Data Quality Still a Challenge in Financial Services

Data Quality Still a Challenge in Financial Services

Despite significant investments in Data Governance programs, Chief Data Officers, and Tools, financial services companies (buy side, sell side, insurance, banking, etc.) of all sizes continue to struggle with the quality of data in their enterprise as evidenced by a recent survey and white paper completed by WBR and Informatica “Modernizing Data Quality & Governance: Unlocking Performance & Reducing Risk” . What did we learn?

  • Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre. Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four- fifths of them have it ranked as a top priority.
  • The top two business functions impacted by poor data quality are regulatory compliance and risk management. Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies cannot quantify the business cost of poor data quality.
  • Financial institutions vary greatly in the maturity of their data governance Data governance
cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
  • Data quality management requires close collaboration between business and IT leaders. That collaboration already exists for 83%
of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
  • Data Quality tools used are outdated and not fit for business users. Despite investments in data quality tools, much of those tools are now 20+ years old. Tools that were designed for IT developers vs. data stewards. As such data stewards and business folks resort to manual processes to get their data right.

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