Data products 101 and slides


DATA PRODUCT DEFINITION
DATA PRODUCT DEFINITION
        
3 IMPORTANT THINGS   USER,DATA
3 IMPORTANT THINGS USER,DATA
        
HARVARD BUSINESS REVIEW   ALGO
HARVARD BUSINESS REVIEW ALGO
        
HOW TO DESIGN DATA PRODUCTS
HOW TO DESIGN DATA PRODUCTS
        
SOFTWARE PRODUCT VS DATA PRODU
SOFTWARE PRODUCT VS DATA PRODU
        
DATAKNOBS APPROACH FOR BUILDIN
DATAKNOBS APPROACH FOR BUILDIN
        
BUILD HIGHER LEVEL CONCEPTS BY
BUILD HIGHER LEVEL CONCEPTS BY
        


Data products 101 and overview


Data as product


Benefits of Data-as-Product for CIOs

As a data architect, I believe that CIOs can benefit greatly from implementing a data-as-product strategy. By treating data as a product, CIOs can:

  • Monetize data assets by selling them to external customers or internal business units
  • Improve data quality and governance by establishing clear ownership and accountability
  • Enable self-service analytics and reporting for business users
  • Drive innovation by encouraging experimentation and exploration of data
  • Enhance collaboration and knowledge sharing across the organization

Benefits of Data-as-Product for Enterprises

Enterprises can also gain significant benefits from a data-as-product approach, including:

  • Increased revenue and profitability through new data-driven products and services
  • Better customer insights and engagement through personalized and targeted marketing
  • Improved operational efficiency and cost savings through data-driven decision making
  • Reduced risk and improved compliance through better data governance and security
  • Enhanced competitive advantage through faster and more accurate insights

Planning for Building Data-as-Product

When planning for building a data-as-product strategy, CIOs should:

  • Identify and prioritize data assets based on their value and potential for monetization
  • Establish clear ownership and governance for each data asset, including data quality standards and security protocols
  • Define a data catalog or marketplace to enable self-service access and discovery of data assets
  • Invest in data infrastructure and tools to support data processing, storage, and analysis
  • Develop a data culture that encourages experimentation, collaboration, and innovation

Data as product service


Data-Product-as-Service

As a data architect, I would like to describe the concept of data-product-as-service to experts. Data-product-as-service is a business model where companies offer data products as a service to their customers. This means that instead of selling data as a one-time product, companies offer access to their data through a subscription-based model.

Advantages: The advantages of data-product-as-service are numerous. Firstly, it allows companies to generate recurring revenue streams. Secondly, it provides customers with access to up-to-date and relevant data. Thirdly, it allows companies to maintain control over their data and ensure that it is being used in a responsible and ethical manner.

Business Models: There are several business models that companies can use for data-product-as-service. One model is the pay-per-use model, where customers pay for the data they use. Another model is the subscription-based model, where customers pay a monthly or yearly fee for access to the data. A third model is the freemium model, where customers can access a limited amount of data for free, but must pay for additional data.

Benefits: Companies can benefit from data-product-as-service in several ways. Firstly, it allows them to monetize their data assets. Secondly, it provides them with a recurring revenue stream. Thirdly, it allows them to maintain control over their data and ensure that it is being used in a responsible and ethical manner. Fourthly, it allows them to provide their customers with up-to-date and relevant data.


Capabilities/platform requie to build data as products in an enterprise


To build data products, you require varity of capabilities. Your goal is to take raw data and convert into meaningful higher level signals. To do this you may be sourcing raw data, integrating with outher sources, may use web scrparing, apply ML and statistical model for preiction etc. You can also use generative AI to generate new data. Here are list of capabilities need to build ne dataset.

Core capability
  • Data ingestion
  • Data Transformation
  • Data integration
  • Statistics to understand data
  • Data science and ML
  • Web scraping
  • Geneerative AI


  • Once you build new dataset, there are additional capabilities require for
  • Lineage
  • Governance
  • Quality
  • Meta data

  • On top of these you also want feedback on data/ recommendation of data
  • Endorsement
  • Certificate

  • When an eneterprise build data product, it also pay attention to
  • Cost spend on producing data
  • Benefit from data



  • Data-as-product-cio    Data-as-product    Data-product-as-service    Data-product-capabilities