Why digital transformation projects fail

An increasing momentum of digitisation across industries is seeing a major cash injection into digital initiatives, but with results failing to materialise many are asking why it is that digital transformation projects fail.

Although digital transformation begins and ends with data, the importance of a well-executed data strategy is often underestimated.

When beginning the transition, it is crucial to revisit your data strategy and assess the way data is collected as well as what data will be collected in terms of the way a machine will use it, rather than the way a human will use it.

One of the biggest problems with digital transformation projects is that they’re approached in a linear form, however unless there is a clear path to the use of data, digital transformation becomes prone to failure.

The foundation for success is a strong data strategy – identifying what data is likely to be valuable to machines and how it should be structured so the machine is able to use it. Endeavour Programme urges organisations to seek the assistance of statisticians and data scientists. Also, consult with machine learning experts to assist with this assessment.

According to our Managing Director David Porter, there are four key reasons data strategies fail:

1. You are collecting data, but are unsure what for or if it has any use to your business –

Second to finance, the construction industry is the most data heavy industry, however over 95 per cent of this data is not used even though it holds great value. Data is a valuable asset and whether an entity is an average small business or one of the big players it should be used to your advantage. Each organisation’s data strategy will be unique to them, however, the primary objective of artificial intelligence (AI) and machine learning (ML) is to extract signal from noise, to find meaning which will enable your organisation to make well-informed decisions.

2. Your data is not properly organised to be usable –

An entity could have terabytes of data and it could be meaningless due to its poor quality and disorganisation. To succeed, companies need quality data, which is relevant, properly defined, and structured. Leave this one to the experts – data sorting is a job for a data scientist or machine learning expert.

3. Your data is set up for human consumption rather than AI improvement –

It is equally important to recognise that the way machines think about things is a different paradigm to the way the construction industry traditionally thinks. Generally, in construction, decision-making revolves around a cause and effect manner. However, machines do so in a probabilistic manner. Making this distinction is crucial to successful digital transformation.

4. Your people are not receptive to digital transformation –

Human resistance is a major barrier where AI is concerned, and it is perhaps due to employees not having a well-researched and well-informed view. Organisations need to transition their people into an unfamiliar environment. Therefore it’s important that this is managed carefully and sensitively. Take your people on the journey with them and do it a pace they are able to manage.