The former United States chief data scientist, DJ Patil defines a data product as, a product that facilitates an end goal through the use of data.
Data Product is the outcome of any data science activity. The data product extracts a meaningful insights from the Bigdata using Machine Learning and Data Science methods. Data product can also be used as model to generate other user oriented or back end system level products. So, Data product can also be defined as system model that can facilitate to understand data for discovering insights and making predictions . The Data products are required whenever there is a need of systems that depend on predictive models , such as : Predicting users future activities based on the past , Recommending user intention based content, Estimating the future demand of a product.
Data products are the applications of data that brings value to the business. Data products may be predictive , descriptive or prescriptive models as well as insights. In business data products helps in generating revenue, cost optimization , risk mitigation , etc.
Steps in designing a Data Product :
- Get Data [ Raw , Structured or Unstructured Data]
- Prepare Data [ Transform to required Structure ]
- Apply Models [ Model Selection]
- Build Data Product [ Full Stack Interfaces]
Examples of Data Products :
- Google Search
- Amazon product recommendation
- Salesforce’s Einstein AI
- Google Analytics
- Bloomberg Terminal
- Linked In