Worried about data governance policies stifling innovation?

Design data governance policies that are flexible and can adapt to changing business needs. Regularly review and update policies to ensure they remain relevant and do not become overly restrictive. Create a safe environment where employees feel encouraged to experiment and innovate without fear of violating data governance policies. Use technology to automate compliance checks

Proper MDM is anything but a hinderance. Without an internal-facing stable taxonomy and catalog with clear indicators for target pull sources per data type (i.e. data lake, warehouse, mart, etc + environment type), you’re inevitably left with an ugly patchwork of cobbled together performance and financial indicators built from a chaotic “best guess” ad-hoc approach to EDA. Modern data governance also REALLY needs to encompass a specific (and meaningful) emphasis on both data literacy and stakeholder training with a locus of key internal personas who leverage data. In the modern world, this is especially critical given the vast proliferation of and requirement for simple EDA at almost every level of production…

Data policies being set at the enterprise level are for the employees of the organisation to do the right things around management and governance of the data. It is also to comply with the regulations so that the desired outcomes are achieved aligning with the firms objectives. Enforcement of the policies, standards and procedures should in no way stifle innovation. At the same time, any new innovation should consider the various data risks like data breaches and cybersecurity risks and the workaround for such scenarios should be clearly outlined in the policies set at the organisation level or at an individual business level. Periodic updates of the policies, standards and procedures should be undertaken on a set frequency.

The key with data governance not stifling innovation is balance. To find that balance, you need as much information as possible. What types of data do you have? What are the laws and rules governing the data, and what are your organization's specific policies? And then focus on risk-based approaches, tailoring governance intensity to data sensitivity. Avoid any unnecessary governance. If you identify an area where innovation is or might be hindered, propose targeted solutions. If possible, use sandbox environments where ideas can be tested with relaxed rules. This freedom allows for greater creativity without compromising sensitive data. Good data governance should enable responsible innovation, not prevent it.

AI excels at routine, repetitive tasks, making it ideal for handling many aspects of data governance. If we make use of AI for compliance monitoring, data classification, and access control, we can automate time-consuming processes. This frees up human resources to focus on creative problem-solving and innovation. As AI continuously monitors data, flags potential policy violations, and generates compliance reports, we can spend more time exploring data, identifying patterns, and developing innovative solutions. By assigning governance tasks to AI, we create space for our creativity to flourish, potentially leading to breakthrough innovations while maintaining critical data protection. This is AI as its best.