Financial Analytics Fundamentals Use Data Science And Analytics In BFSI

In an age of data lakes, enterprise data catalogs are becoming a necessity. Done right, machine learning data catalogs can facilitate discoverability and ensure compliance while fostering the user engagement needed to build a robust data culture. In this webinar, Andrew Brust, analyst at GigaOm Research, and Aaron Kalb, co-founder and chief data officer at Alation, discuss why people are an essential part of successful self-service analytics, and how data catalogs can help foster collaboration and analytics enthusiasm.

Fanatics, a popular sports apparel website and fan gear merchandiser, needed to ingest terabytes of data from multiple historical and streaming sources transactional, e-commerce, and back-office systems to a data lake on Amazon S3. Once ingested, the data would be analyzed to better identify, predict, and fulfill customer needs related to the products Fanatics offers in over 300 online and offline stores. To accomplish this, Fanatics chose Attunity Replicate, a software solution featuring continuous data capture (CDC) and parallel threading for streaming data in real time from multiple sources into a data lake on Amazon S3. The data can then be consumed in Apache Kafka for real-time analytics. Attunity helps Fanatics avoid the heavy lifting of manually extracting data from disparate sources and enables the organization to see results in real time.

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.

Few things bring down the value of data faster than confusion and uncertainty about what it is, where it came from, and whether it is good quality data. Yet as more users seek to access and interact with data and reports for business intelligence and analytics and as data sources become larger and more varied, confusion and uncertainty spread fast. Executives, managers, regulatory administrators, and other key personnel cannot rely on their reports, KPIs, and dashboards. Users cannot even find reports that the organization is producing. Instead, users spend more of their time trying to locate data and reports and correcting mistakes than they do applying data insights to solve business problems.