The Modern Data Architecture in Financial Services

An entire ecosystem of tools and data processing frameworks have grown up around Hadoop. Almost as soon as someone identifies a weakness or limitation―and there have been more than a few―someone else creates a fix. That's one of the reasons the Big Data ecosystem is so complex. And why many large companies hung back before jumping in. They wanted to see if any leaders would emerge from the chaos.

Hortonworks is one of those leaders. And enterprise organizations are starting to invest in Hadoop and the technologies that support it such as the Attivio Semantic Data Catalog . They are building a modern data architecture. See Joe Lichtman’s guest post at Hortonworks Build a Modern Data Architecture with Enterprise Level Governance to learn more about the importance of an agile data stack.

Another part of Hadoop's coming of age narrative is the type of use case it attracts. Right now, large banks are taking advantage of Hadoop's massive scalability to support analytic applications in risk and compliance.

Risk and compliance solutions must accommodate all types of information, from structured data to unstructured text. Content that sits outside of structured databases such as emails, chat logs, text-based documents contains a wealth of information vital to these solutions. Text-based content may contain details that make a difference between a partial view and the big picture when it comes to fraud detection and regulatory compliance.

Effective management of both structured data and unstructured information is a cornerstone of the Modern Data Architecture. With text analytics to extract insight, meaning, and sentiment from raw, unstructured sources, risk and compliance solutions have holistic coverage and the ability to scale up gracefully.

Especially for companies that operate in regulated industries, the value of trusted governance can't be underestimated. For example, many large financial institutions adopted Hadoop early on out of sheer necessity, in spite of its immaturity on the governance front. The volume of data they capture is extreme, so they need to run analytics at massive scale. Nevertheless, many of their primary analytic use cases such as know-your-customer, eCommunications monitoring, and anti-money laundering carry with them substantial information governance and regulatory compliance restrictions.

Attivio is one of the first products to integrate with Apache Atlas―the data governance initiative. Atlas is critical because it’s a metadata store that facilitates governance―making it simultaneously more agile and robust. Attivio uses data discovery expertise and understanding of the origins of data and metadata to help improve the performance of Atlas and automate governance in ways that weren’t possible before.

We're participating in a webinar hosted by Hortonworks on December 1 st at 1pm ET that details how one global banking leader leveraged Hortonworks and Attivio as a central infrastructure for building data-driven applications. We’ll discuss their anti-money laundering solution as an example that dramatically accelerated processing time.