Finalytica

Introduction

In today’s data-driven world, building efficient machine learning (ML) pipelines is crucial for leveraging data to drive insights and business value. In the realm of machine learning, the creation of robust, scalable, and automated pipelines is not just a technical necessity but a strategic imperative.

Introduction

In the dynamic landscape of financial services Industry, the integration of Generative Artificial Intelligence (Gen AI) has emerged as a game-changer. In this article, we delve into the transformative applications of Gen AI, focusing on the role of Large Language Models (LLMs) such as GPT-3, BERT, and LLaMA, among others.

Background:

Having extracted data from various Data sources and establishing robust data pipelines and creating comprehensive data views in previous stages, which we covered in our previous edition of Finalytica last month. In this article and in continuation of the process, we delve into building reports & dashboards and extracting actionable insights from the accumulated data.

Creating Data Views in various environments for complex BI Dashboard Creation

Introduction:

In various BI implementations, we have addressed the need for efficient data pipelines to build complex BI reports from diverse sources. Here’s how we practically implemented robust data pipelines supporting advanced BI dashboards, utilizing cloud platforms like AWS and GCP, as well as on-premise solutions.

Leveraging Narrator on Public domain data for Analytics

Introduction:

Many organizations use Business Intelligence (BI) tools for their conventional & core data, such as Dashboarding for reporting. Narrator is also used by many organizations for their reporting, dashboarding, insighting on the core data on a day-to-day basis

However, in the last few years, Narrator has also emerged as one of the tools where you can host your alternate data like data sourced from public domain or portals etc.