Data Warehouse Architecture: Traditional vs. Cloud

A digital city symbolizing data warehouse architecture.

A data warehouse is a system that gathers data from various sources and makes it available to management and others to support and improve their decision-making. Data warehouses can employ either a traditional architecture or a cloud-based one.  That brings us to the traditional vs. cloud data warehouse architecture topic. Which is best for your…

Read More

Data Testing Vs. Data Observability: What Does Your Data Need?

woman studying data observability vs testing

Poor-quality data is a blight on every organization that depends on data to run its operations. Using inaccurate and incomplete data can affect operations and long-term strategic planning, lead to bad decisions, and make a company less competitive in the market. You can address the problem of poor-quality data through data testing and observability. You…

Read More

10 Common Data Quality Issues (And How to Solve Them)

Data quality is essential for any data-driven organization. Poor data quality results in unreliable analysis. High data quality enables actionable insights for both short-term operations and long-term planning. Identifying and correcting data quality issues can mean the difference between a successful business and a failing one.  What data quality issues is your organization likely to…

Read More

How to Build a Unified, Scalable Cloud Data Lake

A cloud data lake is a powerful AI and ML tool.

Large, flexible, and powerful. No, not the Stay Puft Marshmallow Man. Cloud data lakes can store large amounts of data, are flexible enough to store a wide range of structured and unstructured information, and are a powerful tool for business analytics across diverse industries. This guide is the only one you’ll need to understand what…

Read More

How to Build the Perfect Analytics Stack

Abstract cubes representing an analytics stack.

How effective is your organization’s analytics stack?  You need the right stack of technologies to maximize data ingestion, transformation, storage, and analysis. A less-effective analytics stack can result in poor data quality and often unusable data.  When you build the perfect analytics stack, you’ll be able to more effectively manage your day-to-day operations and make…

Read More

Why Every Catalog Needs a Data Trust Score

Discover why your organization's data catalog needs a data trust score—and how you can benefit via better and more-informed decision-making.

Does your data catalog need a data trust score? With the increasing use of data catalogs in data management, the answer is a resounding yes. Assigning a data trust score to a catalog ensures that the data within is trustworthy for use in both day-to-day and long-term decision-making.  Quick Takeaways What Is a Data Trust…

Read More

Differences Between Data Quality and Data Trustability

data trustability

Do you know the difference between data quality and data trustability? Both are concerned with data quality and usability but differ in how they approach the issue. Data quality tracks the accuracy of data within a data pipeline. Data trustability has a larger scope, using machine learning to identify data errors from data creation to…

Read More

What Is a Data Trust Score?

data trust score

Do you trust the data used by your organization? A data trust score measures how much you trust your data and is based on how accurate, up-to-date, and relevant your data is. In the end, the data trust score reflects your data’s quality—high-quality data is more trustworthy than low-quality data.  Quick Takeaways Organizations have to…

Read More

Five Things to Know About Data Trustability

A number filled pattern in green color

How much do you know about data trustability? Do you know how data trustability relates to data quality and data observability? Do you know how it works within a data pipeline? Do you know why your firm needs it?  Data trustability is the next step in data quality. It goes beyond both data monitoring and…

Read More

What Is Data Observability for Data Pipelines?

A blue color tunnel illustration with binary codes

Data observability is the big buzzword these days, but do you know what it is or what it does? In particular, do you know why data observability is important for data pipelines?  You use a data pipeline to move data into and through your organization. You use data observability to ensure that your data pipeline…

Read More