Learning CenterLearning Topics
Data Analytics vs. Data Analysis
Quantitative vs. Qualitative Data
What is Behavioral Analytics?
Data Analytics vs. Business Analytics
Data Analytics vs. Data Science
The Difference Between Data Analytics and Statistics
The Difference Between Data Analytics and Data Visualization
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What is Data Analytics?
What is Diagnostic Analytics?
Data Analytics Processes
A top-level guide to data lakes
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
Data Warehouse Architecture
What Is a Data Warehouse?
How to Create and Use Business Intelligence with a Data Warehouse
Best Practices for Accessing Your Data Warehouse
Data Warehouse Best Practices — preparing your data for peak performance
How do Data Warehouses Enhance Data Mining?
Data Warehouses versus Databases: What’s the Difference?
What are the Benefits of a Data Warehouse?
Key Concepts of a Data Warehouse
Data Warehouses versus Data Lakes
Data Warehouses versus Data Marts
Difference Between Big Data and Data Warehouses
How to Move Data in Data Warehouses
What Is Customer Data?
Customer Data Analytics
Customer Data Management
Collecting Customer Data
The Importance of First-Party Customer Data After iOS Updates
Types of Customer Data
What Is a Customer Data Platform?
What is an Identity Graph?
Customer Data Protection
A complete guide to first-party customer data
CDPs vs. DMPs
What is Identity Resolution?
What is Consent Management?
Data Access Control
Data Sharing and Third Parties
What is PII Masking and How Can You Use It?
Data Security Strategies
Data Security Technologies
Data Protection Security Controls
How to Manage Data Retention
How To Handle Your Company’s Sensitive Data
Data Security Best Practices For Companies
What is Persistent Data?
Google Analytics 4 and eCommerce Tracking
What Is Google Analytics 4 and Why Should You Migrate?
GA4 Migration Guide
GA4 vs. Universal Analytics
What are the New Features of Google Analytics 4 (GA4)?
Benefits and Limitations of Google Analytics 4 (GA4)
Understanding Google Analytics 4 Organization Hierarchy
Understanding Data Streams in Google Analytics 4
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Understanding the role that data analysis has to play in the data analytics lifecycle is vital to developing a healthy data system that produces value from your incoming data.
The data your business collects for analysis and research will fall into one of two categories — quantitative or qualitative. This article explains what quantitative and qualitative data are and how each can be used in your business.
This article defines behavioral analytics, explains how to do it, and why it is important. It also explains what behavioral data consists of and introduces a variety of behavioral analytics tools.
This article compares data analytics and business analytics, examining the data sources, analytical approaches, and deliverables for each. It also covers how business analytics fits into the data analytics lifecycle.
In this article, we explain the differences between data analytics and data science, and discuss the job roles, skill sets, and responsibilities of both a data analyst and a data scientist.
Data analytics sets up the context and process for analyzing data. Statistics verifies it with rigorous proof. These processes must be well understood in order to efficiently sort, communicate, and validate data-based business decisions.
This article explores the differences between data analytics and data visualization, and explains the primary data visualization strategies you should use and how they benefit your business.
This article describes the data analytics lifecycle — question discovery, data preparation, model planning, building and executing the model, communicating the results, and operationalizing the process — and why each phase is important.
This article explains the differences between data analytics and business intelligence in terms of scope, temporal focus, and frequency. It discusses the benefits of data analytics and business intelligence and covers some useful BI techniques.
This article defines descriptive analytics, explains how it works and its benefits for your business, and it gives real-world examples of descriptive analytics, such as financial metrics, social media engagement, and web traffic reports.
This article gives an overview of data analytics and explains the different types of data analytics, why data analytics is important, and how it’s different from business intelligence.
This article defines diagnostic analytics, explains how to use diagnostic analytics in your business, and gives examples of the kinds of questions diagnostic analytics aims to answer.
In this article, we’ll cover everything you need to know about data lakes. You’ll learn, what is a data lake? How is a data lake different from a data warehouse? Benefits of data lake.
When it comes to choosing a data warehouse for the open-source analytics stack, it is very common to pit data warehouses against each other. Redshift vs BigQuery, Redshift vs Snowflake, etc. are some common examples.
Data warehouses need to be architected in a way that maximizes flexibility and maintains speed. In this article, you'll learn about data warehouse architectures, as well as what you should consider when setting up your own data warehouse.
Data warehouses offer a wide range of benefits. In this article, you’ll learn about the benefits and features of a data warehouse and how to effectively implement, use, and maintain a data warehouse.
In this article, we cover what business intelligence (BI) is, which roles within the company work with it, and explain BI's relationship to the data warehouse.
In this article, you will learn how data is stored in a data warehouse, how it’s accessed, best practices for writing SQL queries, who the major cloud data warehouse vendors are, and the various types of data storage models available.
One of the best modern tools for maximizing the observability and analytic power of your data is the data warehouse. This article will guide you through data warehouse best practices and illustrate how to get the most value from your data warehouse.
