Digital companies create value through digital factories
A digital factory combines mental and functional models that enable a company to effectively build, release, and update software and other digital offerings in a repeating cycle.
Through a blend of design thinking, lean business, agile programming, and development operations (DevOps), digital factories work to quickly produce digital offerings. They start with a Minimum Viable Products (MVP), an early version of a product that launches with just enough functionality to meet customers' needs. MVPs are then fleshed out into beta releases and, eventually, a complete product with regular, continuous integration and delivery of new features and bug fixes.
This process exposes the product's creators to real users and use-cases that might not otherwise be anticipated in a controlled environment, giving developers access to valuable information about what needs to be added, fixed, or improved. This can result in better product and customer loyalty while requiring less time and expense than traditional linear testing and production processes.
Digital factories may need to be separated from other departments, as the business goals and expectations for such factories differ considerably from the rest of the business. The velocity of the development operation is much faster than, for example, the budgeting operation of the company, and so decisions need to be made much more quickly.
You can use the digital factory as a metaphor to expand how your organization thinks about technology, value, and production.
This is critical because organizations that still think of IT as a utility that enables their existing business models will not create the resources and structures, hire and train digitally-talented people, or align the incentives necessary to regularly, consistently, and rapidly produce and evolve digital objects.
For a digital organization, 'digital' can mean many things, including but not limited to: content, products, services, strategic partnerships, datasets, or data processing.
The digital factory metaphor considers technology to be core to value creation, not a supporting function. It prompts the same questions any factory does:
Key methodologies within digital factories often include:
Define what is so in the customer's world by listening to people and then imagining new possibilities
Test ideas as quickly and inexpensively as possible to learn what the right solution is, evaluate assumptions, and learn a way to achieve the desired outcomes
Adapt software to changing conditions
Reliably, securely, and quickly deliver software
Before addressing the intricacies of digital innovation and digital factories, it is helpful first to understand how the development cycle works as a whole. Innovation may look complicated, but one of our oldest technologies—agriculture—provides a surprisingly simple lens for navigating this process: the seasons of innovation.
In each 'season' of the digital factory (pardon the presence of two metaphors), there are:
Before looking at thinking styles as they apply throughout the seasons, you may want to read Introduction to Thinking Styles and Thought Partnership and take a thinking styles self-assessment.
It may also be helpful to read our guides on business models and value models.
Tools Notice:
Specific tools may show the company's tagline for their offering or our paraphrased version for clarity. Specific tools listed may link to their own website, which is outside our control. Causeit, Inc., the creator of this Digital Fluency Guide, does not receive any sponsorship money or affiliate revenue from any listed company. However, members of the Causeit team may own stock in publicly-traded companies shown here, either directly or via ETFs/mutual funds/other assets. Causeit also does not represent any of these companies or make statements on their behalf. The referenced companies/tools are shown primarily as examples of commonly discussed solutions in the marketplace and development communities and are not rigorously ranked or scientifically selected.
In the winter season of innovation, we're focusing on culminating prior work and creating space for new work. This is where we take stock of resources, gather information, and prepare for new things.
In the digital factory, this includes activities like exploring industry trends, conducting research on new technologies, publishing or sharing that research with other parts of the industry, and networking with various people inside and outside the organization.
This is also the phase where it makes sense to consider deconstructing 'legacy technologies,' i.e., older systems that can't adapt to current demands for interoperability and dealing with parts of the infrastructure where outdated software has reached the limits of what it can be made to do through patches and workarounds. Together, these create 'tech debt' that becomes more expensive to fix the longer it exists, not just because of the inflation of the cost of labor but also interdependencies created when other software has been added or modified to accommodate older components.
A typical example of this is software built in-house that was designed to run on older mainframe computers or on-premise servers that do not have the architecture and data organization to exist in cloud-based computing.
Someone who can see different business models and participates in creating hypotheses without being intimidated by a blank canvas.
Digital explorers look at the broader picture of what is possible where user need meets current (or potential) business capability.
