Digital Transformation, Data and Innovation

In my last post I tried to illustrate the importance (and the challenges) of data to digital transformation.  This is often a complex and difficult idea for people to understand - why is "data" so hard?  Why can't computer systems work more effectively?  For example, my father called me over the weekend to ask why his doctors can't get his electronic medical records correct.  Trying to explain all of the databases, the types of data, the different sources of the data and the work required to normalize that data is difficult, even to a guy whose job was in computer systems.

Now, if it is difficult to simply compile and use data (like in an electronic medical record) imagine how much more difficult it is to compile data and use it to make decisions.  The data has value but often has subtle context associated with it that we humans understand, but machines may not, because they haven't been taught the explicit and implicit meanings of data that accompanies the data (what many people call "meta-data").  For example, a sensor or computer may use computer vision to "see" a school bus, and based on its size and shape may recognize that the vehicle is a school bus.

But the computer may lack the intelligence to also understand the shape and size of the children in the bus (small kids - heading to kindergarten, large kids - heading to high school) or the context (empty bus leaving school, full bus arriving at school).  We humans have this insight and understand the less overt concepts around meta-data because we have learned experience.  We know that a school bus has a relatively predictable job - taking kids to and from school - and that at different times and different locations the bus is doing a specific job.

What gets even more complicated is when a predictable activity, like a bus trip, takes place outside of normal hours or normal routes.  When a school bus is used for a field trip or for a sports event, the bus is outside of its normal routes, operating outside of its normal times.  Even we humans may look at a school bus on the highway late in the evening and wonder what they are doing.  Machines can only parse through rules or assumptions when data is out of regular bounds.

Data and digital transformation

There is no digital transformation without data.  It's as simple as that.  Digital transformation is either involved in creating more data (using sensors and IoT devices to gain more data), managing and understanding the data (Big Data, predictive analytics) or using data to make decisions or take actions (autonomous vehicles, robots, AI and machine learning).  None of these things can be accomplished without data.  Once you realize how important and how valuable data is to digital transformation you'll think again about where and how digital transformation can be successful.

I spoke recently with a software partner to ask them how to help clients prioritize what projects they should start for digital transformation.  There were several activities that if they could be automated and digitized would have high return, but obtaining and using the data was proving difficult.  His response was to find the problems or challenges where obtaining and using the data was the easiest, not necessarily where there was the biggest return, because access to good, useful data is that important.

Digital Transformation and Innovation

So, what does this tell us about the impending merger of innovation and digital transformation?  Again, data can be an input to innovation - helping drive the design of new products or identify market needs and gaps, or it can be the result of innovation, creating new products that generate useful data that can be gathered, analyzed and perhaps monetized.

In both activities - digital transformation and innovation - people, consultants, thought leaders - will try to convince you that the digital tools (AI, machine learning, robotics) or key innovation methods or activities are what matter.  Please ignore these arguments.  Where innovation and digital transformation are concerned, what matters is the data.  Data coming into the activity or data resulting from the activity, and the value you can create from the data heading in either direction.

Data, Data everywhere

I'm using a reference to the Rime of the Ancient Mariner for the title of today's post, because there's rarely been a more interesting dichotomy facing most management teams.  As a colleague of mine is fond of pointing out, data is the new oil.  For those old enough to remember the Beverly Hillbillies, Jed struck oil and became rich, and moved to the big city.  In many ways digital transformation and the value of the data it creates will enable many new digital hillbillies to strike it rich, striking gushers of data.

At some level the mere availability of data is valuable.  When no or very little data is available, any data is valuable and precious.  When someone strikes a gusher of data (Facebook, Google, etc) then that data and the access to that data can become profitable.  But what happens in large organizations when data becomes ubiquitous?  What happens when we have thousands of sensors and IoT devices submitting data, along with consumer data and reviews, and social media feeds?  What happens when there's data everywhere, of every type, velocity, validity and all the other V words that experts use (variability, veracity and so on).  Increasingly we aren't sitting on gushers of data, we are swimming in rivers and lakes of data, soon to be oceans of data.  And here's where we circle back to the Ancient Mariner.  We may be afloat in a sea of data, but an awful lot of it isn't valuable or useful, because we can't be quite certain which is recent, valid and most importantly, normalized.

ETL - Phone home
If you had real time access to all the data in the world, and it was all verifiable, accurate and without inherent bias, you'd still face an enormous challenge.  Gaining insight from mechanisms like machine learning and AI will only happen when the machines can read and make sense of the data.  Right now we are generating gushers of sentiment data, quantitative and qualitative data and other kinds of data, that aren't normalized and require some human intervention.  In fact most honest brokers who are dealing in machine learning and AI will tell you that the "long pole" in gaining value from AI and ML is in another acronym: ETL - Extract, Transform and Load.  There are a couple of important activities in that acronym.

