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Data has become one of the most important assets of companies in the digital age; However, the concept of “Data” is so new and broad that even experts in this field approach it with very different definitions and perspectives. This roadmap offers a path to corporates that want to consistently make the best use of one of their most critical and under-appreciated resources – namely, data.

As difficult as it is for data scientists to label data and develop accurate Machine Learning Models, managing models in production can be even more daunting. Data science practices are essential to recognize model bias, retrain models by updating datasets, improving performance, and maintaining core technology platforms. Without these disciplines, models can produce erroneous results that significantly impact the business.

Big Data Analytics uses advanced analytical techniques against massive, diverse datasets from different sources and contains structured, semi–structured, and unstructured data of varying sizes from terabytes to zettabytes.

Predictive analytics transforms data into actionable insights, but only when combined with the decision-making process. As businesses become more adept at predictive analytics, data harmonisation becomes critical to success. What exactly is data harmonisation, and why is it important?