Summary
Data warehousing and governance go hand in hand, yet many companies struggle to integrate them effectively. In this episode, we break down how to align data governance with warehousing strategies, why metadata is key to AI readiness, and how to get started with minimal disruption. If you’re looking to improve data accessibility, security, and compliance while preparing for future AI-driven initiatives, this episode is a must-listen. Tune in now to learn the first steps toward building a scalable, governed data ecosystem!
Transcript
Jocelyn 00:00:
You’re listening to Infoverity’s Trust Us podcast, where you can gear up for your data management journey with bite-sized discussions on industry trends and thought leadership. On each episode, we feature industry experts to help you navigate your path to mastering your enterprise data.
Welcome to today’s episode of the podcast. Today, we’ll be talking about getting started with data governance and data warehousing. To guide us through this discussion, we’ve invited Tony Schafer, Director of AI and Governance at Infoverity. Tony recently joined us, and we’re thrilled to have him here today. Tony, thank you so much for joining us!
Tony Schafer 00:40:
No problem, Jocelyn. Thanks for the invitation.
Jocelyn 00:47:
Could you tell us a little about yourself and your background?
Tony Schafer 00:52:
Of course! I recently joined Infoverity from Databricks. I have extensive experience in data management, data warehousing, ETL, governance, and AI. I’ve worked across multiple industries, including manufacturing, healthcare, and finance. Additionally, I’ve been involved in sales processes across these sectors, giving me insights into both technical and business needs.
Jocelyn 01:25:
That’s an incredible breadth of experience, Tony! Thank you for joining us.
To kick things off, let’s define a few key terms. What is data warehousing, and what is data governance?
Tony Schafer 01:50:
Great question! Data warehousing is fundamentally about storing and managing data efficiently. It involves:
- Bringing in data from various sources
- Storing it securely in structured and unstructured formats
- Enabling access and analytics
- Ensuring data remains up to date
A more modern concept is the lakehouse, which integrates both structured and unstructured data for greater flexibility.
On the other hand, data governance is about ensuring that data is accessible, accurate, secure, and well-managed. Governance includes:
- People, process, and technology
- Metadata managementto track data usage
- Security measures to prevent unauthorized access
- Compliance strategies to meet regulatory requirements
Jocelyn 04:39:
That’s an excellent overview! Where should organizations start investing in data governance and data warehousing?
Tony Schafer 05:30:
Start investing when problems arise—typically when data volume, access needs, or inconsistencies grow too large to manage manually.
Signs that it’s time to invest:
- Multiple teams managing the same data differently
- Inefficiencies in data access and reporting
- Compliance and security concerns
- Scalability challenges
Jocelyn 07:56:
That makes sense. Once a company identifies the need, what are the first steps to take?
Tony Schafer 08:11:
Start with the business problem! Many organizations focus too much on technology first. Instead:
- Identify the core business challenge
- Assess data quality and accessibility
- Define governance policies
- Determine storage and infrastructure needs(On-prem, cloud, hybrid?)
- Implement monitoring and security protocols
Jocelyn 09:37:
That’s a great approach. Now, AI is a major industry focus—how does it tie into governance and warehousing?
Tony Schafer 10:00:
We’re in the early stages of AI adoption, similar to the early internet in the late ’90s. AI relies on high-quality data, making data governance and warehousing essential.
AI Success Factors:
- Metadata management for better AI insights
- Security & compliance to prevent misuse
- Clean, structured data to reduce AI development costs
Companies that invest in governance early will gain AI advantages faster and more cost-effectively.
Jocelyn 15:12:
So the key takeaway is that AI success depends on strong data foundations. What role does Infoverity play in helping clients?
Tony Schafer 16:12:
Infoverity acts as a non-biased advisor, helping companies:
- Assess current data environments
- Design governance and warehousing strategies
- Implement future-proof solutions
- Ensure AI-readiness
Unlike vendors pushing specific platforms, we prioritize the best solution for each client’s needs.
Jocelyn 18:00:
That’s key—ensuring long-term sustainability and scalability. Would you summarize today’s discussion as follows?
- Data warehousing stores and manages enterprise data for accessibility and security.
- Data governance ensures data is accurate, compliant, and used responsibly.
- Organizations should invest in governance when data complexity grows.
- AI success depends on structured, well-governed data.
- Infoverity provides expert, non-biased guidance for long-term data strategies.
Does that sound right?
Tony Schafer 21:02:
Absolutely! Companies need to start with their business problem and let technology follow.
Jocelyn 21:24:
Tony, thank you so much for joining us today! Your insights are invaluable, and we look forward to future discussions.
Tony Schafer 21:39:
Thanks for having me!
Jocelyn 21:42:
For over a decade, Infoverity has been a trusted leader in enterprise data management consulting, with experts worldwide and headquarters in Columbus, Ohio, and Valencia, Spain.
To learn more, visit infoverity.com. Additional contact details are in the show notes.
