Generated AI is a headline act in many industries, but these AI tools power data plays a leading role behind the scenes. Without clean, curated, compliant data, even the most ambitious AI and machine learning (ML) initiatives will fade.
Today, businesses are moving quickly to integrate AI into their businesses. According to McKinsey, in 2024, 65% of organizations reported regularly using generated AI, showing a double increase since 2023.
However, the true potential of AI and ML in an enterprise is not due to surface-level content generation. This comes from embedding the model deeply into customer-facing processes where decision-making systems, workflows, and data quality, governance, and trust are central.
Furthermore, simply embedding AI and ML features and capabilities into basic applications is not good for the enterprise. Organizations need to leverage all aspects of their data to create strategic benefits that stand out from the competition.
To do this, data that powers the application must be clean and accurate to mitigate bias, hallucinations, and/or regulatory violations. Otherwise, they risk training and output issues and ultimately disable the benefits that AI and ML projects were intended to create first.
The importance of excellent clean data
Data is the foundation of successful AI initiatives, and companies need to raise standards for data quality, integrity and ethical governance. However, this isn't necessarily as easy as it looks. According to QLIK, 81% of companies suffer from the quality of their AI data, and 77% of companies with revenues above $5 billion expect a decline in AI data quality to trigger a major crisis.
For example, in 2021, Zillow shut down Zillow's offer. This is because the home could not be accurately evaluated due to defects in the algorithm, resulting in a huge loss. This case highlights a very important importance. AI and ML projects need to work with good clean data to produce the most accurate and best results.
Today, AI and ML technologies rely on data to learn patterns, create predictions and recommendations, and to help businesses drive better decisions. Methods such as recovered high generation (RAG) pull in real time from the enterprise knowledge base, but if those sources are incomplete or outdated, the model generates inaccurate or irrelevant answers.
The ability for agent AI to rely on real-time consumption of accurate and timely data. For example, autonomous trading algorithms that respond to market data failures can cause millions of losses within seconds.
Establish and maintain a superior data environment
There are three key factors to consider in order to establish and maintain a great data environment that businesses can leverage for AI and ML use:
1. Build a comprehensive data collection engine
Effective data collection is essential for successful AI and ML projects, and modern data platforms and tools such as integration, transformation, quality monitoring, catalogability, and observability are essential to support AI development and output demands. Make sure your organization has the correct data.
Whether the data is structured, semi-structured, or unstructured, the collected data must come from a variety of sources and methods to support robust model training and testing to encapsulate the various user scenarios that you may encounter during deployment. Additionally, businesses must ensure that they follow ethical data collection standards. Whether your data is a first, second or third party, you must agree to the collection and use and source it correctly.
2. Ensure high data quality
High quality, reasonable data data is essential for the performance, accuracy, and reliability of AI and ML models. Given that these technologies introduce new dimensions, the data used must be particularly consistent with the requirements of the intended use case. However, 67% of data and analytics experts say they don't have complete trust in the organization's data for decision-making.
To address this, it is essential that companies have data representative of real scenarios. Furthermore, it is important to recognize and address bias in training data, as biased data can undermine results and equity and negatively impact customer experience and reliability.
3. Implement a trust and data governance framework
The promotion of responsible AI has put a spotlight on data governance. It is important that there is a transition from traditional data governance frameworks to more dynamic frameworks, as 42% of data and analytics experts say they are not ready to handle the governance of the legal, privacy and security policies of AI initiatives.
In particular, when agent AI becomes significantly more pronounced, it is important that agents take specific decisions or address the reasons for taking specific actions. Companies need to focus on explainable AI technologies to build trust, assign accountability and ensure compliance. Trust in AI output starts with trust in the data behind it.
In summary
Data allows these technologies to learn, so AI and ML projects fail without good data. Data strategies are intertwined with AI and ML strategies. Companies need to make an operational shift that places data at all cores, from investing in technology infrastructure to governance.
Those who take time to place data first will see the project thriving. Those who don't will bite with their heels in the face of continuous struggle and competition.
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