learning and review, and yet (according to my studies) it produces much better cost-saving and profit results than a “cram as much work as possible in, be as cost-efficient as possible” approach, for the CEO as well as the developer.

And then there is the really controversial stuff. Sustainability efforts are, by and large, really underperforming, as a recent article in www.thinkprogress.com confirms my impression that gains in the US in carbon emissions are effectively coming from offshoring the problem.  Businesses need to acquire effective global carbon-tracking software, and use it.  Businesses need to join to push shared quality regulations that will move all towards new computer and software technology that is more productive, rather than clinging to the infrastructure of the past that in the long run costs more, as in the electrical/energy grid vs. software-coordinated regional combined solar/wind – and rather than enabling the block-all-regulation political excesses of the U.S. Chamber of Commerce and its ilk.

I’m not saying that all this is doable, or that the case for questioning the approach of the overall business to computing technology is clear.  I am saying that it’s time we recognize the seriousness of the overall problem of which computing spend is a part, and we stop blaming the usual suspects.  Five years is enough.  Thirty years is enough.  In the long run, a cost-cutting strategy is not enough – and the long run is beginning to arrive.   

 

GoodData and Grey's Anatomy: It's Not Always the Vendor's Fault

Recently I received information about an interesting new vendor called GoodData – it appears that they are specializing in BI-type user interfaces for function-specific dashboards (of course, in the cloud and involving cross-database information display).  One of these was GoodSupport Bash, a “bash-up” of customer-support metrics and KPIs (Key Performance Indicators).  And that caused me to reflect on the idea of applying these types of “comprehensive” metrics to customer support.

Customer-support technology has a relatively long software-vendor history:  I remember first seeing the idea in action back in 1982, when Computer Corp. of America’s email system was used to track customer inquiries. Our forms capability allowed us to enter the call info in an email, send it for resolution to the appropriate techie, and then see the “email trail” as the problem was resolved.  Like many small companies, we had a reputation for excellent customer support because the techies were developers who had spare time to actually solve problems and the clout to change the code base to do so.  In fact, I was once told that one “guru” would go to install at new sales and when the customer asked for changes, he would make them without documentation or changing the main code base, so that the next version overrode the customization – it made the customer very happy initially, and very annoyed a year later.

However, in the early 2000s, when offshoring came in, major-vendor customer support was one of the first to move, and the semi-mature customer-support software of the day now had to define support personnel tasks very carefully, as the new Indian and Filipino respondents had some difficulties with American English and more difficulties with being up to date with software technology.  The resultant upheaval in customer support has never really settled down again, imho.  Companies like Microsoft would really like to move away from live-person customer support entirely, but the complexity of the technology means that users keep having to ask questions of live representatives who can be forced to pay attention.  For example, it was not until I got to a live representative that I realized that the Windows 8 version of Windows Media Player does not support DVD playing out of the box (according to PC World, you need the $10 Media Pro or some such).

Well, this in turn meant that phone-based customer support had to be “optimized” in some way, else waits of 2 hours and unresponsive representatives would cause a significant black eye for the company providing the support for its products.  And so, solutions like GoodSupport Bash allow companies to “take the temperature” of their customer support according to all sorts of criteria, like speed to reach a representative, time taken by the representative, and whether the representative is taking the opportunity for up-sell or cross-sell.

Except that I believe that the results of this kind of fine-tuning according to various management theories can sometimes actually be counter-productive – and the fault lies not with the customer-support software vendor.

Grey’s Anatomy and Ice Removal

I must confess that Grey’s Anatomy is one of my guilty pleasures, and I often find it difficult to watch without rotfl.  To me, GA is the ultimate in actor torture.  In just about every episode, at least one and often several actors and actresses must somehow make credible a complete 360-degree turn in their characters, with the words they are given to do this stretching the limits of belief that anyone could talk in this way.  As character after character changes partners or sexual orientation on the verge of a marriage and in the middle of a surgery, I can almost imagine the reaction as that actor or actress sees the week’s script – bracing themselves, and yet unable to anticipate the next contortion.  Although there are limits:  no one has taken a sudden interest in animal husbandry – yet.

Anyway, in this episode, doctors facing a hostile takeover of the hospital went to find out what this new highly-efficient corporation was doing, and posed as patients.  The doctor started reading from a scripted set of questions, and when the “patients” attempted to derail them, started the script over again until they could get it done.  This was done, we are assured, in the name of standardized efficiency.  You will recognize the similarity to some customer support experiences.

