Modern Data Platform RFP

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Below are some key solution options, criteria and assessment ideas to consider. This is a general outline that can then be customised to the individual organisation technology landscape and business priorities.

Key Modern Data Platform Options

There are some great options out there for modern data platforms. When scanning the market, below are key options that should be considered as these platforms are known for their reliability, scalability, and comprehensive service offerings. These are outlined below in no particular order.

  • Google BigQuery
    • Fully managed, cost-effective, powerful analytics capabilities, and seamless integration with other Google services. Strong integration with Marketing Tech.
  • Amazon Redshift
    • Scalable, high performance, integrates well with other AWS services, and supports complex queries.
  • Snowflake (on AWS, Azure or GCP)
    • Fully managed, supports multi-cloud, real-time data sharing, and strong performance for both structured and semi-structured data.
  • Microsoft Azure Synapse Analytics
    • Unified analytics platform, integrates with other Azure services, and supports big data and data warehousing.
  • IBM Db2 Warehouse on Cloud
    • High performance, AI-powered insights, and strong security features.
  • Oracle Autonomous Data Warehouse
    • Key Advantages: Self-driving, self-securing, self-repairing, and optimized for high performance and scalability.
  • Teradata Vantage on Google Cloud
    • Advanced analytics, scalable, and integrates well with Google Cloud services.
  • SAP Data Warehouse Cloud
    • Combines data management and analytics, integrates with SAP applications, and offers strong data governance. Normally only used with SAP S/4.

Selecting a Modern Data Platform

Every business will have different criteria to align with their business and technology objectives. Below is a potential starting point to refine further.

Creating a Request For Proposal (RFP) for assessing a modern cloud data warehouse involves defining clear, objective criteria to evaluate potential vendors. Here’s a detailed list of assessment criteria along with suggested weighting percentages:

1. Technical Capabilities (20%)

  • Scalability: Ability to scale storage and compute resources independently.
  • Performance: Query performance, data loading speed, and concurrency handling.
  • Data Integration: Support for various data sources, ETL/ELT processes, and real-time data ingestion.
  • Data Types: Support for structured, semi-structured, and unstructured data.
  • Advanced Analytics: Built-in support for machine learning, AI, and advanced analytics.

2. Security and Compliance (15%)

  • Data Encryption: At-rest and in-transit encryption capabilities.
  • Access Controls: Role-based access control, multi-factor authentication, and user management.
  • Compliance: Adherence to industry standards and regulations (e.g., GDPR, HIPAA).
  • Audit Logging: Comprehensive logging and monitoring of data access and changes.

3. Cost and Pricing Model (25%)

  • Pricing Transparency: Clear and predictable pricing model.
  • Cost Efficiency: Competitive pricing for storage, compute, and data transfer.
  • Billing Flexibility: Options for pay-as-you-go, reserved instances, and volume discounts.

4. Ease of Use and Management (15%)

  • User Interface: Intuitive and user-friendly interface for both technical and non-technical users.
  • Automation: Automated maintenance, scaling, and optimisation features.
  • Monitoring and Alerts: Real-time monitoring, alerting, and reporting capabilities.

5. Integration and Ecosystem (10%)

  • Third-Party Integrations: Compatibility with popular BI tools, data integration platforms, and other third-party applications.
  • APIs and SDKs: Availability of robust APIs and SDKs for custom integrations.
  • Ecosystem Support: Availability of a strong partner ecosystem and community support.

6. Vendor Support and Services (10%)

  • Customer Support: Availability and quality of customer support, including SLAs.
  • Professional Services: Availability of consulting, implementation, and training services.
  • Documentation and Resources: Quality and comprehensiveness of documentation, tutorials, and other resources.

7. Innovation and Roadmap (5%)

  • Product Roadmap: Vendor’s commitment to innovation and future enhancements.
  • Customer Feedback: Responsiveness to customer feedback and feature requests.

Weighting Percentages

  • Technical Capabilities: 20%
  • Security and Compliance: 15%
  • Cost and Pricing Model: 25%
  • Ease of Use and Management: 15%
  • Integration and Ecosystem: 10%
  • Vendor Support and Services: 10%
  • Innovation and Roadmap: 5%

These criteria and weightings can help ensure a comprehensive and balanced evaluation of cloud data warehouse solutions. Given the changes with cloud data platforms, we recommend that the Cost & Pricing Model is the highest weighting area with the long-term impacts of the solution.

Potential assessments or tests

Here’s a list of assessment or objective tests that can be run for a modern data platform, aligned with the specified weightings for an RFP response (example above).

1. Technical Capabilities (20%)

  • Scalability Test: Evaluate the platform’s ability to scale storage and compute resources independently under varying loads.
  • Performance Benchmarking: Run complex queries and data loading tasks to measure response times and throughput.
  • Data Integration Test: Assess the ease and efficiency of integrating various data sources, including real-time data ingestion.
  • Data Type Support: Test the platform’s ability to handle structured, semi-structured, and unstructured data.
  • Advanced Analytics: Evaluate built-in support for machine learning, AI, and advanced analytics capabilities.

