Data Strategy and Data Quality

22 May 2024

Artificial Intelligence and Generative Artificial Intelligence

Source: IT user

Data quality is crucial for the success of modern organizations. Poor management of internal and external data can lead to missed opportunities, revenue loss, and inefficiency, impacting customer experience. Inaccurate internal data can also disrupt supply chains and hinder talent retention.

Proactively addressing data quality is essential to mitigate risks, especially in an environment where cyber threats are constantly evolving.

According to Gartner, poor data quality can cost organizations up to $9.7 million annually, while IBM suggests that US companies lose $3.1 trillion annually due to inadequate data quality.

Amid economic uncertainty and the threat of recession, data quality becomes even more critical for making informed short, medium, and long-term decisions.

Data and Generative Artificial Intelligence

In the current context of 2024, traditional challenges in data quality persist, such as duplicates, ambiguity, inaccuracy, and inconsistency. Additionally, emerging issues like data obsolescence and security demand agile responses amidst rapid technological evolution and ever-changing cyber threats.

A growing challenge is the exclusive data management by IT departments, rather than adopting a comprehensive organizational approach to data quality. Hence, integrating strategies that include tools like Generative Artificial Intelligence (Generative AI) becomes essential to enhance data quality.

Generative AI can help identify patterns, correct errors, and generate high-quality synthetic data, thereby optimizing data management across the organization. Strategic adoption of these technologies enables businesses to leverage their information effectively, enhancing data accuracy and reliability for informed decision-making.

Improving Data Quality for AI

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how companies leverage their data to enhance operations and generate added value. However, for generative AI systems to be effective, having high-quality data is fundamental.

Generative AI relies on reliable and diverse data to develop accurate predictive models, provide relevant recommendations, and efficiently automate processes.

Hence, the quality, security, and regulatory compliance of data are crucial elements to ensure the credibility and utility of AI applications.

To improve the quality of data used in AI systems, organizations must implement advanced techniques for data cleansing, normalization, and management.

This involves eliminating errors and duplicates, as well as ensuring data integrity and updating throughout its lifecycle, from capture to secure deletion.

Innovation and the Future

To remain competitive and agile in a constantly changing global market, companies must maximize returns on existing technology investments and adopt innovative new solutions. Combining robust data management with advanced technologies like AI will enable them to stay ahead.

Data quality is not just a technical concern but a critical factor affecting all aspects of business. By prioritizing data quality and effectively adopting emerging technologies, companies can strengthen their market position and be prepared to confidently tackle future challenges in a rapidly evolving business environment.

In this regard, having advanced AI tools like OMMA Data can be crucial for optimizing data management and quality, allowing companies to maximize their innovative and competitive potential in an ever-evolving business environment.

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