Category: Open Source At Large

(This is the seventh post in our Open Source At Large series.)

Open source is a powerful tool that offers great benefits to organizations that make, maintain, or deploy software. Most teams know they need open source capabilities. The hard part is finding a focus around adding skills. In most cases, the most important consideration is building capability to know how and when to use FOSS strategies.

FOSS is all about execution. Reaping the benefits of open source investment requires nailing a series of difficult steps ranging from designing an initial strategy to building an appropriate community to leveraging the resulting dynamics for strategic advantage. Many organizations are not prepared to travel that path and reap those rewards at scale. At OTS, we talk about that level of capability as organizational readiness, and we describe the journey to mastery as one of gaining capability.

To do that, we might locate a team in a readiness model. This clarifies where they are and also suggests next steps and likely results. Often, organizations use these models to identify areas for potential growth. We find the models are most useful for firms early in their open source journey, and this post focuses on those early stages.

There are several readiness models. OTS has for many years described climbing the readiness ladder (in various publications). Microsoft’s Jeff McAffer sees common patterns in an engagement model. GitHub also sketched maturity model as the start of a discussion on readiness. Similarly, Wikipedia reproduces an interesting model from Qalipso. There are a number of such models, and it is often worth considering more than one when examining an organization. Of the published models, we like McAffer’s in particular because it includes strategic components, accounts for realistic failure modes, and understands that open source readiness will be unevenly distributed in large organizations.

On many teams, initial open source capabilities might be nascent. Most team members have not had significant (or perhaps even any) experience using open source strategies to create value. The team works in an environment where FOSS investment is rare, and many do not see much reason to change that. That lack of knowledge might translate in some quarters into hostility toward FOSS. People will say “It can never work here” even as open source slowly seeps into more and more of the technology around them.

At this stage, FOSS strategies can be difficult to execute. Internal political risks might be high. Policies might explicitly forbid engaging FOSS. Staff doesn’t know how to begin to work with external open source teams. Many people will lack even a basic understanding of what it means to do open source work. Efforts to work in an open source mode are likely to fail in ways that reinforce the belief that open source is not worth further consideration.

Many factors might move an organization from hostility toward tolerating open source, but movement usually comes from external pressure, changing environments, and staff turnover. Conditions around the organization begin to change, and the costs and risks of refusing to engage start to rise.

Those costs might include the pain of maintaining internal forks of external open source projects (or, more precisely, the pain of not maintaining all those forks). Similarly, the benefits of making minor open source investments start to become clear, even if only from watching others make those investments and reap the benefits while your organization loses ground.

However organizations begin to come around, tolerating open source is all about experimentation. Firms usually don’t think of it in those terms. They are making small concessions to necessity. They are seizing unique, one-off advantages. Most don’t think of those small projects as the future direction of the company. They should consider the possibility, though. Sometimes, explicitly labeling such experiments as learning exercises and skill-building allows an organization to maximize the value of their experimental investment. It prioritizes reflective analysis and learning. It gives permission to fail. Those can be useful to companies seeking adaptability. When considering McAffer’s model, we might relabel his “tolerance” phase as “experimentation”.

Experiments come in many forms, but the most common first experiment is using some outside open source code and engaging the open source project. That might involve filing bug reports, offering a contribution, or merely participating in project mailing lists and forums. These are all relatively low-risk ways to begin connecting an organization to outside FOSS projects.

The experimentation phase is usually a skill-building and knowledge-gaining phase because it exercises the skills that cause an organization to shift from merely tolerating open source to trying to harness it. Having those skills provides the vision that starts to shift attitudes.

The problem that arises as companies start to embrace FOSS is that they lack the infrastructure to succeed at it across the entire organization. They are missing policies, auditing, skills, culture, and experience. This is a pivotal, risky moment. A large number of teams will still be in the denial phase. Efforts to move internal culture toward FOSS will be perceived by some as a pointless shift toward the latest buzzword. Those experimental skills will be unevenly distributed internally. Many new open source projects will fail, and this will convince some that FOSS is a failure. Some might even sabotage FOSS projects.

