5 Things Data Engineers Must Know About Data Products

The typical data engineer is balancing many priorities. In addition to the day-to-day tasks like pipeline construction, connecting sources, and establishing data trust and governance, pressure is increasing to contribute to the organization’s bottom line. This may happen by unlocking the full value of the organization’s data or devising ways to accelerate business outcomes. One way to accomplish both is through data products.

If you’ve heard of data products before, you’re in good company — right now, these innovations are at the Gartner Hype Cycle for Data Management. But if you’re not using them yet, you’re not alone there, either. When it comes to data products, most organizations are currently in the early phase of adopting this new data management paradigm.

What are data products?

In a nutshell, a data product is an entity that allows you to gather and package trusted datasets related to the same domain or any grouping that makes sense for a specific business use case. More specifically, data products are curated collections of productized datasets and business-approved metadata and data-centric artifacts like data quality rules and transformation logic designed to solve domain-specific business outcomes. Data products also bring a product management approach to managing data — and are the backbone of powerful, AI-driven data apps including recommendation engines, predictive models, dashboards, and APIs, and are used across industries to extract value from data.

Data products are highly trusted, reusable, and consumable data assets. Their purpose is to foster ownership, accountability, and data trust. For a deeper exploration of the basics of data products, please read the first post in this series, “What Are Data Products, Anyway?”

Data products: Five things data engineers must know

One very attractive benefit of data products is that they solve the re-usability problem - data engineers can reuse a data product for another use case, saving both time and cost. But from a data engineer’s perspective, there are a lot more benefits to enjoy as well.

1. Break free from silos. Often, data engineers are isolated from other data consumption teams in the data journey. With data products, data product teams can be composed of both data engineers and data consumers, who work together to build data products iteratively.

2. Reduce or eliminate bottlenecks. Silos aren’t the only byproduct of legacy data architectures. With the emergence of data lakes, warehouses, and lakehouses, centralized data teams became the bottleneck as all data requests flowed into a centralized pool. While data products will not replace centralized architectures (Snowflake, for example, is not going away because some use cases are better for that), data products help data teams to elevate the value of their data and deliver business outcomes faster.

3. Improve over time. Data products are living things. They are iterative – data engineers get continuous feedback to improve the product, adjust for different use cases, and fine-tune as the consumption needs and requirements in the field change.

4. Access easily for reuse. A data product catalog is a repository of multiple data products, composed of three layers to serve different data professionals. For the data foundation layer, data engineering teams deliver foundational capabilities that not only support moving, transforming, and governing data; but also allow for rapid iteration, lower TCO, and faster use-case delivery.
   
   
   

5. Create real, lasting value. Data engineers live in the world of pipelines, but when getting bogged down in pipeline creation, management, and repair, it can be challenging to provide and extract value out of enterprise data. Data products allow engineers to create something really valuable because they are domain centric (engineered around a specific use case) and easily consumable (meeting the consumption agenda of the data consumer). In addition, if you’re a data engineer specializing in AI, data products are foundational, allowing you to quickly prepare AI-ready data that is of high quality to drive AI projects.

Prior to the emergence of data products, there was a huge consumption gap between data producers and consumers — closing that bridge was key to successful data outcomes. Now, data products can serve as that bridge between data consumers and producers.

What do I need to implement data products?

Like many advanced data management capabilities, the age-old divide exists between build and buy. For companies looking to move quickly and embrace data products, while continuing to benefit from their existing investments, you need a solution that can seamlessly integrate with the existing ecosystem — be it a data lake, data warehouse or lake house. At the same time, integration itself isn’t sufficient. You need data transformation quality and governance to curate and prepare datasets to maximize value and meet domain-centric business outcomes.

Qlik Talend Cloud® Enterprise edition is one such solution, allowing you to integrate data from any source or platform and transform it into high-quality data products ready for business consumption. You can learn more about some of these capabilities within Qlik Talend Cloud here: https://www.qlik.com/us/products/data-products-catalog

Learn. Play. Adopt.

Looking for hands-on experience to confidently pitch the value of data products to your Chief Data Officer or Chief Data and Analytics Officer? There’s no better place to start than an interactive tour of data products, courtesy of Qlik. In this eye-opening interactive tour, you take a seat as a data product manager at Bricoworld, a fictional logistics and shipping retailer, and experience some key data product capabilities in Qlik Talend Cloud firsthand. Afterward, you’ll be ready to champion the adoption of data products in your organization and accelerate business outcomes.