Data product development involves all activities of the data science project process

Including not only human users, but also computers and other software and hardware systems

Data products exist in many forms

Single latitude conversion

Multidimensional conversion

key links

Key technologies

data driven

data intensive

data paradigm

Data products

Information products

Knowledge products

Smart products

Content products

Application products

Service products

Decision-making products

work creatively

think critically

Ask curiously

Data object encapsulation

Data system development

Integrated applications

Ancillary services

Derived services

Three points for technology, seven points for management, and 12 points for data.

Data is the raw material for the development of data industry

The wisdom of data scientists is the main source of added value in data product development

User experience is the main evaluation indicator of data products

creative design

data insights

Visualization

story description

Virtualization

on demand services

Personalized service

Security and privacy protection

user experience

policy Analysis

Product development must have high artistic quality

Target user-centered product development

D.J. Patil

drop-down list

single button

Smart reminder

Other solutions

Amazon Mechanical Turk

Return results in search engines

Catering advertising information in search engines

Book advertising information in search engines

The importance of user experience

Errors and nonsense are often more likely to be perceived by target users and create a wrong perception of the entire data product.

What information needs to be provided by users and whether this information meets the needs of data product development

When requiring users to provide personal information, the scope of collection, purpose, promised utilization methods, and future services returned to users should be clearly informed.

Various forms of existence such as data acquisition, storage, integration, analysis, application, presentation, archiving and destruction. process of evolution

A collection of related control activity performance and risk management during data resources and their application process

System execution of data operations

The organization’s vision, purpose, goals and principles for conducting data work

A framework for abstractions such as data element structures and interfaces and their interrelationships

A set of processes that transform raw data into action and knowledge

Data about the data or data element

There is a set of attribute provisions that define data units that identify representations and allowable values

Core business entity data that needs to be shared across systems and departments in the organization

Data is valuable

Data management needs align with business needs

Data management relies on multiple skills

Data management is life cycle management

data strategy

data governance

Data quality

Data operations

Platform and architecture

auxiliary process

Executed level

Already managed

level defined

Measured level

Optimized level

start up

diagnosis

Establish

action

study

visible

accessible

understandable

association

trustworthy

interoperable

Safety

A data strategy not only needs to define the goals of data management, but also needs to provide specific action plans on how to achieve these management goals, as well as a mechanism for dynamically adjusting data management goals.

Define a data-driven organization or cultivate a data-driven culture, use data as the driving factor for the organization's decision-making activities, enhance the organization's agility, and thereby improve the organization's core competitiveness.

data intensive problem

China

Europe

USA

U.K.

Germany

Japan

Understand your data

Identification and analysis of data stakeholders

Establishment of data department

Formulation of codes of conduct

Determination of data management policies and objectives

Definition of job responsibilities

Emergency Plan and Emergency Management

Level protection and classification management

Effective supervision and dynamic optimization

plan

implement

examine

Improve

DGI data governance framework

key resources

important resources

primary resources

All resources

Data sources, selection bias

Data Processing and Preparation Bias

Algorithm and model selection bias

Bias in the interpretation and presentation of analytical results

Big data familiarity

Data attacks and Google bombs

Cambridge Analytica data scandal