Why Value Stream Management matters

Why Value Stream Management matters
Kit Friend
Kit Friend

This guest post was written by Kit Friend – Agile Coach and Atlassian Strategic Partner Lead for EMEA at Accenture

The views, opinions and positions expressed within this guest post are those of the author alone and do not necessarily reflect those of Accenture or their clients.

Let’s recap briefly why this Value Stream thing is getting everyone excited (and not even particularly recently – SAFe’s value stream architecture credits key elements to 2004’s Implementing Lean Software Development: From Concept to Cash by Mary and Tom Poppendieck, and much of the logic is far older).

Though there’s a huge amount of complexity in actually mapping the things within an existing complex enterprise, the concept itself is fairly simple: effectively, it’s far better to organise your efforts around creating and iterating a channel that links the raw ingredients your company can access, all the way to the people willing to pay for them (usually your customers).

I like to describe this in terms of two competing companies running watermills. It doesn’t matter how impressively or hugely a company builds segregated ditches from their water source, the company that builds a thin channel all the way from source to destination is going to win every time.

Value Stream Canal Image

On top of this, which company is going to discover a critical fault or efficiency in paddle wheel bearings first? It’ll be the one with something working in the market and the ability to make small changes quickly of course.

So far, so good. But rallying your large siloed enterprise around the concept of a better way is just the theory box ticked, to mobilise change (particularly working the pandemic-shaped world of 2020 and beyond) it’s essential to invest in tooling to turn value streams from being a (digital) whiteboard and slideware exercise, into a living part of how you organise teams. Gartner splits these toolsets into:

Value Stream Delivery Platforms
Value Stream Management Platforms


Value Stream Delivery Platforms (VSDPs) Value Stream Management Platforms (VSMPs)
VSDPs ‘provide a fully integrated set of capabilities to enable the continuous delivery of software. These capabilities may include project or product planning, build automation, continuous integration, test automation, continuous deployment and rollback, release orchestration, and automated security policy enforcement, and may provide visibility to key value stream metrics.’ VSMPs ‘enable organizations to optimize end-to-end product delivery lead time. These platforms provide greater visibility and traceability into the flow of all product delivery processes, from ideation to release and operation
Amazon Web Services
Microsoft Azure

Essentially, the Management Platforms seek to provide some intelligence to help link the lakes of data and activity being generated by the people and tooling doing the delivery.

It’s enticing to think that we should be able to jumble this all together and then point some sort of neural network at these kinds of problems, but giving all the data meaning and enabling teams to take action from it isn’t quite so simple.

Common issues faced by teams trying to tackle this include:

  • Disparate toolsets and stacks (challenging to pull data from)
  • Multiple data sources and data hygiene issues
  • Challenges in processing this data into accurate, transparent, and useful metrics
  • Getting people to understand how to use what they are seeing

The last can often be the most difficult. Where large organisations are still struggling to understand how to implement agility consistently at various levels, a lot of data can be a dangerous thing.


How to Avoid Creating a (Data) Monster

As the Large-Scale Scrum Guide stipulates:

It is common for organizations to look for tools to solve their problems even though tools are rarely the cause of the problem. Avoid solving problems with tools unless you truly, deeply understand the problem and consider a tool to be the right solution for that particular problem. 

Over-emphasising the role of tooling can distract organisations from investing in supporting the people and process changes they deeply need for the tooling to make any sense.

Instead, particularly in a maturing organisation, it is wise to view the role of tooling as providing visual clues as to the problems you need to tackle. For example, a cumulative flow chart might help identify where bottlenecks in your team’s flow are BUT a Gantt Chart and complex permissions on ticket movement won’t de-risk your deliveries.

Faced with the opportunity to create hundreds of fascinating graphs, charts, and metrics in a few clicks, it’s tempting to overwhelm teams and stakeholders with screens full of data without focusing on what is actionable. This inevitably results in the ’email newsletter problem’: people only read and digest the first couple of items.

When curating your tooling to support a value stream, it’s important to come to terms with individualised approaches. It’s far better to have a flexible set of tools that allow users to easily create visuals that are relevant to them.


Outcome vs Output

So you’ve assembled your Value Stream, got some fancy tooling, and have a bundle of Agile teams hard at work. What could go wrong?

The next piece in the puzzle is to ensure they aren’t busy creating chocolate teapots and fish bicycles efficiently. Christophe Achouiantz sums this up neatly in the following graphic:

How Outputs generate Outcomes that generate Impacts by Christophe Achouiantz
How Outputs generate Outcomes that generate Impacts by Christophe Achouiantz


It’s vital to implement both elements of the value stream tooling to continue to optimise the output segment here.

However, teams that ignore a product-led work model and forget to introduce a feedback loop to measure and integrate the performance of their products when live will find themselves disappointed.

The rise of OKRs reflects the challenge of meshing these very different worlds of data together. However, the majority of organisations still struggle to follow through on tracing the benefits data which is locked in before delivery begins.

(An aside: Be careful with language when comparing the concepts of ‘efficient’ and ‘effective’ with international teams. Howard Johnson and I encountered an amusing problem whilst teaching in Sweden, where both terms translate to the single Swedish word ‘effektiv.’ Thankfully this hasn’t stopped Swedish companies like Saab from becoming icons of agility.)


So what?

How can these host of challenges and vague warnings translate into something workable? Many of the tips that apply to pragmatically mixed agile transformations also apply here:

  1. Engage your teams, and save them time: Try to avoid anything that creates additional overhead for your teams. In particular, anything requiring manual time logging should be viewed with suspicion. Engage your teams early and find out what data would help them take action to fix existing pain points.
  2. Plan on it being messy, full of surprises, and… not what you plan: Don’t make the mistake of waterfalling your agile tooling. Embrace an approach where elements can be created and validated early, changes made, and customers engaged.
  3. Minimise your tools and maximise your experimentation: Remote working expert Molood Ceccarelli cites the importance of not flooding people with too many tools. This is a critical philosophy in a world where remote working is likely to become the norm. Balance this with not narrowing down your options too early: use pilots and trials to allow teams to provide real feedback before you narrow down your choice of tools.
  4. Embrace the cloud: Many organisations still struggle with opening up their data. This blocks their ability to make the most of a flexible range of SaaS and PaaS services. Tackle this early by challenging assumptions (there are examples of adoption in pretty much every country and industry), and get sponsorship for the journey to the cloud. Spinning up large platforms within your infrastructure is unlikely to produce the best outcomes for supporting your teams.


About Plandek

Plandek is an intelligent analytics platform that helps software engineering teams deliver value faster and more predictably.

Celebrated by Gartner and Forrester as a ‘leading global vendor’, Plandek mines data from delivery teams’ toolsets and allows them to optimise their delivery process using both intelligent insights and predictive analytics. 

Co-founded in 2017 by Dan Lee (founder of Globrix) and Charlie Ponsonby (founder of Simplifydigital), Plandek is based in London and currently services the UK, Europe and North America.

Find out more about Plandek here: The Plandek Difference.

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