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Technology Debt Crisis 2025

Capital loves code. Over the past decade, there’s been no better way to get fat returns on your cash than to invest in code. Tech stocks have buoyed markets. And code fever shows no signs of abating.

But the cool new code we write today becomes the massive technology debt of tomorrow. And there’s nothing sexy or profitable about debt service.

Don’t believe me? Just visit the data center of any global financial institution. There you’ll find a mainframe running COBOL programs from the 70’s. That’s right, the 70’s. You know, that time before computers.

Now multiply the technology debt from the 70’s by a few orders of magnitude. Think of all the code we’ve written in the intervening decades and all the places it’s running. Add to that all the code we’re writing now and will write in the coming years. The apps, the databases, the AI.

It’s unsustainable. It’s not an immediate pain-point. So like climate change, infrastructure, and all our other long-term challenges, technology debt is a can that capital just keeps kicking down the road.

Meanwhile, we keep producing massive future technology debt at an unfathomable rate. Worse yet, we encourage young people to become coders and add to the burden.

A lot of this code is bad. A lot of it is unsecure. And all of it – 100% — is future debt.

Technology debt is not an abstraction. It has a real cost. You have to allocate time, money, and talent to old code. Or, alternatively, you can ignore it – in which case you pay for your neglect with poor business performance, non-competitive customer experience, and outages.

Technology debt is why you pay Microsoft a tax just for owning a CPU and having to run Word. It’s why the IRS shuts down just to modify a few pieces of business logic. It’s why Walmart had to spend $3.3 million on

Of course, no one thinks they’re creating debt when they roll new code into production. After all, their code is brand spanking new. It has been birthed by state-of-the-art code cultures with all the right attributes: Agile, DevOps, Continuous Delivery.

But it’s still all debt. And you’ll never re-platform your way out of it. Heck, I’m writing this on a platform that dates from the Reagan administration.

Can we avoid technology debt entirely? Of course not. But we can:

• Price debt into the code we produce today – instead of persisting in our denial-based valuation of intellectual property.
• Better service the technology debt we already have, including our mainframe code – some of the most important code on the planet running on some of the most awesome machines on the planet.
• Stop being so juvenile and manic about coding. Not every kid needs to be a coder. We need plumbers and welders, too.

In fact, maybe we should ease up on coding for minute while we address what is perhaps our most egregious “technology debt” – the roads and bridges and airports and water/sewer systems we have neglected for decades.

We can’t code our way out of debt. We can, however, choose to more prudently manage it.

Big Data, Bad Metrics

As technology provides us with more access to more data, a lot of
attention is being directed towards leveraging that data to improve outcomes. The popular notion is that by gleaning insights from so-called “Big Data,” we can make better, faster fact-based decisions
—and that these decisions can move the needle on everything from business performance to human longevity.

This notion is borne out to some extent by empirical evidence. If you collect and analyze customer data, you can sell more. And if you measure your food intake, you can lose weight.

However, not everything that readily lends itself to measurement is important. And not everything that is important readily lends itself to measurement.

Measuring the Wrong Thing
In the June 2014 issue of Harvard Business Review, Clay Christensen and Derek van Bever make this very point about investment. They assert that flawed metrics often drive companies to eliminate jobs rather than create them. They demonstrate that ratios such as return on invested capital (ROIC) incentivize corporate decision-makers to simply whittle down denominators (by, say, outsourcing), rather than do the hard work of adding to the numerator. They further argue that a metric like ROIC is obsolete, because capital is not the scarce resource it once was.

The result is short-term investing that fails to generate growth or jobs. And these results will, if left unchecked, ultimately work against the interests of the very capitalists for whom return ratio metrics have become orthodoxy.

Similarly flawed metrics dominate the contact center industry, where managers chase KPIs such as shorter call-handling times or higher customer satisfaction scores—neither of which have much to do with desired business outcomes such as greater long-term customer loyalty or more market-aligned innovation. But, because these metrics are easily understood and easy to capture, they drive counter-productive and toxic behaviors.

