October 4, 2024
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Data Architecture News, Articles, & Education

The Hidden Language of Data: How Linguistic Analysis Is Transforming Data Interpretation

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From Fortune 500 companies to local startups, everyone’s swimming in a sea of numbers, charts, and graphs. But here’s the thing: While structured data like sales figures and customer demographics have long been the backbone of analytics, there’s a growing realization that unstructured data is the real goldmine. Think about it. Every tweet, email, customer review, and social…

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Data Product vs. Data as a Product (DaaP): Understanding the Difference

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Data quality (DQ), which ensures that data is fit for business and consumer needs, remains a significant challenge and is growing more complex. According to a dbt Labs 2024 report, 57% of survey respondents identified data quality as a challenging aspect to data preparation, up from 41% in 2022.  To address these data quality challenges, companies increasingly…

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What Is a Data Fabric?

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Data fabric is an innovative approach to data architecture, and simplifies data management. At its core, data fabric is built on the principle of unification. This standardization serves two purposes: It creates a single-entry point for data consumers, and it enables seamless access to information, regardless of where that data is stored, computed, or administered. These outcomes happen through data…

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Mind the Gap: Start Modernizing Analytics by Reorienting Your Enterprise Analytics Team

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… and your data warehouse / data lake / data lakehouse. A few months ago, I talked about how nearly all of our analytics architectures are stuck in the 1990s. Maybe an executive at your company read that article, and now you have a mandate to “modernize analytics.” Let’s say that they even understand that just…

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AAA Webinar: AI and Data Management

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Download the slides here>> About the Webinar In this webinar, we will dive deep into the unique challenges of managing the quality, security, and transparency of AI applications. We will also discuss how to use the power of AI to up-level your current data management practices. About the Speaker Nick White CEO and Founder, Data…

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Foundations of Forensic Data Analysis

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Forensic data analysis involves collecting, modeling, and transforming data to identify and highlight potential risk areas, detect non-standard or fraudulent activities that use data, and set up internal controls and processes to minimize a variety of risks. Data forensics can also be used in instances involving the tracking of phone calls, texts, or emails traveling…

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Traditional Data Engineering Best Practices Aren’t Cutting It Anymore

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I have been in data (in the fuzziest sense of the word) since about 2009, whether that means data engineering, management, analysis, strategy, or visualization. Over that time, things have changed drastically. In my first “real” data position, I was asked to identify and organize fallout from a claim auto adjudication engine to identify ways to…

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Streaming Data Dilemma: 5 Reasons Some Companies Aren’t Streaming Data Yet

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In the era of big data and bigger AI, businesses are relying more on the importance of real-time data processing and analytics. Streaming data is a powerful paradigm for handling continuous, unbounded streams of data in real time. However, despite benefits like reduced latency, improved responsiveness, and the ability to make data-driven decisions on the…

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Mind the Gap: Analytics Architecture Stuck in the 1990s

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Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the data chasm. This month, we’ll look at analytics architecture. From day one, data warehouses and their offspring – data marts, operational…

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Securing Data in Transit for Analytics Operations

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Most enterprises today store and process vast amounts of data from various sources within a centralized repository known as a data warehouse or data lake, where they can analyze it with advanced analytics tools to generate critical business insights.  Modern data warehouse platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, and IBM Db2 are built with…

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