Three reasons for an effective enterprise data quality strategy

Kyung Choi
Master Principal Sales Consultant

How can financial institutions ensure that they take full advantage of today’s data-rich world? In the past, these institutions solved data quality problems on a project-by-project basis, such as to satisfy regulatory compliance or incorporate new or acquired systems. However, in recent times there has been an explosion of financial data availability, both in volume and granularity. This has led to heightened expectations by both internal company divisions and external customers to utilize this data for a competitive market edge. Investing in an enterprise-wide data quality strategy provides the ability to trust and rely on data-driven analytics – improving company efficiency and providing an advantage against competitors still struggling to handle vast streams of available data.

An effective data quality strategy provides significant benefits, including the ability to demonstrate clear data governance, maximize business agility, and minimize project cost.

Data governance

Without an overall strategy in place, data quality is frequently resolved only at the project level, resulting in localized data knowledge and expertise. An enterprise-wide data quality process incorporates data governance in the form of stored metadata, which provides non-technical insight and explanation behind underlying data logic. This enables well-defined data transparency at an enterprise level, avoiding the all-too-common inability to understand the “black box” behind the mechanics of specific data issues. Moreover, this window into existing data quality provides compliance-related projects with a ready-made building block to demonstrate data governance to auditors.


Ensuring company-wide data quality enables trust in the accuracy of data-driven insights when optimizing existing customer strategies or targeting new opportunities. For example, the evolving area of customer segmentation can be refined by machine learning and artificial intelligence but requires consistently clean data to enhance model accuracy.

A comprehensive data quality strategy also reduces project effort on duplicative data cleansing tasks and condenses project production timelines, thus providing a decisive advantage compared to market competitors.

Cost reduction

Solving for data quality issues at an enterprise-level expedites projects by re-using already cleansed data – reducing effort, cost, and timelines. In addition, data quality issues resolved during projects can be reclaimed back into the enterprise, deriving incremental data benefit for future initiatives.

How Oracle can help 

Data governance, agility, and cost reductions are just a subset of the abundant benefits of implementing an enterprise-wide data quality strategy. Data-driven technology and data volume will continue to grow exponentially – providing a window of opportunity for companies to scale appropriately and excel in the marketplace. The Oracle Financial Services Data Foundation can accelerate this transformation by enabling a single version of the data truth to business users across the enterprise via a single, transparent, and auditable analytical platform.

To learn more about how Oracle can help improve data quality at your institution, and provide an advantage in the marketplace, feel free to reach out to me to have a conversation.

For more information, please visit:
Oracle Modern Risk and Finance: oracle.com/risk
Oracle Financial Services: oracle.com/financial-services

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