A processor-implemented method for enabling automation of data analytics in an Internet of Things IoT platform, executed by one or more hardware processors , the method comprising: constructing, a domain knowledge graph for each of one or more domains and identifying a relationship among information across the one or more domains; receiving, input data comprising type of problem and one or more solutions associated with the type of problem, and sensory information associated with at least one of the one or more domains from a plurality of sensors deployed in the IoT platform, and domain knowledge from one or more sources, the domain knowledge is specific to the input data and corresponds to the one or more domains , wherein the one or more solutions are indicative of a performance metric; representing, using an ontology-based format technique, the input data as a plurality of graphical knowledge representations ; determining, a relationship between the plurality of sensors with their associated attributes and the domain knowledge across the plurality of graphical knowledge representations by identifying jargon in the domain knowledge across the plurality of graphical knowledge representations; and translating domain-specific jargon into interpretable data through look-up of the cross-domain knowledge using one or more translation techniques, wherein the translation techniques include at least one of word2vec techniques and node2vec techniques, constructing a database for each of the one or more domains based on the translated domain specific jargon, wherein the constructed database comprises a mapping between the interpretable data; applying, one or more Machine Learning ML models on the determined relationship ; optimizing the one or more ML models based on the performance metric ; and determining, using the optimized ML models, root cause analysis and interpretable root cause information associated with the root cause analysis, from the relationship determined across the plurality of graphical knowledge representations . constructing, a domain knowledge graph for each of one or more domains and identifying a relationship among information across the one or more domains; receiving, input data comprising type of problem and one or more solutions associated with the type of problem, and sensory information associated with at least one of the one or more domains from a plurality of sensors deployed in the IoT platform, and domain knowledge from one or more sources, the domain knowledge is specific to the input data and corresponds to the one or more domains , wherein the one or more solutions are indicative of a performance metric; representing, using an ontology-based format technique, the input data as a plurality of graphical knowledge representations ; determining, a relationship between the plurality of sensors with their associated attributes and the domain knowledge across the plurality of graphical knowledge representations by identifying jargon in the domain knowledge across the plurality of graphical knowledge representations; and translating domain-specific jargon into interpretable data through look-up of the cross-domain knowledge using one or more translation techniques, wherein the translation techniques include at least one of word2vec techniques and node2vec techniques, constructing a database for each of the one or more domains based on the translated domain specific jargon, wherein the constructed database comprises a mapping between the interpretable data; applying, one or more Machine Learning ML models on the determined relationship ; optimizing the one or more ML models based on the performance metric ; and determining, using the optimized ML models, root cause analysis and interpretable root cause information associated with the root cause analysis, from the relationship determined across the plurality of graphical knowledge representations . <EOS>