2. Security and Compliance (15%)

  • Encryption Test: Verify the effectiveness of data encryption at rest and in transit.
  • Access Control Assessment: Test role-based access control, multi-factor authentication, and user management features.
  • Compliance Audit: Check adherence to industry standards and regulations (e.g., GDPR, HIPAA).
  • Audit Logging: Evaluate the comprehensiveness of logging and monitoring of data access and changes.

3. Cost and Pricing Model (25%)

  • Cost Simulation: Run simulations to estimate costs based on different usage scenarios (e.g., storage, compute, data transfer).
  • Pricing Transparency: Assess the clarity and predictability of the pricing model.
  • Billing Flexibility: Evaluate options for pay-as-you-go, reserved instances, and volume discounts.

4. Ease of Use and Management (15%)

  • User Interface Test: Assess the intuitiveness and user-friendliness of the platform’s interface for both technical and non-technical users.
  • Automation Features: Test automated maintenance, scaling, and optimisation capabilities.
  • Monitoring and Alerts: Evaluate real-time monitoring, alerting, and reporting features.

5. Integration and Ecosystem (10%)

  • Third-Party Integration Test: Assess compatibility with popular BI tools, data integration platforms, and other third-party applications.
  • API and SDK Evaluation: Test the availability and robustness of APIs and SDKs for custom integrations.
  • Ecosystem Support: Evaluate the strength of the partner ecosystem and community support.

6. Vendor Support and Services (10%)

  • Customer Support Test: Assess the availability and quality of customer support, including response times and SLAs.
  • Professional Services Evaluation: Evaluate the availability and quality of consulting, implementation, and training services.
  • Documentation Review: Assess the quality and comprehensiveness of documentation, tutorials, and other resources.

7. Innovation and Roadmap (5%)

  • Product Roadmap Review: Evaluate the vendor’s commitment to innovation and future enhancements.
  • Customer Feedback Analysis: Assess the vendor’s responsiveness to customer feedback and feature requests.

These tests and assessments will help ensure a comprehensive evaluation of potential cloud data platforms, aligning with the specified weightings for your RFP response.

Key risks to consider

Rather than going with a “happy days” approach, it’s always best to consider the key risks to make sure these are covered in any assessment and implementation plan.

Here are some key potential pitfalls to consider that may impact your above weightings:

1. Cost Overruns

  • Unexpected Costs: Cloud services can incur unexpected costs, especially with data transfer, storage, and compute resources.
  • Underestimating Usage: Misestimating the amount of data or the frequency of queries can lead to higher-than-expected bills.

2. Data Security and Compliance

  • Data Breaches: Storing sensitive data in the cloud can increase the risk of data breaches if not properly secured.
  • Compliance Issues: Ensuring compliance with regulations like GDPR, HIPAA, and others can be complex and costly.

3. Integration Challenges

  • Compatibility Issues: Integrating the cloud data warehouse with existing systems and applications can be challenging.
  • Data Migration: Moving large volumes of data to the cloud can be time-consuming and may require significant effort to ensure data integrity.

4. Performance Issues

  • Latency: Cloud data warehouses can experience latency issues, especially if the data is accessed from geographically distant locations.
  • Scalability Problems: While cloud platforms are designed to scale, improper configuration or unexpected usage patterns can lead to performance bottlenecks.

5. Vendor Lock-In

  • Dependence on a Single Vendor: Relying heavily on one cloud provider can make it difficult to switch vendors or adopt a multi-cloud strategy in the future.
  • Proprietary Technologies: Using vendor-specific technologies can limit flexibility and increase switching costs.

6. Complexity in Management

  • Learning Curve: Teams may face a steep learning curve when adopting new cloud technologies and tools.
  • Ongoing Maintenance: Continuous monitoring, optimisation, and maintenance are required to ensure the cloud data warehouse operates efficiently.

7. Data Governance

  • Data Quality: Ensuring data quality and consistency across different sources can be challenging.
  • Access Control: Managing access control and ensuring that only authorized users can access sensitive data is crucial.

8. Disaster Recovery

  • Backup and Recovery: Implementing robust backup and disaster recovery plans is essential to prevent data loss.
  • Downtime: Cloud services can experience outages, and having a plan to mitigate downtime is important.

9. Change Management

  • User Adoption: Ensuring that users are trained and comfortable with the new system is critical for successful adoption.
  • Process Changes: Adapting existing processes to work with the new cloud data warehouse can be challenging.

10. Hidden Dependencies

  • Third-Party Services: Relying on third-party services for certain functionalities can introduce additional risks and dependencies.
  • Interoperability: Ensuring that the cloud data warehouse works seamlessly with other tools and platforms is essential.

By being aware of these pitfalls and planning accordingly, organizations can better navigate the complexities of implementing a new cloud data warehouse and maximise its benefits earlier.

Wrap up

In today’s data-driven world, modern data platforms are essential for organisations aiming to harness the power of their data rapidly. By leveraging modern technologies and robust architectures, these platforms enable efficient data management, easy integration, and insightful and reliable analytics to be produced. As businesses continue to evolve, investing in a scalable and flexible data platform will be crucial for maintaining a competitive edge and driving innovation.