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Assumptions and possibilities documented; testing these leads to higher likelihood of offerings that are genuinely useful and feasible
Assumptions and possibilities documented; testing these leads to higher likelihood of offerings that are genuinely useful and feasible
Number or financial amount of projects, offerings, initiatives or other resource drains which need to be retired or overhauled
Number or financial amount of projects, offerings, initiatives or other resource drains which need to be retired or overhauled
Number of complete business ideas articulated as 'on-paper-tested' business models
Number of complete business ideas articulated as 'on-paper-tested' business models
Number of specific value propositions articulated as 'on-paper-tested' models
Number of specific value propositions articulated as 'on-paper-tested' models
Potential network or market size
Potential network or market size
The number of points of information in a forecasting or trendcasting network
The number of points of information in a forecasting or trendcasting network
The spring season of innovation focuses on formulation—imagining new things based on broader research, planning the upcoming work, and planting the year's crops.
In a digital factory context, this could mean introducing new features or fleshing out the details of new offerings. It is also the time to set a particular destination (with important milestones) on the product roadmap and determine critical questions that need to be solved, de-risked, or tested.
This stage requires networked leadership and cross-function collaboration as the business needs to look at the big picture, talk to customers to determine their needs, and create a financially viable product—all at the same time.
During this season, another vital function is ensuring all development requests from across the business are collected and prioritized effectively against specific business outcomes.
Objectives and Key Results, or OKRs, are helpful at this stage. It may be easy to focus on a particular software need that may not provide a significant movement in the business or create a lot of value.
Conversely, some development may be necessary to defend existing value creation, such as security patches that may create problems later if left unattended.
Artifacts for this season may include prototypes where a concept is developed, but feasibility hasn't yet been tested.
The Spring Season of Innovation ends when things start to appear in material forms, such as a Minimum Viable Product (MVP) that's ready to test with real users.
An innovation toolkit, like Spigit, or lightweight modeling tools like the Strategyzer online toolkit/web app for value proposition design and business model generation, can help organize work on this process, even as a distributed team, and provides structure to stay focused on tangible opportunities. Specialized tools are not required. However, a well-configured general-purpose project management system like Asana or even a well-built spreadsheet is a good start.
Business model elements move out of the Formulation/Spring stage when the first signs of work become visible to the people the business model ultimately intends to serve. This might take the form of published research, pilot projects, or minimum viable products. Teams know they have progressed towards the end of this stage when the learnings they are extracting from their tests validate a complete business model, indicating product-market fit.
Designers
Whether web designers, graphic designers, or user experience designers, these people create user interfaces and manage elements of the customer experience.
Product Manager
Product management as a field arises where different services and functions are brought together into a cohesive offering or product, rather than a smattering of smaller features or revenue models. This process is known as productization.A Product Manager sees the entire lifecycle of a product and all the people it affects, such as particular users or developers, and connects the dots between the business and technology elements of that discussion.
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Number of initial, partially-functional digital releases.
Number of initial, partially-functional digital releases.
How many potential target customers have been identified (eg from an existing customer list or prospect list)
How many potential target customers have been identified (eg from an existing customer list or prospect list)
Number of preliminary algorithms or data models; can be affected by quality and quantity of data sets
Number of preliminary algorithms or data models; can be affected by quality and quantity of data sets
Number of data points (total) across data sets
Number of data points (total) across data sets
Number of APIs for internal stakeholders (measures the quality and utility of network of systems)
Number of APIs for internal stakeholders (measures the quality and utility of network of systems)
Number of business models and value propositions tested with the marketplace to a uniform stage (such as customer identification interest, or validation)
Number of business models and value propositions tested with the marketplace to a uniform stage (such as customer identification interest, or validation)
Category of metrics for the number of members in a network (such as people, devices, organizations, or data points)
Category of metrics for the number of members in a network (such as people, devices, organizations, or data points)
Number of successful patents filed, which can increase network size or network uniqueness
Number of successful patents filed, which can increase network size or network uniqueness
Potential network or market size
Potential network or market size
Fully-written user stories (demonstrates listening to users which will lead to increased quality)
Fully-written user stories (demonstrates listening to users which will lead to increased quality)
In the Summer Stage, developers and software-centric parts of the digital factory work with Product Managers to refine Minimum Viable Products into something scalable and Generally Acceptable (known as a GA release). This version comes after the Beta version has been finished, polished, and made ready for widescale adoption.
At this point, the organization's support and cybersecurity teams must be able to defend and support the new software against hackers, bugs, and any other issues that may arise.
