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Data and Analytics Tutorial

Data and Analytics Overview
Under Construction

Data and Analytics Success

Data and Analytics Strategy
Project Management
Data Analytics Methodology
Quick Wins
Data Science Methodology

Requirements

BI Requirements Workshop

Architecture and Design

Architecture Patterns
Technical Architecture
Data Attributes
Data Modeling Basics
Dimensional Data Models

Enterprise Information Management

Data Governance
Metadata
Data Quality

Data Stores and Structures

Data Sources
Database Choices
Big Data
Atomic Warehouse
Dimensional Warehouse
Logical Data Warehouse
Data Lake
Operational Datastore (ODS)
Data Vault
Data Science Sandbox
Flat Files Data
Graph Databases
Time Series Data

Data Integration

Data Pipeline
Change Data Capture
Extract Transform Load
ETL Tool Selection
Data Warehoouse Automation
Data Wrangling
Data Science Workflow

BI and Data Visualization

BI - Business Intelligence
Data Viaulization

Data Science

Statistics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics

Test and Deploy

Testing
Security Architecture
Desaster Recovery
Rollout
Sustaining DW/BI

Data and Analytics Introduction

Learn Data and Analytics Fundamentals


In this part of the Data and Analytics Tutorial we will discuss:

What is Data Analytics?

Data Analytics is a broad architecture for collecting, storing, and utilizing data for the enterprise. It includes Contemporary Data Architecture elements including: Data Pipelines, Data Lakes, Atomic Warehoues, Dimensional Warehouses, Business Intelligence and Data Science.

What is Business Intelligence (BI)?

Business Intelligence (BI) is the practice of supporting decision making through the presentation and analysis of data.  BI supports analytical processes such as:  customer segmentation, product mix analysis, budgeting, and operations improvements.  Techniques and tools associated with BI including data mining, dashboards, statistical analysis and regression modeling.

The article titled Business Intelligence and the Data Warehouse provides further information about BI.  Data is the needed raw material for material for BI and that is where Data Warehousing comes into play.

What is Data Warehousing?

Data Warehousing is a total architecture for collecting, storing, and delivering decision support data for an entire enterprise. Data warehousing is a subset of Data Analytics and is described point by point in this series of tutorials.

William (Bill) H. Inmon has provided an alternate and useful definition, “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”

As a total architecture it includes people, processes and technologies to achieve its goal which is to provide decision support data that is:

Benefits of Data Warehousing

Data warehousing and business intelligence efforts improve decision making which in turn provides numerous benefits including:

Decisions Impact The Bottom Line

Decisions can impact the bottom line by reducing cost and increasing revenues. Costs may be reduced by:

Revenues may be increased by:

Operational Data vs. Data Warehouse Data

The analytic data stored in the data warehouse is different from operational data. Operational data is:

In contrast, data warehouse / analytical data is:

High Quality Data

High quality data has the following characteristics:

See our article Data Sources for Data Warehousing and Business Intelligence to learn how to assess and improve the quality of data.

Decision Support Goals

The Decision Support function typically has the following goals:

These decisions can support a number of stakeholders

What a Data Warehouse Is and Is Not

Let's make clear what a data warehouse is and is not. A data warehouse is:

A data warehouse is not:

 


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