The development of data products is quickly jumping to the top of the priority list for most data leaders. These products present a wide range of economic and brand opportunity for enterprises, but many are still unclear how they can develop and use them.
A product or platform that uses data to solve particular business problems or specific use cases can be called a data product. In other words, data products are applications, tools, and devices that leverage data to overcome product bottlenecks or provide different services more efficiently. There are many types of data products, each with its unique set of characteristics and uses.
Depending on the type of data used, products can be grouped into different buckets. For example, Netflix uses data to power its highly accurate recommendation engine to ensure viewers keep returning to the platform. Uber uses data to implement dynamic pricing based on traffic conditions, distance, and high demand. The data products from Netflix and Uber are perfect example, but the type of data used is different, and so is the output required to deliver their products.
So, to better understand the different types of data products, we need to get to the core of the product’s functionality.
Types of Data Products Based on Functionality
Predictive modelling use historical data to make predictions about future events. For example, a predictive model might be used to predict which customers are most likely to churn, or to forecast sales for a business. These models can be built using a variety of techniques, including machine learning and statistical modeling.
This is a common type of data product, which we often see in day-to-day life. Recommendation systems utilize data about people/users and their behavior to recommend products, and services that the user might be interested in. These systems can be based on collaborative filtering, which uses data about the actions of other users to make recommendations, or on content-based filtering, which uses data about the attributes of items to make recommendations.
Data visualization products allow users to explore and understand large datasets by providing them with interactive visualizations, such as charts, maps, and heatmaps. This enables users to easily access and analyze key data and metrics, often in real-time. Dashboards and reporting products are commonly used in business intelligence and analytics applications, as well as in operational systems such as customer relationship management (CRM) and enterprise resource planning (ERP) systems.
Ever since Google came into existence, the term “Search Engine” became a common occurrence in everyday conversations. Search engines use natural language processing and machine learning to understand the intent behind a user's query and return relevant results. Search engine data products are used to improve the search results and make it more relevant to the user.
Anomaly detection systems are data products that are designed to detect unusual or unexpected patterns in data. These systems can be used to identify fraud, detect equipment failures, or flag potential security threats. Anomaly detection systems can be based on a variety of techniques, including machine learning and statistical modeling.
Conversational Intelligence Systems
Chatbots and virtual assistants are examples of data products that use natural language processing and machine learning to analyze, comprehend and respond to user input in a human-like language. These systems can be used to automate customer service, provide information, or perform other rudimentary tasks.
Data Observability for the Many Types of Data Products
On the whole, data products come in many different forms and can be used to solve a wide range of problems. Whether you're looking to improve business operations, better understand your customers, or automate a process, there is likely a data product that can help. The key is to understand your needs and identify the data product that can best meet those needs.
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.
Check out how data observability from Acceldata is helping leading brands develop great data products.