Extract - find the data and get it out of the originating system.  This could be data from sensors or devices on other devices like your iPhone or Alexa.  Where the data is generated is often very different from where it is stored, thanks to the cloud.  We've got to find that data and consolidate everything we know about a certain individual or segment or product, and get all of that data into one place.

Transform - Your data can take hundreds of forms - binary, quantitative, analog, digital, hexdecimal, images, voice, text and so on.  Machines can be taught to read and recognize any type of data but they can't easily determine the validity and value of different types of data.  Thus, we must normalize the data to some degree - help machines understand why a picture is worth a thousand words.  And it's this work that will be the biggest barrier to full adoption and use of machine learning and artificial intelligence. In fact we may need machine learning and artificial intelligence just to find, clean, evaluate and normalize all the data we are generating.

Thus, as strategists and innovators we are left in an interesting predicament - the more data we have, the more potential value we have, and the more the problem of actually finding and using the data that matters increases.  This is in fact an exponential problem, because the data is increasing at a rate faster than we can determine how to make sense of it.  At some point only machines will be able to interpret and understand all of the data we generate, so it behooves us to begin to either standardize the data formats, sources or streams (which isn't viable due to competitive differentiation) or to improve the ability to find, clean, standardize, rank and normalize the data we have.  Otherwise we'll sit on oceans of potentially viable data unable to extract the value, as new oceans of data are created.

Having more data than a competitor doesn't convey an advantage unless you can make sense of the data and use it more effectively.  In fact in many cases having more data may make it more difficult to make good decisions and as the volume of data accelerates and the range of data types increases, it will become every more difficult to simply keep pace with the data.  Like the Ancient Mariner you'll be awash in data, floating in data but without an insight to drive your business.

Who is responsible for managing this data?

Here's another interesting challenge - thinking about who is responsible for managing this data.  The traditional IT team has been overwhelmed with simply keeping the operational systems running.  Your email, core systems, financials and other operational systems require constant attention, and constantly upgrading to the latest releases and protecting the data from hackers is a constant struggle.  Does your IT team have the bandwidth and skills to capture, manage and make sense of the data?  Should a data scientist report to your Chief Information Officer?  If not, then where should people who are good at managing data and making sense of the data reside?  What should they do?  Who directs their work?  I'm not sure there's a good answer in many companies to this important question.

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Understanding future conditions is vital to create innovative solutions

I'm just wrapping up another project that's focused on trend spotting and scenario planning, working to help a client company understand the emerging competitive conditions their business will face in the next 5-7 years.

While foresighting, trend spotting and scenario planning are exceptionally valuable, many innovation projects simply ignore the benefits they can receive from doing this work.  Instead innovators plunge in to create the products and services they believe customers need.  This approach is why so many innovation projects fail to deliver value - no matter how compelling the innovative product you create is, if the circumstances or environmental conditions change, consumers and their needs will change as well.  To create a really compelling product or service, you need to be able to understand or predict the future conditions and tailor your innovation to those conditions.

Why understanding the future is so important

Many potential customers tell me they'd like to understand the future - in fact they'd pay good money for a forecast that is highly probable.  However they don't believe they are good at predicting what might happen and therefore don't spend time trying, or worse they simply assume the future will be an awful lot similar to current conditions.  They are often shocked when we demonstrate how rapidly customers, technologies, needs and conditions are changing.

Of course it's possible to create a compelling product or service and have it well-received in the market without doing foresighting or scenario planning, it's just much more likely that you'll miss evolving needs or opportunities and the product you create will fall flat.  If we accept that people acquire goods and services to fulfill "jobs to be done" or to satisfy needs, then we must also accept that jobs and needs are fungible and change over time, and that new entrants and new substitutes arise all the time.  Ignoring future conditions that will shape needs, wants and especially ability to pay is a recipe for disaster.

Insightful yet inexpensive

Further, foresighting and trend spotting when done correctly creates an opportunity to gather insights on what might happen and how the company should be prepared to act.  Foresighting is valuable to understand emerging needs, but also useful to understand emerging threats and opportunities.  Doing this work is relatively simple with good participation and facilitation, and creates insights that shape your innovation activities.  It is powerful and insightful, while also being a relatively inexpensive investment.

Its importance and value are increasing

Foresighting and trend spotting are becoming ever more important, as the nature of change is changing and the rate of change is increasing.  Digital transformation will create disruptive change in many companies and will create new types of demand.  New generations of consumers are emerging with different concepts about acquisition and ownership.  Understanding the evolving future and identifying emerging needs before or as they happen is more important than getting a new feature on an existing product which may be obsolete or unnecessary by the time you get the new feature installed.

