Digital transformation is creating a world where businesses must test new, exponential strategies while maintaining incremental business functions to keep the lights on. How can we manage this balance between old and new, and choose which path to modernization is right for us? To be prepared to lead our companies into the future, we need to increase our digital fluency. If we don’t, we risk becoming obsolete or worse, making critical mistakes that harm real people. We have to be digitally fluent so that we can speak and translate the language of the technologies—and technologists—shaping our livelihoods and organizations.
What is digital fluency? Most approaches to ‘going digital’ focus on only one or two elements of the larger equation of digital fluency. Maybe we start with finding out what tools are available, or get a briefing on business model trends. That’s like learning a few phrases in a foreign language while you are away on vacation—useful, but not the same as being able to have a spontaneous conversation.
If we want to lead in a digital world, we need more than catchphrases—we need fluency, or we’ll never be able to cause the ‘disruptive’ movements people talk about. Fluency includes not only words but also culture, ways of thinking, and a lot of practice. Similarly, effective digital strategies require more than a cursory knowledge of tech terms—they must introduce new paradigms to bridge the gaps between technological and human elements of change. We must decode the technical elements to help business leaders understand their tech counterparts. And we must help IT leaders and developers understand the changing world their business counterparts are facing by connecting the dots between emerging technologies and their impact on markets and organizations.
The mental models of the 20th century won’t allow us to see the future clearly. We need to upgrade our thinking in order to realize the opportunities of the digital age.
To create digital value, we must understand how data is structured and how it moves from system to system. Otherwise, there is no way to know if we can monetize it ethically.
Digital business models and value propositions require new thinking about who creates value and how it is delivered.
Selecting and implementing tools for digital value creation is not as easy as it may seem. The right tool can save a lot of work—while the wrong tool can distract you from your goals.
A new set of skills is required for digital value to be created. Technical, intellectual, interpersonal and leadership skills need to be acquired and evolved.
We start by raising the minimum level of fluency in the organization we are working with. If people have great thinking about social products or how to collaborate online, but have few software tools to do so, the software will be the lowest common denominator. The same is true if you have exponential tools rich with useful features—if the right thinking isn’t in place, no one will understand the possibilities of the technologies, and the organization won’t get very far.
Perhaps the most fundamental area where we need to update our mental models is from an incremental model of change to an exponential one. Incremental change (10%) is constant, while exponential change (10x) has an increasing rate. Incremental change is linear and additive, while exponential is non-linear and multiplicative.
While the incremental is about 10% improvements, the exponential is about 10x acceleration.
Almost all exponential strategies are driven or supported by digital technologies, which is why we need to work on our digital fluency across every aspect of our organization. The higher each pillar of digital fluency is, the more likely we are to be able to achieve exponential results.
The higher your digital fluency, the more likely it is that you or your team can create digital value.
No matter where you or your organization lands, focus on raising the lowest common denominator of your digital fluency to unlock new opportunities and performance. In most cases, over-emphasizing any one pillar of digital transformation will result in expenditure of time and money without being able to generate sustainable, exponential results. To begin, explore each pillar at a high level so that you can establish a focus for your first efforts.
Let’s start with thinking. The mental models of the 20th century won't allow us to see the future clearly. We need to upgrade our thinking in order to cause exponential results.
Shifts in thinking about technology tend to follow a pattern. When cars first came into being around 1900, people had no frame of reference for what designers were calling 'automobiles.' So they became known as “horseless carriages.” Why? When people needed transportation, they thought of horses. The concept of a world full of automobiles as commonplace as horses, as visionaries like Ford and Daimler imagined, was so far beyond comprehension that it seemed liked a fantasy. So we had to view the future through the lens of the past—as a horseless carriage. Only much later, with widespread understanding of automobiles, did the network of roads of the modern age come about.
This is an example of 'unlearning.' Before the exponential value of a new way of doing things with advanced technology can be realized, we usually need some time to identify and let go of existing mindsets and practices.. It's not just enough to learn new knowledge about digital technologies—we have unlearn our old practices.
