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Ten Data Platform Themes from theCUBE Research 2024

The following is a working list from theCUBE Research on potential topics we’ll cover around data platforms in 2024. We welcome community input to these themes and will iterate throughout the year as warranted.

1. Customer Spending Patterns in Data Platforms

  • Spending momentum and market presence in data, analytics, ML/AI.
  • Customer perceptions on data and analytic priorities.
  • Data challenges with generative AI
  • Competitive positioning of leading and emerging data platforms
  • Ecosystem momentum

2. Building Intelligent Apps with Data

  • Customer strategies for combining transaction and analytic data.
  • Historical systems of truth leveraging data to make decisions and take action.
  • Data as the new “development kit.”
  • Using generative AI to develop applications that represent a digital version of an organization.
  • Architectures to enable facile data access to multiple data types

3. Beyond Data Warehousing: Next-Generation Architectures and the Sixth Data Platform

  • Investigate architectures that transcend traditional and cloud data warehousing, focusing on facile data integration, incorporating multiple data types and query options with governance and security.
  • Explore the evolution of data lakes, lakehouses, and mesh models as foundations for supporting diverse analytical and operational workloads.
  • Separating compute from data in open formats.
  • Enabling digital representations of organizations, tying together people, places and things across an ecosystem of employees, customers, suppliers and partners.

4. Real-Time Data Processing and Analytics

  • Examine the technologies and methodologies enabling real-time data streaming, ingestion, and analysis, emphasizing low-latency and high-throughput systems.
  • Assess the impact of emerging standards and protocols on real-time data interchange between platforms and applications.

5. Technological Advances in Distributed Computing: Scalable Joins and Computations

  • Dive into the latest distributed computing frameworks capable of performing massive, efficient joins and computations across geographically dispersed datasets.
  • Analyze case studies of successful implementations, focusing on the technical challenges and solutions in distributed data processing at scale.

6. Governance, Compliance, and Security in the Data Ecosystem

  • Develop comprehensive frameworks for data governance that ensure data quality, lineage, and compliance with global regulations.
  • Explore cutting-edge security models and encryption technologies that provide robust protection for data at rest and in transit, addressing both internal and external threats.

7. Hybrid Data Models: Integrating Structured and Unstructured Data

  • Investigate the design and implementation of hybrid data models that accommodate structured, semi-structured, and unstructured data, facilitating complex analyses.
  • Evaluate the role of NoSQL databases, graph databases, and time-series databases in supporting varied data types and structures.

8. The Role of Generative AI in Shaping Customer Data Strategies

  • Assess the implications of generative AI technologies on data collection, processing, and insight generation, with a focus on enhancing customer experiences.
  • Explore ethical considerations, privacy concerns, and the potential for bias, ensuring that AI-driven data strategies prioritize customer trust and transparency.

9. Privacy-Enhancing Technologies for Data Security

  • Delve into the development and application of privacy-enhancing technologies (PETs), such as differential privacy and secure multi-party computation, in safeguarding user data.
  • Examine the balance between data utility and privacy, evaluating how PETs can enable data analysis without compromising individual privacy.

10. Interoperability and Standardization Across Data Platforms

  • Open data formats and customer preferences.
  • Cost optimization for data engineering and pipeline activities.
  • Address the challenges and strategies for achieving interoperability among diverse data platforms, focusing on standardization efforts and open-source initiatives.
  • Analyze the role of APIs, data formats, and protocols in facilitating seamless data exchange and integration across ecosystems.

Image: Dmitry

Keep in Touch

Thanks to Alex Myerson and Ken Shifman on production, podcasts and media workflows for Breaking Analysis. Special thanks to Kristen Martin and Cheryl Knight who help us keep our community informed and get the word out. And to Rob Hof, our EiC at SiliconANGLE.

Remember we publish each week on theCUBE Research and SiliconANGLE. These episodes are all available as podcasts wherever you listen.

Email | DM @dvellante on Twitter | Comment on our LinkedIn posts.

Also, check out this ETR Tutorial we created, which explains the spending methodology in more detail.

Note: ETR is a separate company from theCUBE Research and SiliconANGLE. If you would like to cite or republish any of the company’s data, or inquire about its services, please contact ETR at or

All statements made regarding companies or securities are strictly beliefs, points of view and opinions held by SiliconANGLE Media, Enterprise Technology Research, other guests on theCUBE and guest writers. Such statements are not recommendations by these individuals to buy, sell or hold any security. The content presented does not constitute investment advice and should not be used as the basis for any investment decision. You and only you are responsible for your investment decisions.

Disclosure: Many of the companies cited in Breaking Analysis are sponsors of theCUBE and/or clients of theCUBE Research. None of these firms or other companies have any editorial control over or advanced viewing of what’s published in Breaking Analysis.

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