Practicing the Future

Instead of wandering blindly into the emerging future, you should be practicing it regularly.  Conducting trend spotting and scenario planning activities won't guarantee a perfect understanding of the future, but can give you good insights into emerging market conditions and the potential for new segments, new customers and importantly new threats or competitors.  Having seen how things may unfold will prepare you to put new capabilities in place and to anticipate when the tipping point arrives.

If you want to understand how to do this work well, or need help doing a foresighting or scenario planning activity, contact us.  We have tremendous experience doing this work and identifying the emerging opportunities that will shape future innovation.

Digital Innovation meets As a service

For a while now I've been considering the impact of all the emerging digital transformation tools on innovation.  Artificial intelligence, machine learning, IoT, blockchain and a host of other technologies will have a bit impact on how corporations conduct work and create new insights and new products and services.  However, I'm increasingly of the opinion that we are guilty of focusing on the technology and ignoring the real benefit of these technologies and other IT-influenced changes, and what customers really want.  As these technologies are implemented, the real benefit will be the data they generate and companies will have to confront the question - how do we gather, use and most importantly, monetize all of the data?  And this, I think, is where the real impact on innovation will be felt.

Two converging themes

There are two really interesting and potentially impactful converging themes in innovation, both of them led ultimately by the increasing power of information technology and ubiquitous connectivity.  The first is digital transformation - the ability to both generate vast amounts of data from sensors and IoT devices, as well as to manage the data and make sense of it using other technologies like Artificial Intelligence, Machine learning or predictive analytics.  The second is the increasing demand for solutions, not products.  I think increasingly people will want to acquire solutions "as a service" or will be happy to share data about device usage in order to receive a less expensive product. 

Companies can provide products "as a service" by changing value propositions and business models, and can further lower the cost of the service by collecting and monetizing the data generated by usage or by pushing other offers to the user of the device.  An entire generation is entering the workforce that grew up with Facebook and Google, so these data exchange models are well understood and already accepted.  The big challenge is for "as a service" solutions to move from the purely digital world (search, social networks) to physical products.  In some senses the shift has occurred for larger capital goods like aircraft engines.  GE doesn't sell the engines, they sell flight hours of operation.  Michelin has a "tires as a service" offering, which is where this really becomes interesting, because tires are a consumable commodity.  If we reach the point where consumables can be offered "as a service" then almost any physical product can be offered as a service, which will have to be supported by new business models.  Further, many of the "as a service" models will be funded to some extent by data, either harvested from the device or information pushed to the device.

Why a new "whole solution" emerges

If these ideas above are true, they have significant impact on innovation and how it is conducted today.  In the past, Geoffrey Moore created the idea of the "whole product" to cross the adoption chasm.  If the arguments above are true, we need to consider a new "whole solution" model to compete in the digital innovation economy, where the value proposition of the physical product shrinks but is augmented by value from the data that surrounds it, the customer experience that empowers it, new business models that sustain it and ecosystem partners who fulfill the promise.

Yes, companies that make physical products will continue to make physical products, but increasingly they'll find that customers expect a more holistic "whole solution" which will incorporate data (from the digital transformation application).  That data may originate from sensors on the product, from a bluetooth connection between the product and the smart phone or device the owner possesses, or eventually from ubiquitous 5G, which just creates a virtual network between any internet enabled device anywhere.

Customer experience
Beyond the physical product and the data that enables, surrounds or funds it, the customer experience will need to change.  In the Michelin example, tires as a service indicates that my experience expectations are actually higher than if I manage the tires myself, because my expectation is that Michelin has experts on staff ready to identify any issue, and who can guarantee that I get the most value and longest life from my tires.  In this example my expected experience is that I do nothing and simultaneously get more, which means my experience expectations are vastly increased.

Business Models
Finally, those products, data and experiences will come wrapped in a different business model - or, more likely two or three different business models.  Again, Michelin is a great example.  Michelin still sells tires to consumers without any support or "as a service" offering, as well as providing an "as a service" offer.  The revenue models, service models, pricing, support and warranty options for these two delivery models are significantly different, and any company that embarks on an "as a service" offer will encounter those who prefer to acquire and own a device or product, and those who are happy to use it as a service, so multiple business models will be required.

The challenge for innovators

I'll submit that the converging factors - increased data generation and management, and increasing expectations of products as service - are here and will continue to converge.  This means that innovators must decide how and when to include data as a component of the offering, and how to shape and ensure customer experience and be prepared to offer multiple, concurrent business models based on the same product.  In other words, are innovators ready to vastly accelerate innovation thinking and options, and work well beyond the innovation requirements of the physical product to include data, customer experience, business models and other factors?

If not, what will it take to develop the innovation teams and skills in order to compete in this market?  The impact to innovation is real and cannot be denied.  Can you and your team rethink and revise how you innovate?  If you need help, we can help think through not only the core product, but how and where data is important, the customer experiences expected by consumers and the requisite business models, as well as critical ecosystem partners.