Data Products vs Data As A Product

What Exactly is a Data Product?

A data product is a product in which data is the primary facilitator of its main objective. This implies that a data product is any technological product or element that depends on data to reach its ultimate goal or purpose.

A data product, in essence, is any platform or tool that analyzes data and provides results. The key motivator for enterprise adoption is accomplishing corporate goals through empowered decisions made with insights from data products.

Data products are frequently associated with corporate entities, such as clients, suppliers, equipment, site, or warehouses. Because a corporate entity’s data is commonly dispersed across several source systems, a data product requires data integration, harmonization, and constant synchronization with the fundamental source systems.

Types Of Data Products

Although data products are classified in various ways, they are most typically defined by how companies handle the type of data and the sort of function they provide.

1. Raw Or Unprocessed Data

This is the most fundamental type of data acquired by the system. If the data is raw, it signifies that it has not yet been processed or used. However, companies can still process this data as a data product to increase its worth.

2. Derived Information

This is a more personalized form of raw data in which additional procedures are done to make the raw data more understandable, such as computing the average or total of a specific property.

3. Algorithms

Only an algorithm can give an outcome by reading and processing data. Some of these techniques may need the execution of a machine-learning model on the provided data.

This category includes the following two data products: insights/decision support and automated decision-making.

A Data Product Example

Salesforce’s Einstein AI, which delivers customer predictive analytics, and finance terminals such as the Bloomberg Terminal are typical data products. However, meaningful data applications do not have to be enterprise-level to influence a business. As a result, companies frequently create internal data products to ensure data protection, integrity, and flexibility.

More Examples Of Data Products

– Any online shopping site could be a data product if the displayed items change based on my prior purchases and searches.

– Google Analytics is a data product since the insights it provides to users are based on data.

– A data warehouse is a data product that combines raw data, derived data, and insights.

– A self-driving automobile is an example of automated decision-making.

Understanding Data As A Product

Consider data in different ways for a time. Rather than complicated stats and figures, see each data set as an item or product on a local supermarket shelf. As a customer, there are a few things you’d want to know when you go into a local supermarket.

First, you’d expect that the products are well-organized and easily accessible. For example, when shopping for apples, you wish to find them on the appropriate racks. That’s just a regular aspect of your supermarket shopping experience. The same holds true for your data.

What Is Data As A Product?

Data as a product is all about looking at the data you gather and analyzing how it will affect individuals downstream – your data citizens, end users, and others. Viewing data as a product is similar to viewing your shopping components as products.

You must look after your data, organize it, and make it simple for data citizens and end users to locate, trust, and use. And it is up to you, as an organization, how you organize that data and treat it as a product.

Best Practices for Treating Data As A Product

The Data Delivery Lifecycle

Data teams should take a cross-operational product lifecycle approach to embrace a “Data as a Product” strategy. The data product delivery lifecycle should adhere to a lean manifesto by being short and continuous to provide immediate, additional value to data users.

Establish The Data Product Plan

Define the data requirements in the context of the business objectives, data privacy and governance limitations, and the inventory of current data assets. Then, the design for data formatting and componentization comes into the picture.

Construct The Data Product

Create the data product based on the needs by finding, integrating, and gathering data from its sources, then concealing it as needed. Next, develop web applications APIs that allow consuming programmes with the appropriate credentials to access the data product and design pipelines to safely distribute the data to subscribers.

Validation Of The Data Product

Focus on testing the data to ensure it is comprehensive, accurate, and reliable and can be securely accessed by large-scale applications.

Maintenance And Repair Of The Data

Monitor statistical measurements, pipeline efficiency, and stability, and collaborate with data engineering to solve problems.

Conclusion

Studying these notions reveals that they all rely heavily on precisely collected data. “Data product,” on the other hand, is a broad concept, whereas “data as a product” is a subset of all conceivable “data products.” To put it another way, “data as a product” is derived from the “data product.”

Moreover, now you have a better knowledge of what data as a product and data products are; and can utilize the knowledge to create, purchase, or deal with such concepts in the coming years.