Democratizing Data Through Data-as-a-Product: Use Cases & Architecture
In today’s competitive world, organizations need to be able to pull data from everywhere in order to gain a competitive advantage, whether it’s running operations or generating insights for the business. Data drives every aspect of enterprise growth, and it is only growing more important as the industry develops.
However, working with data remains a challenge for non-experts. When there is a small team of experts that handle all requests, this creates a bottleneck that most enterprises can’t afford. Training employees without technical backgrounds also takes time and resources, so it’s getting more imperative to be able to deliver data that is accurate, timely, and accessible to anyone.
Gartner defines data democratization as “the delivery of digital leadership and responsibility both outside the IT organization and distributed across the organization.” It is listed in Top 10 Trends for Tech Providers for 2022 as a recommendation to solve resource shortages, decrease bottlenecks and increase the ability of business units to handle their own data requests. Adobe lists data democratization as “crucial” to modern businesses, and Forbes discusses how it helps companies know better to grow better.
When it comes down to it, the people who use data daily are experts in that data; they know what data they need and how to apply it. That’s why it’s so important to put data into the hands of those who use it in the way that best suits them. Democratizing data is the first step in not only speeding up data pipelines throughout an enterprise, it also sets the foundation for innovations and growth. By letting people explore and retrieve their data, they are more likely to look outside their usual context and find new ways to solve problems and create value.
Data products are contained, ready-to-use data entities that are the foundation of data democratization. What defines a data product is the additional features that allows the product to be found, tracked, changed, delivered, or used in any way.
Data products are created from raw data of any format or velocity. Batch, stream, real-time or API source in any format are transformed by adding metadata and creating a consistent interface for all data types.
By adding metadata such as schema, descriptions, validation, characteristics, and samples, data is made easier to find and sort according to need. Since data products can be treated the same and organized by type or tag, exploring relevant data also becomes easier. This also streamlines delivery, as data products can be output in any format requested.
Data products are the future of data democratization, letting non-techincal people find, request, and get the data they need in the required format. By automating the data pipeline process, not only is the power put into the hands of those who use data, but data engineering teams are also freed up to handle issues or create value, increasing the strategic use of resources. For more on creating and using data products, check out our post What Is A Data Product?
Data as a Product in Data Mesh and Data Fabric
Data products can be either auto- or user-generated, and once a product template is set up, it can be standardized to make the terminology and metadata consistently formatted and sorted for more efficient organization and delivery. Data products can be included as a part of a data architecture or solution to streamline any kind of data pipeline.
Data as a product is one of the four principles of data mesh and is a fundamental part of how a data mesh solution functions. Data products are created by domains, with each domain being responsible for meeting the needs of the users. Domain teams are in charge of curating and processing their data into data products, as well as making these data products available to users.
Data fabric pulls in raw data and tags and processes it. The data preparation and delivery layer then uses metadata to identify and transform the raw data into data products to be delivered to appropriate users. This automated generation and delivery of data products are curated by custom request to deliver the data product as requested in the format needed.
Data solutions that combine different elements can also use data products. The creation of data products can be built into custom solutions and configured for delivery in different parts of data solutions to suit a specific enterprise or use case. Data products streamline any data pipeline and add a pre-configured level of governance and quality control that are manually built for standard data pipelines.
Data as a Product Use Cases
With real-time data fueling dashboards and spreadsheets, making data easily discoverable and usable is intrinsic to smart decision making. Data products make that easy; let’s look at some examples of how.
Using Nexla, a unified data solution centered on automated data product generation, Poshmark was “able to query data in a much more scalable and manageable perspective, increasing the company’s analytics insights delivery efficiency by 10X,” said Kyle Martin, Head of Data Management & Analytics Platform. Poshmark now automates pipeline building and monitoring throughout its data delivery process. Nexla’s self-service platform allows data users at Poshmark to easily access ready-to-use data from internal and third-party sources without spending additional engineering resources. Check out the full case study for yourself.
For another example of how data products effect data with real-life use cases, this ecommerce journey map breaks down where in the process data products are used.
If you’re ready to discuss data products as part of a unified modern data solution, get a demo or book your free data mesh consultation today and learn how much more your data can do when everyone can use it whenever they need it. For more on data and data products, check out the other articles on Nexla’s blog.
Definition sourced from Gartner.com with permission.
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