However unlikely the situation, the GA actors and actresses, trained to make the unlikely plausible, made it very clear how customer support driven by metrics appears to the customer.  The caller reporting something that often is unusual must be fit into a series of questions that attempts to cover only the usual.  Callers who do not fit are effectively ignored – you could share the frustration as the “patient” attempting to vaguely describe symptoms is asked to spend much time talking about things that are not problems, and the feeling that no one is listening.  At one point, the “patient” confesses that he is a doctor, pretends he’s already a new part of the company, and asks why these procedures; the “representative” assures him in a scripted manner that the procedures are more effective – except it’s clear that he must answer that way, no matter what he thinks, or lose his job.

Sound implausible?  Well, I actually went through a similar experience the other day, when I tried to see if a national gutter specialist could provide snow and ice removal on an emergency basis.  Sure enough, I wound up with a representative who took some time to realize that I was not calling about gutter cleaning, finally said that the company did not provide the services, and then spent three minutes attempting to upsell me on a bigger gutter cleaning contract, despite the fact that (a) I said twice that I was not going to do so, and (b) I had just been told that they didn’t do something else I needed.  It was clear that this was one of the metrics by which he was judged:  did you run through all the questions?  Did you upsell? You can imagine what I felt about the company afterwards – and it wasn’t the representative’s fault.

Sloan Management Review, iirc, had some interesting data on the effect of this type of thing on customer loyalty.  Often, the most loyal or biggest-spending before the disappointment were the quickest to jump ship – they felt “entitled”, as it were.  But even the truly loyal needed customization that truly paid attention to their needs, else they too found it difficult to stay.  Branding only goes so far.

My point here, however, is that none of this was the customer-support software vendor’s fault.  On the contrary, whether the company’s support strategy is good or bad, the typical indication from users is that something like GoodData Support Bash will make it more efficient.  If the strategy is bad, however, it may also make it less effective, or more harmful.

The Bottom Line:  Metrics Is As Metrics Does

It is now a truism of management theory that metrics create behavior – employees game everything to their own advantage, or to minimize their own disadvantage.  And so, users of customer-support software like GoodData Support Bash badly need to remember that the most effective use of any customer-facing tool is not to create representatives who will deliver the company’s idea of customer happiness via efficiency, but to find analytics to understand the customer better.  Only after that knowledge is gained and the company has used that knowledge to improve customer support responsiveness to real customer needs should efficiency metrics be considered.

To put it more concretely:  among the metrics in your dashboard, is at least one telling you honestly the degree of customer satisfaction with the interaction?  And are you mining data from that metric telling you what’s good and bad about your present solutions?  If not, are you really making things better with your improvements in customer-support efficiency?

Or are you making them worse?

Life in a Software World

Various tech-industry “visionaries” have proclaimed that we are entering an “age of software”, pointing to the importance of software to today’s solutions and the world economy.  By and large, I agree that “all things software” is more and more a differentiator among companies, a focus of work, and a prevalent element of life outside of work.  However, it seems to me that no one has fully defined just what life in a “software world” will mean – what it has meant for those who participated in its formative stages.

Below, I lay out five things that I believe are worth considering as unique characteristics of enterprises in a “software world”.  They are my own point of view, based on 12 years in firms as a software developer informed by the business theory I learned at Sloan, plus 22 years as a computer industry analyst.  Everyone will have his or her own list; I’d just like these potential aspects of the “software world” to be considered along with the rest of those lists.

Project Management, Not Manufacturing

In a typical manufacturing firm, the norm was and is production of pieces of hardware.  In a so-called “services” firm, the norm is delivery of pre-designed service “solutions”, and therefore there is a reasonable analogy to manufacturing.  In a software firm, the cost of actually creating copies of a software program is pretty close to zero.  Instead, the focus is on creation of the next version or fixing bugs in this version.  In the manufacturing/service firm, the focus is often on optimizing the process of producing the same thing over and over.  In the software firm, the focus is on developing new features so customers don’t walk away.  And so, these firms seek to optimize new-product development – that’s the critical success factor for a software firm.  Does anyone suppose that if Google had simply cut the costs of servicing its search engine to the bone as its main focus, it would have survived, much less thrived?

