What is a modern data platform?

6 minute read

A modern data platform connects, structures, governs and makes business data usable across analytics, automation and AI. It brings together data ingestion, storage, processing, metadata, governance, access control and serving layers so people, applications and AI-enabled workflows can work from trusted data.

For operational companies, the value is not only better reporting. The value is that ERP data, supply chain data, production data, customer data and planning signals become easier to trust, reuse and act on. That makes the platform a foundation for faster decisions, better data quality, automation and future AI agents.

Legacy data landscapes often create predictable problems: siloed data, brittle ETL, slow reporting, unclear ownership, high scaling costs and weak governance. A modern data platform should reduce those problems by giving the business a controlled way to turn raw data into reliable data products, decision support and operational workflows.

Key takeaways

A modern data platform is not only a technical stack. It is an operating foundation for trusted data, analytics, automation and AI-ready workflows.
Architecture should follow business workloads. Choose lakehouse, warehouse, data mesh, data fabric or hybrid patterns based on latency, ownership, governance and team capability.
Governance is not a later step. Metadata, lineage, access control, ownership and quality rules should be designed from the start.
ERP data matters. For companies running systems such as Infor CloudSuite M3, the platform must make operational data reliable enough for decisions, planning, automation and AI agents.
Start with one focused pilot. Pick one domain, one business question, one source system and one measurable outcome before scaling the platform.

What a modern data platform is and why it matters

A modern data platform captures, stores, processes and delivers reliable datasets that support both operational and analytical processes. The business value lies in clearer decisions and faster reactions – based on solid data foundations instead of fragile scripts and Excel files.

When organisations begin to see data as a product, planners and analysts get consistent data sources for daily decisions and models.

Older data landscapes often create clear problems that slow down the business. Link these problems to concrete platform capabilities so that stakeholders see how modernisation actually solves business challenges, for example by replacing slow ERP reports with streaming ingestion, a transactional lakehouse and a semantic layer.

Below are common problems and how a platform can solve them:

Data silos. Isolated sources make comparisons between teams difficult. A unified storage layer combined with a semantic layer provides consistent definitions and a common version of key metrics, reducing duplicate work and reporting chaos.
High costs when scaling. When storage and compute are tightly coupled, costs rise quickly as data grows. By separating storage and compute, you can scale them independently and control costs with cloud object storage and flexible query engines.
Fragile ETL. Manual pipelines break when upstream systems change. Event-driven ingestion and versioned transactional formats enable replaying data and recovery from errors, reducing downtime.
Delayed insights. Long batch cycles mean teams work with outdated data. Low-latency processing and streaming transformations provide near real-time KPIs and alerts so the business can act faster.
Lack of governance. Absence of metadata and lineage reduces trust in datasets. Catalogues and federated governance rules document ownership, lineage and access, supporting audits and compliance.

CIOs, data architects and IT managers within industry, retail, food & beverage and distribution should prioritise modernisation when handling dozens of systems or thousands of SKUs. Low-risk pilots such as domain-based reporting, a lakehouse proof of concept or an ERP-to-analytics connector can validate assumptions and reduce migration risk. Use the pilots to gather metrics that demonstrate business impact before scaling further.

Modern data platforms and AI-ready data

AI does not remove the need for structured, trusted data. It increases it.

For AI-enabled workflows to create business value, the data platform needs to make data understandable, traceable and usable. That means clear ownership, reliable pipelines, documented definitions, quality checks, access control and lineage. Without those foundations, AI initiatives often get stuck in pilots because teams cannot trust the data, explain the output or connect it safely to daily work.

An AI-ready data platform should support:

trusted source data from ERP, operational systems, documents and external sources
reusable data products with clear owners and definitions
governed access for people, applications and agents
lineage and quality controls so outputs can be explained
APIs or serving layers that make data usable in workflows, not only dashboards
monitoring so teams can see when data quality, cost or performance changes

This is especially important for companies where operational decisions depend on ERP data, planning data, production data, inventory, finance or customer processes. In those environments, AI becomes useful when it is connected to reliable business context and controlled workflows.

