Kumaran1987

Guidewire Data Studio Guidewire Data Studio is part of the Guidewire Data Platform, which is an enterprise-grade, cloud-native big data solution designed specifically for property and casualty (P&C) insurers. Here's an overview of its details and how one can learn about it: Details: Purpose: Guidewire Data Studio is aimed at helping insurers unlock data potential by providing tools for data ingestion, curation, and analysis. It enables insurers to ingest data from both internal and external sources, unify and transform this data, and then use it for advanced analytics, reporting, and decision-making. Key Features:  Data Ingestion: Collects data in near real-time from various systems, including Guidewire and third-party applications. Curation: Uses multiple curation engines to prepare data for use, whether in real-time or batch mode. Data Lake: Stores both raw and curated data in a scalable environment. Data Catalog: Manages metadata to make data discoverable, secure...

Introduction to Generative AI for Data Analytics Generative AI (GenAI) is a subset of artificial intelligence that focuses on creating new content, ideas, or insights by leveraging existing data. In the context of data analytics, GenAI can augment traditional methods by generating predictions, insights, or simulated data that improve decision-making, trend analysis, and operational efficiency. Here’s an elaboration with practical examples: --- 1. Data Synthesis for Training and Testing Use Case: Creating synthetic datasets when real-world data is sparse, incomplete, or sensitive. Example: A healthcare analytics company generates synthetic patient records using GenAI to train predictive models without violating privacy laws like HIPAA. The synthesized data mimics real-world scenarios and maintains statistical fidelity to actual patient data. --- 2. Predictive Analytics Use Case: Forecasting trends or events based on historical data patterns. Example: In retail, GenAI predicts future dem...

 In this blog post, sharing guidewire claims how many claims got created per city  and per state basics Databricks it is available here :  Claim happened from most US State Insights data analytics Template V1.0 - Databricks SQL Sample code  select AX.CITY, count( AX.CLAIM_NUMBER) as TOTAL_CLAIMS_PER_CITY from ( select distinct C.CLAIMNUMBER as CLAIM_NUMBER, ST. NAME as STATE, ADDR.CITY from guidewire_raw_db.cc_claim C left join guidewire_raw_db.cc_contact CONT on C.INSUREDDENORMID = CONT. ID left join guidewire_raw_db.cc_address ADDR on CONT.PRIMARYADDRESSID = ADDR. ID left join guidewire_raw_db.cctl_state ST on ADDR.STATE = ST. ID ) AX group by AX.CITY order by 2 desc From these SQL Query Resultsets Feed into Tableau Public Edition This Tableau Public view Available here  https://public.tableau.com/views/Guidewire_Insurance_Claims_Insights_one/InsuranceClaimsPerCity?:language=en-US&:sid=&:redirect=auth&:display_count=n&...

 In Guidewire, We have majorly four centers: 1. Contact Manager 2. Policy Center 3. Billing Center 4. Claim Center Business Context wise follow the same order to remember that business & data integration ways it is best approach, While loading sample data with individual instances load sample data in the same order 1,2,3,4; Once after loading the sample data stop the server and then after do the integration between multiple centers in both directions  1. Contact Manager to All remaining xCenters and vice versa, reverse directions also possible 2. Policy center to integrate with Billingcenter and Claimcenter as well as viceversa 3. Billingcenter & claimcenter there is not possible to integrate !! Coming to Business context how to easily recall above sequence    1. Contact Manager:   It is used to create diverse contacts broadly into Person / Company types; Contact Manager with AddressbookUID, PublicIDs are available, any business contact is im...

  As part of LLM KRL ( LLM Know Respect Language ) model this is the product feature choices fastest way to translate and comprehend !! Enabling Human Communication Multi Cultural and Multi Language support, still native people feel free to talk/ consult and communicate with respectful and fastest word exchanges !! Designing an LLM (Large Language Model) and GenAI (Generative AI) architecture for faster translations involves a combination of strategies to optimize model selection, training, inference, and deployment. Below is a step-by-step architecture design that includes the latest techniques for scalable and efficient translation. 1. High-Level Architecture Key Components: Data Preprocessing : Text cleaning, tokenization, and language alignment. Model Backbone : Efficient transformer-based models (e.g., MarianMT, BLOOM, or distilled versions of GPT). Training Optimization : Mixed precision, parameter-efficient fine-tuning (PEFT), and low-rank adaptation (LoRA). Inference ...

 The concept of clairvoyance or heightened perception, especially in the context of exploring parallel realities of human emotional feelings , delves into both metaphysical and psychological realms. While there's no scientific confirmation of parallel emotional realities, experimental and conceptual approaches can help us explore this idea. Here’s an organized framework for understanding and experimenting with clairvoyant feelings in the context of parallel emotional realities : 1. Clairvoyance and Emotional Perception Clairvoyance is often described as the ability to gain insight beyond ordinary sensory experiences, sometimes perceived as tapping into parallel or higher-dimensional realities. When linked to emotions, it involves: Heightened Emotional Empathy : Experiencing others’ feelings as if they were your own. Detecting unspoken or subconscious emotional states in others. Simultaneous Emotional States : Feeling conflicting or parallel emotions at the same time, such as joy ...

 Below is a comprehensive list of feelings and emotions that humans can experience over a lifetime, categorized by their nature. This list includes both primary emotions and nuanced ones that emerge from combinations or cultural influences. 1. Basic Emotions (Paul Ekman’s Six) Happiness : Joy, contentment, amusement, satisfaction, delight, euphoria, bliss. Sadness : Grief, sorrow, loneliness, disappointment, melancholy, heartbreak, despair. Anger : Frustration, rage, annoyance, resentment, irritation, indignation, fury. Fear : Anxiety, dread, panic, nervousness, apprehension, terror, worry. Disgust : Revulsion, contempt, disdain, aversion, loathing. Surprise : Shock, amazement, wonder, astonishment. 2. Positive Emotions Love : Affection, adoration, passion, infatuation, tenderness, devotion. Gratitude : Appreciation, thankfulness, indebtedness. Hope : Optimism, anticipation, inspiration. Compassion : Empathy, sympathy, kindness, altruism. Pride : Achievement, sel...