Using recent advances in data analytics techniques and tools, this paper proposes a novel data-centric paradigm for riser fatigue analysis in which practitioners can efficiently make use of large hindcast and measured metocean datasets directly in the design process. In the conventional fatigue design method for offshore risers systems, in order to reduce the computational cost practitioners tend to condense large hindcast metocean datasets into small wave datasets, so-called wave scatter diagrams, to represent the long-term variation in significant wave height, Hs, and peak period, Tp, at a site of interest. These methods tend to predict rather conservative riser loading conditions and hence fatigue lives due to the information lost in the condensing method, which can limit the application of steel catenary and lazy wave risers (SCRs and SLWRs) with larger diameters (outside diameter, OD > 18") whose design is governed by fatigue. This paper proposes enhanced frameworks for more accurate prediction of the fatigue life of SCRs and SLWRs located in swell dominant regions by enabling the direct use of large measured and hindcast metocean datasets in the fatigue design process of risers. Firstly, we introduce a novel detailed framework that uses an “ANN-based technique” with a “representative (P50) year” concept to enable large metocean datasets to be directly used for accurate estimation of riser fatigue life that is suitable for detailed design stages. Secondly, we propose an alternative innovative framework based on the use of Monte Carlo methods for more efficient long-term fatigue assessment of risers. Finally, we demonstrate the usefulness of the proposed frameworks by calculating the riser fatigue lives and comparing them against those obtained via conventional wave-condensing strategies. For the case studied here, the results show that using the proposed framework can significantly increase the estimated riser fatigue life over the values estimated using conventional wave condensing methods.