Remove Data Integration Remove Data Strategy Remove Data Transformation
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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Build data validation rules directly into ingestion layers so that insufficient data is stopped at the gate and not detected after damage is done. Use lineage tooling to trace data from source to report. Understanding how data transforms and where it breaks is crucial for audibility and root-cause resolution.

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Estes Express shifts gears on customer experience by streamlining data operations

CIO Business Intelligence

To fuel self-service analytics and provide the real-time information customers and internal stakeholders need to meet customers’ shipping requirements, the Richmond, VA-based company, which operates a fleet of more than 8,500 tractors and 34,000 trailers, has embarked on a data transformation journey to improve data integration and data management.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. Digitizing was our first stake at the table in our data journey,” he says.

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Sure, Trust Your Data… Until It Breaks Everything: How Automated Data Lineage Saves the Day

Octopai

This challenge is especially critical for executives responsible for data strategy and operations. Here’s how automated data lineage can transform these challenges into opportunities, as illustrated by the journey of a health services company we’ll call “HealthCo.”

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How CFM built a well-governed and scalable data-engineering platform using Amazon EMR for financial features generation

AWS Big Data

In this post, we share how we built a well-governed and scalable data engineering platform using Amazon EMR for financial features generation. To share data to our internal consumers, we use AWS Lake Formation with LF-Tags to streamline the process of managing access rights across the organization.

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Fabrics, Meshes & Stacks, oh my! Q&A with Sanjeev Mohan

Alation

Everybody’s trying to solve this same problem (of leveraging mountains of data), but they’re going about it in slightly different ways. Data fabric is a technology architecture. It’s a data integration pattern that brings together different systems, with the metadata, knowledge graphs, and a semantic layer on top.