Q&A From Expert Panel:
Benefits of Data Democratization

How to Promote Data Sharing and Governance

In our recent webinar, experts from the data industry gathered to discuss the critical importance of democratizing data access. We summarized the discussion in a comprehensive Q&A.
Moderated by Neda Talyai, the panel included Tiankai Feng (ThoughtWorks), Christian Schneider (Dataciders), and Dr. Andreas Böhm (One Data).

This article captures key insights and takeaways from their discussion. You will get perspectives on how to effectively manage and leverage data governance to foster innovation, efficiency, and data driven decisions.

Key findings from the discussion

Data democratization

  • Enhances decision-making and innovation
  • Requires a cultural shift and leadership support
  • Involves stakeholder engagement and storytelling

Data governance

  • Continuous monitoring and auditing to prevent unauthorized access
  • Uses automation for routine tasks to focus on strategic initiatives
  • Agile implementation to show quick value and gain buy-in

Data products

  • Structured data assets meeting specific business needs
  • Ensures quality, compliance, and security
  • Integrates with governance for lifecycle management and business alignment

AI and data governance

  • AI automates tedious governance tasks, enhancing efficiency and accuracy
  • Governance for AI ensures high-quality, unbiased training data
  • Monitor AI models for fairness and ethical considerations

Challenges and solutions

  • Promote open data sharing with security and privacy safeguards
  • Role-based access controls and privacy-by-design principles
  • Data quality and transparency build trust

Your data experts

Christian Schneider Quinscape Dataciders

Christian Schneider—I’ve been in the data business for 20 years, focusing on data integration and strategic data governance. Currently, I’m a Data and Analytics Evangelist at Dataciders and CEO of QuinScape, working on making data more accessible and valuable within organizations.

Tiankai Feng Thoughtworks

Tiankai Feng—I’m the Head of Data Strategy and Governance Services at ThoughtWorks Europe. Previously, I led product data governance at Adidas. My experience spans across various roles including data product ownership and data analytics. This gives me a holistic view on data management and consumer needs.

Andreas Böhm One Data

Andreas Böhm—I founded One Data 11 years ago with a mission to make data impactful for businesses. My journey in data started at 23. Since then, I focus on creating data-driven solutions to drive business value.

Insights on democratizing data access

According to Gartner®, data sharing can significantly enhance the effectiveness of data and analytics teams. Why do you think this is, and how does data democratization play into it?

Andreas: Organizations need to view data as a strategic asset rather than a cost center. When data is shared openly, it becomes a catalyst for business optimization and innovation. You must empower data and integrate them with business units. Then, they can leverage data effectively, fostering a culture where data is seen as a profit center.

Gartner® also suggests shifting from limiting data access to promoting data sharing by default. How can data governance strategies support this while ensuring security and privacy?

Christian: This shift requires a robust and agile data governance strategy. Implementing role-based access controls, segmenting data, and embedding privacy considerations throughout the data lifecycle are crucial steps. Continuous monitoring and auditing of data access also help in maintaining security and trust.

Tiankai: Data governance needs a rebranding. Historically seen as restrictive, you should view it as an enabler of data usability. Communicating its value through storytelling and showing tangible benefits can help change this perception.

How can one enable open data sharing considering data privacy concerns?

Andreas: It’s about balancing tool-based enforcement and process flexibility. Start small by creating data products that adhere to governance policies. Test and iterate to find what works best for your organization. Gradually expand as you prove value and gain buy-in.

Tiankai: Focus on the business use cases. Not all data needs to be granular and personal; often, aggregated or anonymized data suffices. This reduces privacy risks while still providing valuable insights.

How do data products intersect with governance?

Christian: Data products encapsulate data to meet specific business needs, ensuring quality, compliance, and security. Governance structures provide the framework for creating and maintaining these products, from design through to deployment and maintenance.

How can data products enhance data democratization?

Andreas: By promoting existing data assets as data products, organizations can showcase their value and foster a culture of data sharing. This visibility helps in identifying and leveraging valuable data assets more effectively.

What role does AI play in data governance?

Christian: AI can automate many repetitive tasks in data governance, such as data classification, anomaly detection, and data quality monitoring. This frees up time for data professionals to focus on strategic initiatives.

Tiankai: Additionally, AI governance is crucial. We need to ensure AI models are trained on high-quality, unbiased data. They must be monitored for fairness and ethical considerations. This requires integrating AI efforts with robust data governance frameworks.

What are your thoughts on marrying qualitative outcomes to numbers for demonstrating ROI?

Tiankai: I usually apply three principles. First, apply data governance situationally and with context. Focus on the most critical data first and expand gradually.
Second, move from a push to a pull mechanism. Automate routine tasks and focus human effort on strategic decision-making.
Third, use automation extensively. While not everything can be automated, leveraging technology can significantly reduce the manual burden and improve efficiency.

What are some best practices to achieve a kind of ‘data governance rebranding’ in modern organizations?

Tiankai: Align your data governance initiatives with three key value drivers: increasing revenue, reducing costs, and avoiding risks. For example, better data quality can lead to products being easier to find on e-commerce platforms. In the end, you’re increasing sales. Work closely with business counterparts to quantify these benefits.

Christian: I agree. Engage stakeholders early and often, and ensure they understand the value of data governance. Transparency and effective communication are key to building trust and gaining support.

Andreas: Also, start small and demonstrate quick wins. This helps in building momentum and proving the value of data governance initiatives incrementally.

Watch the whole discussion on demand →

We thank the participants for sharing their practical insights. This discussion highlights the business impact of data democratization, robust governance, and the strategic use of AI.

To deepen your understanding on data democratization and governance, we recommend reading the related whitepaper from One Data, ThoughtWorks, and Dataciders →

Feel free to reach out to Christian, Tiankai, and Andreas on LinkedIn for any follow-up questions. They are more than happy to help you with your data strategy!

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