Mid-sized businesses face several common data governance challenges that can hinder their data initiatives. These include:
- Siloed and Disorganized Data: Mid-market companies often accumulate large volumes of data from different sources and in various formats. Without governance, data becomes fragmented and inconsistent, making it hard to get a “single source of truth.” Teams may struggle with duplicate or conflicting data and waste time reconciling reports. To overcome this: integrate data from silos and standardize it. For example, adopting a master data management approach or a central data repository can ensure everyone works off consistent information. Many firms set up a single source of truth backed by governance policies to eliminate silos and data inconsistencies.
- Lack of Data Visibility and Access: Disorganized data leads to poor visibility. Employees often can’t find or access the data they need in a timely way, especially if data is spread across departments. This hampers agile decision-making. Solution: improve data discovery and documentation. Implementing a data catalog is one popular step to give a unified view of what data exists and where. A centralized catalog or inventory of data assets helps users quickly locate data and understand its definitions, which is vital for a mid-sized team’s efficiency.
- Resource Constraints and Skills Gaps: Mid-market companies may not have large data management teams. It’s a challenge to designate full-time data governance staff, so governance duties might be on top of someone’s day job. Likewise, employees might not be trained in data management best practices, leading to errors or “shadow” processes. Solution: focus on data literacy and training to build a data-centric culture. Educate staff on data policies and tools – for instance, hold workshops so business users learn how to access and use governed data properly. Some mid-sized firms create a small cross-functional data governance committee or “data champions” group to spread awareness and stewardship responsibilities. Over time, as the governance program shows value, you can justify dedicating more resources or hiring data governance roles.
- Treating Governance as a One-Off Project: A big pitfall is viewing data governance as a “set it and forget it” project. Many mid-market organizations initially try to implement an entire governance framework at once and consider it done. This often leads to overwhelm and failure – data governance is never truly finished because new data and new business questions keep emerging. Solution: adopt an agile, ongoing approach. Start with high-priority areas (e.g. customer data definitions, basic quality checks) rather than tackling all data at once. Build momentum with quick wins, then expand the governance scope. Regularly revisit and update policies as the company grows or regulations change. By treating governance as a continuous process embedded in the company’s culture, mid-market firms avoid the “big bang” mistake and adapt more easily to new challenges.
- Lack of Executive Buy-In: Sometimes data governance isn’t seen as a priority compared to other initiatives, especially if ROI isn’t immediately obvious. This can lead to weak support and compliance with governance policies. Solution: tie governance efforts to tangible business outcomes and risk mitigation. For example, show how improving data quality can speed up sales reporting or how better access controls protect the company from costly breaches. Industry research shows that organizations with strong data governance are more likely to outperform – McKinsey found “breakaway” companies are twice as likely to have robust governance practices driving their analytics success. Using such evidence can help win leadership support. It’s also wise to designate an executive sponsor for the data governance program to champion its importance.
By recognizing these challenges and proactively addressing them, mid-market companies can greatly increase their odds of data governance success. The overarching theme is to start practical and build iteratively, involve the right people (assign data owners and executives sponsors), and leverage tools that make governance tasks easier rather than relying on manual effort alone.