For more on how unlearning can help us embrace new ways of thinking and access huge potential value, read Unlearning in our Thinking for a Digital Era Guidebook.
Advanced technologies may not look impressive at first, while our thinking catches up with their potential. The language of “more, better, faster” lets us know when we are approaching technology with a limiting, incremental, analog mindset—and indicates where we need to raise the minimum level of fluency so we can see what’s really possible.
For an example of analog vs. digital thinking, consider the internet. Early users thought of the internet as an incremental improvement on analog ways of doing things: more information (online newspapers), a better way to promote products (the banner advertisement), and a faster postal system (e-mail). Most people couldn’t foresee what paradigms the internet makes possible today (like social networks, massive multiplayer gaming and remote robotic surgery, to name but a few) until a critical mass of people—from many disciplines—became fluent in how it worked and discovered new digital ways of thinking about value, information and community.
To further explore digital ways of thinking and see how they can be effectively used to move your project or business' growth rate from 10% to 10X, check out our Exponential Thinking guidebook.
Examining far-future thinking can tell us about our current thinking. Futurists can help us imagine exponential new possibilities because they take current incremental trends and ask how they would add up to something exponential, asking 'what would that make possible? and then that?' for many iterations. Science-fiction creators suspend tested, rational ideas just enough to imagine something we don't know how to make possible yet. From their imagined futures, we can work our way back to the present. No matter which angle we come from, we have to stretch beyond the incremental frame of 'better, faster and cheaper' that we can see from our current vantage point. To do this, it can help to come together with other thinkers.
The benefits of connecting with other thinkers, including the various forms these relationships can take, are explored in our Thought Partnerships guidebook.
Digital transformation is partly a function of harnessing the power of network effects to connect thinkers.
Here, we mean network effects to be the exponentially-increasing value of a network as more nodes are added to them. For example, a telephone network with three members is exponentially more valuable than a telephone network with only two members; the same can be said for online social networks or transit networks. Similarly, a network of thinkers who understand digital possibilities becomes exponentially more valuable as more members are added to it.
When raising the digital fluency of an organization, attend to the size, quality and growth (or attrition) rate of your network. Some organizations find it helpful to determine key groups. For example…
Digital “Champions,” business leaders who bring resources and remit
Digital “Explorers,” who discover new opportunities and share about new models of value creation
Digital “Makers,” who have the technical know-how to make prototypes
Measure the number of people in these groups, how well-connected they are to each other, and if you are gaining or losing members.
As someone bringing digital fluency to your organization, it might be helpful to think of yourself as a matchmaker or outfitter to these various members.
In order to create digital value, we have to understand how data is structured and how it moves from system to system. Otherwise we can't figure out if it can or can't be monetized, and if it's ethical.
Digital thinkers ask questions about data. What data exists or could exist in our ecosystem? How could datasets connect to each other to create new value? How are we managing informed consent and ensuring our data use doesn't harm our partners or customers?
We call this subset of digital fluency data fluency: a shared understanding of how data is disclosed, manipulated, and processed, and the implications thereof.
To understand how data works, we need to understand the data supply chain, so we're going to apply some computational thinking to help us think through how all of these pieces fit together. There are three stages of the data supply chain:
1) Disclosure, whether by a person or a sensor or a system;
2) Manipulation, which is where we process data and understand what's possible with it or analyze it in some way; and
3) Consumption, where data is used by a stakeholder or fed back to users as insight about themselves.
At each phase from acquisition to storage, to aggregation, to analysis, to use, to sale and disposal, there are key implications and handoffs that have to happen that make sure that ethics are preserved and the efficiencies occur and that the data is still accurate.
The first stage is data acquisition—data is collected from sensors, systems, and humans.
For the purposes of this article, let’s use the example of a driverless car or autonomous vehicle as the context for data’s motion—its journey—through the supply chain. In the acquisition stage of data, the car captures raw data from its on-board sensors, like cameras or speed sensors. It's just bits and bytes, and no work has been applied in terms of processing or thinking about it.
