Data Governance Trends and Predictions: What to Expect in the Next Decade

Cybersecurity incidents can involve corporate espionage, fraudulent digital documents, or data leaks exposing employees and customers to identity theft threats. Therefore, no stakeholder will support a brand failing at essential data governance and standard compliance. Embracing those norms ensures companies can mitigate IT, accounting, and legal risks without problems. This post will highlight the data governance trends and predictions for the decade. 

What is Data Governance? 

Data governance encompasses legal, financial, and cybersecurity compliance to prevent unfavorable outcomes that threaten enterprise competitiveness, reputation, and trade freedoms. Its policy-related aspects involve macro considerations like consumers’ privacy rights or preventing tax evasion. 

However, brands can also leverage data governance consulting to optimize in-house processes for micro-compliance aspects. For instance, they might define employee roles, data modification rights, network usage, and standard operating procedures. These activities facilitate an ease of encouraging accountability at the workplace. 

Global organizations must explore how each geopolitical territory’s laws impact data governance requirements, compliance trends, and framework amendments. Otherwise, they will likely attract penalties for failing to change processes and meet local authorities’ expectations. Moreover, governance inconsistencies can alienate consumers in the target market based on controversial media coverage.  

An agile data governance model develops cross-functional teams with a fixed member tally. On the other hand, an adaptive governance approach can systematically adjust operations based on new regulatory norms. Nevertheless, given the demand for federated data governance, innovations balancing democratization and authoritative hierarchies have gained momentum.  

Top Data Governance Trends and Predictions Leaders Must Expect This Decade 

1| Continuous Revisions to Governance Frameworks 

A data governance framework relevant to a firm’s operations guides stakeholders in creating, managing, and modifying the right executive roles or bodies. You will find precise vocabulary in a framework to decide how to handle data events like major database leaks or accounting irregularities. 

Furthermore, leaders can utilize data governance frameworks or DGFs to restrict the scope of collaborative data sharing with company outsiders. This strategy increases your enterprise’s resilience to corporate espionage and temporary workers misusing intelligence assets. As new compliance threats frequently undermine corporations, revising DGFs to optimize workflows is vital. 

Finally, the recurring need to review the terminologies and role scopes arises due to ever-changing legal and political risks. More stakeholders and policymakers expect businesses to invest more in enterprise data security to protect consumer interests. Therefore, many nations regularly change and amend related IT laws. 

At the same time, a global organization loses compliance ratings during the transition period after new regulations govern the business space. Managers prefer slight, incremental adjustments to governance policies instead of witnessing significant disruption because of occasional framework updates. These factors indicate all business-specific data governance frameworks must keep evolving. 

2| Data Democratization for Decentralized Governance 

Centralized governance models are time-consuming. After all, employees must invest remarkable time, effort, and resources to get approvals from the higher-ups on multiple levels. This approach also exhibits noticeable delays between requesting and securing file access permissions for minor database modifications.   

Democratization principles describe a more agile or less restrictive attitude toward data access, update, and transfer. Empowering employees through a decentralized governance model also increases opportunities to foster self-learning, leadership, and collaborative innovation. Understandably, global businesses seeking to unlock the actual potential of multidisciplinary teamwork will embrace democratized or decentralized data governance models.  

3| Ethical Implementation of Artificial Intelligence and Data Mining 

Data ethics help managers avoid controversies concerning user privacy rights, historical biases, and cultural differences. Besides, it indirectly improves legal compliance. However, the global business community mainly wants to focus on ethical data operations to prevent the problematic use of artificial intelligence (AI). 

AI-powered chatbots, software commands, and electronic devices promise a straightforward human-machine interface irrespective of an individual’s coding aptitude. Therefore, using AI to automate and experiment with data processing activities is theoretically harmless. Unfortunately, certain malicious actors can deliberately misconfigure or exploit AI systems to compromise a business’s IT systems. 

Consider AI-generated reports and insights. Should an analyst accept the results of AI reporting tools without inspecting their reliability? No. Responsible, ethical implementation of AI and big data technologies is inseparable from most data governance trends. Companies recognize that. They will develop data quality assurance and moderation controls to overcome the challenges in  AI integration. 

4| Skill Crisis in Data Stewardship 

Data governance frameworks and data quality management (DQM) tools will only be effective if skilled professionals lead the implementation efforts. An experienced data steward monitors data fitness and examines on-ground situations that a DGF or DQM strategy might cause. 

Data stewardship requires technical, managerial, and legal skills to ensure harmony across a company’s data resources. As a result, data stewards must prevent discrepancies between branch offices’ records, master data, and analysts’ reports. They must collaborate with company lawyers, IT teams, and data governance officers (DGOs). Data stewards must excel at classifying datasets as regulated, restricted, or public. 

On a related note, a subject matter expert or SME will require additional software and business administration skills to qualify for a data steward role. Remember, an SME’s recommendations are not obligatory. So, data stewards can employ a quality assurance criterion based on business priorities. Meanwhile, a subject matter expert might have domain expertise suitable for an advisory role instead of an executive one. 

The skill crisis in the data stewardship space has forced many SMEs to tackle technological challenges beyond their capabilities. This situation signifies the urgent need to differentiate subject matter experts from data stewards. To become competent data stewards, they must undergo adequate training to acquire tech and law skills. 

Conclusion 

Data governance trends for this decade include frequent global and regional framework changes. In addition to a renewed emphasis on democratization, companies have evaluated their data operations from ethical perspectives. For instance, more stakeholders want hyper-enthusiastic users of AI-powered applications to integrate bias prevention and moderation policies. 

Finally, skill gaps and job mismatches between SMEs and data stewards lead to workflow disruptions at many enterprises. Therefore, clarifying legal and tech qualification requirements during role assignments is essential. 

When organizations embrace the right data governance frameworks and collaborate with talented data stewards or DGOs, they will witness extraordinary improvements in resilience.