Core components: ingestion, storage and processing

Ingestion must support both batch and streaming as they meet different SLAs and use cases. Batch suits large periodic loads such as ERP exports and historical reconciliations, while streaming is needed for real-time signals from IoT devices, application logs and change data capture from transactional databases.

Tools like Debezium or AWS DMS are often used for CDC, while Kafka, MSK or Kinesis handle high throughput. SaaS connectors simplify onboarding of third-party APIs. For a practical overview of modern ingestion methods and architecture patterns, see Fivetran's guide to modern data architecture.

When it comes to storage, you should start with durable object storage like S3 and then add a transactional layer for ACID guarantees, versioning and time travel. Implement Delta Lake, Iceberg or Hudi to enable safe concurrent writes, schema enforcement and reliable rollbacks.

This makes storage cost-effective while supporting analytics and reproducible experiments. Decide whether most of your queries will run in a warehouse for fast SQL performance, or in a lakehouse if you need ML support and large historical scans.

Processing often follows an ELT-first model: land raw data first, then transform close to the compute engine running queries or models. Use streaming transformations for enrichment and alerts, and orchestration tools like Airflow, Glue or Step Functions for scheduling, retries and lineage.

Version control pipelines, automate tests and data quality checks, and collect operational metrics so transformations remain safe and auditable.

These three layers form the core of the platform. Metadata and governance then make datasets searchable, traceable and ready for downstream users.

Metadata, governance, and serving: trust and access

Metadata acts as the control plane for data products. Catalogs document datasets, reveal lineage, and display schemas so teams can find and trust data without manual checks. Tools like Unity Catalog, AWS Glue, and Purview centralise these functions and also show usage statistics that help consumers assess the suitability of datasets. For a concise overview of the fundamental principles that govern modern architectures, see the six modern principles of data architecture.

Schema enforcement and controlled schema evolution reduce the risk of downstream failures and ongoing incidents. By enforcing schema at write time where possible and using versioned transactional storage formats, you get controlled evolution and time travel. Supplement this with automated validation tests at ingestion and simple contract checks before production deployment.

Governance scales best when central guardrails are combined with clear domain ownership and role-based access control. Central teams publish policies and tools, while domain teams build and operate data products according to these rules.

Start with access control, encryption in transit and at rest, and lineage-based compliance checks to establish a baseline level of trust.

The serving layer should reflect user needs: semantic layers and curated metrics for analysts, REST or Graph APIs for applications, as well as reverse ETL for operational systems. Measure adoption, latency and accuracy for each serving level to understand where value is created.

These metrics determine whether the semantic layer, APIs or reverse ETL actually deliver the desired business impact.

ERP data as a foundation for AI and automation

For many operational companies, ERP is one of the most important sources in the data platform. Systems such as Infor CloudSuite M3 hold core business context: items, customers, suppliers, orders, inventory, finance, planning and process rules.

That data becomes more valuable when it can be extracted, structured, governed and reused in a modern data platform. It can support reporting, forecasting, exception handling, data-quality work, planning decisions and AI-enabled workflows.

The practical question is not only how to move ERP data into a lakehouse or warehouse. The practical question is how to make that data trusted enough for business use. That requires clear ownership, common definitions, quality checks, access rules and a serving model that makes the data available to the right people and systems.

When companies start exploring AI agents, this foundation becomes even more important. ERP-connected agents need reliable operational data, defined permissions, logging, monitoring and human review points. A modern data platform helps create the controlled data foundation those workflows need.

Architecture patterns: lakehouse, data mesh, data fabric, and event-driven

Choose a pattern that matches business goals, data gravity, and the team's skills. The options below cover common choices and when they work best in practice.

Lakehouse: uniting lake and warehouse

Lakehouse is based on a shared storage layer with transactional metadata so that BI, reporting, and ML can work on the same raw and curated datasets. Formats like Delta and Iceberg add ACID guarantees, schema enforcement, and time travel, enabling reliable backfills and reproducible experiments.

For many teams, lakehouse reduces cost and complexity by avoiding copies between separate lake and warehouse silos.

