Gaining cross-product, cross-asset insights presents daunting technology challenge.

The ability to consolidate multiple versions of the same data enables significant cost savings as well as preventing expensive errors from occurring due to differing datasets on opposing sides of a process.

“These applications, combined with data standards and semantics to homogenize complex trading environments, enable a firm to unlock the value of structured data by achieving a single version of the truth,” said Michael Chow, director of corporate development at GoldenSource.

As volumes of data continue to increase exponentially, organizations across all industries are wrestling with the problem of transforming mountains of bits and bytes into decision-enabling information.

The challenge for financial organizations is transforming monumental amounts of data into actionable information and to manipulate it.

“This may seem endemic of the never ending saga of doing more with less; however, advances in technology are enabling firms to capture, store, manage and analyze datasets previously too unwieldy for traditional solutions,” Chow said. “This is what underlies Big Data.”

Most firms are good at storing and securing data, but struggle with cleaning, verifying and reconciling data across the organization.

“A proper data warehouse acts as a ‘single source of truth’ for storing and retrieving reliable accurate data from across the organization,” said Matt Blakely, business intelligence technology practice lead at SWI, a software consultancy.

“It also forms the basis for building advanced analytics that truly gauge organizational performance, efficiency, and improvement opportunities,” Blakely said.

Modern technologies for data warehousing and business intelligence are mature in terms of handling structured data.

“With the proper design these systems can handle many terabytes of data and still provide fast performance, secure storage, high availability and other features that users expect today,” said Blakely.

However while unstructured data such emails, tweets and documents has existed for some time – the amount of unstructured data has exploded in recent years (at least 80% of the data produced today is unstructured) and the needs and usages for incorporating this data into data warehouses and BI analytics is relatively new. As such the market is less mature in this respect.

Database vendors are catching onto the need to store, manage and search unstructured data. In addition to new data types, newer databases (e.g., SQL Server 2012) have added rich support for unstructured documents – e.g., the ability to store, search and manage millions of folders and files within the database itself.

“In the next few years, it will be critical for firms to take a 360 degree view of their data to eliminate potential blind spots and to strengthen operational efficiency internally,” Chow said.

In addition, having a robust counterparty hierarchy will be necessary to enable firms to roll-up exposure quickly.

“Without it, they won’t be able to answer critical questions, such as, “What is my exposure to Lehman, AIG or MF Global, etc.”?” said Chow.

Can Big Data deliver great business results, or will firms need to employ a “smart data” model?

“Whatever definition you subscribe to currently, the debate around data is a positive sign that firms are becoming more aware of data and extracting value from their data assets,” said Chow. “Being able to harness the latent potential of large datasets in a structured, purposeful manner will undoubtedly yield lucrative opportunities, where cheap debt and exotic instruments have lost much of their steam.”