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Data Quality Requires Cross-Team Collaboration

Data quality is more than just a buzzword—it’s the foundation of strong decision-making and the success of your organization.  At its core, data quality refers to the accuracy, consistency, completeness, and reliability of your organization’s information. High-quality data means you can trust...

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Understanding Data Decay, Data Entropy, and Data Drift: Key Differences You Need to Know

We rely on data to inform decision-making, drive innovation, and maintain a competitive edge. However, data is not static, and over time, it can undergo significant changes that impact its quality, reliability, and usefulness.  Understanding the nuances of these changes is crucial if you aim to...

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Unstructured Data Management: Why It’s a Different and Tougher Beast Than Structured Data

When it comes to managing data, unstructured data is the wild card. It’s messy, unpredictable, and doesn’t fit neatly into the boxes and grids we’ve relied on for years.  Unlike structured data, which is easy to organize and analyze, unstructured data is chaotic—think videos, emails, images,...

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What is Data Quality and Why Does It Matter?

Whether you’re making strategic decisions, optimizing processes, or developing artificial intelligence-powered solutions, reliable data is the foundation of meaningful results. Without it, even the most advanced tools and technologies fall short. But what exactly is data quality, and why does it...

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How to Manage Outdated and Duplicated SharePoint Data

Managing outdated and duplicate data is a headache nearly every SharePoint user faces at some point. Files pile up, older versions hang around longer than they should, and before you know it, your SharePoint environment feels like a digital junk drawer.  When files are scattered and...

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Why Ignoring Data Decay Could Cost You Big in AI Initiatives

Data decay is the gradual loss of data quality over time, leading to inaccurate information that can undermine AI-driven decision-making and operational efficiency. Understanding the different types of data decay, how it differs from similar concepts like data entropy and data drift, and the...

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data mesh

What is a Data Mesh?

A data mesh is a modern approach to data architecture that decentralizes data ownership and management, thus allowing domain-specific teams to handle their own data products. This shift is a critical one for organizations dealing with complex, large-scale data environments – it can enhance...

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How to Find Your Path: Choosing Between Data Science and Data Analytics

The terms “data science” and “data analytics” are often used interchangeably, but they represent distinct fields with different goals, processes, and skill sets. Understanding the differences between these two disciplines is crucial for professionals who work with data, as...

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Understanding Data Lakehouses for Advanced Data Management

A data lakehouse is a modern data management architecture that’s designed to handle diverse data types and support advanced analytics. It’s a valuable tool for data scientists, project managers, AI professionals, and organizations that rely on data-driven decision-making.  As businesses...

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Generative AI Is the Poison and Antidote for Unstructured Data Quality

When it comes to data quality, unstructured data is a challenge. It often lacks the consistency and organization needed for effective analysis. This creates a pressing need to address data quality issues that can hinder your ability to leverage this data for decision-making and innovation. As you...

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What is Parquet? Columnar Storage for Efficient Data Processing

Choosing the right data format can significantly impact how well you manage and analyze your data, especially in big data environments. Parquet, a columnar storage format, has gained traction as a go-to solution for organizations that require high performance and scalability.  Parquet offers...

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Data Lake vs. Data Warehouse: Which Data Strategy is Right for You?

The ability to manage, store, and analyze vast amounts of data is critical to your organization’s success. As you generate more data from diverse sources, you must choose the right infrastructure to handle this information efficiently.  Two of the most popular solutions are data lakes and...

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