<img data-cke-saved-src="https://www.cioapplications.com/newstransfer/upload/590h7300-Recoveredhgf.gif" src="https://www.cioapplications.com/newstransfer/upload/590h7300-Recoveredhgf.gif" "style="margin-right:10px;&quot;">Last but not least, as important as the technology, is the team in place to help pivot the thinking from data analysis into data science in an effort to scale Machine Learning and its cognitive capabilities. Data science as a practice will need to be institutionalized, and the inherent desire to treat this team as advanced report makers needs to be deterred. This is key for any enterprise to create and curate a data landscape that is conducive for ML applications.

And while we have individual companies who have succeeded in setting up this right framework, our society as a whole is yet to think beyond the ‘buzz’ of A.I. and Machine Learning, and consider this discipline a new normal, a means to surviving the pace of change and the competitive landscape.

As such, it is our duty to educate our executives on the proper framework conducive for Machine Learning survival. And when asked the inevitable questions on, how much will this cost and when can I expect an ROI, the response should not be a quantitative discussion. The first reply back should be which ROI are you referring to because there are three necessary components we need to reflect on, as we advance towards modernization:

• Monetary ROI – revenue potential and savings cost associated with ML applications
• Relevancy ROI – if we don’t do it, we won’t be relevant with the pace of change in our market
• Cultural ROI – pivoting our company from data analysis into data science for market differentiation

As such, while we are all data rich, very few of us are data driven and the applied science of Machine Learning is the start to any of these ROI questions, while the section above describes the right framework.

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