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Data Analytics

Data Analytics vs. Data Analysis

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.

Quantitative vs. Qualitative 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.

Data Analytics vs. Business Analytics

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.

Data Analytics vs. Data Science

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.

The Difference Between Data Analytics and Statistics

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.

The Difference Between Data Analytics and Data Visualization

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.

Data Analytics Lifecycle

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.

Data Analytics vs Business Intelligence

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.

What is Behavioral Analytics?

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.

What is Descriptive Analytics?

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.

What is Data Analytics?

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.

What is Diagnostic Analytics?

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.

Data Analytics Processes

For a successful data analytics project, it’s important to have streamlined processes in place. In this article, we detail the different steps of the data analytics process and give valuable tips to guarantee the success of your data-driven project.

Data Warehouse

A top-level guide to data lakes

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.

Redshift vs Snowflake vs BigQuery: Choosing a Warehouse

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 Warehouse Architecture

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.

What Is a 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.

How to Create and Use Business Intelligence with 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.

Best Practices for Accessing Your 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.

Data Warehouse Best Practices — preparing your data for peak performance

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.

How do Data Warehouses Enhance Data Mining?

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.

Data Warehouses versus Databases: What’s the Difference?

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.

What are the Benefits of a Data Warehouse?

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.

Key Concepts of 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 versus Data Lakes

Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.

Data Warehouses versus Data Marts

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.

Difference Between Big Data and Data Warehouses

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.

How to Move Data in Data Warehouses

In this article, we’ll explore the various ways that data enters, moves through, and exits warehouses. You’ll also learn how a data warehouse strategy helps businesses understand their current position and set benchmarks to drive long-term growth.

Customer Data

What Is Customer Data?

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

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

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.

Collecting 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.

The Importance of First-Party Customer Data After iOS Updates

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.

Types of Customer Data

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.

What Is a Customer Data Platform?

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.

What is an Identity Graph?

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

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.

A complete guide to first-party 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.

CDPs vs. DMPs

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).

What is Identity Resolution?

Identity resolution is the process of combining personal information gathered by a website to generate a complete view of customers for enhanced data analysis.

Data Security

What is Consent Management?

This article explains how consent management enables you to establish trust with your users and ensure your data operations are within regulatory compliance.

Data Access Control

This article explains data access controls and why your organization must implement them to safeguard your valuable data and protect you from liability.

Data Sharing and Third Parties

Third-party data is important for building market insights and requires careful handling. This article explains third-party data terminology and best practices.

Cybersecurity Frameworks

A cybersecurity framework will help you prevent cyberattacks and protect your data and reputation. Here’s a guide to the most common cybersecurity frameworks.

What is PII Masking and How Can You Use It?

PII masking protects sensitive user information by changing data across a database. This article discusses techniques and concepts in PII masking.

Data Security Strategies

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 Technologies

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

Data protection security controls include physical, technical, and administrative controls. This article outlines best practices for security controls.

How to Manage Data Retention

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.

How To Handle Your Company’s Sensitive Data

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.

Data Security Best Practices For Companies

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.

What is Persistent Data?

”Persistent” data is stored on media from which it is accessible for the long term. This article explains persistent data terminology and best practices.

GA4

Google Analytics 4 and eCommerce Tracking

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.

What Is Google Analytics 4 and Why Should You Migrate?

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.

GA4 Migration Guide

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.

GA4 vs. Universal Analytics

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.

What are the New Features of Google Analytics 4 (GA4)?

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.

Benefits and Limitations of Google Analytics 4 (GA4)

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.

Understanding Google Analytics 4 Organization Hierarchy

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.

Understanding Data Streams in Google Analytics 4

Data streams in Google Analytics 4 replace and improve on several features from previous versions of Google Analytics. This article explains what data streams are, how to set them up, and some things you should keep in mind when working with them.

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