Narrow Results

To build high quality datasets and unlock the value of unstructured data, a systematic approach for data capture is necessary. Cognitive automation (CA), that is, automation of processes with artificial intelligence (AI), enables the information extraction from unstructured data to provide relevant insights and further processing with AI. This study provides an overview of this new technology and shows how it can be used to transform existing business models. Our case studies in the insurance auditing, healthcare, and banking industries show the potential managerial impact of CA, which prepares these legacy industries for their digital future’s challenges and opportunities. We present the novel data extraction pipeline for textual and visual data and demonstrate its efficiency in extracting information from the company’s unstructured data. We show its performance in quality, cost, and time compared with current industry standards and provide management insights for business applications using CA.

We provide an overview on the recent advances in textual analysis for social sciences. Count-based economic model, structured statistical tool, and plain-vanilla machine learning apparatus each have their own merits and limitations. To take a data-driven approach to capture complex linguistic structures while ensuring computational scalability and economic interpretability, a general framework for analyzing large-scale text-based data is needed. We discuss the recent attempts combining the strengths of neural network language models, such as word embedding, and generative statistical modeling, such as topic modeling. We also describe typical sources of texts and the applications of these methodologies to issues in finance and economics and discuss promising future directions.

"Innovation today is not just product innovation, rather it is about services, processes, business models or cultural innovation." Faced with growing knowledge management needs and its difficulties in organizing and retaining, enterprises are increasingly realizing the importance of interlinking critical business information distributed across structured and unstructured data sources. The structured and unstructured information that are distributed across the organization are automatically combined by the two technologies called EROCS (Entity RecOgnition in the Context of Structured data) and SCORE (Symbiotic Content Oriented information REtrieval)[1]. This enables organizations to generate new contextual and actionable insights to help determine appropriate business decisions. This survey paper highlights the importance and scope of EROCS in the IT industry and in the field of business. A highlight of this technology, which clearly differentiates it from the traditional named-entity recognition systems, is that EROCS identifies an entity even if it is not explicitly mentioned in the document. It exploits the context information present in the document to match and identify entities."