Intelligence by design

How skysparc can help you successfully implement a data warehouse project

Across the finance sector, informed decision-making based on accurate market intelligence has always been a key ingredient of long-term success. But in recent years, advances in technology have enabled organizations to make giant strides in data capture, storage and analysis. At the same time, the need to interrogate data in pursuit of new insights and intelligence has intensified.

Many finance sector organizations are hoping to achieve greater insights by consolidating and cross-referencing data across departments, with specific examples including

  • Central banks looking to analyze and monitor historical trends in time-series data to improve efficiency of market operations, as well as for policy and oversight purposes;
  • Asset managers comparing and predicting the impact of unusual market events on fund and/or asset performance;
  • Corporate treasury departments tracking performance of FX hedges over time to improve risk management processes.

Today’s data management challenges

With sources and volumes of business-critical data only likely to increase in future, many large organizations have embarked on enterprise-wide data warehouse (DWH) initiatives to create a pool of core historical data to be drawn on by a variety of business users.

But creating an effective data warehouse is considerably more complex than simply channeling data from live systems into a catch-all database for future analysis.

Key challenges include:
  • Extraction: Market, transaction and static data from multiple sources must be extracted securely, consistently and comprehensively;
  • Accuracy: Data must be cleansed to ensure all errors are identified and addressed before being uploaded to the DWH;
  • Performance: Large quantities of data must be uploaded regularly, typically daily, to the data warehouse from live systems and extracted within tight time windows in accordance with end-user needs, which may change at short notice;
  • Scalability: DWH projects must be sufficiently robust and flexible to handle future business needs and new as-yet-unspecified data sources and file formats;
  • Flexibility: User-friendly, responsive interfaces are required to enable new reports and analyses to be generated, tested and run at short notice, as needs evolve;
  • Consistency: Data from multiple sources should be normalized for storage and retrieval purposes, using standardized definitions across sources;
  • Synchronization: Data from live systems must be accurately reflected in the DWH, even when backdated changes are made.

Many DWH projects have run into difficulties and have been shelved. Organizations that under-estimate the scope and scale of a DWH implementation project risk wasting valuable investment dollars and internal resources on a facility that is incapable of producing reliable data for business users. Slow delivery of inaccurate data at high cost quickly adds up to a ‘white elephant’ IT project, but perhaps more important is the opportunity cost and the risk of falling behind peers and competitors in terms of analysis and intelligence.

OMNIFIMAPPINGREFORMATTING1242 Based on this information, OmniFi thenqueries only the relevant data.DWH1 When uploading new data to the DWH, OmniFi locates only the data that has changed since the most recent upload / query.3 OmniFi formats, maps and / or normalizes the data received to ensure consistency. OMNIFI IS ABLE TO IDENTIFY AND REQUEST A PRECISE DATASET, THEREBY INCREASING THESPEED AND EFFICIENCY OF THE UPLOADING PROCESS.THIS SMART DATA INTERROGATION AND EXTRACTION IS BASED ON OMNIFI’S FLEXIBLE CONFIGURATION AND SKYSPARC’S UNARALLELED EXPERTISE. 4 OmniFi forwards the processed data toupdate records contained in the DWH in a highly automated, seamless and timely fashion. WALLSTREETSUITE / INTERNAL SYSTEMS3

Streamlined extraction through intelligent analysis

OmniFi facilitates the extraction of raw data from the Wallstreet Suite and other internal systems, and performs an initial transformation, before delivering it to the data warehouse’s staging area for further processing.

What makes our solution different? In short, intelligent data extraction. Rather than extracting all the data, OmniFi updates the date warehouse by first analysing the most recent data flows (typically from the past 24 hours) in order to identify only the data elements and fields that need to be retrieved.

Because OmniFi focuses on the data that has changed since the previous extraction (effectively performing a ‘delta’ extract) it reduces extraction times from weeks to hours. This means OmniFi can tackle the common challenge of making backdated changes to previously uploaded transactions by intelligently identifying only the fields and entries directly impacted.

OmniFi is able to extract every piece of data residing within every module of the user’s Wallstreet Suite, from static reference data to market data to calculated data.

Harnessing the power of data

Extracting the greatest value from available data is one of the major challenges – and opportunities – for today’s financial institutions.

For central banks, flexible and responsive reporting and analytics capabilities are fundamental to the conduct of core responsibilities such as reserve management, market operations and oversight of systemic risk.

For banks, asset managers and corporate treasuries, the ability to analyse and cross-reference data from multiple sources ‘on demand’ supports more efficient and accurate hedging and investment strategies.

As a long-term trusted provider across the finance sector, SkySparc has developed both proprietary technologies and best practice methodologies based on our consultants’ extensive experience in establishing data warehouse facilities, as well as other automated reporting projects.