The following is a transcript of the “Digital Fluency in 19 Minutes” Video above.

My name is MJ Petroni. I’m the Chief Exponential Officer for Causeit Inc, a futurist think tank and innovation consultancy on the West coast of the U.S.

I have an unusual degree. I'm a cyborg anthropologist. It’s a real area of study, I promise! In addition to being useful for giving my parents a panic attack when I told them that I was going to study cyborgs in university, it’s also quite relevant today. That’s because cyborg anthropology includes study of the mental models underlying advanced technologies, like AI and big data.

In practice, what I get asked for most is how to raise the digital fluency in an organization.

Digital fluency occurs in five key areas

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 requires deeper understanding. It includes not only skills and knowledge but also culture, ways of thinking and a lot of practice. One part of effective digital strategies is bridging the gaps between human and technological 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.

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 which harm real people. So 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.

The Lowest Common Denominator

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.

For example, one of the first uses we had for electricity was lighting our buildings. Electric lighting was a breakthrough in its own right, but it was nowhere near the potential of electricity itself. We already had lighting, we just made it better. But we couldn’t conceive of what electricity makes possible today until a critical mass of people—from many disciplines—understood more about how it worked and discovered new ways of thinking about energy, movement and information. 

Advanced technologies can create as many problems as they solve 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.

Building a Network of Digital Fluency

Digital transformation is partly a function of network effects. 

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. 

When raising the digital fluency of an organization, you want to attend to the size, quality and growth (or attrition) rate of your network. Some organizations find it helpful to determine key groups. For example…

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 groups.

We need to start to think of digital as a capability that's not in one department, but across the board. Existing businesses are often used to thinking of technology as a utility function—something that enables their analog business.

Thinking

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 realize the opportunities of the digital age. Examples of these mental models include network effects and exponentiality, platform thinking, many-to-many and peer-to-peer communication. 

Another key distinction is the difference between data at rest versus data in motion: think about the difference between something static, like a file in a file cabinet, versus something live, like a stream of real-time video.

We also need to have the right thinking to know how we can leverage exponential technologies to solve incremental business needs.

And perhaps most important to updating our thinking is diversity of all kinds, so that we apply technologies to serve everyone, not just a select few.

Computational Thinking

Computational thinking is another great example of where we need to move from traditional business approaches to something that bridges how humans think and how machines think. Computational thinking starts with what's called decomposition.

Skills

As you can see, before you can get to making programs, you have to do the work beyond just coding. Skills of all kinds are required; not just technical skills, but also intellectual and interpersonal skills. 

Technical skills are sometimes obvious, but certainly there are other places you can go to find out more about that.

Whether you’re learning about practical elements of databases or advanced tech like machine learning, always ask the question: what's the way of thinking here? 

Intellectual skills that we want 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 you might call an options management system that integrates decisions in lots of different parts of the business.

Interpersonal skills we want to develop are like working across distance, expressing emotion in written form, and of course, specifics like video etiquette. However, make sure you also look at leadership-level skills related to more tactical interpersonal skills.

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/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 update old habits around control, professionalism and perfection.

Business Model

Business models are key to digital efforts. We have to understand how data can or cannot be monetized. Monetization could be as simple as paying for access to data and insights or through advertising-based subsidies. 

Digital business models and value propositions require new thinking about who exactly creates value and how it's delivered. 

In order to know whether data can or cannot be monetized, we should learn how platforms enable co-created value, such as when individuals create content on top of a shared infrastructure like YouTube. 

It can help to think of your business as a network orchestrator or a platform host. But you don't necessarily have to be a tech company in order to host a platform.

One shift to pay attention to is the move from pipes to platforms. In a pipes mindset, you deliver value to others—and they consume. But in a platforms mindset, you're enabling others to create value. This is can be thought of as co-creation, where each party has a unique role.

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. 

Assets & Things

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. 

People & Services

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.

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 they get valued more than coffee vendors—they’re not selling coffee, but an experience that happens to include coffee.

Ideas & Technology

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 or 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.

Connections & Networks

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

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 tons 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.

Data

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. 

Ask ourselves, What data exists or could exist in our ecosystem?

