#012 - The 7 Paths to Data Modernization: which is right for you?

Hey there,

Choosing the wrong data modernization approach is the #1 reason why 70% of these projects fail.

Today, I'll walk you through the complete spectrum of data modernization options:

• 7 proven approaches ranked by risk level and implementation complexity
• Clear indicators for when each approach is most appropriate
• Real-world examples from financial services, retail, and government sectors

Let's break down your options...

7 Data Modernization Paths To Transform Your Infrastructure With Minimal Risk Even if You Have Legacy Constraints

To select the right modernization approach, you must understand the full spectrum of options and how they balance transformation potential against implementation risk.

Let me guide you through each option, from lowest to highest risk:

1. Encapsulate

What it is: Leverage and extend the application's features by encapsulating its data and functions and making them available as services via an API.

Best for: Systems that function well but must connect with modern applications or provide self-service access.

Real-world example: Implementing a GraphQL API layer over your existing Oracle database to expose customer data to mobile applications without modifying the underlying system.

Risk level: ?☆☆☆☆ (Lowest) Typical timeline: 2-4 months Success factors:

• The core system is stable and performs adequately
• The main issue is connectivity, not functionality
• Need quick wins with minimal disruption

2. Rehost

What it is:?Redeploy the application component to other infrastructure (physical, virtual, or cloud) without modifying its code, features, or functions.

Best for: Organizations looking for quick cost savings, better performance, or preparing for end-of-life hardware/software.

Real-world example: Migrating an on-premises SQL Server data warehouse to SQL Server on Azure VMs or AWS EC2 instances using backup/restore.

Risk level: ??☆☆☆ Typical timeline: 3-6 months Success factors:

• The primary issues are infrastructure-related
• Current functionality meets business needs
• Need to demonstrate quick ROI

3. Replatform

What it is:?Migrate to a new runtime platform, making minimal changes to the code but not the code structure, features, or functions.

Best for: Organizations looking to reduce technical debt or adopt cloud-native benefits without reinventing their data architecture.

Real-world example: Converting an Oracle data warehouse to Snowflake using automated schema conversion tools while maintaining the same data model.

Risk level: ??☆☆☆ Typical timeline: 4-8 months Success factors:

• Current data models work well, but technology is limiting
• Looking for improved TCO without business disruption
• Data structure is sound, but platform capabilities are inadequate

4. Refactor

What it is: Restructure and optimize the existing code (although not its external behavior) to remove technical debt and improve nonfunctional attributes.

Best for: Organizations with sound data architecture but inefficient implementations that cause performance or maintenance issues.

Real-world example: Converting batch-oriented overnight ETL to near-real-time data streams using Kafka and Spark Structured Streaming.

Risk level: ???☆☆ Typical timeline: 6-12 months Success factors:

• Data architecture is fundamentally sound
• Performance or maintainability is the primary concern
• Technical debt needs addressing without disrupting interfaces

5. Rearchitect

What it is: Materially alter the code to shift it to a new application architecture and exploit new and better capabilities.

Best for: Organizations whose current architecture cannot meet evolving business needs or scale requirements.

Real-world example: Shifting from ETL to ELT architecture using a data lake (Azure Data Lake, AWS S3) with Databricks processing OR Transforming a monolithic Teradata EDW into a domain-oriented architecture with separate analytical stores in Snowflake for finance, customer, and product data.

Risk level: ????☆ Typical timeline: 12-18 months Success factors:

• Current architecture is limiting business capabilities
• Need improved flexibility and domain-specific optimization
• Willing to invest in significant structural changes

6. Rebuild

What it is: Redesign or rewrite the application component from scratch while preserving its scope and specifications.

Best for: Organizations with clear requirements but systems that have become unmaintainable or impossible to extend.

Real-world example: Reimplementing an Oracle-based customer analytics platform as a cloud-native solution using BigQuery with dbt transformations.

Risk level: ????? Typical timeline: 12-24 months Success factors:

• The current implementation is fundamentally flawed
• Business functions remain relevant, but technology doesn't
• Strong requirements management capabilities exist

7. Replace

What it is: Eliminate the former application component altogether and replace it, considering new requirements and needs at the same time.

Best for: Organizations whose business needs have fundamentally changed or when legacy systems cannot be adapted to new requirements.

Real-world example: Sunsetting a legacy data mart architecture in favor of a Databricks lakehouse platform with new data models and analytical capabilities.

Risk level: ????? (Highest) Typical timeline: 18-36 months Success factors:

• Business needs have fundamentally evolved
• Current system constrains rather than enables the business
• Organization can handle comprehensive change management

That's it.

Here's what you learned today:

• The 7 modernization paths range from low-risk encapsulation to high-risk replacement, each with appropriate use cases
• Lower-risk approaches can deliver quick wins with minimal disruption for specific pain points
• Higher-risk approaches enable transformation but require greater investment in time and change management
• The best modernization strategies often combine multiple approaches for different components

Remember, successful modernization isn't about being the most technically ambitious; it's about matching the approach to your specific drivers and constraints. For many organizations, a mix of approaches across different components delivers the optimal balance of risk and reward.

PS...If you're enjoying The Data Modernisation Playbook, please consider referring this edition to a colleague who's wrestling with legacy data systems. They'll thank you for saving them from a costly modernization misstep.

That's it for this week. If you found this helpful, leave a comment to let me know ?

I am Khurram; I help businesses modernize legacy data systems for AI-ready, data-intelligent futures | Founder & Principal Consultant – BigDataDig

See you next Tuesday.