Data mesh: domain-driven data products

Data mesh treats domains as product teams that own and publish datasets with clear contracts and SLAs, supported by a self-service platform. Federated governance shifts the focus of central teams from owning all data to enabling standards, quality controls, and interoperability.

Choose mesh when domains are large and independent, and evolve towards this as product culture and tools mature.

Data fabric and hybrid overlays

Data fabric is a metadata-driven overlay that integrates distributed sources without heavy data migration, enabling discovery, virtualisation, and policy enforcement across cloud and legacy systems.

Fabric fits hybrid landscapes or situations where data copying becomes too expensive, and can complement a lakehouse when consolidated, queryable storage is needed. Use fabric for discovery and policy enforcement, but rely on lakehouse for robust analytics and ML workloads.

Event-driven and lambda-like hybrids

Event-driven architectures stream changes in system state through pub-sub systems to create low-latency pipelines and scalable processing. Combine streaming with periodic batch backfills to ensure complete history and data consistency.

Decoupled producers and consumers increase robustness and enable independent scaling and recovery.

These patterns help you align architecture with business needs and team capacity. Choose a first pattern for the pilot and then adapt as operational maturity and ownership models develop.

Data products, ownership and governance

A modern data platform works best when important datasets are treated as products. A data product has a clear owner, a defined purpose, documented meaning, quality expectations and a known group of users.

This makes the platform easier to scale because responsibility is visible. Business teams understand which data they can trust. Technical teams know which pipelines, definitions and access rules matter most. Leaders can connect platform investment to business outcomes instead of only infrastructure activity.

Useful data product questions include:

Who owns this dataset from a business perspective?
Which decisions or workflows depend on it?
What does good quality mean for this data?
Which users, applications or agents should be allowed to access it?
How will lineage, changes and incidents be monitored?

This is where governance becomes practical. It is not only policy. It is the operating model that makes data usable at scale.

Vendor and cloud choice: comparison between Databricks, Snowflake, BigQuery, Microsoft Fabric and AWS

Differences between vendors are essentially about which types of workloads they are best suited for and the strengths of the team. Databricks is strong for ML-first engineering with open table formats and advanced data science workflows. Snowflake focuses on SQL warehousing and simple multi-cloud operation. BigQuery offers serverless scale for ad hoc analysis.

Microsoft Fabric integrates closely with Power BI. For guidance when choosing between lakehouse and traditional warehouse, see Microsoft's decision guide on lakehouse vs warehouse.

Choose a platform based on clear criteria: volume and latency needs, AI maturity, existing cloud choices and the team's expertise. Run a proof of concept over 60–90 days that reflects a real production workflow and validate governance, metadata and TCO assumptions.

Compare pricing models such as pay-per-query, compute credits or provisioned clusters, and build a simple TCO model based on the expected query mix and forecasted data growth.

Focus on whether the vendor supports your data and AI goals, the right balance between control and managed services, as well as useful tools for metadata and governance. Use the POC to test team onboarding, data ownership processes and operational tasks before deciding on full migration.

For an in-depth review of strengths, architectural choices, and cost models between these options, read our article on the best modern data platforms 2026, where we compare the platforms based on real workloads, governance requirements, and total cost of ownership.

How to start: a practical roadmap

The safest way to modernize a data platform is to start with one valuable business workflow and expand from there.

Map the business problem. Choose one decision, report, forecast, process or operational workflow where better data would create visible value.
Identify the source systems. Document the ERP, operational, customer, planning or external data needed for that workflow.
Define ownership and quality rules. Agree who owns the data, what the key definitions mean and which quality checks are required.
Choose the first architecture pattern. Decide whether the pilot needs a lakehouse, warehouse, data mesh, data fabric, event-driven pipeline or a simpler hybrid setup.
Build one controlled pilot. Deliver one data product, one dashboard, one API or one workflow improvement with clear success metrics.
Measure business and platform value. Track one business metric, one adoption metric and one technical metric before scaling.

The first pilot should be small enough to deliver quickly, but important enough to prove the operating model. Good starting points often include ERP-to-analytics pipelines, planning dashboards, inventory visibility, forecast support, master data quality or a controlled AI-readiness use case.