Build an internal data lake to centralize data from ERP, CRM, website, shop, files, logs, database and operating systems. Enterprises own all raw data, cleaned data, metadata, access rights, and processing pipelines on your own infrastructure.
✓ No GB ingest fees · ✓ Internally stored data · ✓ BI/AI ready
Data sources often integrate
The entire Data Lake is built entirely on the company's infrastructure (On-premise) and can operate with an independent storage system (Air-gapped). This is the optimal model to protect business data, customer data, operational data and eliminate dependence on third-party Cloud platforms.
When data is scattered across Excel, ERP, sales software, websites, file servers and databases, businesses cannot have reliable reporting or a secure AI platform.
Each department stores data in one type. Accounting, sales, warehouse, marketing and operations have to export Excel and merge manually, which is prone to errors and takes many hours each week.
Customer files, orders, contracts and financial reports are shared through many channels. There is no centralized authority, audit log or clear storage policy.
AI needs data that is clean, contextual, and has clear access. If data has not been collected, standardized and catalogued, AI projects are likely to fail or create risks of information leakage.
Data Lake helps save raw data, standardize important data, manage metadata, decentralize permissions and provide data for internal BI, dashboards, machine learning or AI.
Connect data from databases, files, APIs, internal applications, websites, shops, logs and Excel reports.
Save data in raw, cleaned, and curated regions to both retain the original source and have data ready for analysis.
Manage data meaning, origin, owner, sensitivity level and usage status of each dataset.
Control who can view, download, query, or process data by role, group, and sensitivity level.
Provides clean data for administrative reporting, operational dashboards, sales, inventory, finance and marketing analytics.
Create a secure data foundation to deploy internal search, enterprise chatbots, predictive analytics, or private AI assistants.
From data loading to catalog, authorization, pipeline, dashboard and AI-ready dataset.
Businesses can build an internal data lake instead of sending strategic data to the Cloud and paying for storage, compute, query or data transfer.
| Criteria | NAD Data Lake | Cloud Data Lake | Data Warehouse SaaS | Discrete Excel/BI |
|---|---|---|---|---|
| Data storage location | On-premise / Air-gapped | Third Party Cloud | Third Party Cloud | Disperse |
| Fees per GB storage/ingest | Does not depend on vendor | Have | Have | permission Hidden in the operation |
| Save raw data | ✓ | ✓ | Usually transformed | ✕ |
| Catalog & governance | According to profession | By package | By package | ✕ |
| Internal data decentralization | ✓ | IAM cloud dependency | Vendor dependent | Difficult to control |
| BI/AI Ready | ✓ | ✓ | ✓ | Craft |
| Long-term costs | Optimize Data Sovereignty | Increases with data | Increments by query/user | Manually laborious |
NAD deploys Data Lake as an internal data capacity: data stays in the company, pipeline according to business, clear decentralization and ready to serve BI/AI.
Businesses have autonomy over storage, compute, access and retention instead of depending on the vendor's price list.
Customer information, orders, finances, operations and internal files are stored in the enterprise infrastructure.
The data set is cleaned, decentralized and cataloged to help reduce reporting discrepancies between departments.
Start small with a few key data sources, then expand to logs, files, applications, and AI data.
NAD deploys in a pragmatic way: choose the most valuable data sources first, standardize the pipeline, then gradually expand.
Inventory data sources, owners, formats, update frequency, and reporting/AI needs.
Design storage architecture, data zones, pipelines, decentralization, catalogs and retention.
Build ingestion, storage, data cleansing, sample dashboards and access testing.
Hand over documents, operating instructions, procedures for adding data sources and periodic review schedules.
Start with a few key data sources, then expand to the entire enterprise system.
Suitable for businesses starting to gather data from 2-3 main sources to make centralized reports.
Suitable for SMEs that need centralized data, catalogs, recurring pipelines and administration dashboards.
For businesses that need large data platforms, detailed decentralization, multiple pipelines and data ready for internal AI.
No GB ingest or query fees. Server, storage, pipeline and operational support costs are separated according to actual needs.
Schedule a free consultation for NAD to survey data sources, propose Data Lake On-premise architecture and appropriate implementation roadmap.