Establishing trust in data is critical. Organizations are now employing AI, Machine Learning, Blockchain to ensure data reliability and integrity.
Establishing trust in data is an essential requirement for businesses and entities for whom credible, reliable information is the lifeblood. As enterprises seek to manage data as an asset, it becomes increasingly vital that data sources are trusted and verifiable.
I wrote a few weeks ago about the MIT initiative to establish a framework for trusted data, and the resulting position paper, “Towards an Internet of Trusted Data: A New Framework for Identity and Data Sharing”. The authors highlight the criticality and need for “trustworthy, auditable data provenance” where “systems must automatically track every change that is made to data, so it is auditable and completely trustworthy”. One of the key recommendations of the study was to improve the process and quality of data sharing. One suggestion was to move the algorithm to the data, explaining “The concept here is to perform the algorithm (i.e. query) execution at the location of data (referred to as the data-repository). This implies that raw-data should never leave its repository, and access to it is controlled by the repository/data owner”.