How could datasets connect to each other to create new value? 

What are our APIs and what role do they play in data-driven use cases? 

How are we managing informed consent and ensuring our data use doesn't harm our partners or customers?

Data Fluency

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:

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.

Acquire

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 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. 

What data could you gather? 

Where will you get it from?

Store

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.

Where will you store your data?

Aggregate

When we aggregate data, we combine disparate data sets to create a larger data set that's greater than the sum of its parts. This is the fun part, but also the more complicated part. In the driverless car example, the aggregation stage begins when the car gets to its owner's home and syncs, uploading its raw data to the manufacturer server. How will the manufacturer combine this data from different sources and types? 

How will you combine data from different sources and types?

Analyze

Once data is collected, stored, and aggregated it’s time to analyze it. We examine data—and sometimes transform it—to extract information and discover new insights. In the automotive example, a company's big data algorithms analyze raw data from all vehicles in an area and compare it to map and traffic data to see how their cars are faring in different parts of the city. 

The analyze stage is the point in the journey where data turns into information. This is a crucial shift. When we talk about data, we may sometimes think of it as being the same all the way through the supply chain, but data is largely useless until you apply the right analysis to it. Think about the difference between the raw data generated by your banking transactions vs. fraud alerts that come from your bank analyzing it effectively.

What analysis will you do of the data?

What is the question you would ask of the data if it were a person? 

Will you change it or add to it in any way?

Use

How can you apply the insights gained from data analysis and use it to make better decisions, which effect change or otherwise help you deliver a product or service? In the automotive example, collision avoidance and navigation algorithms might be updated across all the vehicles based on the raw data that we're getting from these various cars out on the road.

What will you do with the findings of your analysis? What switch will you flip? What choice might you alter? What investment might you make—or revoke?

Share/Sell

Now we get to the million-dollar question: Can we ethically share or sell data? 

How do you provide access to datasets—or the insights from those data sets, which are not the same thing—to other people, organizations or systems? 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?

There are data ethics and informed consent considerations all the way through the data supply chain. For one, good modeling of a ‘data supply journey’ is a critical part of operating mindfully and ethically, which in the long run is always better business sense. Secondly, the analysis phase of the data supply chain is a vital one for ethics considerations. Sometimes we can analyze and get insights into data without ever passing on raw data. If we can do analysis at the point of data ingestion into the data supply chain, we may never need to pass private information along at all. On-device analysis characterizes Apple’s approach, as they prefer to do much of their seemingly-magical processing (facial identification, voice recognition, etc) with powerful and secure processors on users’ phones, rather than passing raw data (like face biometrics or voice recordings) to the cloud often—like their competitors Amazon and Google.

Will you share or sell any of the data to other parties?

Will you share data back to the source?

Dispose

Disposal of data is an important consideration at the end of the data supply chain. It’s never the most exciting, and it doesn’t generate any immediate value, so it’s often overlooked. However, regulatory requirements and common decency both demand that we think through how data will be disposed of when it’s no longer useful. While whole disciplines of cybersecurity are devoted to these details, business stakeholders need to consider one key concept: centralization vs. federation. With data at rest—‘files in a file cabinet’—we can imagine shredding files when they’re not needed anymore. If we’re honest about it, most organizations only got rid of paper files when they ran out of space. Digital storage, however, isn’t visible to us, and has such marginal cost that we might not even realize what data we still have laying around gathering proverbial dust.

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. As mentioned earlier, 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. 

How will you protect the data? Will you dispose of the data when you are finished?

Take the quiz to find out how digitally fluent you are

The higher your digital fluency, the more likely it is that you or your team can create digital value.

If you want to jump in, we have an assessment that you can do on behalf of your company, or just for yourself. You can use it to identify your organization's lowest common denominator of digital fluency, whether it's thinking skills, tools, data, or business model, so you get an idea of what to upgrade first. 

That's at causeit.org/how-digitally-fluent-are-you.

Or you can just email us at talk@causeit.org and let us know where you're working on digital fluency, because we'd love to hear and integrate these kinds of examples into our talks. 

So with that find your lowest common denominator to upgrade to your highest digital self and we'll look forward to learning from you. Thanks.