The next stage of the data supply chain is storage—recording data to a trusted location, which is both secure and easily accessible for further manipulation. Storage is often some version of the cloud, or perhaps a specific server. Sometimes it’s a flash drive or local memory on a sensor. But wherever we store data, we need to make sure that we understand how that's going to connect to other systems.
In the automotive case, raw data might be stored in an unprocessed form in the vehicle's local memory. Think of it like a hard drive in the car.
When we aggregate data, we combine disparate data sets to create a larger data set that's greater than the sum of its parts (often referred to as Big Data). This is where exponential possibilities begin, but so does exponential complexity.
In the car example, the aggregation stage begins when the car gets to its owner's home and syncs, uploading its raw data to the manufacturer's server.
Once data is collected, stored, and aggregated it’s time to analyze it. We examine data to extract information and discover new insights. We might append this analysis to the original data, and/or use it to discard extraneous data.
In the automotive example, the company's big data algorithms analyze the raw data from all vehicles in a geographic area, and compare it to data from road maps and transit systems.
Apply the insights gained from data analysis and use them to make better decisions, which effect change or otherwise help you deliver a product or service. This stage may also involve changing or adding to the data.
In the automotive example, collision avoidance and navigation algorithms might be updated across all the vehicles based on the raw data that the auto manufacturer is getting from various cars out on the road. Vehicles then use the updated algorithms to make better decisions.
During the share or sell stage we send data—or insights gleaned from data—back to its source or to third parties. There are significant ethical considerations to take into account in this stage.
In the example of the automotive data supply chain, the vehicle maker might make subsets of their data, or data-derived insights like "these are the parts of the city that have a lot of traffic," to other manufacturers, map services, or even regulators. This creates a feedback loop to influence the world around us. However, sharing and selling data is the stuff of headlines for a reason—if our driving data is telling someone exactly where and when we drive, do we really want that shared to other manufacturers or map services—or advertisers?
When we think about ‘data in motion’ and the many distributed and synchronized, or ‘federated,’ copies of data out there, it may be nearly impossible to find and delete all instances of the data a user has disclosed or had generated about them. If we can avoid storing the data in the first place—as Apple does by doing face recognition on a user’s own phone rather than in the cloud—we may not have as much to deal with. Therefore, it’s critical to consider how data deletion and disposal will occur, even if just to prompt us to not unnecessarily store sensitive data in the first place.
In the automotive example, disposal of individual data about driving routes could occur after a set period of time, leaving behind only the insights gleaned from it (such as avoiding a certain street during rush hour).
Digital business models and value propositions require new thinking about where value is created—or co-created—and how it's delivered.
Let's talk about four key ways that businesses create value. The most recognizable is via assets and things, then people and services, ideas and technology, or networks and connections. All have their relative merits—and varying degrees of exponentiality.
An asset and things model asks the question, “what are our assets and how do we best protect and leverage them?” That might be tangible assets like real estate or money, or products a company acquired or manufactured.
A people & service model asks “how do we engage the best talent and deliver the best experiences?” Customers, employees and partners are the primary source of value. These businesses typically have a higher valuation than an equivalently-sized asset & things business.
That’s Starbucks’ core strategy. Instead of focusing on the monetization of coffee (an asset), they offered a service: hosting an experience that's a third place between work and home. That's why the company is valued higher than just a coffee vendor—they’re not selling coffee, but an experience that happens to include coffee.
The ideas and technology model asks “how do we create and share intellectual property? How do we design and build exponential value with machines and data?”
This is like tech companies Nvidia and Intel. What they're doing is taking concepts and creating something replicable and protectable about them like a copyright or a patent, software, hardware, or algorithm that can then be used over and over again.
Ideas and technologies have an exponential return. This is why, when you track chipmakers Nvidia or Intel, they have massive revenue over time compared to traditional businesses—because they've created something evergreen that they can use over and over.