How’m I Doing?
At a recent technology conference, I found myself sitting next to a bright young market analyst who waxed enthusiastic about the Quantified Self. He went on at some length about how the principles of data capture and analysis could be applied to the individual in order to improve physical, financial and even emotional outcomes.

My question to him was a simple one: “What is your ethical metric?”

The question was, of course, rhetorical. And it served its rhetorical purpose by leading to a discussion about whether we are in fact primarily physical, financial and/or emotional beings—or whether we might be in some way moral beings as well. We then discussed how we might rightly incorporate moral imperatives into a life in which physical, financial and emotional imperatives play such a dominant role.

Ethical goodness and long-term socioeconomic impacts don’t easily find their way into our spreadsheets and BI toolkits. So in our zeal for metric-driven personal and institutional behaviors, let us also exercise wisdom. Otherwise we may succeed in making our numbers—but fail in making our world.

DataOps: Why Big Data Infrastructure Matters

Many organizations approach Big Data almost exclusively from a data science perspective. They know that the massive data they now have access to contain high-value insights, and they’re trying to determine what kind of analytics they need to extract that insight.

But if you don’t also re-think your infrastructure, you’ll be in trouble. You can’t simply throw data science over the wall and expect operations to deliver the performance you need in the production environment—any more than you can do the same with application code.

That’s why DataOps—the discipline that ensures alignment between data science and infrastructure—is as important to Big Data success as DevOps is to application success.

Stale results are no results

Speed counts. In fact, some results are completely useless if they aren’t delivered in real time. So if your infrastructure can’t deliver near-wirespeed performance, you’ll never be able to reap the full potential business value of your ever-expanding data resources.

Of particular importance is data intake. Big Data projects often get derailed because everyone was too busy thinking about analytic processing workloads on the back end. But intake can be the thornier problem. It’s not easy to prep large volumes of disparate data for analytic processing. Data has to be validated and rationalized. And, again, this has to happen fast.

Incorporating legacy mainframe data into Big Data environments can be especially challenging. EBCDIC-to-ASCII conversion is a beast to normalize—and it can take days to execute that conversion if you don’t have the right infrastructure in place.

The cloud is not enough

Scalable, low-cost commodity cloud infrastructure—both from as-a-service providers and in the datacenter—is awesome. Unfortunately, that capacity can’t solve every performance problem.

In fact, infrastructure models that require you to move lots of data in and out of memory are going to create bottlenecks that will ultimately prevent you from achieving the real-time results your business demands.

So, sure, there’s a lot you can do with Hadoop running on cloud VMs. But you’re not going to make your Basel II reporting deadlines if those are the only cards you have to play.

No future for one-hit wonders

It’s also important to bear in mind that Big Data success isn’t about just getting one type of analytic result from one set of data sources. It’s about adaptively performing multiple types of operations—including operational analytics, predictive analytics, mobile data serving and transaction processing—on whatever combination of data sources may be relevant to any given business objective, today and in the future.

That means your infrastructure has to deliver superlative peformance on everything from social sentiment gleaned from unstructured content to anomaly detection gleaned from IoT telemetry.

In other words, infrastructure isn’t commodified just because VMs and open-source solutions are. Infrastructure that cost-efficiently delivers differentiated performance by adaptively aligning with ever-changing Big Data workloads will definitely provide a competitive advantage. And if you don’t have that, even the best data science can’t help you.

Enterprise Mobility and Time-Independent Work

Because we use the word “mobile” to describe the increasingly useful wireless devices now at our disposal, there is a tendency to understand their impact primarily in terms of location-independence. If we closely observe how work is actually performed in the enterprise, however, we may find that it is by leveraging these devices to make work time-independent that we are more likely to achieve transformative value.

Consider an account rep about to embark on a roadtrip to call on customers in a number of cities. Such a rep is no more “mobile” today than in the past. Physical movement from place to place still depends on trains, planes and automobiles. Other than perhaps providing access to information about a last-minute gate change, a mobile device does nothing to enhance our rep’s physical mobility.

