From Data to Value: Understanding Data Management Through a Real World Use Case [Full Book]

                     From Data to Value: Understanding Data Management Through a Real World Use Case [Full Book]
               



Tópico:
                     From Data to Value: Understanding Data Management Through a Real World Use Case [Full Book]
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

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Today, data has become a particularly valuable resource. It allows companies to compete in the market and drive innovation, improving the quality of products and services offered.

Data processing lets teams automate processes. It also supports decision-making, offers a significantly more personalized experience to the end user, and detects patterns in many areas such as banking fraud or risk mitigation. Companies need to know how to capture and use data effectively, safely, and legally.

You likely are or have been a user of various products and services. And you know that processes involving data are fundamental to almost everything around us. You're likely also already familiar with terms like Big Data, Data Analytics, Artificial Intelligence, and Machine Learning.

But unless you're an expert in one of these fields, some of these concepts might seem overwhelming. These are large areas of study, after all.

And even if you're trained in one of these areas, it's difficult to know all the details about each field, as the data world is vast.

One way to understand this world of data a bit better is by dividing it, and establishing a distinction between the areas of Artificial Intelligence and Data Management. This isn't the only way to proceed, but I've found it helpful to separate the set of disciplines and techniques for information processing into these two blocks.

On one side is Data Management, which encompasses everything related to the capture, storage, protection, and analysis of data.

Meanwhile, on the other side is Artificial Intelligence, which focuses on developing techniques that allow a machine to emulate human capabilities like reasoning or learning to solve a problem, whether interacting with data or not.

Here, interaction refers to an algorithm acquiring "knowledge" from data, but not all artificial intelligence functions.

In any case, this book offers a comprehensive overview of Data Management, helping you understand all the terms and related concepts involved in using, processing, and analyzing data.

It won't just provide an abstract explanation of the field and its contents. It'll instead help you understand it holistically and offer a more practical and realistic view. We'll also study a use case to put into practice everything we discuss.

Table Of Contents

• Our Case Study

• Data Management Fundamentals

• Data as an Asset

• Data, Information, Knowledge, and Value

• The Data Lifecycle

• Data Management Principles

• Data Management Capabilities

• Data Governance

• Data Ownership

• Data Stewardship

• Decision Rights

• Data Policies

• Data Standards

• Data Accountability

• Data Ethics

• Ethical Data Use

• Consent and Transparency

• Fairness and Non-Discrimination

• Responsible Data Sharing

• Ethical Risk Management

• Data Security and Privacy

• Data Classification

• Identity and Access Management

• Encryption

• Data Masking

• Privacy Controls

• Audit and Compliance

• Security Operations (SecOps)

• Data Architecture

• Enterprise Data Architecture

• Data Domains

• Data Flows

• Operational Data Architecture

• Analytical Data Architecture

• Cloud and Hybrid Data Architectures

• Data Modeling and Design

• Conceptual Data Models

• Logical Data Models

• Physical Data Models

• Entity-Relationship Modeling

• Dimensional Modeling

• Data Model Governance

• Data Storage and Operations

• Databases

• File and Object Storage

• Data Warehouses

• Data Lakes

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