Digital Transformation: the Elephant in the Python

Lately I've been thinking a lot about the latest fad in business thinking - digital transformation.  As technologies become more pervasive, and our ability to gather and process a lot of information increases, it makes sense to think about how digital solutions may change the way we work, either by replacing monotonous tasks, automating entire business processes, anticipating future trends and hundreds of other ways.

As more companies place more sensors in their products, the products will start reporting on their mean time to failure and maintenance needs.  Companies will gather data from those sensors and provide feedback on optimal usage, maintenance and repair or replacement needs.  The Internet of Things (IoT) will mean that many, many devices are connected to the internet and providing a constant stream of data that can be analyzed, so that we can learn more about any factor of our supply chain, distribution chain and consumer consumption of products and services.  When you add in the promise of blockchain, it's possible that all of those transactions will be conducted in a fully automated and transparent ledger.  What's not to like?

Taking the curmudgeonly approach

This digital future sounds incredible, because it is.  We can now automate far more than ever before.  We have more data at our fingertips than ever before.  With that data we should be able to schedule people and machines more effectively, anticipate future shifts and reduce costs and cycle times.  We should know more about our entire value chain than ever before.  The real question is:  can all of this technology and data provide the insights it promises?  Is there a weak link in all of this promise?  I think there is.

Digital transformation is already happening.  It's not something that will arrive all at once, but piecemeal throughout your business.  You've already installed some IoT devices, and probably have a relatively robust ERP system to govern your internal processes.  To some extent you are already on your way to becoming a 'digital' business.  But there is more you can do, with additional sensors that capture data about customers, or blockchain applications that track the provenance of products and services.  You can and will install AI or machine learning algorithms to help you improve the efficiency of your operations.  As I said, this stuff is already happening, and it is a double edged sword.

The promising edge of that sword is integration and the availability of data and, with the right processing, new information and insights.  The difficult edge of that sword is integration of all the data producing equipment and the ability to normalize the data so that it is all integrated and useful.  And this is no small challenge.  Once every IoT device, smart device and distributed system comes on line, they will generate terabytes of information, and often in different data configurations.  Since there is no standard, someone must normalize all of this data and decide which data is truly valuable, and how it will influence or impact AI and ML algorithms.  You could literally be awash in data from thousands of different devices, all of which are producing data that has potential value, but in different data standards and configurations. 

Who does the integration?

Think about your current integration challenges.  Today many corporations have manual consolidations across different IT systems because it's easier for humans to review and consolidate financials or other data sets, rather than create rules about how the data should be aggregated.  If you have challenges with your existing operational data, collecting it, aggregating it and normalizing it, what do you think will happen when hundreds of new data sources - both internal and external - come on line, and new systems like AI or ML try to start making sense of this data?

Worse, an already overworked and overwhelmed team - the IT team - which in most cases is simply trying to keep the older operational systems afloat - will be called on to normalize and integrate all of this data, and ensure that the AI and ML systems get good, clean data to make assessments and projections.  Oh, and they'll need to install all the hardware and software, determine data flows and continue to run the regular IT systems that keep the company running.  Why is no one talking about the big challenge of data integration that is required for digital transformation? 

The elephant in the python

Data integration isn't just the elephant in the room that no one is talking about, it is the elephant in the python.  Digital transformation assumes that the data that is produced at any place in the business is instantly available to everyone, creates maximum efficiency and helps people and machines make better decisions.  For that to happen data needs to flow accurately and freely, and be free of conflicts or errors.  All systems need immediate access to the data and AI and ML need to be able to process the data and make decisions in real time.  What I just described is a fantasy for most large corporations today, and it will be a fantasy for quite some time.  Data simply doesn't flow that way, and today we are talking about a mere trickle of data as compared to rivers, oceans of data that will spring forth as the IoT devices begin to pop up everywhere. 

Most firms don't have the ability to manage the volumes of data we are talking about, don't have the ability to normalize the data and certainly don't want to introduce all of these diverse streams of data into any decision making process or algorithm without a lot of oversight.

Consultant Job Creation

When people tell you digital transformation will make your company more efficient, be wary.  It strikes me that it will create massive data traffic jams internally, leading to the need for far more consulting labor to straighten out the data flows.  It will require far more people to sit in judgement of the data, to ensure the data is valuable and useful, to keep algorithms from using incorrect or flawed data sets to make decisions.  Digital transformation is the pot of gold at the end of the rainbow for many hardware and software producers, who are overstating the benefits (of which there are many legitimate benefits) and vastly understating the challenges having to do with integration of data, the normalization of the data and the sheer volume of the data.

Does IoT, blockchain, AI, ML, sensors and other technologies have a place in your business?  The answer is unreservedly yes.  Should you be concerned about the use of these technologies and the impact on your business?  Again, yes.  Where is the biggest challenge?  I'll stipulate that most companies simply don't have the ability to manage the amount of data that will be created, and can't make that data (and the more important information and insight that should be gleaned from the data) available to the right people to make the right decisions quickly.  What's the value of digital transformation if it does not make the business more productive?

The future belongs to whoever creates it

You know something has become passe when you see it used as a meme on Twitter frequently.  That's actually what prompted this diatribe.  I doubt there's any meme I dislike more than "The future belongs to...".  In recent years we've been told that the future belongs to the swift, the smart, the agile, and more recently to the digitally transformed.

