Business success is meaningless without data. Qualitative data has earned its place at the top of the metaphorical list of precious organizational commodities. Without accurate, precise, relevant, and reliable data, enterprises could find themselves at a dead end. Data and numerous data products are helping businesses develop new, innovative revenue streams, and from an operational perspective, they help reduce costs, improve process efficiencies, and manage complex interconnected ecosystems.
With the emergence of data observability as a category that enables businesses to manage and monitor complex data environments, the ability to build data products has accelerated and actually become much easier. As a result of the surge of data product development, there is corresponding confusion regarding the meaning of just what a data product is and how it’s different from data-as-a-product.
To understand the difference between these two very different terms, we need to start by looking at the goals and implications that data products and data-as-a-product solutions have on an organization.
Data Products and Data as a Product: What’s the Difference?
The primary difference between data products and data-as-a-product is the way “data” on the whole is perceived. Data products are viewed as products that help amplify a goal through the use of data. In other words, with the help of accurate data, data products can help businesses achieve enterprise-wide goals. Data-as-a-product, on the other hand, is the process of looking into the data collected and understanding how it affects the business downstream - the end user, data consumers, and others.
Data products are often used within organizations to support decision-making and are developed using data analytics techniques to extract insights, patterns, and trends from large volumes of data. For example, a marketing team might use a data product to analyze customer behavior and develop targeted marketing campaigns. Similarly, a logistics team utilizes data products to optimize inventory levels and improve delivery times. Depending on the use case, there are a variety of data products available to pick and choose from.
Examples of data products include dashboards, algorithms, visualizations, reports, and other features that provide insights to decision-makers so that they can better understand vast and complex data sets.
Data as a Product (DaaP)
To simplify data-as-a-product, try looking at it as individual items you’d see on the shelf of a local supermarket. Like how each item on the supermarket shelf has its specific function in a customer’s day-to-day life, data as a product is a bundled dataset that serves a particular requirement within a business or vertical.
For example, a company might package and sell data sets that provide insights into consumer behavior, market trends, or economic indicators to help manage production, inventory, or logistics. Another company might have data that contains information regarding consumer demographics and product preferences to help organizations build products that have value to the public consumer. The possibilities are endless, and companies can create data sets that are tailored to the needs of their customers. Also, data-as-a-product can be a lucrative revenue stream for companies that can collect and package data effectively (provided regulatory requirements, compliance factors, and laws are adhered to).
As the number of companies relying on data to inform their decisions has increased, the demand for data-as-a-product has grown significantly.
Data Observability Drives Data Products
Whether an organization uses data products, or bundles up data to sell it as a product, the fundamental core of both remain the same - data! Without the most accurate, and relevant data, data products will not function effectively, and can neither be dispatched to analytical applications for utilization. Data quality becomes a focal point for success.
Acceldata’s multi-layered data observability solution enables enterprises to gain comprehensive insights into their data stack to improve data and pipeline reliability. This helps business teams to build and operate great products by monitoring compute performance, spend efficiency, and delivering reliable data efficiently.
Get a demo of the Acceldata Data Observability platform to see how to accelerate your data product efforts.