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Extract Product Traces

Identify and separate systems by product line.

Many applications are built to serve multiple logical products from the same physical system. Often this is driven by a desire for reuse. “Hmm, consumer loans look quite similar to business loans” or “clothes are a product, so are made-to-measure curtains, how different can they be?”. A major problem we come across is that superficially the products look similar but they very different when it comes to the detail.

Over time, single systems serving multiple products can become over generic, with code evolving to handle all possible combinations of all possible products. For example, for a generic system that is designed to handle n products with n changes per product the amount of testing that must be done to check all possible combinations is n factorial. That's a number that gets big quickly. It also explains why many of the applications of this type that the authors have encountered have had very little in the way of automated test coverage, instead relying on huge, often manual regression suites. It's simply not possible to test so many different codepaths.

The problem is often therefore one of economics. It is difficult to accept that developing and maintaining a system per product might make better economic sense than developing and maintaining a single system. By splitting by product we are taking advantage of the fact that changes to multiple products can be made simultaneuously, and reducing risk by avoiding the combinatorial explosion of changes that may introduce defects in unwanted places.

Of course this is a trade-off – we don't want separate applications for red trousers versus black trousers, but we may want one for off-the-peg versus made-to-measure, or Home insurance vs Pet insurance.

It is also common for different product lines to have very differerent
value streams as described in Extract Value Streams.

How it Works

Identify the product or product lines within the system. This will
form the thin slice to be built / migrated. Look to identify all the
capabilities that the existing system provides and map them to the new
product. We tend to look through different lenses when identifying
capabilites, data, process, users etc.

Identify shared capabilities. Identify if the different product
lines have BusinessCapabilities that are shared. There are several ways of
approaching this, which we cover in UnderstandingYourBusinessCapabilities.
Our advise as ever is to value Use over Reuse, so if in doubt try and limit
the number of shared capabilites as much as possible

Choose who goes first. Which product lines are you going to move
off first? One approach we like is to think in terms of risk. After
understanding the risk to the business of migration, we often like to take
*the second riskiest product line*. This may feel counter-intuative and that
we should take the least risky option first but actually we like to identify
a product that is meaty enough to keep the business' attention and cause
them to prioritise funding, but not so risky that the business will fail if
there are problems.

Identify your target software architecture. It is very rare that
we advise big bang replacements, which in this case would mean building out
all the software for all of the products at once. Instead, look to identify
the appropriate architecture for the thin slice identified in step 1.

Identify your technical migration strategy. As we discuss in the
section on technology migration patterns, there are a number of different
options that can be deployed depending on current constraints. If it's a
simple web-application then it may be possible to use ForkByUrl. In other
cases where ForkingOnIngress is appropriate it may be better to choose the
MessageRouter pattern. Keep in mind that a TransitionalArchitecture may be

When to Use It

You have a system with easily identifiable product lines that would
benefit from:

  1. Being worked on independently. Breaking up the system into separate
    products means that teams can be formed around the individual products
    allowing for progress to be made without the typical problems of change
    co-ordination including merge hell and long regression cycles.
  2. That have different non-functional characteristics. You want to be
    able to offer different SLO's for each product. For example different load
    requirements for a given latency.
  3. Different rates of change. Some product lines are stable while other
    products are undergoing active development. Breaking up the system means
    that you do not risk changes to the stable products.

Insurance Example

In the insurance domain, different product types have very different
characteristics. For example Vehicle insurance is typically high volume
but low margin, whereas Home insurance is the opposite. It is also highly
competative so the ability to make changes quickly is very important. One
insurance company we worked with had developed a 3-tier architecture that
served as the quoting engine for all the different products the insurer
offered including Vehicle, Life, Home and Pet lines as shown in figure

Understand the outcomes you want to achieve

The product owners for the business were increasingly frustrated at the
lead time to change which was long and getting longer. They decided to get
Thoughtworks in to take a look at their architecture and development
processes to see if we could identify why things were so bad. The value
stream map for the development process identified a number of constraints
that were contributing to lead times exploding. While each technical
domain was decoupled from the others, the different business domains were
tightly coupled together. This meant adding a new requirement for the
Vehicle product would often impact Home, Life and so on. These changes
equired both careful thought and extensive regression testing before the
usually fraught deployments. The multi-product architecture also imposed a
limit on the number of people able to work on the codebase safely, further
slowing progress.

Finally, due to the volume requirements of the Vehicle product line and
the growth of the business, the underlying data store had to be scaled
often, requiring downtime to all the other products hosted by the

Decide how to break the problem up into smaller parts

As a result, the insurer decided to migrate away from their n-tier
architecture organised around technical capabilities towards one organised
by product lines. The products were already identified: Vehicle, Home,
Life and Pet insurance. Once these were understood the different
capabilities that they each needed were identified, for example the
individual question sets, quoting and customer account as well as more
technical capabilities such as Authentication and Authorisation. Customer
Account was identified as a key shared capabilty that all of the product
lines would use, a good example of adopting the Coordination approach from the EA Magic Quadrant which is described in more detail in The One True Ring.

The next thing that needed to happen was to identify where to start. In
order of revenue to the company, the product lines rated as Vehicle, Home,
Life and Pet. Whilst in terms of customer numbers the order was reversed.
As a result it was decided that Home insurance would be the first product
line implemented separately. This balanced risk to the business of
something going wrong with enough revenue to make the choice

Successfully deliver the parts

The above figure shows the event-driven architecture that the team
started to build out. Communcation with the Quoting Engine and with the
Customer Management capabilities was via events passed over a RabbitMQ
message bus. These events were also propagated to the existing enterprise
data warehouse for reporting purposes.

As the team built out the new systems to the side of the existing one,
preparations were put in place to cut over traffic from the legacy system
to the new one. One downside to moving the product line in it's entirety
was that the question set had to be implemented in full before customers
could be switched over. Due to this contraint the new system went into a
Beta phase where certain customers were offered the option to opt-in to
use the beta version. Those that opted in also had the opportunity to
provide feedback on the new look and feel. As the new system was
progressively enhanced and the final cosmetic features were added, a
go-nogo decision was taken and customers were gradually redirected to the
new system over the course of several weeks. First one percent, then five,
then ten percent etc. This allowed the team and business to grow in
confidence that the new system was performing as expected, both from a
functional and non-functional point of view. Finally, the new system was
serving one hundred percent of traffic for Home Insurance. The team then
turned to ongoing product development.

Change the organization to allow this to happen on an ongoing basis

After the success of the first migration, attention turned to the next
challenge, moving Vehicle insurance using the same approach and the
pattern was repeated until the migration was complete and the old system
turned off.

Meanwhile, and gradually, the whole technical organisation moved away from a project-based approach to development to a product-centric one. This of course had teething problems. Product Ownership is a skill that needs to be built over time and so the transition was a gradual one. They also adopted the same approach to traditional IT Operations, and under the guidance of the CTO and Chief Architect moved towards a platform product engineering approach for on-demand infrastructure and then data and analytics.



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