3 success criteria for a data transformation programme

Apr 13, 2020 | in Data Strategy
2 mins read
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Data transformation is an integral component of every digital transformation initiative. Unfortunately many fail to deliver for the business.

The most challenging aspect of a digital transformation is the scale of change. It often entails a new way of working with data for the entire organisation.

Having worked at organisations large and small, many of them iconic brands, we've observed some traits that differentiate a successful data transformation from one that fails to deliver. What the successful ones have in common are a cogent data strategy, an organisational embrace of agile ways of working and putting in place a team with the right skills and tools.

Data strategy

At its heart, a Data Strategy is the organisational approach to using data as a strategic asset. That means that any data initiative is considered in terms of the value to the business and alignment with strategic goals. This is coupled with cross-functional ways of working and architectural principles that marry enterprise consideration with the flexibility to be agile.

A data strategy acts as a true north and helps anchor the emotional biases towards technologies, processes and business use cases that inevitably arise during the execution phase.

Once a Data Strategy is in place, the data transformation piece becomes vastly easier. In fact it materialises as a by product of delivering data products which have a huge business value.

Agile ways of working

Agile is an overused term which can encompass a plethora of concepts - Scrum, Kanban, Lean, DevOps / CICD, TDD etc. Each has its associated practices and rituals.

What's often missed is the point of agile - to learn quickly. The meaningful metric of agility is the speed of those learnings. A truly agile culture uses those learnings to fail quickly and thereby minimise lost time and costs. Or it will find ways to continually optimise and improve business performance.

For a data transformation initiative to be agile, it must have the right:

  • Framework (tools and processes)
  • Ways of working (highly cross functional / collaborative)

Skills and tools

Putting in place a modern - typically cloud based - data platform will simultaneously test the enterprise along several dimensions. For mature organisations with long standing data functions this can be challenging.

The skills and concepts required for a cloud based data platform will differ significantly from traditional on-prem, proprietary technologies. Open source frameworks prevalent in data analytics and data science such as Python, R and Scala need to be mastered. And the concept of using cloud based services requires developing a different mindset to that of on-prem based resources.

Rather than see these as a threat, existing employees should be enabled through a mix of training and working alongside experienced consultants.

Conclusion

A data transformation requires far more than an architectural road map. For the transformation to successfully land, a congruent data strategy, embrace of an agile culture and the right skills need to be considered as well.