Reportedly, more than three-quarters of organizations are using AI in at least one business function. This illustrates how organizations have recognized AI’s ability to redefine operations and streamline processes.
But even with these perceived benefits, the effectiveness of AI depends heavily on the integrity of the data driving it.
Senior Vice President of product management at Precisely.
Organizations are enthusiastic about AI’s future, but tangible returns are yet to be realized by many. Ensuring optimal data quality and strong data governance practices as part of a robust data integrity strategy is critical to fully harness AI’s capabilities and avoid costly missteps.
Establishing a strong quality and governance mindset across the organization is also crucial as there needs to be clarity around policies, roles, and overall business goals.
Data quality: achieving accuracy and reliability
Among these key practices of data integrity, data quality can often pose a big challenge. Although AI influences 60 per cent of organizations’ data programs, only 12 per cent report having data of sufficient quality and accessibility for effective AI implementation.
This highlights the urgent need for strategic investment in data quality management.
AI is only as strong as the data behind it – incomplete, inaccurate, or outdated data derails even the most sophisticated large language models (LLM). This becomes critical when personalization is involved in generating strategic recommendations or providing tailored customer experiences.
By leveraging poor quality data, businesses risk delayed decision-making, increased costs, and, subsequently, customer dissatisfaction.
For example, algorithms used within streaming services depend on detailed customer demographics – such as age, location and preferences – to provide relevant recommendations that build trust with the user.
However, when data is inaccurate, it creates skewed suggestions which fail to meet customer expectations. Consequently, ensuring data accuracy, timeliness and reliability is crucial. Without well-maintained data, AI models will not produce the valuable insights needed for innovation and growth.
While no two organizations face these exact same challenges, many struggle with similar underlying issues. This includes collecting duplicate entries from multiple sources or storing generic data that – albeit correct – lacks value.
By embedding automated validation processes that compare incoming data to real-world benchmarks, organizations can ensure their data remains trustworthy, valuable and ready to use. This facilitates stronger data-driven decisions, improved cost efficiency, and influxes in revenue.
The importance of strong data governance
Reliable and accurate data must also be supported by a strong governance framework that ensures compliance with evolving regulation.
In today’s tech landscape, organizations around the world are managing increasingly complex data laws including evolving GDPR commitments and recent legislative changes, such as the EU AI Act and Data (Use and Access) Act.
While these are designed to address the emerging risks associated with AI and data management, they also pose a significant challenge.
The remedy is effective data governance: it helps organizations understand their data, where it is stored, who owns it, and how it is used. It is required at the foundation of any successful AI adoption strategy. Without the right data governance systems in place, organizations cannot meet ethical and privacy regulations.
From a practical perspective, a tailored approach to data governance is crucial for managing multiple internal systems. Combining and using data across varied sources often creates complications due to differences in formats, standards and quality levels.
Strong governance enables advanced analytics and AI-driven decision-making to break down data silos and improve operational efficiency. It also ensures workflows run smoothly and that AI models deliver reliable results, in line with privacy and security policies.
As AI adoption continues to rise, creating a robust governance strategy has never been more crucial. AI systems rely on large volumes of diverse data; any gaps in accuracy, consistency or compliance could escalate into significant risks, including breaking data regulations.
Data governance provides the structure needed to verify whether source data is accurate, relevant and compliant.
With a strong data governance framework in place, organizations can efficiently leverage critical data for more precise AI-driven insights, decision-making and reporting.
Organizations that prioritize data governance initiatives report a wide array of benefits: 58 per cent have already seen improvements in data quality, improving insights and analytics. As a result, adoption is continuing to rise; currently 71 per cent of organizations now operate with a defined data governance program in place.
True data governance maturity requires businesses to go beyond industry standards. Recognizing compliance as a stepping stone will allow companies to design unique data decision-making structures, aligned with their unique goals, organizational design and culture.
Not only does this demonstrate strong ethical decision-making and commercial awareness, but excellent data leadership.
Benefits of implementing data observability for real-time data management
Even with data quality and governance frameworks, it’s critical to implement tools that can continuously ensure data is trustworthy and accurate. As data ecosystems continue to grow in both size and complexity, real-time monitoring is key to avoid invalid and incomplete data.
Data observability tools address this challenge by proactively scanning data pipelines for anomalies and inconsistencies.
AI and machine learning (ML) techniques track data and detect unexpected patterns that compromise AI model performance. For instance, if an organization typically receives 10,000 new customer sign-ups per day but this abruptly shifts to 1,000 or 100,000 users, it signals a major data issue.
Observability tools flag anomalies and intervene early to remedy this uncertain situation before the model’s accuracy is impacted. They also ensure that data transformations – including enrichment or normalization – are executed correctly to preserve data integrity throughout the organization.
Of course, each organization’s path to unlocking AI success is different. Adopting official data integrity frameworks allows organizations to define data standardization, establish long-term data quality assurance processes and deploy data observability tools that support real-time monitoring.
Building a culture of data excellence for long-term success
To ensure the full value of AI is unlocked, organizations must prioritize quality, governance and observability in their data integrity strategies, in addition to focusing on integration and enrichment. These elements are essential for creating reliable and trustworthy AI and they should not be considered as optional.
By taking these steps, organizations can benefit fully from AI in accurately personalizing the customer journey, facilitating smarter decision-making, and bolstering operational efficiency.
By prioritizing and understanding every element of data integrity, organizations can transform their AI maturity and drive competitiveness in an increasingly data-driven world.
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