At a time when companies are increasingly dependent on artificial intelligence (AI) and advanced data capabilities, the importance of IT services is more crucial than ever. Nonetheless, despite the progress in technology, business leaders are growing more dissatisfied with their IT divisions.
As per a research conducted by IBM’s Institute for Business Value, the trust in the efficiency of fundamental IT services among senior executives has significantly decreased. While AI holds the potential for transformative capacities, especially generative AI (gen AI), the journey to realizing these advantages is filled with obstacles, particularly in data management.
Evidently, many executives believe that insufficient data quality, accessibility, and security are exposing their businesses to unwarranted risks. Let’s delve into this emerging issue of data liability and explore how companies are working to mitigate their risks.
The increasing apprehension regarding IT services in the era of AI
The demands from IT departments have undergone a significant change in recent years due to digital transformation and the widespread use of AI. With businesses leaning more on technology to gain a competitive advantage, the pressure on IT departments to deliver has surged. However, this heightened expectation has not resulted in a greater confidence in IT services.
The study by IBM indicates that among technology leaders, only 43% claim that their organizations are proficient in delivering unique products and services. Additionally, only half of tech leaders believe that their teams possess the knowledge and skills to integrate new technology. Specifically, for generative AI expertise, 40% of tech CxOs express an increased level of anxiety over the last six months.
This dissatisfaction extends to concerns surrounding data management. A concerning statistic from the IBM survey displays that merely 29% of tech leaders are confident that their enterprise data abides by the requisite standards of quality, accessibility, and security to scale generative AI. This illustrates a substantial gap between the expectations of business leaders and the actual capabilities of IT, especially concerning data-driven AI systems.
Data liability in the era of AI
Data serves as the cornerstone for AI operations, yet it also poses a significant liability if not managed correctly. Ensuring data quality, accessibility, and security is crucial to guaranteeing that AI applications function as intended. Inadequate data management can lead to inaccurate models, biased outcomes as well as security vulnerabilities, all of which can have profound repercussions for businesses. In fact, 43% of business leaders surveyed by IBM have expressed escalated concerns about their technological infrastructure due to gen AI in the past six months.
Furthermore, companies are facing mounting regulatory scrutiny concerning how they gather, store, and utilize data. As a result, data compliance imposes additional pressure, requiring companies to not only leverage data effectively for AI but also conform to regulations and laws regarding data protection.
The ramifications of inadequate data management are extensive. Aside from potential financial losses stemming from inefficient AI models, companies could encounter legal complications due to data breaches, mishandling of sensitive data, or non-compliance with data regulations. Mishandled data liability could turn into a persistent headache for both business leaders and IT departments.
Introduction to governance, risk, and compliance (GRC)
One solution to address the mounting data liability issue is through a robust governance, risk, and compliance (GRC) framework. GRC serves as an organizational strategy aligning IT practices with business objectives to manage risks and maintain regulatory compliance. By integrating GRC into the core of IT operations, companies can proactively tackle the challenges associated with data management and scaling AI.
These three pillars constitute the foundation of the GRC framework:
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Governance pertains to the set of regulations, policies, and procedures ensuring that corporate activities are aligned with business goals. Effective governance ensures that management can direct and influence activities across the organization, aligning business units with customer needs and corporate objectives.
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Risk management encompasses the identification, assessment, and mitigation of financial, legal, strategic, and security risks. In the realm of AI and data, risk management plays a vital role in identifying vulnerabilities like software deficiencies or subpar data practices, which could jeopardize the integrity of AI models.
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Compliance ensures that organizations adhere to internal and external regulations, whether industry-specific or mandated by the government. A robust compliance program ensures that the organization remains in compliance with data privacy laws such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
Read the related Gartner report
Utilizing GRC to tackle data liability
Another significant advantage of GRC lies in its capability to escalate discussions around data management from being solely an IT concern to a boardroom agenda. Data management is frequently viewed as a technical matter only. However, it holds implications for business performance, legal exposure, and customer trust. GRC fosters collaboration among IT, finance, legal, and business units, ensuring that data management is recognized as a core strategic priority.
GRC establishes clear policies for data governance, ensuring consistent data handling throughout the organization. This involves the implementation of data fabric architectures and enterprise data standards, laying the foundation for scaling AI applications effectively. These architectures aid in dismantling data silos and facilitating seamless data integration, enabling AI systems to access and leverage data in real-time.
Addressing the disparity in AI development
An often neglected aspect of data liability is the significance of diversity in AI development. The IBM study underscores the necessity of bringing more women into IT and AI roles to ensure diverse viewpoints in AI development and data management. This approach acts as a safeguard against biases that may infiltrate AI models due to excessively homogeneous development teams.
Marisa Reghini Ferreira Mattos, Chief Technology and Digital Business Development Officer at Banco do Brasil, notes, “If 70%, 80% of IT professionals are men, it’s obvious that AI is going to be coded with bias.” Encouraging women to venture into IT and AI realms as subject matter experts broadens the talent pool, shaping AI evolution in a manner that incorporates diverse perspectives and addresses broader societal concerns.
Role of GRC software
Effective implementation of GRC demands suitable tools. For instance, GRC software can streamline operations like risk assessments, compliance management, and audits. These platforms provide businesses with a centralized approach to overseeing data governance, tracking compliance with regulations, and assessing risks in real-time. By automating GRC-related tasks, organizations can alleviate the administrative load on their IT teams and assist in meeting compliance obligations.
Additionally, GRC tools offer valuable insights by linking data management practices with business outcomes. According to the IBM study, high-performing organizations that correlate technology investments with measurable outcomes, effective strategy, and cross-functional collaboration report a 52% increase in revenue growth. This underscores the significance of not only deploying GRC but also evaluating its impact on the business.
The data management edge
As businesses proceed to embrace AI at a larger scale, the necessity for efficient data management cannot be overstated. Data liability is maturing into a major concern for business leaders, and the repercussions of inadequate data practices can be severe. However, by embracing a robust GRC framework, organizations can mitigate these risks and transform data management into a competitive advantage.
GRC furnishes the structure necessary for governing data, managing risks, and ensuring compliance, thereby empowering businesses to flourish in the AI-powered future.