This chapter discusses the foundational theory and practical applications of language models in the realm of natural language analytics (NLA). It explains that understanding semantic relationships, clustering similar words, or inferring sentiment based on word proximity embed the subjective and qualitative nature of language, categorized as soft data, which may be pivotal for nuanced decision-making in organizations. Key Soft Indicators (KSIs) are introduced as a tool that quantify soft data like customer satisfaction or organizational culture, complementing the more traditional, quantifiable Key Performance Indicators (KPIs). Furthermore, the chapter discusses the integration of advanced computing techniques to reduce data and analysis latency, enhancing the speed and efficiency of decision-making processes in big data environments.