Self-service data platform (SDP)

What is a self-service data platform (SDP)?

A self-service data platform (SDP) enables the independent discovery, access, and analysis of data sets, without requiring support from central data teams or high levels of technical skills. Within the data mesh concept it also provides the ability to easily create and manage data products without requiring additional tools – these products can then be shared through the platform. 

Built and maintained by the central IT or data team, a self-service data platform increases the value generated by data through self-service business intelligence, particularly by scaling its usage and by providing all the tools to quickly create and publish new data products.

The SDP delivers three key functions to organizations:

• It enables data discovery for all users
• It supports the creation of data products
• It ensures data governance processes and policies are applied

What are the benefits of a self-service data platform (SDP)?

A self-service data platform benefits the organization in multiple ways:

• It enables better-informed, faster decision-making through seamless access to reliable, comprehensive data
• It increases productivity by reducing the workload of data analysts, enabling them to focus on more complex tasks
• It improves collaboration, breaking down silos across the business 
• It enables the faster creation of standardized data products, allowing greater interoperability and higher value while removing bottlenecks from the process
• It underpins greater agility and competitiveness, as the organization can respond more quickly to changing market trends
• It supports data democratization and the growth of a data culture by providing everyone with intuitive access to data in their working lives
• It ensures compliance and governance, by applying data governance rules across all data in the platform

What features does a self-service data platform (SDP) require?

An effective self-service data platform should offer these seven capabilities:

User-friendly interface to aid discovery

It should be easy for non-technical users to confidently locate and access relevant data through powerful intuitive data discovery and search. The interface and experience should be as simple as an e-commerce marketplace, building trust and increasing usage.

Access to all data sources

The SDP has to be comprehensive, which means it must connect to all data sources within the organization. To make this process seamless and straightforward, the platform should offer pre-built connectors to all major business applications, cloud storage, data lakes/data warehouses and other sources. This avoids the need to write complex scripts to extract data from their sources, reducing technical workloads.

Ease of management

Given the potential number of data assets and data products within the SDP, their management has to be made as simple as possible. For example, the platform should include intuitive tools to extract, enrich, and clean raw data, ensuring it meets corporate quality guidelines.

Provide a range of data visualizations

To ensure data is usable and understandable, the platform has to make it simple for data to be visualized through formats such as reports, maps, dashboards and other graphics. These capabilities have to be accessible by end-users, enabling them to create their own bespoke visualizations, and for data teams to build visualizations.

Enable data lineage

The platform should enable data teams to monitor overall usage of the platform and track how and when data products and data sets are being used through data lineage features.

Allow the standardized creation of data products

The platform should include all the tooling and support to enable data products to be created and shared as part of a data mesh approach.

Security, access management and data governance

A self-service data platform must also provide methods for restricting data access to authorized individuals at a granular level, protecting information and ensuring regulatory compliance. It should integrate closely with corporate data governance frameworks to enforce standards around the management and use of data.