The Crucial Role of Data Quality in Generative AI?

At SAP Sapphire 2024, Generative AI took center stage. SAP CEO Christian Klein highlighted how the company’s latest AI implementations are set to change how people work, emphasizing that we are entering a new era of AI usage. It included announcements of partnerships with other major payers in the industry, including Amazon, Microsoft, Nvidia and Google Cloud. The company's integration of AI across its business portfolio, including its proprietary Generative AI assistant Joule, highlights the potential impact that AI can bring to enterprises using SAP.

In the conversation around?AI implementation, data quality is often brought up as a point of discussion. Have you ever wondered why the quality of data is so crucial for AI functionality? The quality of your data is a key concept that plays a significant role in shaping the effectiveness and reliability of AI. Understanding this relationship can open up a world of possibilities in the realm of AI-driven solutions and drive growth and innovation.

Understanding Data Quality

To get started, we need to understand?data quality??and its importance to companies' decision processes.

Data quality is measured by how relevant, accurate, and complete the data is, ensuring it is ready for use for the intended purpose. High-quality data is crucial for making informed decisions and ensuring the accuracy and effectiveness of vital business processes.

Quality data is assessed by several dimensions that determine its usefulness for a specific purpose.

Key Dimensions of Data Quality

To define the quality of the data, we use the eight metrics. To ensure quality, it needs to comply with all these elements. Let’s explore each and why they are important to ensure good data.

1. Accuracy: Refers to the extent to which data correctly describes the real-world objects or events it is intended to model. Accurate data is error-free and provides a true representation of the facts.
2. Completeness: Complete data contains all necessary information and is not missing any elements that are crucial for its intended use.
3. Consistency: The uniformity of data across different datasets or within a dataset. Consistent data does not contain contradictions and is harmonized.
4. Reliability: The dependability of data over time. Reliable data is stable and predictable, allowing for consistent use across multiple applications and over long time periods.
5. Relevance: The appropriateness of data for its intended purpose. Relevant data is pertinent and useful for the specific needs of the user or the application it supports.
6. Timeliness: The degree to which data is up-to-date and available when needed. Timely data reflects the most current information and is readily accessible for decision-making processes.
7. Validity: The extent to which data conforms to the correct formats and values. Valid data adheres to the defined rules and constraints, ensuring it is within acceptable and expected ranges.
8. Uniqueness: The degree to which each?data element is unique?and not duplicated. Unique data ensures there are no redundant entries.

But, now that we have a better understanding of what quality data is and its importance, we can dive into its significance for Generative AI success. Good data ensures AI-generated content is accurate and reliable, which helps maintain the integrity of outputs and prevents errors.

Now, let’s explore what Generative AI is and how it works.

What is Generative AI?

Generative AI is a technology that creates new, original content by learning from existing datasets. The content it produces, known as synthetic data, depends on the quality of the existing data. That’s why having high-quality data is essential. It ensures that synthetic data is accurate, reliable, and useful for its intended purpose.

To ensure the new data generated by AI is reliable for decision-making, we need to prove our existing data set, assessing it against the eight data quality metrics. Let’s explore two examples of how that translates into real-world applications.

Scenario 1: High Data Quality

1. Existing Dataset:?The dataset contains well-researched, accurate, unique, and unbiased news articles.
2. AI-Generated Content:?The AI generates a new article about a recent event, mimicking the style and quality of the original dataset.
3. Outcome:?The new article is reliable, accurate, and maintains the context of the original high-quality dataset.

Scenario 2: Poor Data Quality

1. Existing Dataset:?The dataset contains articles with duplicate, misinformation, or outdated information.
2. AI-Generated Content:?The AI generates a new article based on the flawed dataset.
3. Outcome:?The new article repeated the misinformation, presenting biased views and being factually incorrect.

Understanding Data Quality in Generative AI

Imagine you're a chef creating a gourmet dish. Using fresh, high-quality ingredients results in a good meal, while using subpar items creates a disappointing outcome. Similarly,?Generative AI relies on the quality of its original dataset to produce accurate and reliable synthetic data.

Synthetic data, created by generative AI algorithms, is designed to be more flexible than real data. These algorithms can be programmed to produce larger, smaller, fairer, or richer versions of the original data, making synthetic data highly adaptable for various AI and machine learning applications. This flexibility allows for better control and enhancement of data quality, ensuring more effective and reliable AI models.

By prioritizing high-quality datasets, we can harness the full potential of Generative AI.

How to ensure data quality?

Assessing your current database?is crucial for ensuring data quality. Companies can begin by conducting a thorough audit of their existing data sources and processes. Identify?data governance practices and eliminate outdated, inaccurate, or biased data. Enterprises often opt to partner with experienced data quality and governance consultants for this endeavor.

3 Steps of Data Management Factory

Auritas offers comprehensive data quality assessment that ensures your data is accurate, complete, and reliable. Leveraging Auritas' expertise allows businesses to enhance their AI models' performance and achieve better decision-making outcomes.

Auritas Data Management Factory methodology ensured data quality in three easy steps:

1. AssessData Quality Assessment:?Creating and applying data profiling rules based on industry specifications and regulatory requirements.Data Governance Maturity Assessment:?Reviewing current data standards, processes, and controls to ensure comprehensive data quality and governance.
2. AddressExisting Issues:?Collaborating with the organization to develop and prioritize master data objects to be cleansed and enriched.Automated Cleanse:?Cleansing data in Auritas Labs and returning it to the organization for inspection.Manual Cleanse:?Addressing incomplete or missing data through preprocessing and subsequent cleansing.
3. SustainProcess Excellence:?Optimizing processes?to sustain high data quality and prevent future data issues.Operationalizing Data Governance:?Implementing tools such as SAP Master Data Governance and SAP Information Steward to maintain data quality standards

Ensuring high data quality is fundamental for the success of Generative AI. Auritas' comprehensive data quality assessment services are vital in enhancing AI performance and supporting better decision-making. By maintaining high data quality standards, organizations can avoid the pitfalls of poor data, ensuring that they use AI to help drive growth.

Start your journey and get your?data quality assessment.

Explore how LivaNova was able to?clean and secure data quality with a centralized source of information?through the SAP MDG implementation by Auritas.