The power of trust in analytics explore questions and challenges around trust, and the anchors and value of trusted analytics.
In this first of an ongoing articles series on the power of 'trust' in data and analytics (D&A), we explore some of the critical questions and challenges emerging around trust such as the customer view, trusted data science, policy and regulation and cyber security, among others.
Since big data and the ‘Internet of Things’ erupted a few years ago, we have seen unprecedented interest in the power of D&A to inform business, personal and societal decisions. As we become increasingly reliant on D&A, questions of trust arise: how much can you trust the analytics you rely on and the actions they trigger?
In our view, trusted analytics is founded on four key anchors: quality of the data and processes, accepted use of the data in the right context, accurate predictions and insights; and integrity. Nonetheless, addressing and assuring trust in D&A is a continuous and holistic endeavor. Besides, the focus on trust has increased as consumers are gaining a clearer understanding of the value of trust in sharing their personal data with companies.
We believe trust in D&A is going to be defining factor of a successful enterprise, will your organization uncover additional value by understanding the significance of trusted analytics?
The connection between customers, trust and analytics in banking and how the four anchors of trust can help ensure trusted customer relationships
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