Better AI outcomes and stronger decision-making through high-quality data
Data is the fuel of digital transformation. Its quality and availability determine whether your engine misfires – or delivers peak performance. Poor data quality does not only result in inefficient business processes and high costs for manual cleansing; it also seriously undermines trust in reporting. This becomes particularly critical when using GenAI and Agentic AI. Low-quality data leads to unusable outputs or “hallucinations”.
The positive news: artificial intelligence is both the challenge and the solution. On the one hand, we need to make data ready for AI. On the other, AI opens up entirely new ways to tap into unstructured data and ensure consistently high data quality. We support you with proven, real-world approaches – creating the foundation for more accurate AI results and reliable, data-driven decisions.
“High-quality data is a non-negotiable prerequisite for effective and innovative AI solutions.”
What sets PwC’s Data Quality Management services apart
The limits of traditional data quality management
For a long time, Data Quality Management (DQM) relied on static, manually defined rules. A dataset had to match a given schema, contain no duplicates and be complete. In a world where data volumes are growing exponentially and the diversity of data sources – from IoT sensors to social media feeds – is exploding, this approach is no longer sufficient on its own. Large amounts of data remain untapped.
Data analysts spend much of their time “wrangling” data – cleansing and preparing datasets instead of focusing on actual analysis. Business processes are disrupted by faulty information, and management is forced to make decisions based on reports that can only be trusted to a limited extent.
Generative AI: both challenge and solution
With the rise of Generative AI (GenAI), these challenges become even more pressing. When Large Language Models (LLMs) are fed low-quality, biased or outdated data, they generate highly convincing nonsense. Data quality is therefore a decisive source of competitive advantage in the AI era.
At the same time, AI makes it possible to extract relevant information from unstructured data such as emails, PDFs or service tickets. It understands semantics, can infer missing values from context and helps to ensure consistency and accuracy. This enables organisations to tap into a new segment of enterprise data that was previously very difficult to analyse in a cost-effective way.
“The key to high data quality is moving from a rules-based to an AI-driven approach – with automated checks across the entire data lifecycle.”
Ready for uncompromising data quality?
We help you implement AI-driven data quality management.
How we support you with Data Quality Management
- Improve data quality with AI
- Unlock unstructured data
- AI-driven DQM
Improve data quality with AI
We enhance your data quality through the targeted use of GenAI across the entire quality assurance lifecycle. Our approach goes beyond the limits of traditional methods by integrating unstructured data – such as documents and text – into your data management. GenAI supports pattern detection and rule derivation and, where appropriate, context-aware cleansing and enrichment. In this way, we turn raw information into a trustworthy basis for your decisions.
Unlock unstructured data
Many organisations leave significant potential untapped because their data quality tools focus exclusively on structured data. We extend your DQM to cover unstructured data as well. Using GenAI, we classify, cleanse and structure information from sources such as full documents or free-text fields. We ensure that these data sources also meet the quality standards required for AI applications and precise analytics.
AI-driven DQM
Are you still relying on traditional, rules-based approaches for data quality assurance? We guide you through the transition to modern, AI-driven Data Quality Management. We assess your maturity, identify critical data paths and implement best practices for using GenAI. The goal is to seamlessly embed quality measures into your data pipelines (DataOps), so that quality becomes an integral part of data creation.
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