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Asset Control’s provides a standard data model that tracks regulatory developments and keeps integration with content providers up to date. Its business rules capabilities can be used for risk factor classification, taxonomies, proxy management, risk factor preparation and eligibility tests

Asset Control’s provides a standard data model that tracks regulatory developments and keeps integration with content providers up to date. Its business rules capabilities can be used for risk factor classification, taxonomies, proxy management, risk factor preparation and eligibility tests

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The View from Sales: A-Team Data Management Summit

Urgencies, Strategies and Realities in Financial Data Management Showcased by Industry Leaders at the A-Team Group Data Management Summit in New York City, November 2015.

For data professionals in the financial industry, it is an exciting time to be engaged in the discussions, solutions and future direction of the central role that data management now commands.

We are focused on gaining strategic information advantages, allowing financial strategists and their firms to provide investment returns for the clients who fund them, attract new investors and innovate through their utilization of data and the deployment of it.

Data Urgency

Data urgency is being fueled by an explosion in the velocity of data that is geometrically increasing. The necessity to provide data lineage to regulatory authorities and investors is accelerating the financial industry’s mind shift from a transaction-centric to a data-centric business model. The financial industry differs from other industries in how data is viewed and managed, and this difference is contributing towards an urgency for action that needs to be taken.

What category does your firm fit in to?

Benchmarking companies based on their current state of Data Strategy. Ivan Matviak, Head of Data, Analytics and Software at State Street provided insight into six data aspects, surveying 400 companies to discover how individual firms are currently positioned regarding data management. These include infrastructure, insight, adaptability, compliance, talent and governance.

Financial firms were categorized in three areas:

  • Data starters
  • Data movers
  • Data innovators

Innovators had the highest degree of confidence in the 6 categories and 49% of innovators said that data is their single most strategic priority within their firm.

Don’t Panic

Creating and structuring an effective long term data governance model and strategy is not something that organizations should be taking lightly or on tactical impulse. However, thought and – most importantly – action need to be taken to address the barriers inhibiting proper data governance.

Address outdated systems that store siloed data, manual work that is driving up costs, shifting manual work time into more productive strategic use of human talent and capital, stay focused on cyber security and data piracy, implement data governance and ensure practices are understood throughout the organization.

Data Landscape

Debra Glasser, Chief Data Officer at J.P. Morgan discussed the data landscape from a historical and present day perspective. 90% of the world’s data accumulated from the beginning of recorded history has been generated in the last two years.

We are now generating as much data every two days; next year it will be every 10 minutes. Metadata and lineage were highlighted in their importance for compression and auditability.

Central Processes

Breaking down cultural, technological and operational silos are common obstacles that were discussed during multiple panel and speaker presentations.

Management faces significant challenges regarding data lineage. There is value in simplified architecture, clear understanding of data piracy, vendor restrictions, and direct action on areas that inhibit cohesion.

Regulatory Stress and Fatigue

Regulatory request fatigue is creeping from the sell side to the buy side. While regulatory requirements are significantly less for the buy side than the sell side, the principles of directional nature of BCBS239 and business differentiation through quality data governance and transparency beckon firms of all sizes on either side to take notice and look introspectively.

Getting by with what a firm currently has will not cut it as this environment continues to standardize.  Event speakers emphasized that requirements should be viewed as a journey, pick a starting point and continue onward. The journey itself never ends rather it slows and becomes maintained after earlier sprints.

Utilities

Utilities get a lot of air time, particularly KYC utilities. The goal of an industry utility is to facilitate economies of scale amongst those participating in the utility. Utilities involving financial data are concentrating their efforts on industry data solution frameworks that can be shared to create cost savings benefits to participants.

These cost savings do not come in the form of lower data costs, but rather from operational and technical efficiencies. The providence of financial data utilities can only be determined by time. Considerations for utilities involve agreement among member firms for rules governing data, legal and instrument level identification, individual business strategies.

Standards and Flexibility into Data Architecture

Standards in the financial markets industry are a relatively new topic and heavily debated.  While industry standards regarding data governance are not uniform, there are common themes that were discussed in a panel setting at summit. Data accessibility, governance, confidentiality, end user and regulatory wants and needs regarding data. Well-structured data models allow organizations to execute repeatable processes. This makes configuration simpler and more flexible as new regulations and changes in general take place.

There is a tremendous amount of infrastructure that exists.  Brian Buzzelli, Head of Data Governance at Acadia Asset Management noted that only recently has the concept of understanding the data about data emerged. Collaboration requires standards, standards require collaboration.

Accelerating Enterprise Innovation and Transformation through Data and Analytics

Derek Strauss, Chief Data Officer at TD Ameritrade provided a keynote closing discussing this topic. Recognition that innovation requires effective data governance, enterprise analytics, enterprise data architecture, enterprise data development.  Building an effective data architecture, centralizing data, managing organizational change were among many detailed points discussed.

Firms need to be agile, data and analytics are continually evolving, challenges are continuous and change will be driven by industry innovators. Make sure you keep up to speed.

 

About the author

mcaccavale@asset-control.com  |   T: +1 212-673-8887

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