As the role of generative AI expands across business functions, new and increasingly complex barriers to consumer trust are emerging. Organizations can incur significant reputational damage (and revenue loss) by training AI models on low-quality data, failing to comply with evolving regulations, or storing sensitive data in vulnerable AI systems. To mitigate these risks, proactive organizations are developing stringent policies for data hygiene.
Systems for cleaning, monitoring, and evaluating data can protect against AI-related threats to privacy and brand reputation. However, only 27% of businesses evaluate bias during data ingestion, and a mere 17% have established data governance committees. At a time when consumers are wary of misinformation and executives associate AI with an increased risk of cyberattacks, greater vigilance surrounding data collection, management, and usage is imperative.
Tightening control over the data pipeline
The market for data pipeline tools is projected to reach USD $45.6 billion by 2030. By deploying technology for routine data pipeline audits, businesses can achieve more consistent and credible output from their AI systems. Audits alert stakeholders to pipeline anomalies, such as incomplete or corrupted data entries, enabling prompt corrective action and helping to eliminate "junk data" from the organization's systems.
More broadly, pipeline audits give businesses an opportunity to optimize their data sourcing practices. This helps improve the relevance and accuracy of AI models, whether they are used to generate customer profiles for salespeople, make product support recommendations to consumers, or perform any number of vital business functions.
Through audits, businesses should aim to ensure that their data sources are not only credible, but sufficiently diverse. This is crucial for preventing AI bias, which can quickly erode consumer trust and diminish a brand's reputation.
Defining leadership roles for data hygiene initiatives
Comprehensive data hygiene requires collaboration—and to align departmental contributions with company expectations, central leadership may be necessary. For some organizations, this will mean appointing a team or executive to promote vigilant and compliant data management.
Data hygiene leaders may be responsible for advising customer-facing roles on best practices for data collection and input. They may also be charged with standardizing responses to any inadequate data that may be detected within the organization's pipeline. Most likely, they will also need to work with the stakeholders that utilize AI outputs to make key decisions. When these stakeholders are trained to recognize abnormalities (such as an incomplete customer profile or an outlier in survey data), they can prevent issues from escalating and impacting customers.
Already, more than half of organizations have appointed a committee or executive to manage the risks associated with AI. But without a strong focus on data, the impact of these roles may be limited. Misinformation, regulatory noncompliance, or any lack of transparency with consumers regarding data collection can be detrimental to a business. This means that the most effective leaders will take a data-centric approach to managing AI risks.
Implementing Explainable AI
Data credibility can vary drastically—and that's before businesses account for possible data tampering, misrepresentation, and misinterpretation within their pipelines. Traceability and documentation are crucial to a business' ability to regulate its data processes and ensure the validity of its AI insights. This is likely why a growing number of businesses are embracing Explainable AI (XAI). When implemented effectively, XAI can have a profound impact on the quality of information that organizations ingest, and ultimately generate, enhancing both internal and external trust for their AI models in the long term.
XAI enables organizations to demonstrate how various results are achieved by their AI systems. This improves an organization's ability to assess whether its data sources and processes meet its quality standards, and inform reliable, accurate AI responses. If errors or anomalies occur, XAI makes it easier for organizations to identify and address their root causes.
In terms of tangible benefits, XAI offers the potential for revenue and EBIT growth rates of 10% or higher. As consumers grow increasingly concerned about the data businesses are collecting, and how it is being used, this effect is understandable. Enhanced transparency and precise documentation give credibility to businesses' claims of safe, ethical, and purposeful data management.
Unifying people, processes, and technology
A multi-faceted, cross-functional approach can be crucial to the successful implementation of an org-wide data hygiene strategy. As more employees seek to upgrade their skills for the era of AI, and the market for AI-centric tools continues to expand, businesses have unprecedented resources available for strengthening their data processes. By developing strategies that unify talent, technology, and clearly-defined data policies, modern businesses can stand out to consumers and establish themselves as credible, ethical, and trustworthy AI adopters.
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