datagility 4 – data agility

The way in which we re-engineer our organisations must not only be able to cope with what we know now, but also be able to adapt to a future that is unknown.

This is the fourth blog in the Data Topic series based on the book datagility. The previous blog is datagility 3 – data driven delivery.

So far, we have described the infrastructure that is required to make data agility become real.

But the future is largely unknown. Therefore, a key part of our organisation’s DNA must be the ability for it to learn. For the individuals within it this is relatively easy. We can provide constant learning as part of the culture to constantly extend their current abilities and experience. And in fact, we must do this to enable and optimise the organisation’s ability to adapt.

The Adaptive Organisation

If organisations can only become organised through defined processes, then these same processes must also enable its ability to adapt – the current ways of working cannot be optimised for the way the organisation will need to operate in five years’ time.

Process Adaptation

The following image show how an organisation with adaptive processes can constantly make micro-adjustments, and possibly macro-adjustments, to track the strategic direction of its future.

Also illustrated for comparison, is a ‘parallel universe’ organisation that is unable to adapt. We can see that this organisation has not tracked the shifting strategic direction, encumbered by its existing practices and systems. As a result, it experiences increasing dislocation between what the strategy demands and what the real-world operational processes deliver.

Any organisation that is not inherently able to adapt will be characterised by increasing stresses for its people, inappropriate processes, sub-optimal delivery of its products and services. Ultimately, of course, this may well spell its downfall.

Currently, we still need to rely on people to understand the environment in which our organisations operate. For example, appropriate SMEs are good at working out a regulation’s impact on the organisation. However, delivering these changing requirements into the system landscape is often a far more difficult procedure, and one that is typically costly and error prone.

The following diagram illustrates a basic generalised model of the learning process, i.e. one that is able to adapt its ‘idealised model.

The Eagle Team

We still need to describe how the feedback flows and their associated processes in the preceding diagram are practically implemented. Given that we are not yet quite ready to let the machines figure everything out for us, we need to have a way of determining imminent changes that will come from our internal and external environments.

I like the name the eagle team for the people in an organisation who are vigilantly looking out for bumps on the strategic road ahead. For me it conjures up an image of a hovering bird of prey intensively scanning the landscape for the slightest perturbations. As soon as one is detected, it swoops down to investigate the cause more closely.

The sources for their investigations will be from funded tranches of work, roadmaps, changes in the external business and technological environments, and possibly at a lower level, from delivery stream backlogs.

The Intraprenuerial Lab

We have met the eagle teams and seen how they can assist our organisation’s smooth journey along its strategic roadmap. But how do we ensure that the overall strategic direction is still valid? Or more to the point, how do we adapt the direction of the strategy to ensure we are taking advantage of all relevant breaking innovations.

We cannot rely on the strategic direction being purely set by stakeholders who are unaware of the constant stream of data and technological innovations.

It is critical to set up a function that is alert to, and able to analyse relevant future trends that will be appropriate for your organisation. Its remit should be to look for any practical benefits for the organisation beyond the hype of, for example, Big Data, AI, ML and the Internet of Things.

Simply optimising the way our organisations operate will not guarantee a sustainably successful future. They must also continually improve their insights into their operational worlds and particularly their customers.

In recent times organisations have used richer and more varied data to begin to understand their customers in ever finer detail. This must continue but be extended to focus on understanding how their customers operate and what their commercial realities are. This will enable our organisations to innovate products and services that help their customers to be more successful.

For example, could you develop a desktop ‘Assistant’ App that will advise them how to optimise their operations better, based on what your organisation knows about them and other data you supplement with this. This approach will create a much more beneficial symbiotic relationship to the advantage of all.

Domain Driven Delivery

Organisations need to end the corrosive cycle of tactically delivering un-reusable components, because there are few reusable components available.

Larger organisations have a complex internal structure that can result in complexity in their system landscape. The divisions of the organisation along geographic and/or business lines, for example, can drive fragmented system delivery. This fragmentation in turn results in an increasing inertia to change that can effectively thwart any dream of true data agility.

If determined efforts are not made to prevent it, this can become a self-reinforcing set of behaviours that ultimately create a ‘tactical first’ culture. We must take strong action to intervene and bring the organisation back on the path to strategic data agility.  

Workstream-Centric Delivery

Once funding is secured, each delivery initiative tends to create a ‘we must get this done at all costs’ mentality. As a result, the delivery typically loses any strategic or enterprise vision, and so focusses on delivering the easiest, ad-hoc and point based solutions.

As subsequent work streams cannot find reusable capabilities, they too create the components that their work stream requires – from scratch. Unchecked, this approach can become a snow-balling institutionalised culture of ‘tactical first’.

Senior stakeholders in the organisation must prevent this culture taking root. And if it is already prevalent, then they must take active steps to uproot it and replace it with a sustainable approach.

Data Domain-Centric Delivery

There is a simple yet profound switch in cultural mind-set required which puts the enterprise needs above individual initiatives. This is often a seismic and fundamental shift that can only be delivered once senior stakeholders understand it, buy into it and mandate it.

We need to rethink and re-engineer our delivery to stop being initiative aligned and instead be data domain aligned.

Rather than delivery pods being spun up for programmes, they should be far more permanent and aligned to data domains.

The data domains would sensibly be aligned to the Business Domain Model entities that we saw in . These are the domains whose definitions should have been agreed by the business and thus immediately provide business alignment and context for delivery.

Ultimately, this transformation will drive a dramatic reduction to the cost of delivery and maintenance. At the same time this it provides a radical boost to agility and also guarantees sustainability. The data domain-first approach is illustrated in the following diagram.

In contrast to workstream centric delivery, we can clearly see that components are developed once and then shared across the work streams. Funded tranches of work will typically be able to simply assemble reusable components to support new orchestrated high-level process definitions.

To deliver this radical shift in mind-set very rapidly, it would be enough to simply change the funding model overnight!

But by itself this would result in chaos. Associated cultural and organisational transformations are required to create the pre-requisite enabling bedrock foundations. Once these are in place, the re-engineering of delivery becomes relatively easy.

What the following diagram shows, is the fundamental shift in the funding model that will transform our organisation to become fundamentally data agile in terms of system delivery.

On the left we see the typical funding behaviour that predominates in many larger organisations and drives waste, increases fragmentation and thwarts agility.

The right-hand side illustrates the future funding model that will reverse these forces and liberate true data-centric agility. This diagram also indicates that the effect on the organisation’s system delivery spend should also radically be reduced as re-use of components becomes the predominant approach.


What we have learned in this blog is that we need to

  1. engineer adaptability into our organisation’s and its peoples’ DNA
  2. define adaptable and learning processes where constant improvement is driven by feedback data
  3. create one or more eagle teams to act as look-outs for bumps on the strategic road ahead
  4. cultivate a culture of change as BAU and establish an Intrapreneurial Lab ‘function’ to drive innovational thinking and delivery
  5. switch delivery from workstream-centric to data domain-centric

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