Any organisation that wants to rapidly adapt to change with sure-footed agility needs to be able to rapidly adapt its DNA.

But what on earth do we mean by an “organisation’s DNA”?

So that we can answer this question, we need to view organisations through a zoomorphic lens.

Organisations have complex internal systems that allow them to survive and thrive by responding to changes in their business environments; if we think of them like this, their similarity to organisms becomes obvious. And we can use this mental model to figure out how to adapt our organisations more rapidly, but using an assured framework of ideas.

We know that an organism’s form and function largely depends on its DNA – it is this blueprint that, to a high degree, prescribes its ability to respond to change. But its DNA can also be adapted, enabling modified responses that are better adapted to changes in its environment. Typically for organisms these adaptations only happen over a number of generations – more rapid adaptation has only been possible where the DNA has been re-engineered by human intervention.

But our organisations also have a blueprint. We may not recognise it because it is informal, or episodic, or personality driven. Nonetheless there are ways of working that deliver responses to business events.

In these times of seismic environment changes for our organisations, their ability to adapt rapidly has been brought into stark relief. We know that in the months and years ahead, the continual testing of their agility will be their most constant challenge. Many of our organisations will need to make fundamental changes to the way their blueprint is engineered so they can adapt rapidly enough to simply survive – never mind thrive.

Defining An Organisation’s DNA

Okay, so we know we need to be better at adapting, but how? What is it that we need to analyse in detail? How can we set about fixing what until now didn’t even appear to be broken?

Our organisation’s ability to re-engineer its blueprint relies on it being able to answer the three simple standard questions of:

1. What are we doing now?
2. What do we need to do?
3. What approach will we get us to this destination?

The self analysis required to answer these questions must be based upon the objective understanding of the way the organisation operates today. And this analysis must use the organisation’s data as the basis for its analysis. As we know, it is this operational data that provides the lifeblood of our organisations. But, if an organisation does not fully understand its operational data, it cannot possibly use it to provide a reliable basis for driving change.

To get a firm grip on its data relies upon articulating the three DNA Data Domains that operate on it to:

1. Define
2. Control and
3. Monitor

Let’s look at these domains in a little more detail.

The Three DNA Data Domains

If we are determined about becoming more agile and also believe that data defines the lifeblood of our organisations, then we must concentrate on becoming data centric and using our data to become truly agile. Let’s look at the role of the three DNA Data Domains in making this approach practical.

Defining Data

We must define our operational data so that we can maximise its benefit.

It is this underpinning domain that we rely on to provide, for example, the free flow of data throughout our organisations or holistic reporting across the enterprise. Without defining our operational data there is little hope of us being able to:

• deliver re-usable components
• enable strategic analytics outcomes
• develop an agile, loosely coupled system landscape
• adopt new technologies including Cloud and Machine Learning 

To create effective data definitions requires an organisation to document its business processes and map them with their associated data. It is this core pair of definitions that fundamentally define the way an organisation responds to business stimuli and events.

Figure 1 – Process and Data Interactions

To get a firm grip on our data relies upon articulating the three data DNA domains of data, those that allow us to:

1. Define it
2. Control it and
3. Monitor it

Let’s look at these domains in a little more detail.

Our structural business data models must form the heart of the data definitions. Once established, these can be used as accelerators, for example, to create data flow contracts for the data that flows through the organisation’s veins.

Figure 2 – Data Definitions Driving The System Landscape

Critically it will also enable the shared meaning essential to ensure our analytics-based strategic decisions are also sure-footed.

Controlling Data

For all of our organisations, effective management of their data is critical for their ongoing success. And, of course, for most it provices inescapable guaranteed compliance with ever changing legislation and compliance regimes. Also, it is not hard to argue about the constant need to improve Data Quality, or maintain appropriate access to our data.

Therefore we must take effective control of our data. A few examples for these control domains include:

• hosting jurisdiction
• privacy controls
• retention policies
• access and data modification entitlements

Obviously, the more that we can automate these controls, the more confident we can be that they are effective, and the easier it will be to adapt to constantly changing requirements. But notice that managing our data is critically dependent on establishing its definition as a pre-requisite – how can you possibly manage data you cannot define?

Monitoring Data

To understand what is happening in our system landscape, we need to constantly monitor data changes in it.

For example, these operational monitoring capabilities will allow us to compare operational data with:

• its definitions
• its basic profile including data quality
• its compliance with policy and regulation
• respect to its sources and destinations

The following diagram illustrates how we can use data flows to monitoring and compare the actual data state with the required data state. In addition, we can see how the variations can be used to improve its conformance with its DNA specification; improving Data Quality metrics for example.

Figure 3 – Data Feedback Loops Driving Data and Process Changes

At a higher level, the monitoring of our operational data should be used to provide insights and this is where the realm of data analytics can, for example, be used to drive:

• strategic directions for the organisation
• suitability and ease of use of our products and
• improved customer experience

Adapting Our Organisation’s Data DNA

We can see how all three domains are essential for a healthy organisation. But it is crucial to realise that the three domains are intertwined and cannot be considered in isolation.

Looking at figure 3 again, we can see that it is a simple representation of using data to drive adpatation. This approach must be built into any data centric organisation, and replace any poorly understood or governed process definitions we discover as part of our current state analysis

Naturally the three domains all fundamentally require data. But this data is not part of the operational data as such. It is a special framework of data that provides the context for the organisation’s operational data.

Organisational DNA Data

Data professionals use the term metadata to describe the data that defines operational data.

Unfortunately this term is typically not well understood beyond a relatively small group in most organisations. Even amongst these groups, there is rarely consensus over what metadata means, or why its shared understanding should form a core part of organisational data literacy. However, if we want to become data centric in order to drive agility, we must promote the use and power of metadata as a fundamental enabling step.

In my experience using the term metadata in our conversations inhibits rather than enables successful progress. Framing its concepts in more intuitive terms for an organisation, will incrits adoption and integrate it into your organisation’s common data language.

Maybe the preceding concepts can gain traction in your organisation by using themes such as:

• Define – defining our data’s meaning to deliver a shared data understanding and a common data language
• Control – executing effective data governance and management processes to ensure compliance and increase data benefit to operational and strategic processes
• Monitor – capturing data metrics and producing analytics outcomes to drive operational efficiencies and strategic direction

Taking Control

To become truly data centric an organisation must understand how data defines its DNA. This realisation must be followed by determined actions and strategic delivery to re-engineer its DNA to enable its truly agile future.

Our operational data is framed by our metadata. These two data domains are the Yin and Yang of our data estates. In the same way we create frameworks and management of our operational data we must evolve mature processes that actively manage out metadata. But here we must not confuse maturity with level of completion – far from it.

We need to engineer agile adaptability of our metadata in order to engineer organisational agility!

As the pre-requistie enablers to this state though, we must elevate the shared understanding of the underlying concepts and in particular, those that are data centric. These must be framed in terms of the organisation’s shared Data Language raising collective Data Literacy and ultimately driving a Data Culture transformation that is genuinely Data Centric.

I hope that the ideas here are useful for you and may help you evolve strategies that will deliver benefit to you and your organisation.

Also I wish you the very best and trust that you and your loved ones are keeping safe in these times.

Further Reading

If the ideas in this article resonate with you, then you may be interested in looking at the following books allied to this topic:

• datagility – powering data-driven success for tomorrow’s organisations
• Enterprise Data Architecture: How to Navigate its landscape
• The Data Model Toolkit: Simple Skills To Model The Real World