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SERVICE 09
Data Platform & Analytics
Data Platform / Analytics
Solving the "we have the data, but we can't use it" problem.
From DWH and data lakes to BI and MLOps, we design and build the foundation that turns your data into decisions.
Process
01
Current-state survey & requirements
02
Architecture design
03
Implementation & data-quality design
04
Adoption support & operations
Who this is for
Getting past "we have the data, but we can't put it to work"
PAIN 01
You have the data, but the effort to visualize and analyze it is enormous
Copy-and-paste from Excel and business systems has become the norm. Producing a monthly report takes two weeks.
PAIN 02
You deployed a BI tool, but no one mastered it and it's gone unused
PAIN 03
You want to start with AI / machine learning, but your data foundation can't support it
We can step in even when your data is scattered and far from organized.
Tell us where you stand →
The SYSTEMI approach
Building a data platform people actually use
Beyond technology selection, we design use cases, data quality, and the operating model together as one whole.
01
Current-state survey & requirements
We survey data sources, how the business uses them, and existing BI across the board, then map the gap against the decisions you want to enable.
Output
Data-source inventory / Use-case mapping / KPI ↔ data mapping
Output
DWH architecture / ELT design / Data model / Access control
02
Architecture design
We select from BigQuery, Snowflake, Redshift, and others by fit to your requirements, and design the boundaries between data lake, DWH, BI, and ML.
03
Implementation & data-quality design
We build ETL/ELT pipelines, data-quality checks, and metadata management so you have a continuous supply of data you can trust.
Output
ELT pipelines / dbt / Quality monitoring / Catalog
Output / What happens next
BI dashboards / Operational runbooks / Training
04
Adoption support & operations
From dashboard design and usage guides to internal workshops, we embed data into how your decisions get made.
What sets us apart
Can the platform be one people actually use, all the way through to adoption?
A data platform isn't finished when it's built. It only delivers value once it's woven into the business.
| Data-platform specialists | BI vendors | Contract developers | SYSTEMI | |
|---|---|---|---|---|
| Use-case definition | △ Tech-centered | △ Tool-driven | △ After the spec is set | ◯ Starts from observing the work |
| Data-quality design | ◯ | △ | △ | ◯ Including dbt / testing |
| BI adoption support | △ Separate contract | ◯ | △ Out of scope | ◯ Designed by the same team |
| AI / ML connectivity | ◯ | △ | △ | ◯ Designed with AI use in mind |
AI × Data Platform
Using AI to lower the barrier for the people consuming data
Natural-language queries
Natural language like "show me last month's sales by location" is converted to SQL, so even people unfamiliar with data can make data-driven decisions.
Automatic dashboard generation
Tell it your goal and an LLM generates a dashboard template, offering options for which metrics to track and how to visualize them.
Anomaly detection & root-cause analysis
An LLM breaks down the drivers behind a change in the numbers and presents it as a report, shortening the lead time from noticing something to acting on it.
Related cases
Where we make the biggest difference
A mix of cases we can disclose publicly and illustrative model cases.
Working with Kumonoucyusen to turn operations and data into a unified platform
Challenge
They had no foundation to consolidate the data coming out of their business systems at a granularity they could actually use.
Outcome
We helped build out a data consolidation platform and analytics environment, then moved them to a continuous-improvement model.
MODEL CASE
FDE in action
For a company stalled after only deploying BI, building the DWH, data quality, and adoption guides end to end
Why they reached out
They had a BI tool, but data quality was too low for it to be usable in decision-making.
What we organized
Mapping the business against use cases → DWH design → introducing dbt plus data-quality tests.
See all cases →
DELIVERABLES
What we produce on the front line
Examples of how the materials we hand over are structured. We organize them into decision-ready inputs you can use directly in the next phase.
DOCUMENT 01 — Data platform requirements & architecture specification
Data_Platform_Spec_v1.0.xlsx
Data sourcesAn inventory of business systems, SaaS, logs, and external data
Use casesThe decisions you want to enable, and the data, granularity, and refresh cadence each requires
DWH architectureA recommended option from BigQuery / Snowflake / Redshift, with rationale
ELT pipelineThe design for ingestion, transformation, testing, and scheduling
BI architectureDashboard structure, users, and refresh operations
DOCUMENT 02 — Proposed architecture diagram
Example data platform — Source → DWH → consumption
Ingestion
Business DBsCore / product☁️
SaaS APIsSalesforce, etc.📜
LogsApp & behavioralStorage
🧮
BigQuery / SnowflakeDWH
S3Data LakeTransform / quality
🔧
dbtTransform + test
Airflow / GlueSchedulingBI
📈
Looker / MetabaseDashboardsAI / ML
ClaudeNatural-language queries🧠
Vertex AI / SageMakerMachine learning* dbt plus testing continuously safeguards data quality, delivering data you can trust.
* Natural-language queries give non-engineers access to the data too.
* Natural-language queries give non-engineers access to the data too.
Frequently asked questions
Common questions about data platform & analytics
Which should we choose: BigQuery, Snowflake, or Redshift?
We select based on data scale, your existing cloud, team skills, and cost structure. We work backward from your requirements: BigQuery for small-to-mid data volumes, Snowflake when you need workload isolation, Redshift if you're AWS-centric, and so on.
Can you build out the AI / machine learning foundation as well?
Yes. Centered on Vertex AI, SageMaker, and Databricks, we cover feature stores, model serving, and MLOps. We prioritize a design that avoids the "we can do ML but can't operate it" trap.
How do you handle integration with existing systems?
We select ETL/ELT tools (Fivetran, Airbyte, or a custom build) to fit your requirements. If you need real-time integration, we also consider CDC (Change Data Capture).
How do you ensure data governance and security?
We design to fit your requirements, covering data catalog setup, access control (IAM / row-level security), audit logging, automatic PII masking, GDPR compliance, and more.
Related services
FDE · Forward Deployed Engineering →
AI & Automation Adoption →
Service Growth Support →
Tell us where your data efforts stand today.
Even if your data is scattered or going unused, we'll start alongside you by defining the use cases.
Talk to us about your data platform (free)