ROT Data: A Barrier to AI Optimization in Business

The Challenges Posed by ROT Data in AI Optimization

In today's business landscape, companies face a critical challenge: managing ROT data (redundant, obsolete, or trivial data). Often overlooked, this data complicates the effective use of artificial intelligence (AI) in enterprises. Increasing regulatory demands and the mass of unnecessary data create an environment where AI struggles to produce optimal quality results.

Most companies have integrated AI into their operations, whether by creating a business account with one of the leading large language models (LLMs) or launching custom pilot projects. AI has become a trusted partner. However, despite its potential, AI is not infallible. The results it generates may seem correct, but they are often based on poor-quality data, which can compromise their reliability.

The Importance of Quality Data for AI

AI, while perceived as a revolutionary technology, heavily relies on the quality of the data it uses. To provide accurate and useful responses, it must rely on valid and relevant data. Unfortunately, many companies feed their AI systems with ROT data, which explains why 95% of generative AI projects fail.

The proliferation of data, exacerbated by the rise of AI, has led to a situation where companies lose control of their informational assets. Without proactive management, ROT data continues to accumulate, hindering the integration and development of custom AI solutions. Unlike LLMs and other off-the-shelf AI solutions, which are easy to use and simple to implement with built-in safeguards, custom internal solutions require a more pragmatic approach. These often struggle to navigate complex business rules and the constant refinement needed to access clean data and avoid relying on ROT data.

The Consequences of ROT Data on AI Projects

Custom AI solutions, unlike off-the-shelf tools, require a rigorous approach to navigate the complex rules of the business. If not properly managed, ROT data can undermine these projects from the start. They generate inaccurate results and slow down processes, posing risks to security and compliance.

Companies must ensure that their AI systems do not rely on ROT data. This involves implementing strict safeguards to ensure that only relevant and secure data is used. In the absence of precise and strict safeguards around the data that feeds AI, custom solutions inevitably end up relying on ROT data, resulting in slow and incorrect outcomes.

The Threat of ROT Data to Cybersecurity and Compliance

ROT data does not disappear on its own and can contaminate other data without detection. The lack of uniform regulation on a global scale can give companies a false sense of security, but it exposes them to long-term risks. Due to the gap between international AI regulations, companies may feel they have one less thing to manage. However, this short-term relief has long-term consequences for their understanding and visibility of their data.

Currently, 92% of organizations lack sufficient visibility into their AI identities, complicating compliance and data governance. This lack of clarity can also pose cybersecurity issues, as uncontrolled access to data by AI could become a major attack vector. Without regulatory or compliance requirements pushing them to prioritize governance, companies tend to overlook it.

The Need to Clean ROT Data

To prevent cybersecurity and compliance risks from becoming a reality, companies must take steps to eliminate ROT data. This involves reassessing the state of their data and implementing strategies to clean it. Companies should focus on the state of their data, exposing and questioning those that need to be cleaned to improve AI outcomes while also safeguarding their organization from future risks.

By improving data quality, companies can not only optimize AI results but also protect their organization against future risks. This requires a deep understanding of the data and the establishment of safeguards to ensure that custom AI projects are successful. A better understanding of the data allows for the implementation of safeguards for custom AI projects, ensuring that the data on which the technology relies is not only relevant but secure.

The Impact of Regulatory Requirements on AI

As AI continues to evolve, it is increasingly subject to regulatory and governance requirements. The concept of "explainability" is becoming crucial, as companies must be able to understand and explain how their AI systems work. Indeed, unless they master the ins and outs of their data and AI, companies will struggle to explain how the technology truly operates.

With 181 zettabytes of data created globally last year, it is essential for companies to sift through their data to improve access to relevant information. This requires concerted efforts to eliminate ROT data and strengthen data governance. Taking this step is not trivial: last year, 181 zettabytes of data were created, captured, copied, and consumed worldwide. To improve access to relevant data lost amid the forest of ROT data, it is time to make clear cuts.