Executing on an Effective Data Agenda

Here's how to utilize data to achieve profitable decision-making.

Regardless of size and shape, and whether they are from Main Street or Wall Street, all financial services institutions struggle with a common issue–turning data into a strategic business asset. This is a bewildering challenge, with up to 90% of all data having been collected over the last two years, mainly through digital mediums, such as email and social networks. While everyone has unique strategies to harness their data, all organizations will use it to:

-- Protect assets and reputation

--Compete fiercely in the marketplace

-- Optimize and simplify operations

While these goals seem straightforward, organizations need to consider three external pressures that affect all players:

Business Operations

Every c-suite executive understands the necessity of a comprehensive data agenda, but it is the technology decision-makers who feel the most urgency, as they are tasked to modernize legacy environments, while also consolidating data environments and reducing total costs. Other considerations include cyber-security measures to protect stored data, data leakage prevention, and checks and balances against unmitigated decisions made by automated systems.

The Intelligent Customers

Today’s customers have evolved, and are smarter than ever before. They have access to product information and pricing, and expect a tightly integrated experience across traditional and digital platforms. The need be innovative with new products and develop life event relationships with customers is essential, as competition grows fiercer by the day. Additionally, there is competition from non-traditional players, such as Google Wallet, Apple iTunes, Paypal, Square, Mint and SpendSmart, that provide tailored products and optimize customer experience because data is at the core of their business.

Regulatory Impacts

There are also regulatory requirements which demand increased transparency, detailed financial reports, data quality improvement and secure data protection protocols. As an example, according to the Dodd-Frank Act, companies must link structured and unstructured data from the front office for over-the-counter derivative deals; email threads to buy or sell must now be linked to an executed trade. Dodd-Frank also requires systemically important financial institutions to create living wills to facilitate an orderly resolution, which includes the management of their data stores, in the event of material financial failure. According to the Basel Committee on Banking Supervision, globally systemically important banks must assess their capabilities across four primary categories: governance and infrastructure, data aggregation, risk reporting and supervisory. Because of these three pressures, organizations have allowed for siloed data environments to meet various business needs. Many are now realizing that fixing the foundation of their data programs will achieve more with less. The proactive players are undergoing transformation programs to re-architect for velocity, volume and variety of data across unstructured and new forms of non-traditional data.

The first step is defining a data management target operating model. In today’s regulatory-driven environment , where finance, risk and operations numbers must reconcile, this model is as important as a business target operating model. And, this is not a standalone model, but an integrated one across the business value chain. There are five primary dimensions.

Data governance: Leading organizations have measures and enforceable policies in place to maintain organizational accountability to data, with a data executive like a Chief Data Officer leading the effort. The goal is to move towards greater transparency of the data agenda to the board.

Data quality:

Defining ownership, accountability and key performance metrics that measure the health and wealth of your data is key, as is setting up remediation processes to reconcile compromised data.

Data management:

Metadata is no longer a one-time event; it is created and updated within the systems development life cycle process. Make sure to be aware of the entire data life cycle, from capture to long-term storage, and all of the extractions in between. Consider all types of data, including transaction, reference, unstructured and social media.

Data usage:

Understand all the uses of collected data. Is it for basic reporting or advanced analytics, regulatory reporting and management information, or quantitative analysis and scenario planning? Develop a common taxonomy so that the data and reports can be used across all business lines.

Data architecture:

Governance, quality, management and usage protocols need to be defined under a unifying data architecture, which drives infrastructure details, such as size and scope of the entire agenda. Enterprise-wide data architecture has been hard to create, and harder to implement, but necessary.

The data agenda carries heavy consequences – and it needs to grow and further innovate. Financial services institutions are becoming as much about the data as traditional data companies we know – and, they need to take advantage of their most strategic asset in the right ways. The data operating model is as critical as the business operating model. Defining and implementing a target data operating model will lay the foundation for effective data management that adds value to the business. The financial services institutions that are taking this seriously have elevated the importance the matter and made it subject to board-level discussions. Turning data into a strategic business asset cannot be done through project-based data management, but instead in a comprehensive manner that is coordinated across the enterprise.

Hyong Kim is a partner/principal at Ernst & Young. For more information, Kim can be reached at [email protected]