AI Governance - Introduction
In August 2020, we ran a simple poll on LinkedIn. We first needed to understand if people felt that organizations' desire to leverage advanced analytics and artificial intelligence (AI) led to increased risks related to data (data risks). We believed that it does, but we wanted to understand what the general perception was.
Artificial intelligence and advanced analytics are now regarded as essential to how data can help organizations compete, innovate, and drive productivity improvements. At the same time, CIO's and CDO's want to ensure that governing data and analytics are now designed to be bite-sized, by the business, for the business to not slow things down.
Given the increased concerns over privacy, security, ethics and risk management, we believe it is a critical success factor that organizations work as a team and in an integrated fashion for effective AI Governance.
Our other question in the poll then was whether respondents felt that Data, Analytics and AI Governance were carried out at the corporate level or in departmental silos.
The Unified Approach
Prodago's Framework for Data Governance allows organizations to operationalize and unify all the siloed efforts. It is one thing to set policies, but ensuring these are followed is getting more difficult. There is now more data created every day than ever before, more people who need it, more devices (and environments) where it sits, and more applications where we need it, like AI.
Once more, the organization looks to Data Governance (it is the right place after all) to figure out how to monetize data and at the same time manage all the risks that come with such a push. This is what we solve.
Because we go one level deeper to identify operating practices, we know what work needs to be done to meet a requirement (ex. mitigate the risk of bias in an AI model), we can track it and govern it.
So our survey was meant to understand where organizations were in their thinking and their journey.
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What this means for you
It was no surprise to us that the explosion of AI and advanced analytics in organizations and the desire to apply it in every function and process would increase risk. After all, data gets copied around (amongst other things), and speed-to-value is prized. 92% of respondents agreed. So AI Governance may help.
How do we manage these risks? Who gets involved?
The answer is Data and AI Governance, and the business is everyone. The Chief Risk Officer needs to mitigate data risks as part of their overall corporate risk structure, though she is likely not an expert in data. The Chief Privacy Officer, probably a lawyer, is responsible for setting legal requirements and demonstrate that they are applied and solved, often by others over whom she has no control. The CDO manages many aspects of the data for analytics and the process of governance, but by no means does she have the full visibility of how the information gets used or the external constraints that may be impacting the organization. The CIO can provide technology foundations to support others and be responsible for technically securing data. Still, without contextual adaptation, this can become a considerable burden on the business and affect the time-to-value of analytics.
An integrated approach is indeed desirable, if not to say a fundamental condition
Out of those that felt they were more at risk, a full 74% highlighted that despite Data Governance being a reasonably mature subject, with "integrated" frameworks, top-down organizational structures, and best-practices abounding everywhere on the internet, it is still being executed in silos and in a disconnected fashion.
An integrated approach is indeed desirable, if not to say a fundamental condition, and the poll revealed that 60% of respondents operating Data and Analytics Governance in silos agreed with this and likely saw it as "the chasm to cross."
Closing
Our findings imply that organizations are exposed to data risks and want to do a better job of Data and Analytics Governance. Still, they struggle to unify all their efforts in a common framework and language where everyone gets to contribute their expertise in a combined and seamless approach, and everyone understands the big picture.
Without this common way of governing the use, management and protection of data, the ability to operationalize Data and Analytics Governance will remain elusive.
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