The summer season of innovation ends when new products or services become 'business as usual' or are integrated into the rest of the organization.
Thinking in the Summer Stage
Explorer thinking begins to focus on integrating innovations into the whole of an organization and begins scouting for the next round of ideas for iterating or pivoting.
Planner thinking helps coordinate selecting which ideas move forward and which have to be let go, especially managing funding and work effort.
Energizer thinking keeps the team focused on the end goal and supports the team through difficult decisions and challenging integrations with the larger organization.
Connector thinking is crucial to map current team members to the departments and partners needed to scale innovations up and redeploy those who were primarily focused on launch.
Expert thinking enables producers and others to solidify complex designs and systems.
Optimizer thinking can rapidly iterate on designs to increase reliability and profitability.
Producer thinking is essential in churning out the work needed to scale innovations.
Coach thinking is instrumental in helping new members of the team orient themselves. It helps existing team members adapt to the increasing complexity and decreasing freedom in the process or allows them to move to new roles.
Technology partners
Any stakeholders who are not inside the company but with whom there is at least some degree of interdependence or dependency. These include but are not limited to third-party software developers, data firms, apps, services, and hosting providers.As companies develop their own software factories, they may lessen their reliance on others but potentially create internal costs and talent pipelines that need to be managed. Conversely, being over-reliant on technology partners is a risk as those partners may look to exploit that reliance or be unable to provide essential services. Understanding the interdependence between your firm and others and using that understanding to plan damage limitations in case of catastrophe can be extremely useful.For example, if there were a major earthquake in San Francisco, would any technology partners based there have resilience plans allowing work to continue? If the software was developed using teams based in Ukraine, are they able to continue?
Software Developers (Digital Makers)
A developer refers to someone who builds and manipulates software code (or sometimes digital hardware). The job of a software developer is to convert human needs into programs. They code particular instructions for machines to follow, either from scratch or by assembling sequences of ready-made components. The developer mindset is based on computational thinking, as much of their time is spent working with machines to solve problems. Working with developers can be similar to working with quants in that they both specialize in working with data to find insights that other types of colleagues may not be able to see. Software developers are core to the software development experience, and there are several disciplines within the field. Speak with IT leaders to understand the different kinds of software developers needed. Due to the nature of their work and the thinking styles required, the developers' way of thinking about problems is often very different than other stakeholders, especially those in the 'front end' parts of the business. This is a good thing when all parties recognize it.
Security and regulatory stakeholders
These teams specifically pay attention to things like security breaches. Regulatory stakeholders may have a lot to say about how data is handled or what may or may not be passed between companies for security reasons related to data ethics or data sovereignty. Heavily-regulated industries like finance and healthcare must include both security and regulatory stakeholders early on in the software development process. Then, as new ideas and new features are imagined, regulators can work with teams to ensure compliance. This insures against a lot of lost effort when a particular use case is found to break local or international rules such as GDPR or PIPL. Data security and “moving security left” are covered in more detail in the Dev Sec Ops section.
Data Scientist
Put simply, a statistician who knows how to work with computer code. Data scientists create or expand algorithms, machine learning models, and other elements that allow us to test hypotheses about data, users, etc. A data scientist can run general analyses of opportunities available in the marketplace, but most of their work will be focused on big data or little data initiatives. You can learn more about elements of data science in our data expedition.