But without enterprise mobility, this rep would have had to prepare for such a trip by putting together reports in printed form or as files loaded onto a laptop. This might mean extra hours front-loaded on a Sunday night or early Monday morning before the trip. Our rep would also have to be super-diligent about putting together all the right information before leaving—because it would be difficult or impossible to add to that information once on the road.

Enterprise mobility dramatically time-shifts this work. With the right combination of mobile BI and content access, our rep can defer information assimilation tasks until just before each appointment. This allows our rep to avoid the intensive front-loading of these tasks and to walk into each appointment with more timely content. Perhaps even more important, our rep can apply lessons learned on Monday and Tuesday to appointments on Wednesday and Thursday—introducing a kind of adaptive, improvisatory quality to account management that was never possible before.

Of course, like the rest of us, our rep can also check emails early in the morning, late at night, and during the sundry other moments the day affords in restaurants and taxis. So what has changed about our work is not that we can collaborate with colleagues on another coast. We’ve always done that. What is new is that someone in San Francisco can ask me a question at the end of their work day and get an answer from me even though, given the fact that there is a three-hour difference in our time-zones, I’m already kicked back after dinner watching a ballgame on TV.

This is not news to those of us who carry our workspace in a pocket and a shoulder bag. But it is something important to consider when planning and pre-imagining the new mobile enterprise. When work leaves the office, it also leaves clock—and this atemporality has profound implications that mere mobility does not.

No, Lincoln Did Not Say That

While the social media I use for business are thankfully free of duck-face selfies and zoomemes, they have become increasingly polluted by an even more abhorrent phenomenon: the false quote.

These false quotes are often ensconced in a graphic format, as if the right combination of font and color might lend them the authority of statuary inscription. And they seem intended for virality—since it would be unthinkably selfish to deny one’s social network the benefit of such a great person’s insight.

Lately, I’ve found myself politely informing posters about the fakeness of their quotes. In fact, I’ve become something of a “quote nazi.” So I am making this post not to defend my own indefensible behavior, but to make a case against a phenomenon that I believe may be more pernicious than its practitioners realize.

Intellectual integrity is non-trivial

I appreciate that posters of false quotes are trying to share something good. But I’d suggest that there is a good which takes particular precedence. This is the good of truth.

Unfortunately, posters almost invariably defend their false quotes by claiming that they got them “somewhere else.” To this I am obliged to reply that “somewhere else” is not a reliable source. That is why we have primary sources, as well as secondary sources that cite primary sources.

Truth matters. When we abdicate responsibility for our own intellectual integrity, we feed the economy of falsehood—by which we have all be burned and, if we are insufficiently cautious, will soon be burned again.

Gandhi vs. Drucker

Another problem is that false quotes often invoke authority inappropriately. Does anyone really think Gandhi was especially knowledgeable about customer service? Did Einstein possess special insight into human psychology?

And, anyway, argument from authority (argumentum ab auctoritate for you rhetoric geeks) is actually a logical fallacy.  Things aren’t true just because someone says they are true—even if that person is Ken Jennings.  They are true because they stand up to the clear light of reason.  Aphorisms may point us in the direction of the truth—but unless they have a powerful internal logic, they only tell us about what one person thought.

Motivation for what?

Finally, there is the troubling predilection posters of false quotes seem to have for wanting me to be all I can be and to have my best life now.  It is as though Al and Mo got together at Esalen and then moved to Sunnyvale to launch a Quantified Self startup.

Somehow, I doubt that the problem those of us fortunate enough to be on LinkedIn and Twitter have is insufficient careerism or self-concern. I have a feeling Al and Mo might share that doubt.

So, please, stop claiming that Mark Twain said “The key to getting ahead is getting started.” What he really said was “Never put off till tomorrow what may be done day after tomorrow just as well.” Now those are words to live by.