As a project-based organization, how confident are you that you are collecting quality data on your projects? What might that even entail? As well as project length and value, and which projects go over time or over budget, you might track at a more granular level. What types of tasks tend to overrun; what skills do these tasks involve? Which skills are continually overutilized, and which are underutilized? At what points during projects do problems tend to arise – is there a pattern?
This is the kind of data that can come together to create a really vivid picture of your project operations. Indeed, if you have this kind of detailed data, a predictive system Cork Kids Bicycle Shop could use this to give you some project management assistance. Wouldn’t it be handy if you could get a warning in plenty of time before a critical task overran and caused a delay?
a-vision-of-the-future-of-project-management-forecast A vision of the future of project management with the way AI is developing, this is not a farfetched vision. But, once again, the reality stands that the AI’s project management suggestions will only ever be reliable and useful if it is trained on good data. And with so many data points to capture, the risk of error is extremely high.
On average, 47% of newly created data records have at least one critical error. People do not intuitively appreciate the value of good data, and most of us are not very good at inputting data manually. Our eyes can skip over fields; colleagues from different countries might enter dates in different formats; we might make typos…the various ways we can make mistakes in data input are as unique and unpredictable as we are!