Many organisations are starting to notice the immense potentials with Artificial Intelligence, Internet of Things, Machine Vision, Machine learning, and other technologies.
A large number of organisations want to deploy these technologies and tend to start with the end result, i.e. hiring AI, IoT, MV or ML specialists or a set of data scientists without necessarily taking into account the readiness of their data.
Yes, of course, organisations don’t have to wait with innovative projects or their digital transformation until they reach a particular level of “data maturity.” However, it is a crucial “must-have” to take up the management from the start in order to avoid the cases of high investments with little rewards.
Even though it’s crucial these days to get to deliverable fast and continuously innovate, it’s also important to make sure you take the right steps with your data journey and ensure that your data has the right level of readiness and correctness.
Many companies invest in innovative technologies and find out (after 1 or 2 years) that their investments ultimately delivered too little business value.
This unique step-by-step approach led to much wider adoption of data management in the organisation and much better focus on the required data capabilities and priorities in terms of innovation and digital transformation.
Taking the right approach won’t stop an organisation from innovating, but will make sure the outcome is the right and sustainable one for the organisation in the long run.