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Number of successful algorithm sets (or models) developed by a data science team
Number of successful algorithm sets (or models) developed by a data science team
Number of app downloads (total, or in a given time period)
Number of app downloads (total, or in a given time period)
Number of integrations of apps and services that reached a beta state
Number of integrations of apps and services that reached a beta state
Number of functional early software releases
Number of functional early software releases
Category of metrics used to measure the ability to serve current and future demand
Category of metrics used to measure the ability to serve current and future demand
Improvement of retention for new cohorts (users taking a core action in/for the product)
Improvement of retention for new cohorts (users taking a core action in/for the product)
Number of users explicitly willing to pay for something, even if they have not paid for it yet (can be measured by interest forms, deposits, etc)
Number of users explicitly willing to pay for something, even if they have not paid for it yet (can be measured by interest forms, deposits, etc)
Number of business models and value propositions tested with the marketplace to a uniform stage (such as customer identification interest, or validation)
Number of business models and value propositions tested with the marketplace to a uniform stage (such as customer identification interest, or validation)
Ease with which two sides of a marketplace or community can find each other
Ease with which two sides of a marketplace or community can find each other
Category of metrics for the rate of growth (or attrition) for a network
Category of metrics for the rate of growth (or attrition) for a network
Category of metrics for the number of members in a network (such as people, devices, organizations, or data points)
Category of metrics for the number of members in a network (such as people, devices, organizations, or data points)
Category of metrics for the utility and attractiveness of a network
Category of metrics for the utility and attractiveness of a network
Number of times an app is downloaded and installed to a user's device
Number of times an app is downloaded and installed to a user's device
Number of (potential) producer-consumers in a marketplace (who both can contribute to and take from the market or community, like eBay users)
Number of (potential) producer-consumers in a marketplace (who both can contribute to and take from the market or community, like eBay users)
Number of (potential) buyers in a marketplace
Number of (potential) buyers in a marketplace
Number of (potential) sellers or providers in a marketplace
Number of (potential) sellers or providers in a marketplace
Number of APIs available to strategic partners and clients
Number of APIs available to strategic partners and clients
Number of APIs available to the public (a specific measure is needed)
Number of APIs available to the public (a specific measure is needed)
Instances of users repeatedly engaging in ineffective clicks because an app or site is malfunctioning
Instances of users repeatedly engaging in ineffective clicks because an app or site is malfunctioning
Number of users who have completed a sign-up process
Number of users who have completed a sign-up process
The number of third-party apps or other digital offerings on a Multi-Sided Platform or marketplace
The number of third-party apps or other digital offerings on a Multi-Sided Platform or marketplace
Time between a feature being requested and it being deployed
Time between a feature being requested and it being deployed
A measure of network quality on content sites
A measure of network quality on content sites
Measure of network quality on content sites
Measure of network quality on content sites
Number of units sold in a period divided by the number of items available at the beginning of the period
Number of units sold in a period divided by the number of items available at the beginning of the period
Realization involves continuing to scale and deploy software, connecting to other services and business functions, and optimizing performance to run faster, cheaper, and more securely.
Continuous Improvement and Continuous Delivery frameworks come together at this point to continually improve software and release new updates, bug fixes, and better features.
Software security is a priority during this season of innovation. Considerations include functional security against hackers, better data handling, and fraud prevention. The security function of any digital product or service should be considered from the first day of planning.
Once the software is deployed, bugs, and the need for fixes, become much more common. This is due to the software being tested by everyday users with more use cases than imagined during the early phases of building. It is critical to ensure that support teams, product managers, and other business stakeholders are connected to fix problems and optimize offerings.
As well as scaling up new software, it is important to consider the need to scale down specific software tools. Over time, we might need to retire older pieces of software to make room for new software.
As this conversation evolves, the cycle completes with a shift from Fall to the Winter season of innovation.
Explorer thinking is applied to track the business environment and look ahead to the next big cycle of innovation.
Planner thinking coordinates regular, sustained fulfillment of the innovation process so it can become “business-as-usual.”
Energizer thinking motivates everyone to stay on track in the ongoing work of sustained production and makes sure there is a clear connection between everyday work and big-picture vision and values.
Connector thinking ensures teams have what they need and are supported throughout the organization. End users, service, and support teams have lines of communication with engineers and product managers.
Expert thinking is applied to keep the design of products and services at a high level of quality.
Optimizer thinking identifies iterative improvements to products and processes, ensuring the most value is reaped from everyone’s effort with the least amount of friction possible.
Producer thinking is focused on the fulfillment of value propositions to customers and internal key activities.
Coach thinking helps teach others how to do their job well and supports people through stressful moments as demand and time constraints increase pressure.
Digital Support
In other words, customer support teams and developer support teams. People who face outside the company to customers or clients and ensure any issues with your product are fed back and dealt with by the appropriate development team very quickly. Without appropriate resources allocated for support liaisons, vital feedback from users may be lost. In the case of security breaches or other data handling failures, the time between the issue being discovered and developers distributing a fix for it is critical. It can represent a significant liability for the brand.For example, cheap, internet-connected security cameras sold through third-party marketplaces. If someone finds a way to compromise the security of the software, all of those cameras are at risk. If there's no way to inform the manufacturer, they cannot notify existing users or withdraw those cameras from the market.