Everyone’s saying it more and more, and in increasingly tired metaphors: data is a new currency. This article covers key ways in which data warehouses enhance the data mining process.
Understanding the sometimes-subtle differences between types of data infrastructure can be challenging. This article will explain the differences and the best usages of a data warehouse as opposed to a database.
A data warehouse is a software construct that pulls data from different sources into a single target for business intelligence analysis and support for strategic decisions. In this article, we examine the benefits of implementing a data warehouse.
This article covers key concepts of the data warehouse, comparing its capabilities with that of relational databases, data marts, and data lakes — all common solutions to the challenges of consuming large, varied types of data.
Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.
In the worlds of business intelligence and outcome modeling, the terms data warehouse and data mart are often used interchangeably. The differences are worth knowing, though, so in this article we’ll compare and contrast the two.
Although terms “big data” and “data warehouse” are often compared directly, there is a categorical difference between them. We will cover the difference in this article.
This article gives a definition of customer data, as well as explaining the different types of customer data, how customer data is collected, why it is important, and whether collecting customer data is legal.
Customer data analytics is generating inferences from data that’s coming directly from your customers, or is inferred through their actions in your SaaS product, website, and campaigns. This article helps you choose the right processes for the job.
Customer data management (sometimes known as “customer database management”) is a common umbrella term for the strategies a company uses to collect, control and use its customer data.
It’s no secret that data has become the most precious commodity to today’s leading businesses. When properly mined, data can enable powerfully effective marketing and game-changing growth strategies.
This post highlights importance of first-party data after iOS updates and how this data is superior to third-party data while helping your achieve better results.
Customer data is both a valuable business asset that can be used for marketing and business growth, as well as a sensitive source of information about individuals that must be protected.
This article defines a customer data platform (CDP), and explains the different types of CDPs. It also weighs the pros and cons of buying software vs building your own.
Identity graphs are used to filter redundant customer data and connect potentially billions of points in a complex, scalable web of people and behavior.
Customer data protection is vital for avoiding PII data breaches and their legal and reputational consequences. In this article, find out how to protect your customer data.
With Google and Apple killing the ad cookie in an effort to improve data privacy, one thing is clear: third-party data access is on its way out. Learn more about how RudderStack can help your company get started with your first-party data journey.
Customer data management has recently undergone changes with the tools used to handle customer data. This article will help illustrate the difference between two of these tools — customer data platforms (CDPs) and data management platforms (DMPs).
This article explains how consent management enables you to establish trust with your users and ensure your data operations are within regulatory compliance.
This article explains data access controls and why your organization must implement them to safeguard your valuable data and protect you from liability.
Third-party data is important for building market insights and requires careful handling. This article explains third-party data terminology and best practices.
A cybersecurity framework will help you prevent cyberattacks and protect your data and reputation. Here’s a guide to the most common cybersecurity frameworks.
PII masking protects sensitive user information by changing data across a database. This article discusses techniques and concepts in PII masking.
A data protection strategy can avoid costly data breaches and protect your reputation and business assets. Learn the key components of a data security strategy.
Data security is vital to the continuity of your business. This article explains the data security technologies you should implement to protect it.
Data protection security controls include physical, technical, and administrative controls. This article outlines best practices for security controls.
In this article, you’ll learn more about what data retention is and why a data retention policy is valuable to your organization. You’ll also learn some of the core ideas behind data retention policies and some best practices for creating your own.
Damages from sensitive data loss can be devastating for companies. It can destroy customer trust and lead to financial and legal consequences. In this article, you will learn about sensitive data within a company and how best to protect it.
The viability of modern companies is rooted in data security. This article will explain the data security best practices you must implement in your business.
All interactions on your site or app are now recorded as events, as part of the new data model that Google Analytics 4 relies on. This article explains how eCommerce tracking has changed in GA4, compared with UA.
Google Analytics is a powerful data analytics platform. In this article, we discuss Google Analytics 4, the newest version of Google Analytics, how it can be beneficial to your business, and why you should prioritize migrating to it.
In July 2023, Universal Analytics will stop collecting data, so you need to migrate to Google Analytics 4, the next generation of Google Analytics. In this article, we break GA4 migration down into phases to guide you through your implementation.
Google Analytics 4 (GA4) has several differences from Universal Analytics. It’s more compliant with privacy regulations, it’s made events more customizable, and it’s spun out some functionality into other products.
The Google Analytics platform is undergoing some major changes with the introduction of Google Analytics 4 (GA4). This article explains the biggest new features of GA4 and how they will benefit your online business.
Google Analytics 4 (GA4) is the next generation of Google's web and app analytics platform, replacing the previous version, Universal Analytics (UA). This article outlines GA4’s benefits and limitations in comparison to its predecessor.
The Google Analytics hierarchy is different in Google Analytics 4 (GA4) compared to Universal Analytics. This article explains the changes to the hierarchy in terms of accounts, properties, and data streams, and how user permissions work in GA4.