But if ongoing success is a matter of new-product development, then it follows that success comes from successful development-project management, not optimization of the supply chain.  Apple grows profits and revenues while most if not all hardware/service companies achieve only flat revenues and lesser profit growth, and clearly the iPhone and iPad rather than Mac production optimization explain this. 

In fact, I believe that this changeover is also happening in so-called manufacturing and service companies.  50 years ago, it was still possible to say that the bulk of workers in a company were production workers, with support staff a poor second.  Today, pick any company and you have far more people in clerical and new-product development plus management roles, and those “clerical” and “managerial” tasks like administrative assistant, web designer, and product marketer are to a much greater degree about producing new solutions.  And so, the management of innovation projects is becoming much more important in traditional firms.

Accounting:  No Inventory, No Capital Stock

All right, that’s a bit of an overstatement, but not much.  Software requires very little if any hardware to produce these days:  just download it from a web site a small chunk of whose servers you have leased from a public cloud.  That cost is there no matter how many copies are downloaded of the single stored copy there.  What widgets of inventory?  What capital stock of hardware-producing machines and factory buildings? What FIFO vs. LIFO?

It seems to me that this software world therefore puts our favorite financial metrics out of whack.  Is inventory turnover really telling us that the firm is about to fail?  How about sales turnover?  If we are investing more in capital than in labor, aren’t we optimizing a non-existent manufacturing process rather than new-product development?  So are our metrics telling us, not that a firm is succeeding wildly, but that it is headed for failure?  And how do we tell what a successful new-product investment strategy is from our books?  For the last 20 years, since the Harvard Business Review suggested computer companies’ futures were in services rather than hardware, IBM has been focusing on both innovation and services rather than hardware, with strong metrics and some profit growth – but, especially lately, revenues have been flat overall, with only software showing strong growth over the entire period, and services effectively beginning to prosper only when IBM delivers innovative software. 

I confess that I have no strong sense for what the new accounting and the new financial metrics should be.  There needs to be some way to measure new-product development success and detect failure, and isolate it in the company’s books – but my textbooks tell me that projecting the revenues from software development is highly speculative.  Maybe so; or maybe Microsoft does have at least some handle on how many copies a new version of Windows will sell, so it’s not as bad as all that.

It’s About Growth, Not Optimization

One of the biggest shocks in the two software companies I worked in for 4+ years was the way that expectations escalated.  Every year was the baseline for the next year, no matter how good, and I was expected to do more in some way:  produce more code, do more tasks, whatever.  In fact, after the first two companies I started to assume a general curve, in which the 3^rd year was necessarily disappointing to my bosses – who, by the way, were never the same at the end of the year as they were at the beginning – and sometimes I wondered if the fourth simply laid the grounds for the end of my employment.  It wasn’t that I wasn’t producing; it was that I wasn’t producing that much more. And, in point of fact, until a change of strategy my fourth year there, it looked as if Aberdeen Group was going the same way.

Recently, I met a car salesman who, as in many traditional jobs, had been there for many years.  It was clear that he was not constantly faced with rising expectations; the fact that he continued to excel compared to others was reason enough to keep him.  Clearly, an auto dealer is not yet a software company. Why the difference, I keep asking myself? 

I would suggest that in a software world, optimization of the machines that support a process simply does not speed up software development significantly – and yet, managers want to grow both revenue and profits.  The obvious answer:  everyone must do more. No matter that it does not fit the mold of unpredictable new-software development, or that it is a one-size-fits-all approach to labor; it must focus on growth of production rather than accepting optimized production, in order to conform to the expectations of the firm.

I don’t say this is good or bad, although I have my opinions.  It does suggest to me, however, some reasons why agile software development seems so inefficient and yet produces such great bottom-line results.  That is, agile development removes the ability to demand more from each person – it changes the metric from lines of code per day or some such to customer satisfaction.  And so, the company can have its bottom-line growth without efficient, ineffective programmer optimization. Because today, more and more companies, like software companies, are thinking dynamic, not static:  seeking growth in unpredictable markets with changing consumers, not seeking to optimize the supply chain or manufacturing process in more stable markets.