McAffer sees this phase as one of hype, and perhaps that’s because it’s also when an organization embraces open source without quite being ready to execute. Firms in this phase tend to engage FOSS in shallow, unsophisticated ways simply because they don’t yet have the experience to make better strategic use of open source opportunities. The way to move past this stage is not to reduce the hype (though that might help) but rather to increase readiness.

Managers at this stage — especially middle managers — need guidance on using FOSS as a strategic component and on managing teams with more external deliverables. Developers need technical infrastructure, easy-to-follow licensing policies, and permission to engage externally. Perhaps more importantly, they need to develop new habits of working in the open and sharing even early, rough versions of their work. Beyond just technical teams, Human Resources needs hiring and compensation guidance as both skills and performance evaluation criteria shift. Providing all of those pieces is the process of gaining proficiency. Adding skills, process, and policy is how that happens, and it requires management approval and resources. Companies that don’t provide this support from fairly high in the org chart tend to plateau at this level of readiness.

All of the above describes a path from the very beginning toward eventual mastery of open source. We think of readiness in terms of skills and capabilities, but the truth is that doing open source well is primarily a cultural shift. Organizations using open source fluently quickly find that the open approach becomes their default process. That culture shift will be the topic of a future post. For now, though, where is your organization on the path toward readiness? What are the steps you can take to help prepare for an increasingly open source tech landscape?

(This is the fifth post in our Open Source At Large series.)

Last week, I spoke with the CEO of a company that makes a proprietary category-leading workflow product. He asked me “what should we open source and when?”

This is one of the most common questions we get. There is no easy, standard answer to this question, but I usually start by looking for features of the ecosystem that either are or could be commoditized.

A commodity is something that is the same no matter who makes it. Gasoline is a commodity. Customers don’t really care where they buy gas. They want it cheap and reliable, but the gas itself should be the same stuff regardless of which oil company refines it and pumps it into your car.

In practice, of course, gas from different companies is not identical. Some places enhance it with detergents, mix it with ethanol, or add stabilizers.  But for the most part, consumers ignore these small differences.  You put it in your car and it works.  People pick gas stations based on price and convenience.

It’s hard to charge a premium for a commoditized good. People are not willing to pay extra for a product if they can get the equivalent for less elsewhere. As a result, gas stations compete on price.  This squeezes margins, and so in the end they make their money on the convenience store, just like movie theaters do with popcorn.

Open source software businesses are often in the same position. There’s a core offering that is, to put it bluntly, boring. Anybody could offer this boring core without innovating. You cannot be in the market without matching the core’s offerings, and customers want standard interfaces these days, so there’s not much opportunity to differentiate on the basic core offering.

Database servers follow this model. From a business perspective, storing rows of data is not interesting. Everybody can do it, and nobody really does it much better than anybody else. There are performance improvements at the margins, and we can build interesting services on top of storing information, but basic data storage and retrieval is just facilities maintenance, not the thing your customers choose you for.

Of course, not all data storage is basic. You can store things faster or in ways that make access easier. You can stack data vertically instead of horizontally. You can keep all the data in memory, duplicate it across multiple servers, or sync with client-side storage. These are features that can set one product apart from another. You might be able to charge a premium for them. None of them, though, is actually the boring, basic task of putting data on a disk and making sure you can find it later.

For a business that manages data but can’t find competitive advantage on storing it, the database is a cost. It’s not worth spending a lot of money to develop or acquire a database that improves on the standard set of features because there’s just not much improvement to be had. Best to source those features as cheaply as possible.

Of course, the same logic applies to everybody else in your industry. We’re all trying to get the boring stuff done as cheaply as possible. This is where open source cooperation comes in. We can’t compete on those features, so there’s no point being competitive about it. We use open source to get together and collaborate with all the other folks who want rock-solid databases. Open source lets us do this even if we’re in competition on a range of other fronts.

We see this pattern a lot in free and open source software. Product categories go open source, then contract, then perhaps expand when somebody figures out the next frontier of differentiation. Most recently, we’ve seen this effect around web rendering engines. Every browser’s goal is to depict HTML in standard ways. Aside from being at Google’s mercy, there’s little reason for most companies to make their own rendering engine when there’s a pretty good one that is open for the taking.

