Essential Guidelines when Selecting a Suitable Data Infrastructure for Your Business (4 Key Points)
Creating an initial data stack for a company is a crucial task that requires careful planning and a structured approach. Data engineers should follow key principles to ensure scalability, agility, and governance in the data stack.
Ground Rules for Building an Initial Data Stack
- Modular and Vendor-Agnostic: Keep the data stack modular and vendor-agnostic to avoid lock-in and allow flexibility as new technologies emerge.
- Promote Data Literacy: Emphasize training and education to ensure tools are used effectively across teams.
- Scalability, Integration, and Skill Alignment: Choose tools that are scalable, easy to integrate, and align with the skills of your team.
- Automation and Orchestration: Implement automation and orchestration to reduce manual effort and errors.
- Strong Data Governance: Adopt a strong data governance framework covering quality, security, and compliance.
- Hybrid Team Structure: Adopt a hybrid team structure where infrastructure and standards are centralized, but analysts are embedded in business units for responsiveness.
- Treat the Data Stack as a Living System: Plan for continuous assessment, tuning, and optimization based on usage, performance, and business needs.
Key Steps for Building the Initial Data Stack
- Assess Current State and Data Landscape: Identify pain points and perform a comprehensive audit of data sources, current flows, quality, and infrastructure capabilities.
- Define Clear Requirements and Goals: Set measurable objectives such as faster reporting, cost reduction, improved governance, and better data quality. Align KPIs with overall business goals and stakeholder needs.
- Select the Right Toolset: Choose scalable, mature tools for ingestion, storage, transformation, and BI. Limit the number of overlapping or niche tools and support both batch and real-time data ingestion.
- Build a Minimum Viable Product (MVP) Pipeline: Create a basic end-to-end pipeline and test it with real business queries to validate assumptions and gather feedback.
- Implement Automation and Orchestration: Integrate CI/CD pipelines, infrastructure as code, and workflow orchestration tools to automate schema detection/evolution and consent tracking where relevant.
- Establish Data Modeling and Transformation Best Practices: Use adaptable data modeling techniques and employ version control, testing, and collaborative development for pipeline code and transformations.
- Institute Ongoing Monitoring and Optimization: Track performance, data quality, costs, and user satisfaction via metrics and alerts, and regularly refine ingestion, transformations, indexing, access policies, and storage strategies.
- Define Data Governance Framework: Implement policies and automated enforcement for data quality, security, and compliance across the stack. Balance central standards with domain-specific autonomy to enable speed and control.
- Scale Team and Stack Architecture: Adopt a hybrid team structure to maintain consistency while staying responsive. Build a scalable tech stack that reduces friction, automates routine work, and democratizes access to clean data and insights.
In addition to following these steps, it is important to consider the following factors:
- Consider Existing Vendors: Consider existing vendors your company has contracts with for potential benefits in process speed and better pricing.
- Create a Comparison Table: Create a comparison table for final candidates, including a rough estimate for the cost of each one.
- Recruitment Considerations: Recruiting is easier with technologies used by market leaders or mainstream ones.
- Request Demos and POCs: Consider asking for a demo account or POC for each component.
- Budgeting and Market Research: Set a budget for the data platform and identify dependencies such as DevOps, IT, and Legal. Identify market leaders by learning from similar organizations and understanding which products they chose and why. Market leaders are usually the more expensive option, but looking at similar companies can help find ones that fit your budget.
- Avoid Over-engineering: Don't over-engineer the data stack and avoid getting into endless decision cycles. Create a list of required features for each component and prioritize them.
- Prepare for Evolution: Remember that the data stack is a baseline and will need to be redesigned as the company evolves. Avoid vendor lock-in by saving data in open-sourced formats and your cloud storage.
By following these guidelines and steps, you can create an initial data stack layout that balances operational stability with business agility, facilitating continuous growth and value delivery as the company’s data maturity evolves. It is also essential to interview potential consumers within the organization to understand their needs and expectations from the data platform.
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