It’s About the New New Thing, Not the Success

In some ways, this is a repetition of my first assertion.  In a software world, especially a “virtual” one, lack of hardware means relative lack of chains to keep the customer attached – or to make the customer feel trapped.  It amuses me to see the venom attached to Microsoft Office as a boat anchor for innovation, when in point of fact it is far more innovative than, say, the gas station or the fashion industry.  And that’s the point:  software firms have a significant amount of new content in each version of each product, because they have to.  They don’t focus on success by standing still or recycling:  they focus on adding features.

I see this as a real problem for our present foundations of microeconomics.  I believe that Paul Krugman wrote recently that static manufacturing firms producing widgets in microeconomics indeed did badly as a model of real firms and markets, but that was OK, because it captured the essentials needed for effective macroeconomics. I question whether that is still true in a software world.

Specifically, it is a recipe for underperformance of macro-economies. For example, over-investment in capital that represents the capital stock necessary for optimizing a manufacturing process and under-investment in labor that represents new-product development may show decreased costs, but it also does less well at meeting consumer needs, and hence static IS-LM curves are achieving less sales at a price than they could – the economy is under-performing not just relative to “potential GDP”, but also “potential GDP” at full customer satisfaction.  Or so I wonder.  This wasn’t a clear problem when we had no alternatives; but now we have a software world.

Flexibilists Rule

This is perhaps the most speculative of my suggestions – and I realize that’s saying a lot.  There’s been a lot of attention paid to Brynjolffsohn’s assertion that increased automation via computing has meant the loss of jobs, even including knowledge workers, and it’s not clear if and when that will reverse.  I would suggest that while that may very well be true of manufacturing and service/support jobs, it should not be true of new-product development jobs in a software world, and, in the best software firms, like Google, it isn’t true. 

For one thing, software automation is very far from replacing programmers.  This is something I’ve been arguing for thirty years:  programming is partly creation of new mathematics, and that’s something that we are not near automating (and yes, that’s very distinct from the “I get human semantics” analytics of Watson).  For another thing, programming in a software world is usually about development of new features, i.e., new-product development, and it makes sense to invest there rather than in non-existent inventory management.

I say, it should be true, but I recognize that in many firms, even software ones, programmers are thought of as disposable and capital investment as to be preferred – e.g., the offshoring of development that is still ongoing, even in areas where the supposed lowered wages are pretty much vanished.  That is partly because, I think, these firms tend to think of programmers, and marketers, and so on as narrow specialists, so that as technology and so on changes, it becomes necessary to hire someone who knows the latest language.  That is completely untrue of most of the programmers I know; in an environment free of threat to their jobs and with a little time to burn, they are the most avid consumers of new programming technologies, and a wide variety of other things.  They are flexibilists, not specialists.

The same, it seems to me, can be said of the agile marketing movement, which is – surprise, surprise – strongly associated with the software industry and software-heavy firms.  Agile marketing is more and more about flexible creation and delivery of new product – in fact, a continuous feedback loop of such creation.  And testimonials from practitioners echo those from agile programming shops:  it does better, and people stay around longer.  In a software world, or at least in a better one than we have today, flexibilists rule.

Conclusion

Them’s my preliminary thoughts.  Reactions?

Why Can't the Mainframe (and the PC) Stay Decently Dead?

For more than 23 years, as an analyst, I have been watching various industry observers suggest that the end of the mainframe may be nigh.  For nearly that long, I have been hearing voices proclaiming the nearing death of the PC. For the mainframe, it was the proprietary architecture, the lack of developers, the aging of administrators, or the rise of scale-out.  For the PC, it was the expense of personal storage and its administration compared to the “network computer”, the lack of Web savvy, the large form factor compared to the smartphone, and the unaptness to touch gestures.

So what did the 2012 markets say?  Astonishingly, after a 10-20 % dip in revenues in the first 3 quarters, IBM saw a 56% mainframe-revenue jump in the fourth quarter, to overall revenue growth in 2012, better than the decreasing revenues of its Unix/Linux (System p) and Windows (System x) alternatives.  This extended the mainframe’s 3-year streak of revenue (and therefore income) growth. 

IBM was not alone in seeing these results.  HP apparently saw greater single-digit decreases in Unix/Linux than in PC revenues.  Anecdotal evidence suggests that Oracle/Sun Unix/Linux revenues continued to decrease.  And, of course, touch screen in a PC form factor had only just begun to arrive at the end of 2012 (with Microsoft Office in a hybrid tablet/PC configuration only apparently arriving some time in 2013) – so the PC was competing with smart phones and tablets with one hand tied behind its back, so to speak.

