Over the past few years, we have seen an exponential increase in the amount of information made available to us on a daily basis. “Fake news” and “Deep fakes” have become part of our cultural lexicon and raises a key question: What can we trust? There are several examples from social media and news broadcasts that some deem untrustworthy. The same can apply to the organizations we work with or in. The amount of data being produced within an organization has been following the same exponential growth pattern and business leaders are questioning what data they can trust and what they can’t.
Up until the recent explosion of Data Lakes and new data platforms, trust had been established by a System of Record (SOR). A SOR represented an organization’s view of the truth and has been used for official compliance and reporting regarding the overall health of the business. Once an organization’s data has been democratized, in one or many data lakes, the view of the truth is not as binary and is predicated on the business context. Thus, trust becomes based on a point of view.
Trust is a hard concept to quantify. As individuals we trust different things based on life experience, and, in its most primal form, what will keep us out of harm’s way. Organizations tend to behave the same way, making trust a subjective measure. AI and Machine Learning technologies can be used to sift through large amounts of data and identify patterns, gauging the overall accuracy of the information presented. The patterns found can be used to calculate a “Trust Measure”, but because of the subjective nature of trust, the true value of a “Trust Measure” can only be determined by human oversight and business context. With that, another factor that needs to considered is that trust will only be established if the data is deemed to be unbiased.
Without getting into the nature of bias, organizations need to accept the fact that bias is something that will always exist, but how it influences the information used to make decisions can be mitigated. To safely automate trust, organizations need a clear understanding of how data will be used, who will be impacted by the decisions made using the data and determine any potential for harm to the business or individuals. As such it is imperative that an organization’s business analysts are able to be representative of the data they are analyzing, providing their perspective to mitigate unwanted bias in the data.
Data security and governance are additional elements needed to safely automate trust. Historically, governance efforts in an organization have been an “after thought”, or a separate set of standards applied to a project after a project completes. Businesses need to create a system that incorporates governance in a way that establishes guard rails, allowing teams to do their best work unencumbered by bulky external governance processes. To take it a step further, the governance guard rails need to be applied at all levels of the organization, providing the ability for teams to operate autonomously and mitigate issues that might otherwise go unnoticed in a centralized governance structure.
Automating trust presents a new set of challenges to an organization due to the subject nature of trust. Businesses must develop a better understanding of bias in their data and how different business contexts are applied to that data. With ample misinformation surrounding us all of the time, using AI machine learning, and human oversight, organizations can gain a clearer understanding of what information they can trust and what they can’t.
About the Author
Sean Beard is a vice president at Pariveda Solutions, a consulting firm driven to create innovative, growth-oriented, and people-first solutions. Primarily, Sean works within Pariveda to evaluate and identify potential applications for emerging technology. His work involves a mix of consulting, research and development, and project-based tasks. Sean also self-identifies as a professional hobbyist — he doesn’t just work with technology but considers it to be a lifestyle.
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