Spotting this pattern early is a crucial step in knowing what to open source and when.  You can lead or let somebody else lead — either might be strategically useful, but spotting the pattern lets you choose which position to take.

Sometimes it makes sense to open source what you have mainly as a signal to others that an area is becoming commoditized and so they should open source their stuff too.  That is, you don’t necessarily have to be aiming for a dominant position in a new open source area — in fact, you often don’t have to decide that in advance.  Instead, you open source because an area is about to become commoditized, and the earlier you shift your investments, the better positioned you will be get the type of influence that serves your needs in the inevitable post-commoditization universe.  (For more about the various forms that influence can take, see Open Source Archetypes.)

Once you start looking for the commoditization pattern, you see it all over the open source world. It is a common tool for understanding the strategic position of all kinds of products. In addition to the basic pattern, there are two more big concepts around commoditization worth considering: the cyclical nature of commoditization and the factors that allow resisting that cycle. We’ll cover those in future posts.

Thanks to Microsoft for sponsoring the Open Source At Large blog series.

(This is the fourth post in our Open Source At Large series.)

All the power of open source comes from throwing in with your neighbors, even the neighbors you don’t like very much. For most projects, the main reason to get involved in open source is to create productive relations with as many participants as possible, including rivals. Doing this well requires understanding who is in your ecosystem and how they relate to each other.

Whenever a team comes together to plan their open source strategy, they need a way to gather that understanding. They want to pool their knowledge and get everybody working from the same set of facts. The best tool we’ve found for this is ecosystem mapping.

There are many ways to visualize groups of stakeholders. We generally recommend starting with who and going from there to what. You can capture users, contributors, service and support providers, partners, funders, investors, deployers, integrators, and competing or adjacent efforts. Grab anything important to the questions directly in front of you, and don’t worry about being complete.

Ecosystem maps are lightweight. They should be messy, quick, and replaced often. The best way to make one is to hand-draw it on a large piece of paper or on a whiteboard, ideally as a group exercise. Snap a pic for future reference, but don’t bother taking the time to redraw them neatly. In a fast-moving project, the terrain these maps describe will shift often. And the reasons why you might draw a map will change even faster. It is not unusual to make several different maps of the same ecosystem in an afternoon.

Here is a simplified version of an ecosystem map drawn by the Arches Project. (We reproduced it in Dia for this article, but normally we wouldn’t bother to digitize a map, beyond photographing the whiteboard or paper it was drawn on.)

Notice how the diagram is primarily designed to highlight geography but also uses color and shape to distinguish between different types of participants.

The day the team drew this map, we wanted to understand where the project was succeeding geographically and how to support participants spreading the project into new communities. We suspected that having a set of committed users and service providers doing custom deploys were both important, so we mapped it to kick off a group discussion. As we talked through planning, we referred to the map, adjusted it at times and later even drew another map with a new focus. The diagram was a guide for conversation and let everybody agree on parameters quickly.

This is a map drawn of the Tor Project by Dlshad Othman:

This map is more centered on interactions with the Tor Project. It doesn’t mention geography at all, and it uses enclosing shapes to group types of participants in a venn-diagram. It shows roles and relationships with a heavy focus on the project itself.

There is no one right way to draw an ecosystem map. There are, however, some signs that your map is not set up to capture relationship complexity:

• It is shaped like a star, with all your connections coming back to one central entity. 
• It is more of a cloud than a map. If the map doesn’t depict relationships between entities, it’s not doing its job.
• It tries to answer too many questions at once.  Maps are usually single-use snapshots designed to highlight one aspect of your ecosystem.  As two-dimensional representations made quickly with a limited palette of colors and symbols, these maps can show complex relationships, but not easily accommodate high-cardinality data views.

That said, do whatever works for your purpose! Experiment with different techniques, and draw maps that highlight different types of information. If you make a map using some of these techniques, let us know in the comments. We’d love to see pictures of maps that might turn into future examples as we continue to help people apply this approach to crafting open source strategy.

Thanks to Microsoft for sponsoring the Open Source At Large blog series, and also to Dlshad Othman and the Arches Project for kindly letting us use their maps as examples.