In fact, if 2012 was proclaiming anything, it was suggesting the eventual death of Unix/Linux (no, I don’t believe that either).

In an era in which IT is apparently content to spend the same amount each year on computing, much of it on saving costs – thus increasing computer maker profits but keeping revenues flat – it is not likely that any of today’s form factors, including the smart phone, is going to attain the 50%-+ growth rates that we have seen in the past.  Thus, we may have seen the popping of the Apple stock-market bubble. And therefore, in the near future, I assert that neither the mainframe nor the PC is going into terminal decline – on the contrary, they should prosper modestly, as the mainframe has done in the last 3 years.

So why will all the cited disadvantages of mainframe and PC not lead to steady or sudden terminal decline in the next 2-3 years, but rather to sober growth?  Let’s take each in turn.

The Mainframe Is Fully Reinvented

When I first suggested to IBM that the mainframe needed to become a “hub” in the sense of a fully networked node of especial prowess in certain types of workload rather than in the sense of “either mainframe or something else but not both”, it was only about five years ago.  Since then, the process of turning the mainframe into something that is a full participant in the enterprise architecture has been pretty completely achieved.  All of IBM’s major enterprise software, from administration to security to data management to development, now is on approximately the same track inside and outside the mainframe, and most apps can easily and even dynamically move between mainframe and non-mainframe servers.  As a result, users find it far easier to employ the mainframe flexibly for its strengths in robustness, security, and high-end transactional scale-up. 

Imho, IBM’s mainframe plans for 2013 contain no such dramatic transformations – and don’t need to.  The “bridging” architectures of zEnterprise and (eventually) of PureSystems form a completely adequate foundation for future elaborations in mainframe-including enterprise architectures.  To the end user, developer, and administrator, the mainframe if needed can appear more or less as a transparent part of a fully modern overall enterprise architecture. My only caveat, as noted before, is that the mainframe fails to support Windows in public cloud architectures adequately (again, imho) – but that simply limits growth, it does not portend decline.

And so, the old objections begin to melt away.  Few argue that the mainframe’s proprietary architecture cannot keep pace just as well in the near future with the evolution of hardware/software technology. IBM cites reports that a new generation of system administrators is arriving, with adequate skills, and therefore some new buys are even showing a preference for z/OS up front rather than Linux.  While the mainframe will never rival the hundreds of thousands of apps for Apple’s OS or Android out of the box, there are few major barriers to software today, either. 

To put it in a nutshell:  the mainframe is attracting new customers, even in the US, because the old knocks on the mainframe no longer apply, leaving it to leverage its strengths in a particular segment of the market that will grow with the growth of usage of Big Data.

The PC Keeps On Doing What It Does Best

The argument for the PC’s continued success is of a different sort from that of the mainframe.  I say, rather, that the doomsayers of the PC have not failed to appreciate adequately its long-term strengths.

Let’s start with the “network computing” argument.  Yes, as with the “dumb terminal” before it, the “network computer” is cheaper than the PC.  However, one value of the PC has been its ability to store personal data and applications, whether as a “home within an office” that allows the end user to generate his or her own Powerpoints and spreadsheets, or as a bridge between home and office to allow work wherever.  That is precisely why the countervailing trend of BYOD (Bring Your Own Device) – which is just as much if not more about laptops than smartphones and tablets – continues to ensure a major presence of PCs both at home and at the office.

As for Web savvy, once again the smartphone’s evolution has proven the value of personal physically-next-to-the-user storage – whether it be stored song downloads or phone logs – and hence the smartphone is turning inevitably into a PC – but one in which the small vs. large screen carves up turf between the PC blogger and the smartphone tweeter.  And, of course, my experience with Windows 8, wrong-headed as I believe some of Microsoft’s decisions are (e.g., crippling the desktop screen out of the box), shows that touch not only is valuable to the PC’s core word-processing and navigating/data-organization skills, but that it is not as easily applied to the smartphone’s small screen without a comparable, complementary typing feature (hunt-and-peck still doesn’t do it). And so, not only in terms of Web usage but also in terms of end-user-friendliness, the PC still holds its share of the market; and, for the same reasons, should continue to do so.

