Technology and data strategy
A reliable AI outcome starts with the right platform and data. This unit explains how to align technology to strategy, prepare your data estate, and choose build versus buy so you can move from proof‑of‑concept to production.
Align tech strategy to business goals
Your technology choices should directly enable your strategic priorities. This means defining target architectures that support scalability, security, and performance; selecting deployment models that match your risk and cost profile; and standardizing on landing zones and governance patterns so teams can onboard and iterate quickly.
- Define target architectures that support scalability, security, and performance.
- Choose a deployment model (cloud, on-premises, colocation, or hybrid) that fits your risk and cost profile.
- Standardize on landing zones and governance patterns to accelerate onboarding.
Prepare your data estate
Reliable AI begins with reliable data. Treat your data estate as a strategic asset: break down silos to create unified views, improve quality through cleaning and enrichment, and operationalize data with pipelines, catalogs, lineage, and access controls so teams can trust and reuse data across use cases.
- Break down silos: create unified views across domains.
- Improve data quality: clean, deduplicate, and enrich.
- Create semantic models and dictionaries: make data understandable across teams.
- Operationalize data: pipelines, catalogs, lineage, and access controls.
Tip
Start with the top 3 data domains that unlock multiple use cases, such as customer, product, and supply chain.
Build versus buy
Deciding whether to buy a prebuilt AI capability or build a custom solution depends on your goals, timeline, and risk profile. The following table provides some considerations about when to buy and when to build:
| Buy | Build |
|---|---|
| Need speed to value; standard capability, such as search, classification, or Retrieval-Augmented Generation (RAG) | Unique IP or highly specialized domain |
| Limited in‑house ML expertise | Long‑term differentiation requires custom models |
| Cost of customization outweighs benefits | Data is proprietary and sensitive; compliance needs are complex |
Plan for compliance and security
Build compliance and security into AI from the start. Aligning to relevant regulations, protecting data, and using strong cloud and on-premises controls reduces risk and enables confident, scalable adoption.
- Plan for compliance and security and choose deployment models that fit your needs.
- Ensure data privacy, residency, and encryption.
- Use cloud security services for identity, access, and monitoring; keep on-premises for data sovereignty as needed.
Keep data readiness continuous
Trustworthy AI depends on ongoing data hygiene. Treat data preparation as a continuous cycle—label, monitor, and update—so models stay reliable as conditions change.
- Label and annotate datasets; create feedback loops from production to training.
- Monitor data drift and retrain models with updated data to maintain reliability.
A strong technology and data strategy delivers trustworthy AI outputs, supports long‑term innovation, and positions you to scale responsibly. With data and platform ready, the next step is to gain experience—run pilots, learn fast, and scale with discipline.