Outcomes
- Monetize proprietary datasets securely
- Deliver reliable data APIs to customers
- Improve decision-making with real-time analytics
- Establish data governance and quality controls
Data-as-a-Service (DaaS) Platforms
Turn data into a scalable product, not a side project.
Reorigin Technology helps organizations productize data through DaaS platforms, data APIs, dashboards, and subscription-based analytics products with ingestion, transformation, governance, and monetization layers.
Capabilities
- Data ingestion pipelines
- ETL/ELT and warehousing
- Customer-facing data APIs
- Cleantech and sustainability analytics
- Dashboards and embedded analytics
- Data access controls and compliance
What is a DaaS platform?
A data-as-a-service platform turns a dataset you already have into something another team or a paying customer can call: an API, a dashboard, access tiers, metering, and a rule for what must never leave the warehouse. Reorigin builds that layer from Istanbul.
An internal dashboard is the right tool when the only consumer is your own team and a missed refresh is an annoyance. DaaS is the right tool when someone outside that team depends on the data — a customer who pays per call, a partner who must not see another partner’s rows, a regulator who will ask which version they were shown. If you cannot name the consumer and how they authenticate, start with the dashboard. We will say that in discovery.
Packaging is usually subscription tiers, call or row metering, and dataset entitlements. We implement keys, usage analytics, and billing hooks so you can change the package without rewriting the pipeline. For EU customers we design residency, encryption, and access logs into the platform. Your counsel decides the legal role; we implement the controls they name, including EU-region hosting when the contract requires it. KVKK questions for Turkish data subjects are handled the same way: the control is engineered, the legal opinion is not ours to invent.
The first data product is one consumer and one dataset. The second dataset waits until the first quality gate is running in production. Otherwise the platform becomes a set of dashboards nobody can bill. Discovery names the caller, the fields they may see, and what happens when a feed is wrong. Publishing yesterday's partial file in silence is not a release.
Region is a deployment fact. If the contract says EU, the job and its backup run there. The Şişli studio runs delivery; the data plane is wherever the instruction puts it. Hours are Monday to Friday, 09:00–18:00 Europe/Istanbul. The 10–16 week band becomes a written proposal after discovery. We do not rent staff, and we do not quote a data product from a chart library.
What you get
- A named consumer, an auth scheme, and the fields they are allowed to see
- Ingestion and a quality check that fails closed when a feed is wrong
- Versioned APIs or extracts, not a shared spreadsheet link
- Keys, tiers, and usage events you can bill from
- Access logs and a retention rule
- A residency choice (Turkey, EU, or both) written into the design
- An operator view of failed jobs and who was affected
- Documentation a second engineer can run without us
Timeline and budget bands
A data product is scoped around the consumer and the feed quality, not around a chart library.
| Phase | Duration | Planning band | What ships |
|---|---|---|---|
| Consumer and contract | 2–3 weeks | Inside the first release | Who calls the data, what they may see, how they pay |
| First data product | 10–16 weeks | Scoped after discovery | Ingestion, API or extract, keys, usage events |
| Next dataset | Phased | Separate slice | Same platform, a new entitlement |
How we architect a data product
Fail closed on bad feeds
A pipeline that publishes yesterday’s partial file is worse than one that alerts. Quality gates sit before the customer API.
Entitlements, not filters in the client
What a caller may see is decided on the server. A hidden column in a dashboard is not access control.
Meter the call you might bill
Usage events exist even if the first price is a flat subscription. Changing the package should not require a new pipeline.
Region is a deployment fact
If a contract says EU-only processing, the job runs there. We do not promise residency with a config flag nobody tests.
Related
Related
SaaS platforms
When the data product needs tenants, plans, and billing.
Enterprise AI
Retrieval over data you are allowed to expose, with the same boundaries.
KVKK and GDPR
Controls we design when Turkish and EU personal data meet a product.
FAQ
What is a DaaS platform?
A DaaS platform delivers data as a service through APIs, dashboards, or subscriptions, allowing customers to access curated datasets or analytics on demand.
Can you help us monetize our data?
Yes. We design pricing models, access tiers, metering, billing, and customer-facing experiences for data products.
How do you ensure data quality and security?
We implement validation pipelines, monitoring, lineage tracking, encryption, and role-based access aligned with your compliance needs.
When does DaaS make sense vs a one-off dashboard?
A dashboard is enough for internal reporting. DaaS makes sense when other teams or paying customers need reliable, versioned access — APIs, SLAs, usage metering, and access control. If you cannot describe who consumes the data and how they pay or authenticate, start with a dashboard.
How do you price and package a data product?
Typical patterns are subscription tiers, call or row metering, and dataset entitlements. We implement billing hooks, keys, and usage analytics so packaging can change without rewriting pipelines.
Can you serve EU customers from a Turkey-based team (GDPR)?
Yes. We design residency, encryption, access logs, and processor/controller boundaries into the platform. Legal advice stays with your counsel; engineering implements the controls they require, including EU-region hosting when specified.