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Times a given API has been accessed (may be divided by the number of unique users making the calls)
Times a given API has been accessed (may be divided by the number of unique users making the calls)
Total amount of revenue on a subscription or similar basis
Total amount of revenue on a subscription or similar basis
Amount of recurring annual revenue per customer
Amount of recurring annual revenue per customer
Total revenue divided by number of users in a given time period
Total revenue divided by number of users in a given time period
Number of paying customers
Number of paying customers
Number of customer tickets opened in a given time period (expresses either dissatisfaction or occasionally positive engagement)
Number of customer tickets opened in a given time period (expresses either dissatisfaction or occasionally positive engagement)
Revenue that the marketplace host receives, as a portion of the gross merchandise value passed through a marketplace (eg app store commissions)
Revenue that the marketplace host receives, as a portion of the gross merchandise value passed through a marketplace (eg app store commissions)
Amount of revenue retained after costs
Amount of revenue retained after costs
Instances of users repeatedly engaging in ineffective clicks because an app or site is malfunctioning
Instances of users repeatedly engaging in ineffective clicks because an app or site is malfunctioning
Number of users who have completed a sign-up process
Number of users who have completed a sign-up process
Total revenue created
Total revenue created
Unlike the natural world, the Seasons of Innovation aren't rigidly defined as a one-year cycle, with four seasons of three months each. The early seasons of innovation tend to be shorter, and the fall season of innovation for a business may run for years or even decades.
This means that software or offerings developed up to 50 years ago may still be running, with no easy means of updating or retiring them as circumstances change and replacements are required. By thinking of things in terms of seasons of innovation, the necessity of eventually retiring software is explicit from the very beginning.
To continue the agricultural metaphor, when a crop has no natural predators and overgrows, it is considered an invasive species. As there's no natural way to retain an equilibrium with the surrounding environment, it can pull resources from, or displace completely, other crops that could be growing more naturally.
This also holds true in the business and software world. If a product or service is overly protected due to customer lock-in or because there are no competitors to drive innovation, feature bloat can occur. Feature bloat is when software gets features continually added to it, but nothing is ever removed.
This happened a lot with internal software developed for businesses that were not originally tech-based.
For example, in the 50s and 60s, banks developed software in proprietary programming languages that are not used or even known anymore. These systems were never designed to connect with their counterparts in other banks, nor with the internet, which did not exist in the way it does today.
As a result, some organizations may be operating on software that is decades old with no developers able to update or change it in any way. As long as these systems continue to be used, neither the software nor, by extension, the business as a whole can grow to meet the challenges of the modern day. It's important to cycle through to the 'winter' season of innovation again and challenge organizations to let go of part of their business or technologies that no longer function well to create space for whatever is next.
Participation in digital factories happens on both individual and departmental levels. The best time to get involved is as soon as possible, perhaps even before a department is ready to work on a digital product.
A great place to start is by reaching out to build an active relationship with the people and departments already engaged in creating and testing digital products. Once a digital project is ready to bring to the factory, it's helpful to map out the points in the process where two-way communication will be most important and make sure the tools and practices are in place to make that communication effective. Examples of this may be a bug reporting tool or a regular cadence of short meetings or 'standups.'
Business stakeholders should also agree in advance on managing prioritization and funding decisions that may come up, so development teams can be free to innovate within acceptable bounds.
Finally, it's helpful to think about when—or if—a project will graduate from a software factory to be managed by a different team after launch. Some businesses are organized around a central technology approach where all software operations are managed in one place. Others prefer each product to have dedicated design and software capabilities around some shared services. With any approach, it is essential to be clear from the outset about the long-term maintenance and development requirements.
NOTE: It is important to disambiguate the mental models of digital factories and software factories, as there is some overlap. Throughout this article, the term 'digital factory' refers to all digital-centered activities (especially on software-centric digital value). However, 'digital factory' in manufacturing contexts may refer to automation and on-demand practices such as 'Industry 4.0' or 'Factory 4.0,' which refers to digitizing physical factories and is outside the scope of this article.
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