Podcast Summary: The Agile Brand with Greg Kihlström®

Episode #779: Denodo CMO Ravi Shankar on Why Good Data is Critical to AI Success
Date: December 4, 2025
Host: Greg Kihlström
Guest: Ravi Shankar, SVP & CMO, Denodo

Main Theme and Purpose

The episode explores why the quality and accessibility of data—not just the strength of AI models—determine the success of enterprise AI initiatives. Ravi Shankar, CMO at Denodo, discusses the often-overlooked pitfalls enterprises face at the data layer, the limitations of traditional data centralization strategies, and how a “logical data management” approach can accelerate AI deployment and customer experience (CX) innovation. The discussion delivers actionable insights for marketing, IT, and CX leaders aiming to make their organizations “AI-ready” and future-proof against ongoing disruption.

Key Topics & Discussion Points

1. Ravi Shankar’s Background and Denodo’s Role ([02:05]–[04:00])

• Ravi's extensive experience (30+ years) in data management, previously at Informatica (master data management) and Oracle.
• Denodo’s offering: Global provider of data integration and management, recognized by Gartner and Forrester.
• Key value prop: “A logical approach to data integration to gain a unified view…without having to physically move [data] all into a single repository.” ([03:32] Ravi Shankar)

2. The Strategic Data Disconnect ([04:00]–[07:22])

• Problem: Despite heavy investment in platforms like Snowflake and Databricks, organizations create “bigger silos” rather than achieving true centralization.
• Historical context:
  • 1980s: Central databases (IBM, Oracle) → proliferation of databases.
  • 1990s: Data warehouses (Teradata) for structured data.
  • 2000s: Data lakes for unstructured data (Cloudera, MapR).
  • 2010s: Lakehouses (Snowflake, Databricks).
• Paradox: “If there is only one platform…to store all the data, why is it that companies have Snowflake, Databricks, Oracle, Teradata… That’s the paradox.” – Ravi Shankar ([05:48])
• Root of the issue: IT departments create purpose-built repositories for each department/function, leading to persistent silos.

3. The Business Impact of Data Disconnect ([07:22]–[10:02])

• Where marketers feel pain:

  • Integrating campaign data from CRM, marketing automation, ABM, and web analytics is slow and prone to lag.
  • Example: By the time campaign performance data is integrated and analyzed, it’s already out-of-date (“out of sync with the source already”).

• Retail use case: Real-time promotional campaign analysis becomes impossible if dependent on centralized, delayed data loads—a missed business opportunity.

  “When you use this approach of moving the data to a central place… it’s not going to be ready for us to do that real-time campaign check-in, and ultimately both the retailer and the consumers also lose.” – Ravi Shankar ([09:37])

4. The Logical Data Layer Solution ([12:24]–[15:28])

• Definition: A “logical data layer” connects to live data sources wherever they are, offering a unified view without actual data movement (“connect rather than collect”).
• Analogy: Recording a remote podcast versus flying everyone to a single location ([12:53]).
• Difference from ETL: No need for time-consuming extraction, transformation, and loading. “With data virtualization… you leave the data wherever it is… and consumers can get the data they need without waiting for it to be loaded.” ([13:51])
• Flexibility: Addresses the reality that full consolidation is neither practical nor viable for enterprises.

5. Measurable Results: Speed and Cost ([15:28]–[18:45])

• White paper findings:

  • 10x acceleration in AI rollouts.
  • 75% reduction in integration time.
  • $3.6M savings, with ROI in under 7 months.

  “The logical data integration is like taking your cup to the source… much faster than bringing the entire pitcher to you.” – Ravi Shankar ([16:35])

• Business value:

  • Faster access to usable data enables quicker decision-making, increased revenue, and enhanced customer experience.
  • Campaign insights can be acted upon in real time, whereas slow data pipelines can result in lost opportunities.

6. Future-Proofing with Logical Data Management ([20:33]–[22:34])

• Data abstraction layer: Shields business users from technical complexity and allows IT to modernize infrastructure at its own pace.

  “The logical data management is that middle data abstraction layer. This is the only technology… that provides true data abstraction.” – Ravi Shankar ([21:45])

• Benefit: Enables organizations to keep up with the rapid rate of business while IT systems evolve, reducing risk from future disruptions.

7. Practical Steps for Marketing & CX Leaders ([22:34]–[24:37])

• First step: Start a conversation about data provisioning for AI—ensure it's fast, secure, and governed.

• Critical need: Flexibility to provision data for multiple teams rapidly without loss of governance or security.

  “With the logical data management, it makes this data available to the AI teams much more faster, so they can start realizing the productivity without having to be constrained…” – Ravi Shankar ([24:29])

Notable Quotes & Memorable Moments

• On the industry paradox:
  “If there is only one platform that you need as advertised… why is it that companies have Snowflake, Databricks, Oracle, Teradata… That’s the paradox.” – Ravi Shankar ([05:48])

• On speed and real-time needs:
  “By the time all this data is loaded and the analysis is done… the information that I get is not relevant anymore.” – Ravi Shankar ([09:05])

• On the logical approach:
  “We kind of say: it is better to connect to the data wherever it is—rather than collect the data into a central repository.” – Ravi Shankar ([13:30])

• On future-proofing:
  “This is the only technology that I’ve seen in my 30+ years… that provides true data abstraction.” – Ravi Shankar ([21:45])

• Looking ahead:
  “We will definitely be talking about the companies that are basically wasting money by not using the logical approach.” – Ravi Shankar ([24:51])

Timestamps for Key Segments

• [02:05] Ravi Shankar’s background and Denodo’s mission
• [04:00] The persistence of data silos, even with “modern” data platforms
• [07:44] Impact of data disconnects on marketing and CX operations
• [12:53] Explaining the logical data layer vs. traditional ETL
• [15:59] Measurable business results of logical integration
• [18:45] Translating faster data to business value
• [20:33] How logical data management future-proofs the enterprise
• [22:34] First steps for marketing/CX leaders to become AI-ready
• [24:51] What’s coming next: The tipping point for logical data management

Speaker’s Advice on Staying Agile

• Ravi Shankar:
  “I’m a news junkie. I read a lot… I have a long commute time; I turn on analyst reports… nowadays with AI it can take a text and read it out to me.” ([25:25])

Conclusion

This episode compellingly argues that optimizing the data layer—through logical data management—is mission-critical for any organization seeking real-time insights, rapid AI deployment, and sustainable marketing/CX advantage. Ravi Shankar’s practical analogies and hard data illuminate why “connecting” data, not just consolidating it, is the modern differentiator.

Recommended for: Marketing, CX, IT, data leaders preparing their organizations for the realities of enterprise AI, ongoing digital disruption, and the next evolution of customer experience.