Perspectives on Big Data and Big Data Analytics in Healthcare

Artificial intelligence (AI) and data analytics are top technology priorities as they capitalize on sustainability through data analytics and adaptive AI.2 For over a decade, Mayer-Schönberger and Cukier encouraged datafication of BD, where essentially, virtually anything is transformed into useful data (insights) by documenting, measuring, and capturing digitally.3 Van Dijck asserted that the future of BD and big data analytics (BDA) will lie with machines, where data will be generated, shared, and communicated among data networks.4 After a decade of progress, much of the structured and unstructured data stored in EHRs can be analyzed with the use of natural language processing (NLP) and machine language processing (MLP) algorithms, which can unlock the value of the text and galvanize the extraction of the hidden insights and connectors.1 Transforming unstructured text into real patient insights holds great potential for improving health outcomes. [...]BD is generally associated with value, which means that when large volumes of BD are analyzed, it is possible to extract high value from them.8 The original form of data has low value, but the information identified through its analysis can make a difference in its value. Given BD characteristics, BDA cannot be derived by simple statistical analysis.12,13 In fact, use of advanced BDA tools and extremely efficient, scalable, and flexible technologies are necessary to efficiently manage and analyze the substantial amounts and variety of data.1,14 Technologies such as NoSQL Databases, BigQuery, MapReduce, Hadoop, WibiData, and Skytree have been in use for more than a decade.15 AI tools such as Microsoft Power BI, Microsoft Azure Machine Learning QlikView, RapidMiner, Google Cloud AutoML, or IBM Watson Analytics are offering greater value in BDA. [...]Microsoft Power BI was successfully used to detect specific antenatal data for babies small for gestational age (SGA) and monitor them through a dashboard, thus allowing clinicians to intervene and plan delivery as necessary.16 BD management entails both the processes and the associated technologies that allow for the acquisition, storage, and retrieval of data, which can be done in three stages: acquisition/recording; extraction, cleaning, and annotation; and integration, aggregation, and representation.17,18 Analytics involves the techniques applied in analyzing and acquiring intelligence from BD and can be completed in two stages: modeling and analysis; and int