The networks and connections model asks “how do we enable and amplify the exchange of value between parties?”
eBay, the Amazon marketplace or Apple's app store are great examples. They bring together social networks, and engage in key activities like matchmaking. That’s where you find the highest long-term return. But they take a long time to build.
While it takes a very long time to build those networks, once you have them, they have a large degree of stickiness and a low cost of doing business relative to the total value that's created for customers.
Tools are another important part of the conversation. We need specialized tools for digital value creation, and selecting them is not as easy as it might seem. The right tool can save a lot of work while the wrong tool can distract you from your goals.
That’s because tools embody ways of thinking—like bias towards certain ways of working and creating value. For each business area, digital tool sets can connect past and future mindsets. You can use tools to streamline and improve existing processes and activities to save money, but you also can use them to support emerging use cases, inspire experimentation, enable collaboration, and share resources.
A well-designed pilot of a new tool can spark new thinking and be a way to introduce tomorrow’s exponential mindset into an organization at the same time that you're solving today’s incremental problem.
Take the challenge of organizing data about customers for the purposes of sales, like contact information and upcoming deals. This might be done through a number of spreadsheets or databases siloed in several departments. To solve this problem, software was developed for Customer Relationship Management (CRM). This software allows many users to create and manage lots of data in a structured way. Launching such software across a large enterprise is quite challenging—no solution is one-size-fits-all—but can solve immediate business problems and offer incremental benefits which add up over time. For this reason, it might appear to make sense to make decisions about CRM selection to solve a series of today's defined problems for the sales department.
However, a CRM can also serve as the foundation for much more exponential possibilities, like serving as a user data source about networks of customers and partners which can be analyzed using artificial intelligence tools. In this instance, investing a little extra time and effort toward a social CRM, which tracks network connections between customers, and not just a standard CRM, you can still solve today's problem while laying the foundation for tomorrow's opportunities. In this case, we have to understand how network effects work to create exponential change in order to spot the right features in potential CRMs.
It can be helpful to think of tools organized into stacks—collections of components which, together, solve problems for a business function. Examples of common stacks include finance, [software] development operations, marketing and collaboration.
Within each stack, there are several software components, many of which can be swapped out for other, similar software. For example, within a marketing stack, one team may have a component for basic Customer Relationship Management which was built from scratch while another team may have adopted a social CRM like Salesforce or another third-party solution. Both satisfy certain functions in a larger stack, or collection, of components organized around a common function for the business.
The concept of stacks doesn't even have to be limited to software—the concept can be broadened to include ways of thinking and specific skillsets. In a Development Operations (DevOps) stack, for example, technologists and business stakeholders work together. If they share mental models and skills like product management, agile software development and user experience design, they will be able to get the most value out of their collaboration and their tools.
A new set of skills is required for digital transformation. Technical, intellectual, interpersonal and leadership skills need to be acquired and evolved.
Technical skills are sometimes an obvious place to start. Certainly there are many places you can go to find out more about technology and software. However, it's important to note a shift from one-time learning to learning on-demand. For example, many technical skills are based on software which is being updated continuously—yearly, monthly or even daily. A new practice is needed to update our technical skills regularly; this is why many software platforms offer on-demand 'universities' for their products' features.
Intellectual skills to develop include the ability to see a project through, from a blank canvas all the way through to completion, to identify new opportunities rapidly, and to create what might be called an "options management system" that integrates decisions across different parts of the business.
Interpersonal skills change in the digital age. New norms are needed for distributed teams, expressing emotion in written form, and of course, specifics like video and online document etiquette.
For example, in a Google doc, where everyone can see live activity occurring, people might not want to make a mistake in front of their boss. This is why, even when people know how to use cloud tools, they often create their own private version of documents and then bring them back to share.
That's a symptom of a leadership or trust issue, but it can masquerade as a lack of technical skills.
As leaders, we must upgrade our ability to show up as someone who can improvise quickly and work in real-time. To be more agile, we need to do more than just adopt technologies. We need to network, or share, our power in new ways—including updating old habits around control, professionalism and perfection.
