Data products 101 and slides

Data product facilitates end goal thru the use of data. It focus on how same data is used in multiple use cases to satisfy multiple stakeholders need. It bring product thinking to data. Read Forbes, Harvard quotes on data product. See innovative approach of Data knobs to build data product. Here is summary how create dataset, create app combining with ab testing enable building great data product.

DATA PRODUCT DEFINITION

3 IMPORTANT THINGS USER,DATA

HARVARD BUSINESS REVIEW ALGO

HOW TO DESIGN DATA PRODUCTS

SOFTWARE PRODUCT VS DATA PRODU

DATAKNOBS APPROACH FOR BUILDIN

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Dataknobs - Build Data Products Right From The Start

Build Data Products Right From The Start

An integrated platform to Kreate, Kontrol, and Experiment with your data products, turning complex challenges into manageable solutions.

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The Dataknobs Toolkit

Everything you need to ship robust data products, faster.

Create Signals and Data

Generate and refine the core datasets that power your applications.

Create Knowledge

Build intelligent knowledge bases to drive insights and automation.

Create Websites & Portals

Develop user-facing applications and portals directly on the platform.

Create Assistants & Agents

Design and deploy AI-powered assistants to automate tasks and engage users.

Guardrails & Policy

Enforce governance with robust policy controls and safety guardrails.

Prompt Management

Centralize and control the prompts used by your AI models.

Data Lineage

Maintain full visibility into your data's origin and transformations.

Enterprise Integration

Seamlessly connect with third-party controls and enterprise systems.

A/B Test Websites

Easily run A/B tests on your websites and portals to optimize user experience.

Automated Data Experiments

Validate algorithms and data quality with automated experimentation.

Prompt-based Experimentation

Test and iterate on AI prompts to find the most effective interactions.

Experiment Repository

Track and manage all your experiments in a centralized repository.

Turn the Knobs

Our core philosophy revolves around "Knobs"—powerful levers for tuning, diagnosis, and experimentation. Fine-tune every aspect of your data product with intuitive controls.

AI Workflow Levers

Adjust model parameters, data sources, and processing pipelines in real-time.

Experimentation & Diagnosis

Isolate variables, diagnose issues, and control experiment parameters with precision.

Business & Domain Knobs

Empower domain experts to tune the product based on business logic and market needs.

"Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders... face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need."

— Harvard Business Review

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

Bulding-modern-data-products Co-pilot-aiase Copilot-for-data-products Data-as-product-cio Data-as-product Data-lake Data-mesh-for-data-products Data-product-as-service Data-product-capabilities Data-product-optimization