Successful implementation of this new technology requires an immense amount of data, but to manage it properly, it is critical to ensure that it is reliable, accurate, secure and trustworthy.
ENSURING TRUSTED DATA
Trust in data comes from the ability to demonstrate with complete confidence how it has been prepared, trace the provenance of the data back to its origin, and provide rights management and auditability. This involves ensuring that the data is accurate, consistent and contextually relevant. Without strong data integrity, companies run the risk of making decisions based on faulty information, which can have disastrous consequences for their business performance.
IMPLICATIONS FOR ML AND IA MODELS
Data integrity takes on even greater importance when it comes to ML and AI machine learning models. If these models are trained on datasets that lack integrity, they will not achieve their intended results and may even generate incorrect and faulty predictions, which would negatively affect business outcomes. A holistic approach to data integrity includes accuracy, consistency and context. When data is accurate and consistent, a company can build a long-term competitive advantage over its competitors.
BENEFITS OF DATA INTEGRITY
Data integrity offers a number of key benefits for companies looking to take full advantage of AI and ML. First, it ensures the quality and reliability of the data used in models, resulting in more accurate predictions and more informed business decisions. In addition, it enables organizations to track the origin of data and perform audits, which is crucial in regulated environments or when it comes to data privacy and security.
Data integrity is a critical component to business success in the age of AI. Companies that prioritize the integrity of their data are better positioned to leverage the full potential of AI and ML, getting more accurate results and making more informed decisions.