Even with the advent of amazing information access delivered by generative artificial intelligence (AI) systems, most organizations still consider it difficult to find business-relevant content and analyses. They also perceive data protection (and protection of all analytic assets) increasingly critical and challenging as privacy issues, cybersecurity events, and the proprietary value of data expand. Against this backdrop, new requirements continue to emerge, such as the need to govern AI models and AI-related artifacts, including training data.
Most governance programs address these issues in an ad-hoc fashion when they occur, rather than being proactive and strategic. Ad-hoc approaches in governance programs are suboptimal because they are reactive, defensive, and tactical—and often done outside the context of business outcomes and value. Data leaders need to guide their organizations to be proactive and “on the offensive” in governing data and analytics as a key component of their business strategies and operating models.
Proactive organizations that transform governance from “compliance” to “core competitive differentiator” often achieve success and effectively outcompete others in their markets. As governance programs expand their purview beyond compliance, they become fundamental elements of business strategies and operating models, and represent a key growth step for organizations that want to increase their Hyper-Decisive® maturity levels. Proactive governance programs that focus on alignment with strategic business decisions, outcomes, and value creation can deliver significantly increased value to their organizations.
Data leaders need to consider how to adjust key characteristics of their governance programs to optimize results. For example:
- What is the current reporting structure for the program, and is it positioned close to decisions / outcomes key to the organization?
- What is the scope of the data and analytics governance program, and does it cover the artifacts most important to those decisions and outcomes?
- Where is the governance program spending its time, and on what activities does it focus? Are those activities positively influencing decisions and outcomes?
- Are metrics and key performance indicators (KPIs) for the governance program set in the context of organizational objectives, and otherwise aligned to support business / mission metrics and KPIs?
By determining to what degree their governance programs align to strategic business value, data leaders can identify where and how to improve them to maximize that value.
Successful programs position governance responsibilities in a more business-focused manner, often shifting them out of IT and into a business-functional reporting structure (under a CDO or inside a functional area, such as marketing or finance). In addition, the scope of most governance programs needs to better support business value. Such alignment means expanding program scope to incorporate operational data, master data, and analytical artifacts relevant to critical business decisions and outcomes most important to business leaders. Leveraging AI and generative AI capabilities also increasingly creates a need to govern everything in the pipeline of AI applications—algorithms and models, training data sets, and data quality and data protection.
In addition, governance programs become more business focused by engaging in activities and curating assets that give direct value to business leaders and their teams, such as metadata and data catalogs, data-literacy-related training, and business-oriented metrics. This scope helps data leaders better engage business leadership, who in turn can help change established perceptions and drive the mantra that governance needs to be formal, proactive, and all about business value. Business leaders can help data leaders prioritize and implement changes (such as reporting structure, scope, and activities) to their governance programs.
Being business focused in governance means not only aligning to business outcomes and value, but also actively identifying and avoiding issues. Data leaders and their governance teams can do this by developing and implementing proactive controls—monitoring data and analytics assets against expectations and requirements, so they can take corrective action before significant issues result. In addition, they can implement governance-related metrics that express the business value that such proactive and formal approaches deliver. By showing the direct impact on the business decisions and outcomes that matter most to business leaders and represent the strategic focus of the organization, data leaders can ensure that executive leadership will perceive a governance program as a critical business capability, ensuring ongoing and expanding support and funding for it.
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