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Are you AI-ready? How adaptive data governance can help

More data does not always mean better data. As companies acquire exponentially more data and new AI-powered technologies proliferate, good data management and governance are more vital than ever. Strong data quality management (DQM) processes, driven by responsive data governance (DG), should be a core goal for any data-driven organization.

Data governance and its best practices are changing rapidly. Evolving regional data protection laws, coupled with consumer demand for transparency, have made clear, consistent practices essential. For many organizations, implementing DG is a defensive response to new regulations. But good DG offers a lot more than just a safety net; it can foster efficiency, collaboration, and a culture of data-driven decision making.

Generative AI represents a dramatic shift in how data is collected and used. Corporations will need firm but flexible data policies to respond effectively to rapid changes brought about by new technology. Robust DQM, paired with adaptive data governance (ADG) policies, ensures that decisions are informed by high-quality information, supported by a framework that allows for virtually unlimited customization and flexibility.

The high cost of bad data

Lackluster DQM can have expensive ripple effects. Gartner has found that low-quality data costs organizations nearly $10 million annually; in the US alone, bad data costs businesses more than $3 trillion per annum. Bad data has a direct impact on the bottom line; for almost 90% of US companies, this translates to an average 12% loss in revenue. These costs come from a range of effects, including reputational damage, missed opportunities, low data usage due to lack of trust, high data debt, and increased costs and inefficiencies in data processing.

But for DQM to be effective, especially at scale, it must be guided by DG. Data governance refers to an organization's broad strategic philosophy for managing, engaging, and safeguarding data. It defines not only the policies for how data is handled in the organization, but also the people who are involved and their roles and responsibilities with regard to data and the core principles that underlie the strategy.

Unfortunately, fewer than 50% of organizations have such a philosophy in place. Within those that do, efforts are often geared toward compliance and little else. This kind of defensive DG focuses on cleaning, qualifying, maintaining, and securing data, and while this approach provides essential protections, it often limits agility and complexity in decision-making.

A DG model that fosters a proactive (or offensive) approach can go beyond compliance to help organizations target specific business goals or employee behaviors. Well-designed DG can be deployed across the operational gamut to target objectives like ensuring informational security, adhering to evolving privacy mandates, developing data literacy, maintaining fine-grained access permissions, and offering prompt responses to requests for information.

Adaptive governance

Lean, adaptive governance offers an alternative to defensive, control-based strategies. Instead of a top-down, one-size-fits-all data framework, ADG takes a more proactive and collaborative approach to create clear policies that aim to enable, rather than restrict, data usage. Data processes are developed not only for compliance, but also to align and evolve with organizational goals and priorities. With the flexibility to customize data processes for specific purposes and use cases, lean methods empower decision makers in ways that more restrictive defensive postures can't.

Fairly or not, data policies are seen as disruptive to the procedures already in place, adding extra work for little perceived value, and lack of adherence can derail the most carefully designed data processes. By taking a collaborative approach to policy design, ADG gives data consumers a direct stake in the process of crafting flexible policies that adapt to different departments and contexts. Defining clear roles and responsibilities for data owners, data stewards, and data users and building governance into existing workflows reduces friction and non-compliance and encourages a data-driven culture.

Regulatory compliance may not be all DG has to offer, but it is of vital importance. With 2023 seeing significant new and updated privacy laws in Europe and North America, a recent report shows that only half of US and UK organizations feel prepared to meet new requirements. The flexibility at the core of ADG enables it to respond more rapidly to regulatory changes and adapt more smoothly to different local laws in multi-national companies.

 ADG and AI

The rise of generative AI heralded by the launch of OpenAI's ChatGPT in November 2022 represents a sea change for business operations, with McKinsey estimating that generative AI could add $2.6 trillion to $4.4 trillion per year to the global economy. The public-facing version of ChatGPT attracted an unprecedented one million users in just five days and 100 million in two months. OpenAI has also unveiled major partnerships with clients as diverse as Morgan Stanley and the government of Iceland, and a plethora of other AI services, like Bard, Claude, GitHub Copilot, and Stable Diffusion have followed ChatGPT onto the market, offering new avenues of automation and the promise of increased productivity and reduced costs.

As generative AI tools enable revolutionary new ways to use data, the ways organizations collect, handle, assess, and conceive of data will also change. AI governance is needed not only to maintain compliance and return better ROI for new technologies, but also to uphold the values and ethics of the company. As the place of AI in social and regulatory structures is still in flux, the agility to hone and iterate on policy and process will be crucial to successfully navigating the AI revolution.

In the face of new technology, the old adage "garbage in, garbage out" still holds; reliable, effective AI cannot be built on a foundation of bad data, so parameters must be put in place to measure the reliability, accuracy, and quality of source data. To prevent AI algorithms from becoming "black boxes", it's important to define robust guidance for how they are used and monitored, such as requiring audit trails and setting regular review and iteration cycles. Further, the output of new AI processes must be monitored to catch biases or "hallucinations" and maintain quality over time. Taking an agile approach to AI governance will help organizations maximize value from AI, and adaptivity is vital to respond to emerging risks and best practices.

One size doesn't fit all

Multi-faceted governance is successful for the reasons any other policy or process succeeds; it's relevant and useful. Of course, this is all dependent on transparent, logical data policies, responsive feedback mechanisms, and easy-to-use technologies. The right combination of these increases organizational compliance while ensuring decisions are made with an eye toward risk-awareness and security.

Any move to ADG will take time; it requires a radical re-imagining of an organization's philosophical approach to data and analytics. While the right software is essential to implementing any data policy, ADG is as much about operations, security, risk management, employee experience, and customer engagement as it is about technology. The good news is, since this is a lean framework, the best way to achieve big change with ADG is to start small and build from there.

Zoho offers a suite of intelligent enterprise business software, including an award-winning CRM suite, the industry's only comprehensive analytics and BI platform, and a powerful low-code development ecosystem.