Evolve From Data Governance to Business-Focused Governance

New analytics and analytical technologies such as machine learning (ML) and artificial intelligence (AI) force organizations to extend beyond traditional data governance to encompass analytic content.

Organizations reporting high levels of success with business intelligence (BI) also reflect disproportionate maturity in common trust in data and governance. BI success directly contributes to higher levels of achievement in business or execution of mission. Master data and master data management (MDM) also strongly correlates to success with BI. The nature of governance, however, precludes its implementation as a defined template or discrete set of rules.

Organizations need to define and institutionalize their own evolved dimensions of the reason(s) why they do it, what it is, and how they do it. In practice, this requires that organizations understand the evolved structural and technological distinctions and overlaps among data security, data management, and governance.

Organizations are best served addressing—although they are conceptual in nature—the questions of “what needs to be governed?”, “to what degree?”, and “how best to govern?”. Answers to those questions should be grounded in the value chains central to an organization’s business or mission. The how should flow from the processes critical to the successful execution of those value chains.

Recognizing the fundamental importance of achieving trust in data and analytic content, data leaders should be in the vanguard of organizational efforts to evolve the business value concept of governance , one that is connected to goals and outcomes. Deliberate expansion of governance to include analytic content extends beyond data to include ML and AI models, associated algorithms, data sets used in training the algorithms, and all associated meta data.

Data leaders should work collaboratively with executive leadership and governance teams to work toward establishment of business-focused governance. An ideal locus for this effort is the office of the chief data officer (CDO). If an organization does not have a CDO, it can work with business leaders to identify the data and analytic content that is critical to them and where governance would most benefit the organization. As governance activities are not cost free, organizations also should incorporate cost monitoring and optimization relative to value achieved into any formal governance programs.

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