How to apply machine learning to assist digital transformation projects

Large digital programs usually tend to focus on the technology side of the solutions, particularly IT transformation programs. The focus is predominantly on implementations of new platforms to support the business while forgetting other important aspects, such as people, organisational structure, business processes and platforms, what is going to change and how are they going to change in response.

Attention should be given to the role platforms continue to play in new solutions. Some of the focus needs to be on the domain of the solution rather than just on the tech. For example, the business architecture (a top-level view of how a business will operate), and how new systems will operate in conjunction with existing infrastructure.

System testing for quality assurance is often an afterthought. There tends not to be sufficient focus in the early stages on how a solution will be deployed on the business IT environment.

Consideration must also be given to the organisational change in the wake of a new deployment. This should be structured and planned from the outset so that change is properly managed and becomes relatively seamless.

Only by focusing less on the technology aspect and more on the business side can enterprises successfully implement any organisational rules changes, new business processes and strategies to manage the business transformation. If the business is not set up to manage the transformation and staff are not prepared, the whole program can be jeopardised.

Other factors to consider are more akin to standard project management practices, such as dealing with risks at multiple levels – from technology to the risk of marketing changing due to adversity in the business environment. Given the average timeframe of programs, it is more likely than not that such issues will arise at some point so it pays to be prepared.

Structuring and planning for program effectiveness

During large transformation programs, operatives are spread far and wide so incidents tend to looked at in isolation, rather than examining the root causes. There needs to be more focus on a smaller number of factors that are crucial to the project.

It is very important to have a mechanism to collect information and data that provides a view of project success based on characteristics. With a small amount of high-quality data, you can build a model to interpret new data sets and correlate with existing datasets to draw reliable conclusions into a project’s success or failure, as well as gaining a better understanding of the characteristics of a project – what have we learned and what insights and recommendations should we glean from it?

So what are all the considerations and characteristics of projects? By collecting and assessing high-quality data, through machine learning, the system acquires knowledge and becomes better equipped to provide recommendations on how to achieve success.

This means that humans don’t need to do too much. Organisations do need to invest effort in understanding outputs though and be ready to be able to accept and understand data to take recommended actions.

Use machine learning to focus on practical, measurable outcomes. It is not simply about change management but rather making sure you are using tools and approaches in a way that concentrates on what is important to achieve successful business outcomes.

Article by Michael Devlin published on IT Brief Australia