Data Technology Trend #7: Monetized

This article is a part of a multi-part series Data Technology Trends (parent article). Previous part — Data Technology Trend #6: Actionable outcomes and Next part — Data Technology Trend #8: Data Next — part 1.

Trend 7.1: Data & AI Market Places and exchanges platforms

Data can be best monetized if the organization can build Data & AI marketplace and exchange platforms are it for internal or external use.

1. Strategy: Choosing a plan for the organization to create a Data value strategy

2. Improving data monetization capabilities — Building strong data pipelines, data platforms.

3. Designing the extendible and live information solutions

4. Continuously improving and generating value through data

5. Building and Generating value through data and analytics

6. Continuous metrics measure — Data Lifetime value

7. Data monetization — Generating financial value out of data

One of the best ways to monetize the data is to use “Data Mesh” or “Data Fabric” on the existing disparate data/microservices of data.

With many businesses turning their high quality data into Data Market places, Snowflake offers a marketplace to publish your data.

The Data Market place by Snowflake enables you to share the data from your cloud into the Snowflake Market place where the consumers can consume the data where the consumers get access to quality, real-time data and can build a data warehouse based on that data. There are data sets across wide variety and growing. The data is shared by renowned providers such as FactSet, S&P Global etc.

Key Features:

  • Discover the Data that Drives Insight
  • Reduce Data Integration Costs
  • Access Fresh Data Faster
Sample Reference Architecture

AWS Data Exchange is building as a central market place where you can find and subscribe to data. Quality and refined data from qualified data providers are included as per AWS. Providers such as Healthcare, Finance, etc. participate in this market place.

Key Benefits:

  • Quickly find diverse data in one place
  • Efficiently access data in the cloud
  • Easily analyze new data

Trend 7.2: Data As A Service

Data-as-a-service (DaaS) is a data management strategy and/or deployment model that focuses on the cloud (public or private) to deliver a variety of data-related services such as storage, processing, and analytics.

DaaS leverages the popular software-as-a-service (SaaS) paradigm, through which customers are able to use cloud-based software applications delivered over the network rather than deploying dedicated hardware servers for a specific set of tasks on a specific set of data. DaaS is not only about sharing a common infrastructure to achieve economies of scale, but also about sharing some of the data among various teams to allow greater collaboration and knowledge transfer within any organization. With DaaS, customers aspire to handle most of their storage, processing, and analytics needs in the cloud to help reduce data silos and data sprawl.

Data Bricks Delta Share and Unity Catalog released in 2021 are excellent candidates for Data As A service.

Refer to my article — Data Technology Trends #8: Data Next (Part 3) to read further on this.

Trend 7.3: Data As A Product

Data-As-A-Product Model:

In the Data as a Product (DaaP model), the organization’s data is viewed as a product.

  • Data flow is unidirectional
  • Data has SLA
  • Data will have problems and anomalies, stakeholder’s understanding is the key hence product-driven must be combined with stakeholder-driven development.
  • Any product that is mentioned in Data Market Place, if you publish to the market place, then it becomes “Data As A Product”.

For other articles refer to luxananda.medium.com.

All the views expressed here are my own views and does not represent views of my firm that I work for. Data | Big Data | Cloud | ML