Understanding the Data Product Lifecycle. How to manage data products in Dawiso?

Data products are not just datasets or APIs—they are reusable assets designed to deliver continuous business value. Managing them effectively throughout their lifecycle ensures organizations can maximize their potential, improving governance, discoverability, and usability. This article explores the key stages of the data product lifecycle and how Dawiso facilitates efficient data product management.

What Are Data Products and Why Do They Matter?

Ask ten data leaders what a data product is, and you’ll likely get ten different answers. The term is often used interchangeably with datasets, APIs, or platforms, but a true data product is more than just raw data or the technology behind it. It’s about who it serves and how it creates value.

What a Data Product Is Not:

  • Not just a dataset or API
  • Not just a technology stack
  • Not simply about storage or access

What a Data Product Is:

  • A packaged, usable asset designed to provide value to specific users
  • Designed with a clear audience in mind, ensuring usability and accessibility
  • Built for discoverability, self-service, and integration into workflows

Creating a data product means ensuring the right data assets reach the right users in the right way. This requires careful consideration of placement, packaging, and permissions:

  • Placement & Packaging: A dataset that no one can find, or use isn’t a product, it’s just another buried asset.
  • Permissions & Access: Who should use this data, and how should they gain access? Striking the balance between security and usability is key.

At its core, a data product isn’t valuable because of the data itself—it’s valuable because of what people can do with it.

Understanding the Data Product Lifecycle

Data-driven enterprises have one thing in common: they build data products rather than one-off data projects. Data products are reusable assets designed to deliver continuous business value.

Every data product follows a lifecycle, similar to software development, allowing for iteration and improvement to meet business needs. The key stages include:

  1. Define – A data product is defined by its business objectives, governance constraints (security and privacy), and data asset inventories. The design phase determines how the data will be productized and accessed via services.  
  2. Engineer – This stage involves locating, integrating, and processing the necessary data sources. It also includes creating data services for applications and engineering pipelines to deliver data to consumers while ensuring compliance and performance SLAs.
  3. Test – Before deployment, data products are validated to ensure the delivered datasets meet expectations regarding freshness, completeness, compliance, and performance.
  4. Deploy & Create – The data product is launched, monitored, maintained, and supported to track usage, performance, and reliability. Issues are addressed to ensure continuous value delivery.
  5. Iterate & Optimize – Data product teams refine the product based on insights from monitoring, feedback, and evolving business requirements.
  6. Retire & Archive – Outdated or redundant products are either archived or updated to maintain an efficient data ecosystem.
The Data Product Lifecycle

Stop with the theory…let’s take a look into the practice.

Managing Data Products in Dawiso

Dawiso provides a structured approach to managing data products throughout their lifecycle. The Data Products Catalog in Dawiso ensures that every data product is well-defined, properly documented, and easy to discover. Key management features include:

  • Defining Data Products Under Specific Domains: Organize data products by domain for clarity and governance.
  • Search and Discovery: The catalog allows users to search for and filter data products based on business terms, categories, and access permissions.
In the Data Market, you can search for published products using the search engine at the top.
  • Capturing Detailed Descriptions and Definitions: Each data product is documented with metadata, business definitions, and governance rules.
Definitions are automatically linked to other definitions if the terms are stored in the Business Glossary.
Metadata concerning product ownership and accountability for its timeliness, accuracy, and other aspects is a critical component of data product management.

As a data owner, you are not just a custodian of information—you accept responsibility for ensuring the quality, reliability, and accessibility of your data products. This includes guaranteeing data accuracy, completeness, and compliance with governance policies. Whether it’s maintaining data freshness, upholding SLAs, or ensuring that business users can confidently rely on the data for decision-making, your role is central to creating trust in the data ecosystem.

Changing a data product’s workflow is not just about adjusting the phases of its lifecycle—it also involves defining who has the right to edit and influence it. Dawiso serves as a collaborative space where multiple stakeholders—data architects, governance professionals, and product owners—each play a role at different stages of the lifecycle. Whether it’s shaping the initial design, ensuring compliance, or maintaining its long-term value, everyone has something to contribute.

  • Full Lineage Tracking: Dawiso enables users to view data lineage at both a logical and business level, making it easy to trace data origins and transformations.
  • List of Data Products Items: Below the data lineage in this template, there is also a "List of Data Product Items". The data product contains several tables. You can add more tables via the plus button in the top right corner.
  • Source Database & SLA Management: Users can list and track the source databases of their data products while defining service-level agreements (SLAs) to ensure timely data updates.

The products are linked by a contract outlining the details of their connections. We can access all or only some data product items. A data contract is a formal agreement or specification that defines how data is structured, communicated when data is exchanged, and guarantees source data quality.  

We can also click on data contracts here below in the right panel. Both the input (draws from) and output (passes the data on) of this data product are displayed contracts.

Here we see a preview of the contracts at the bottom of our data product. We can click on the details.
  • Managing Data Contracts: Dawiso provides dedicated objects for defining and managing data contracts, ensuring smooth data flows between data products and compliance with organizational policies.

In Dawiso, data contracts exist between individual data products and are documented like the products themselves.

Each contract identifies a producer (data supplier) and a consumer (data receiver). The Data Contract details the terms of how data transfers occur, such as from Sales - Sales Order to Sales - Product and Sales - Sales Statistics.

By leveraging these management capabilities, Dawiso ensures that organizations can easily create, maintain, and govern their data products, fostering a scalable and self-service data ecosystem.

We have a predefined template in Dawiso. You can utilize a preconfigured workflow for data products, or we can adapt it to fit your existing framework. Both options are available.

Data Product templates help speed up the development process. These templates serve as blueprints for well-defined data products, including essential information such as the product's definition, ownership, data lineage, and service level agreements (SLAs).  

Although we establish a solid foundation for documentation, the technical development and deployment of these products in other tools is your responsibility. With these templates, you can gain a head start in development and ensure that your data products are built on a strong foundation.

At the same time it is easy to customize. A one-size-fits-all approach doesn't always work for everyone. While data product templates provide a strong foundation, Dawiso empowers you with customization. Modify the templates as needed for your unique data product vision.

If you are more interested in the components of data products, check out the article.

Or you can click through the interactive tour yourself and read more about all the capabilities here.

Why Lifecycle Management Matters

Effective lifecycle management prevents data sprawl, improves data governance, and ensures that organizations derive real business value from their data. By using a structured platform like Dawiso, businesses can create reliable, reusable, and high-impact data products.

A well-managed data product doesn’t just store data—it delivers actionable insights, supports decision-making, and enhances collaboration across teams.

Want to see how it works in action? Explore Dawiso’s interactive tutorial on data product management here.

Martin Nevický
Senior Data Governance Consultant

More like this

Keep reading and take a deeper dive into our most recent content on metadata management and beyond: