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The promise of generative AI has pushed companies to quickly implement this new technology across the enterprise. Many organizations are now leveraging more data than previously possible, including huge amounts of unstructured data. More and more data is going into existing systems, but using AI technology without a data strategy and oversight is leading to unreliable results.

Poor data quality is one of the biggest challenges for companies to be successful with AI initiatives. Turns out, the old adage “garbage in, garbage out” is more relevant than ever before.

The impact of poor data quality isn’t just a tech issue. It’s a business issue. Without reliable data analysis based on good data, your company can be significantly affected. Your reputation and brand management could be impacted. You may implement less effective business strategies because decisions are made based on poor insights. Your competitive advantage could be in question because you cannot understand your data and therefore cannot make informed data-based decisions. 

To achieve the operational efficiency and business advantage that genAI technology touts, organizations must first ensure their data integrity. This is not a one-time fix. Instead, having confidence in your data requires an ongoing, threefold approach to building a data quality foundation that includes people, processes, and technology.

The importance of human oversight

An organization’s people and culture are the first key components to create and maintain quality data. Companies should have a dedicated function for data quality. These are data experts who set standards and guidelines for other teams to follow. While technology is important, human interaction drives data quality development and oversight. Technology should support and accelerate that effort.

Embrace shifting left in processes

The second part of ensuring data quality is for leadership to embrace and promote the shift left approach to technology development, where testing for data quality happens earlier in the process. A typical life cycle for creating a tech solution is discovery, architecture, implementation and testing, production, deployment, and operations. Historically, data quality is checked after implementation. To shift left means moving your data quality assessment prior to ending the implementation cycle.

By having your quality testing earlier, you can identify problems sooner. The earlier you identify problems, the easier and less costly they are to fix. Plus, catching data quality issues sooner means less potential impacts on customers and your business.  

Technology for data quality

The third component to managing data quality is technology, and there are various tools available for data quality management. EXL, a global analytics and digital operations and solutions company, works with customers to build their data quality programs. While we offer proprietary accelerators to check for data quality, we know that many companies have custom requirements.

EXL works with global companies in many industries to build and implement data integrity programs to improve business outcomes. We deeply understand data specific to those domains, such as banking, healthcare, and insurance, so we are well-equipped to identify and fix data quality issues. Since each company’s technology environment is different, we often create bespoke data quality frameworks that meet niche requirements.

For one of our banking clients, we developed a system to better enable informed credit decisions and to provide accurate, timely regulatory submissions. Our client was experiencing constant fluctuations and apprehension that their data quality had been compromised. Further, the client did not have a way to promptly identify data anomalies and quickly take corrective actions.

EXL developed an automated reporting mechanism with data quality reports for more than 130 variables and more than approximately 1,300 test criteria. The tool was iterative and could perform multiple quality checks for completeness, conformity, accuracy, validity, and comprehension. With EXL’s data integrity solution, the client achieved an 88% reduction in defects over six months.

With this new data quality system in place, the global banking client significantly improved their confidence in driving business decisions using data analysis. Plus, the company was able to respond more quickly to regulatory inquiries and reduce regulatory submission defects.

Know your data quality maturity level

Every large organization has data that they should be leveraging to make data-driven business decisions. Mature organizations that are confident in the data have a strong presence in all three components of their data integrity foundation—people, processes, and technology.

Before implementing AI-driven solutions throughout your enterprise, organizations should do an honest review of their data quality maturity level to find and improve weak areas. EXL offers a purpose-built maturity assessment program based on industry standards and our years of experience helping clients improve their data quality.  Our data experts will uncover your organization’s weaknesses and craft a strategic roadmap to build a robust data quality foundation.

For more information about EXL’s data quality solution and to learn more about our data quality assessment program, email dataquality@exlservice.com, or visit us .

Cora Broyles is senior AVP of the data product management practice at , a leading data-and AI-led services, digital operations, and solutions company.