APIs, SFTPs, and the Data Lake Illusion in HR Analytics

I was reviewing a dashboard with a client, walking through the weekly trends we were pulling from an SFTP feed. Mid‑conversation she said, “I’d really like to start doing WoW and MoM analysis, but I know we have to wait for the data lake before we can do that.”

That stopped me. It made me realize there’s still a lot of confusion about what a data lake actually provides — and what APIs, SFTPs, and basic logic layers already make possible. The analysis she wanted didn’t require a data lake at all. It required clarity, structure, and someone who understands how these systems really work.

Deep Analysis: APIs, SFTPs, and Data Lakes — What They Actually Do

APIs — Real‑time pipes, not analysis engines

APIs are great for small, frequent updates: new hires, job changes, terminations, status changes. They give you speed and near‑real‑time sync. But they don’t magically create analytical capability.

APIs don’t:

• Build historical datasets
• Create WoW/MoM snapshots
• Clean or reconcile data
• Apply business logic

They simply move data quickly. That’s it.

SFTPs — The quiet workhorse

Most HR data still moves through SFTPs because they’re stable and predictable. They’re perfect for:

• Payroll files
• Benefits eligibility
• Recruiting → HRIS handoffs
• Time & attendance exports

They’re batch‑based, not real‑time — but they’re more than enough for WoW and MoM analysis. If you have a weekly file, you can do weekly analysis. If you have a monthly file, you can do monthly analysis.

The limitation isn’t the SFTP. It’s whether someone is structuring the data correctly.

Data Lakes — Storage, not strategy

A data lake is a centralized place to store raw data from multiple systems. It’s powerful, but it doesn’t automatically create analysis. Without governance and logic layers, a data lake is just a giant bucket of CSVs.

A data lake does not:

• Create WoW/MoM metrics
• Build dashboards
• Clean data
• Align definitions
• Solve integration problems

A data lake enables analysis — but it doesn’t produce it.

The real value comes from:

• Data dictionaries
• Logic layers
• Validation rules
• Business alignment
• Someone who knows how the company actually operates

Without those, a data lake is just expensive storage.

Response to the client

The client’s assumption — “we need the data lake before we can do real analysis” — is one of the most common misconceptions in HR tech. Leaders often think the data lake is the magic ingredient that unlocks insight. But the truth is simpler:

You can do WoW and MoM analysis with the data you already have. If you have structured weekly or monthly files, you can build structured weekly or monthly insights.

The blocker isn’t the technology. It’s the misunderstanding of what each integration method actually does. APIs give you speed. SFTPs give you stability. Data lakes give you storage.

Analysis comes from logic, governance, and someone who knows how to build the story.

That’s the part people forget.