Not that long ago we were told that the future belonged to Nokia, because before 2000 it was the king of the hill in handsets.  Then along came Apple.  Then we were told the future belonged to the Newton, except the Palm and then the smartphone generally won the day.  The fact is that the future doesn't belong to anyone.  Given the rapid pace of change and the emergence of new technologies and solutions, you can't say with much certainty who will win the future.  In fact, as William Gibson likes to say, "the future is already here, it's just not very evenly distributed".

Who "owns" the future?

The best we can hope for is to understand the future and prepare for it, or attempt to create the future we want.  The other options - waiting to see what happens and then responding, or worse, hoping that nothing changes at all - are not viable alternatives.  These options are choices that lead to the fast road to obsolescence.  Santayana said that those who cannot remember the past are doomed to repeat it.  I think the corollary to that statement is:  those who ignore the future are doomed to extinction.

If we can't ignore the future, and it's clear few firms or organizations "own" the future, this means the future should be malleable to some extent, and we have the capacity to discover it.  Therefore it's incumbent on us to try, rather than end up as an afterthought, or an overused metaphor like the buggy whip manufacturers who didn't change as automobile ignitions changed.

Nobody "owns" the future but if we try just a bit we can understand what's likely to happen and in some instances perhaps even influence it.  However understanding the future requires doing work to identify emerging trends in the environment and understanding how they might unfold.  Understanding the future requires you to ascertain what 'could' happen, not simply depend on what  you want to occur.  Understanding the future requires understanding that the future is not determinant, but that there are multiple possible futures.  Once you understand these things, you can begin to see how you might influence the future.

Future chaos

If a weak definition of chaos theory is "a butterfly flaps its wings in Tokyo and it rains in New York" then the idea of small changes now influencing the future isn't so far fetched.  Microsoft influenced an entire industry at a time when Microsoft was very small, convincing IBM to license its operation system.  That one decision had ripple effects that we still experience today.

We are too often far too passive and too convinced that the future will happen to us, rather than becoming more proactive and at least more prepared to act as the future emerges or, better yet, trying to shape the future.  But what is good innovation other than an attempt to shape the future?  Innovation is a current bet on future outcomes - so any innovation is an attempt to change the future.  Why not invest in understanding the future and even influencing the future as part of any innovation activity?

Trend spotting, scenario planning, roadmappping, future forecasting - these are all tools that can help determine what may occur in the future.  Doing this work isn't enough however, because the knowledge will simply prepare you for what may happen.  Taking action on the insights, investing in innovative ideas meant to fulfill unmet customer needs or targeting emerging segments will shape the future, and perhaps allow you to create it and own it.

What unicorns and narwhals tell us about innovation

My lovely wife and I were returning from a long car trip, having one of those wide-ranging conversations about everything and nothing that consumes time during long periods of interstate driving.  The topics were shifting and free-flowing, when suddenly she asked the question:  what's the difference between a unicorn and a narwhal?  After all, they both seem a bit mysterious and they share the uncommon attribute of possessing only one horn.  I liked the question so much I promised on the spot to write a blog post about her question.

The answer, as it turns out, is relatively easy.  Unicorns combine features like a single horn, fantasy and equine nature in a single package that is meant to appeal to a defined population - young people who enjoy fantasy.  Would the unicorn work without the horn?  Probably not, because then it's just another horse.  Narwhals are almost as rare as unicorns of course, and have a single horn (really a tooth), but there the comparison ends.  Narwhals seem more like walruses or large seals, relatively large, amphibious animals that few people have seen or understand.  Narwhals lack fantasy and aren't featured in many stories. You won't find too many stories of magical narwhals flying in to save a damsel in distress. They don't seem to have large fan clubs or venture capital companies describing startups using their name.

The real difference between unicorns and narwhals?  I think it's in the packaging.  Unicorns combine a number of features that appeal to a segment of people and incorporate fantasy or magic.  Narwhals don't.  So ends the lesson.

But it's the features

But there's an interesting perspective to take away from this.  Both unicorns and narwhals share an important and rare trait - they have a strange single horn (or tooth).  This makes the unicorn look fierce and the narwhal look slightly ridiculous, but that's beside the point.  Both have a singular feature that makes them unique in their environments.  And as we all known, features matter.  After all, nearly every product you buy is presented not as a solution, but as a long list of features.  Better screens, faster processors, lower emissions. 

This is what so many innovators get wrong about new products and services.  They focus on the solitary feature - faster processors, better user interface, more application support - and lose sight of the fact that consumers appreciate features (as far as they understand them) but value a total solution much more.  A "unicorn" feature - one that really stands out and is unique in the market - is great, but in the grand scheme of things it may make a product more difficult to use or more difficult to adopt, even though it seems differentiated.  What people love about the unicorn is the total package - the mystique, the fantasy, all of which is augmented by the horn.  When you stick a horn on a small whale, no one really cares.  It's the entire package that matters.

Solutions and Benefits

Unicorns and narwhales are also illustrative of the difference between market pull and technology push.  Innovators who follow a market pull strategy, using innovative methods and tools to understand the needs and desires of customers to help shape a product, discover wants and needs and try to fulfill them.  On the other hand, innovators who follow a technology push strategy often highlight specific capabilities and features they believe a customer needs, whether they've asked or not.

In this regard, a unicorn is like market pull - finding out that people love magic and fantasy, and adding just a few unusual features (a horn on a horse) to make it seem unusual.  A narwhal is like technology push - sticking an outsized horn on a rarely viewed and mysterious sea creature, that few people understand or care about.  In most successful innovations, it's not the features that win the day.  Most consumers don't understand nuances like operating system versions or processor speeds.  They care about the total solution and how it fits into their lives.

Who knew that two mysterious and potentially magical creatures could tell us so much about innovation?