AI might be making all the headlines, but savvy data leaders haven’t forgotten the foundations, including data security and governance.
That’s the finding from the State of Data Security Report, which shows that 35% of data professionals say their organization will implement stronger governance and security controls as the major initiative in 2024.
We recently spoke to James Courtney-Smith, Business Data Architect at Limoss, on our Behind the Data podcast. He gave some fantastic insights into the challenges facing data governance in an era of rapid change and what strategies can be employed to ensure effective data governance in the near future.
"I am a devout follower of data governance,” James says. “And I'm concerned that we are losing sight of its core pillars—security, stewardship, ownership, and data quality. These principles were dominant 5-10 years ago, but are they still upheld today?”
The four pillars of data governance
Security
Data security is perhaps the most important part of data governance. James emphasizes the need to focus on data security beyond just hardening database systems. "Are we mindful of the security around data during transit and rest?" he asks.
Stewardship
Effective data stewardship involves assigning responsibility for data quality and compliance to specific individuals. This ensures that data is managed correctly throughout its lifecycle. According to James, "Having a designated data steward helps maintain the integrity and quality of data.”
Ownership
Clear data ownership policies ensure accountability and clarity. Without clear ownership, data management can become chaotic, leading to inefficiencies and errors. The Data Governance Institute provides some questions to ask yourself when assigning data responsibilities to your team.
Data quality
Data quality directly impacts decision-making and business processes. High-quality data leads to better insights and outcomes, while poor data quality can cause severe issues downstream. Ensuring strong data quality practices are in place is key. As James says in our latest interview, "If the data is not of good quality, it’s going to be a wasted effort.”
The big challenges facing data governance in 2024
The most formidable challenge for data governance is the pace of change in emerging technologies. The integration of AI and ML in data governance, including the Dev Ops-inspired ‘shift left’ approach to data accountability, means businesses must rush to adapt their governance strategies to stay competitive. However, James reminds us to take care of foundational data governance practices first, before chasing the shiny object. He says:
"There’s a lot of information about AI, but does it truly meet our expectations? While it's an exciting field, we must remember it's still an emerging technology, similar to big data in the past. Are we using it to its full potential and understanding it correctly? We need more than just plugging data in to get meaningful insights. Are our business leaders aware of this necessity?”
Before we begin to fulfil AI's promise, we must first tackle something more mundane (but infinitely more important): stakeholder management. Aligning different interests and ensuring everyone understands the importance of data governance can be challenging. For this, James suggests three strategies.
Strategies for effective data governance in the age of AI
Step 1) Educate your organization
“There needs to be a process of education,” James says. Whether key stakeholders or employees, everybody needs to understand their roles and responsibilities and the end purpose of the data to ensure the best result possible. Creating a culture of accountability can help. When everybody owns something, some small part of the process or end result, the mission can assume more meaning and effort.
Step 2) Build an adaptable data governance framework to reflect changes
Creating a comprehensive data governance framework involves defining policies, procedures, and roles for your team. While providing best practices for building a framework goes beyond the scope of this article, McKinsey’s article Designing data governance that delivers value gives concrete steps to do so.
Step 3) Leverage technology to make your job easier
While it’s a fine line between using outside help and being dependent on third parties, strategically leveraging technology partners can help when implementing your improved data governance framework. These tools can automate many aspects of data governance, such as data cataloguing, metadata management, and policy enforcement. They also enable better data lineage tracking and impact analysis, which help maintain data quality and compliance.
Begin your journey to better, cleaner data
Data governance and security is a top priority for data leaders worldwide in 2024. For more information and expert tips, watch the full Behind the Data podcast with James Courtney-Smith.
For all things CloverDX, and to schedule a demo, get in touch with the team.
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