Building Long-Term Success Through Enhanced Data Quality
By Dan Higgins, Chief Product Officer of Quantexa
Today, global businesses face numerous challenges that hinder their ability to achieve sustainable profitability and long-term success.
One of the most significant barriers to achieving and sustaining profitability is a lack of access to high-quality data. According to Gartner, poor data quality costs organizations an average of $12.9 million annually—a staggering figure driven by the fact that nearly 60% of organizations don’t measure their data quality.
The prevalence of low-quality and siloed data not only hampers the efforts of data and analytics teams, but also undermines accurate and trusted decision-making across the organization. This creates significant risks to the financial integrity of the business – placing even greater pressure on chief technology and data officers to get their house in order before future growth plans. In an increasingly AI-enabled competitive business environment, AI adoption is no longer optional for organizations. However, poor quality data and a lack of contextual data remain key barriers to successful AI adoption.
It’s a big endeavour, but an essential one, and the good news is there is a pragmatic approach for executives embarking on this journey.
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Organize your data
The volume and complexity of organizational data continue to grow as businesses collect increasing amounts of internal and external information. Data and analytics teams are working to make sense of these vast data pools, aiming to categorize and redirect them into smaller, more manageable subsets. This process often leads to data being siloed, isolating products and services from the organization’s larger data estate. As a result, companies lose visibility into their data—what they have, where it resides, and what it signifies—leading to a lack of trust in its ability to inform strategic decisions. Despite having a wealth of data at their disposal, many organizations struggle to effectively leverage it for actionable insights.
Rather than breaking data into separate pools, Chief Data Officers (CDOs) should focus on creating interconnected lanes within a unified system. This approach allows disparate data sources to coexist transparently, providing comprehensive oversight. While 97% of organizations invest in data initiatives, only about a quarter report achieving a “data-driven” status. The persistent challenge lies in unifying scattered data at scale. To overcome this, organizations need to align internal and external data—representing real people and businesses—on a single plane. Entity Resolution plays a critical role here, enabling companies to integrate and analyze data cohesively, establish a trusted data foundation to power their AI and analytics and foster clarity and trust in their decision-making processes.
These dimensions should serve as benchmarks for assessing and improving data strength and accuracy, regardless of industry or company size. It’s clear that enhanced data quality enables organizations to unlock critical insights into their operations, improves decision-making and risk management, enhances customer experiences, and frees teams for more strategic tasks.
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Leverage Entity Resolution (ER) and knowledge graphs
The first step in improving data quality is to build context through a unified data foundation, leveraging advanced AI tools such as Entity Resolution (ER) and knowledge graphs. ER is a process that parses and matches data entries to their same “real world” entity, such as individuals, organizations, products, or accounts, and consolidates them into a single, cohesive record. This process integrates both external sources – such as watchlists, credit reports, or corporate registry sources – with internal sources – such as customers, products, or transactions. ER eliminates duplicate information within an organization’s data, forming a unified and accurate view of entities.
Knowledge graphs further enhance this foundation, providing crucial context by mapping relationships between entities, showing how data points are interconnected. Together, ER and knowledge graphs form a powerful combination, enabling organizations to build a unified, contextualized data foundation that drives actionable insights and informed decision-making.
ER and knowledge graphs in action in the banking industry
While these tools benefit all industries, their impact is particularly significant in banking, where institutions face complex challenges, such as combating money laundering, mitigating AI-enabled financial crime, uncovering unique customer insights to grow wallet share, and navigating regulatory compliance. These pressures demand that c-suite executives find ways to reduce costs and maximize the effectiveness of their teams.
By deploying ER and knowledge graphs, banks can integrate customer information—including transaction histories and account data—with external sources like credit reports and watchlists. This connected, contextualized view provides critical insights into hidden risks and opportunities that might otherwise go undetected, significantly enhancing risk management, operational performance, and decision-making.
For banks, contextual monitoring – leveraging a connected view of customers and counterparties – also delivers transformational benefits. According to Deloitte, roughly $800 billion to $2 trillion is laundered globally each year, with less than 1% of the proceeds of crime recovered. Applying contextual monitoring to financial crime detection provides four key advantages:
- Strong data governance: Enables compliance with evolving regulations by providing a robust framework for risk management.
- Unprecedented accuracy: Offers a precise understanding of risks, such as whether a customer operates in high-risk geographies or engages with high-risk counterparties.
- Resource efficiencies: Automates processes, reduces human error, and minimizes false positives, which helps streamline investigative, reporting, and analytical tasks.
- Seamless integration: Ensures risk identification can be applied across multiple business areas, delivering enterprise-wide value.
By adopting ER and knowledge graphs, banks can enhance their ability to monitor, manage and mitigate risks while improving overall operational efficiency. This connected, contextualized approach to data empowers financial institutions to make smarter decisions and achieve long-term success.
Maximize AI and machine learning for decision-making
Before adopting and leveraging emerging technologies such as AI and machine learning, organizations must assess the level of investment against the benefits they aim to achieve. Crucially, realizing the full potential of AI and machine learning for contextual monitoring depends on the quality of data fed into these systems. A unified data foundation is essential for these technologies to operate effectively. By improving their data quality, organizations can maximize the value of AI investments to both automate everyday operations and address their most pressing challenges.
AI models can and should also be leveraged in tandem with ER and knowledge graphs. Generative AI copilots, for example, enable organizations to query large, siloed data through a natural language interface – providing real-time insights while once again automating key investigative, reporting, and analysis tasks. These AI models adapt to organizations and their unique operations – and require human involvement to ensure that each decision is backed by trusted and accurate data every time.
Sustained profitability and long-term success for any organization begins by examining and improving the quality of data they rely on within their operations. By deploying ER and knowledge graphs to build a robust data foundation, this not only maximizes the value of other investments such as AI and machine learning, but also provides organizations with crucial insights required for more trusted, accurate decision-making.
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