#bigquerydataproducts

BigQuery Products

To address that issue, Google Cloud Next unveiled BigQuery data solutions for testing.

By treating data as the product, BigQuery helps you organise, distribute, and exploit your most valuable asset. A ‘Customer Sales’ data product combines regional sales data with customer order data in a carefully selected BigQuery view. Sales Analytics provides a single point of contact, freshness assurances, and business context for campaign analysis as the data product owner. With context and guarantees, data consumers may utilise this data offering to make well-informed client sales decisions.

BigQuery data products simplify data producer-consumer transactions by allowing data producers to bundle one or more BigQuery tables or views that meet a use case and provide them as logical blocks. Although BigQuery currently offers a strong way to share data through datasets listed in data exchanges, its data products provide more abstraction and insight on the business case they address. BigQuery data packages allow consumers locate relevant information in one consumable unit.

A data product helps data producers manage their data as a product, including:

Build for use cases: Determine the client and use case, then utilise one or more resources to produce a data product that solves it.

To retain accountability and client confidence, identify the data product's owner and contact information.

To democratise context, provide backstory on the product's problems, examples of use, and expectations.

Streamline contracts: Allow data consumers to note data quality and freshness to boost confidence and speed understanding.

Manage assets: Control who can view the data product.

Data discovery: Allow data users to easily search for BigQuery data products.

Data distribution: Distribute data to the public through a data exchange or commercial consortiums.

Iterate and enhance products to meet client needs.

Data producers that build use case-based tools and treat data like a product may help data teams work better. This includes:

By establishing standardised and reusable BigQuery data products, data teams may avoid replicating the same datasets or procedures for different clients or objectives. This frees up their time and resources.

Data teams may better align their efforts with business goals by prioritising their work based on the impact and value of each data product.

By tracking data product usage, data teams can better assess and communicate their efforts to the organisation.

Future data solutions might incorporate governance standards and compliance procedures to ensure data is handled appropriately and consistently.

These reduce the effort needed to find the right asset, making data consumers more efficient. Data consumers get faster insight since everyone in the firm can search, browse, and subscribe to BigQuery data products. Clear, trustworthy, and well-documented data makes it easier to identify the right data for a use case, which increases confidence.

BigQuery data solutions give controls and building elements for product data management. Businesses benefit from business-outcome-driven data management because data consumers get insights quicker.