When my kids first asked the dreaded question every parent faces—“can I have a phone?”— I knew I would need to set some rules to keep them safe. Powerful technology can open the door to a marvelous world – and also a risky, less-than-secure underworld.
Similarly, more and more companies are adopting AI and data analytics to win a competitive edge. While data strategy, analytics, and monetization are closely connected, data governance is the necessary foundation for succeeding in each.
The concept of data governance is not unlike setting parental controls on a teenager’s smartphone. When I begrudgingly introduced smartphones to my children in middle school (years after the first request), I was careful to set boundaries and controls to limit their access. I knew the risks of exposing kids to social media and inappropriate content too soon, but I also recognized the value of connectivity.
So how do you implement solid controls that both minimize risk and maximize reward?
Data Monetization Begins with Data Governance
Revenue can be generated from data in a variety of ways, from the sale of raw or processed data to using data insights to identify and provide value-added services and custom solutions for customers. And the “better” the data, the more valuable it is.
Without consistent, reliable data, efforts to monetize it internally or externally can be hindered by inaccuracies, resulting in misguided insights, missed opportunities, and ultimately, lack of trust in its value. A client once asked me a question that is a common problem for companies with a matrix of systems established over time:
“We have all this data, but it’s inconsistent across data sources. How do we ensure that we’re using the most accurate version?”
A solid data governance plan is the answer. It creates the framework for properly managing data, ensuring high quality, integrity, appropriate access, and regulatory compliance.
It also can feel like a herculean undertaking. But by keeping a few key points in mind, you can begin to build a data governance plan that supports your business goals now and in the future.
1. Don’t Skip the Basics
When diving into data monetization and AI-driven insights, the tendency is to focus on advanced analytics. But skipping foundational aspects of data governance can prevent the chance to deliver real value. Establishing core governance standards before delving into more sophisticated data strategies will ensure that your organization maximizes the value of its data from the beginning.
Implementing data governance starts with understanding your data.
This means identifying key data sources, ensuring the accuracy and consistency of data, and assigning clear data stewardship and ownership protocols. Without strong data management and governance measures, data errors can undermine the trustworthiness of insights, compromise decision-making, and reduce benefits gained from AI capabilities, such as data monetization. By defining data quality metrics, creating a comprehensive data catalog, and ensuring that stakeholders know who is responsible for data at every stage of its lifecycle, you can create a sustainable and scalable data governance framework.
2. Regulatory Compliance as a Driver
Understandably, many companies’ first forays into building a framework are driven by regulatory compliance. Banks and other financial institutions, for example, have seen an increase in regulatory scrutiny following the regional bank failures of 2023. This increased oversight, along with upcoming open banking regulations concerning data sharing, privacy, and consumer consent, is pushing banks to strengthen their data governance frameworks to enable the secure and accurate sharing of customer information with third-party providers.
Data governance practices help by setting cohesive policies and procedures to manage and safeguard information, preventing regulatory infractions and reputational damage from data security breaches or non-compliance.
Data governance frameworks built for the sole purpose of compliance, however, have a tendency to be so rigid and restrictive that they are not adaptable to other types of data business needs. They are typically reactive, with very prescriptive requirements and a high degree of focus on standardization and adhering to specific laws and regulations. With these limitations in mind, organizations can evolve governance frameworks to delineate between various types of data to balance compliance with adaptability and enable application to a broader set of business objectives.
Data governance is not just about regulatory compliance. It’s about preparing an organization to use data effectively. With the rise of AI, mature data governance practices are integral to advancing the potential of data-driven insights.
3. Balancing Accessibility with Security
Data governance must strike the right balance between making data readily accessible to those who need it and ensuring that sensitive information is secure.
Just like setting controls on a teen smartphone to limit access to specific applications, businesses need to control who has access to sensitive data. In a bank setting, for example, loan officers need access to sensitive customer financial data, while marketing teams should really only see general trends. Using role-based access controls and monitoring data usage helps ensure that teams can use data to extract valuable insights without putting the company risk. This approach supports both regulatory compliance and building trust with customers and partners, ultimately increasing the potential for data monetization.
Data can be very powerful – in good ways and in bad. Similar to how smartphone controls can help ensure both safety and connectivity, implementing a well-structured data governance framework creates the foundation for safe and effective data use.
The solution lies in building a framework that is adaptable, supports business objectives, and prioritizes both accuracy and security, empowering businesses to unlock the full potential of their data.
Further Advisory helps companies strategize, plan, and deliver their most complex data challenges. From governance to analytics to A.I, we make strategy become a reality.