Note that I say that the relatively large form factor is a plus rather than a minus.  I assert that there is a “finger limit” in which the typing necessary to create large-scale amounts of real content can only be accomplished on a sufficient-sized screen.  Call it semantics, call it deep analysis, call it whatever you want, 50 lines is about the minimum for a decent blog post that does not amount to dipping your finger into a very large pool – and, as in this post, 2-3 old-style pages are more like a comprehensive look at a subject.  Note that folks like Paul Krugman use shorter blog posts as “riffs” to serve as the basis for a comprehensive multi-page paper, not as the definitive word on a subject.  A mashup is no substitute.  And so, the PC’s combination of personal storage and relatively large screen/keyboard should see continued modest growth in demand, in whatever form, over the next 2-3 years – depending, of course, on the world economy.

Sacrificing Some of the Future By Discarding the Present

I am long since resigned to periodic outbursts of “the death of …”, which sometimes are justified (I for one don’t lament the effective death of the dumb terminal, having had the dubious joy of programming for it).  What I do object to is the way in which these persuade many people in and out of the industry that the architectures in question are indeed dead, which means we don’t need to think about them, which means we don’t leverage their unique strengths in the next generation of the world-wide web of computing. 

Specifically, the mainframe pushes the limits of scale-up computing.  While scale-out grid solutions may indeed have achieved some notable successes (I understand that Google has made notable advances by thousands-of-servers divide-and-conquer applied to language comprehension and translation), on a per-processor basis scale-up’s tight integration continues to offer frequent performance advantages over scale-out’s loosely-coupled networking. 

On the PC end, the latest tablet/smartphone user interfaces are crippled by a lack of appreciation of foldering’s static personal data organization as a complement to the Web’s dynamic search-based organization. Moreover, we continue to move away from the idea of a “virtual end-user space” in which the full functionality and data of one’s own PC is available anywhere, any time, whether we are connected to the Web or not (and this will be needed because we are still quite a ways away from always-available and always-fully-functional/personalized Web avatars). 

Most of us remember the Monty Python routine in Holy Grail where one person attempts to fob off an alive person on a “dead collector”, while the corpse in question protests “I’m not dead yet!” To which the seller keeps replying “Shut up.”  For very good reasons, the mainframe and the PC refuse to stay decently dead.  Could we please not keep telling them to shut up?

Microsoft Pledges To Go Carbon Neutral. Wow

I just read an article in the Washington Post stating that Microsoft aims to go carbon neutral in its direct operations, starting in fiscal 2013. As I read the details as reported, my reaction was what you see above: Wow.

Why wow? Well, they have reached the point where they have enough information on these operations to measure their carbon impact. Now, they propose to charge a fee to each business unit based on their lack of carbon neutrality. And that’s fundamentally different from what I’ve seen from any business before.

You see, this approach embeds drastic reductions in net carbon emissions in the company financials, and therefore in the company culture – and that is only one of two approaches that have proven to work in situations like this in the past (the other is “World War II in America”, where the whole population is mobilized to fight a global war on carbon emissions). No other company approach that I have seen delivers the potential message: you don’t do this right now, your business (in this case, business unit) goes under. And that message is precisely appropriate to the situation we find ourselves in.

At this point, some readers should be itching to point out that Microsoft is applying this method far too weakly and with far too narrow a scope. And that is true, too. It appears that the fee is not yet strong enough to clearly deliver the “out of business” message. Microsoft needs to apply this to indirect operations, else its units will simply offload the emissions to outsourcers. Microsoft needs to re-source its energy almost entirely to “clean” energy sources – and that doesn’t mean natural gas. Microsoft needs to reduce the net carbon emissions associated with use of its products (almost entirely on PCs) to 10% of their present level. And, last and least, Microsoft needs to reduce its employees’ carbon emissions from travel to and from work to 10% of their present level. Oh, yes; Microsoft needs to do all this (plus what it is doing) starting right now, and be finished within the next 10 years, and keep those net carbon emissions, from then on, level in absolute terms indefinitely into the future.

Yes, all this is true; and it’s also true of every other business I hear of. Even the most aggressive of countries (I think Germany and Norway might qualify) also suffer from these flaws. That is why I typically say, looking at our situation, that our present best-practices reactions to the climate-change crisis are pathetically, disastrously inadequate. But here, at last, on a very small scale, is a long-term company strategy that imho has any probability at all of being adequate in the short and long run.

Microsoft, wow. Take a bow, you deserve it. Now you need to do it a hundred times better. Starting right now.