AI driven project management – Data Strategy

Over the next few weeks we’ll be hosting a number functions which take a look at the state of the art of time and cost performance of major projects as presented by keynote speaker Dr Alex Budzier (https://www.sbs.ox.ac.uk/about-us/people/alexander-budzier). To parallel these insights I will be posting a number of articles which deal with the differences between traditional dominant methods of project management and data driven project management powered by Artificial Intelligence.

The starting point for this all about the data. If you take nothing away from this series of posts, take this-

Data is a critical ingredient to having an edge in the future delivery of projects.  The best way to be ready for this change is to develop a DATA STRATEGY with a machine learning perspective.

It is difficult to design a data strategy which fits into an artificial intelligence paradigm, if you do not have some understanding of the artificial intelligence paradigm. Like all paradigm changing technology, this is not so simple. For example, there is a common misconception about the relationship between data analytics, and artificial intelligence (both very valuable, but not the same) and there are many different sorts of AI. At the risk of adding to the confusion the diagram below shows our perspective of the various elements in this Industry 4.0 technology as they relate to projects. 

In designing your organisation’s DATA STRATEGY, better immediate and long term results will be achieved if you are able to position your past and future data to take advantage of the way your organisation is likely to use AI technology.   

Here are some simple questions which can help you to start to scope your data strategy.

1.      What data does my organisation have now and how useful is it for future technology leverage. How can we test this?

2.      How does our organisation develop an understanding of the differences between the human perspective of data and the machine perspective? (They are not the same).

3.      What outcomes does our organisation seek from AI technology, and which of these are low hanging fruit, given our existing situation.

4.      What work is involved in configuring and using our legacy data for ML, and how much value will this task deliver to our organisation?

5.      How should we collect and manage our data going forward to maximize the future business value of emergent AI technology?

6.      What are the risks of doing nothing?

The answers to these questions will form the foundation of your organisation’s data strategy. However, the field is new and answering these questions is as tricky as it is powerful. Even the best advisers do not have all the answers and there is no shortage of snake oil salespersons and misinformed commentators claiming “thought leadership”.

What is needed is advisers with track record and real credibility. For organisations seeking the world’s leading advice, I refer you to Oxford Global Projects (https://www.oxfordglobalprojects.com/).  We collaborate with them regularly because they have a very deep knowledge and understanding of the issues faced by large organisations and governments seeking to deliver better projects in the age of data.  

This is a journey. Technology driven change is inevitable. No-one knows how much, or when. But we do not need to. Now is the time for organisations to do more than collect data and put it in clever graphics. Its time to turn your data from a stranded asset into a business driver with an AI informed Data Strategy.