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The latest industry news, interviews, technologies, and resources.

ETL vs. ELT: Choosing the Right Approach for Your Data Strategy
In today’s data-driven world, organizations across all industries are under increasing pressure to leverage data as a strategic asset. From e-discovery processes to case management and advanced analytics, data is integral to efficiency, compliance, and competitive advantage. However, before you can turn raw data into actionable insights, you need a robust data integration strategy. This is where two popular data integration methodologies come into play: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
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ETL vs. ELT: Choosing the Right Approach for Your Data Strategy
In today’s data-driven world, organizations across all industries are under increasing pressure to leverage data as a strategic asset. From e-discovery processes to case management and advanced analytics, data is integral to efficiency, compliance, and competitive advantage. However, before you can turn raw data into actionable insights, you need a robust data integration strategy. This is where two popular data integration methodologies come into play: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
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Data Integration Solutions: How to Choose the Right Vendor
This blog provides a structured guide on selecting the right data integration vendor. It explores key factors such as compatibility, scalability, security, ease of use, vendor reputation, support, and cost to help businesses make informed decisions.

Data
EU Data Act: lay the track, time to act
A major shift in industrial data is coming. The EU Data Act, taking full effect on September 12, 2025, will transform how businesses handle mandatory data sharing. With hundreds of connected products but no infrastructure in place, many CIOs face a critical challenge. As digital expert Pieter van Schalkwyk puts it, this revolution is as impactful as the 19th-century railroad boom—laying the tracks for the future of business value. Are you ready?
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Data
Three critical data quality mistakes and how to avoid them
Tens of millions? Due to legal updates? How the hell was it possible?’’ That's exactly what happened to Uber in 2017.

Data
The Future of Data Quality - Why good enough is not good enough anymore?
There is one thing that every big company has in common. They all want to be ‘’data-driven”. OK. But why? McKinsey’s study shows that data-driven organizations are 23 times more likely to acquire customers, 6 times more likely to retain customers, and 19 times more likely to be profitable as a result. Yet, a harsh reality remains: most companies struggle to realize these benefits due to fundamental data quality and integration challenges. "Most organizations are drowning in data but starving for insights," is a main takeaway from Delloitte’s report. This observation aligns with Gartner's finding that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.

Business
Your decisions are as good as your data quality
Imagine this: you're driving in a luxury car. Beautiful leather seats, advanced GPS and a powerful engine under the hood. Everything is perfect, except for one tiny detail. Your speedometer shows 50 km/h, but you're actually driving at 80 km/h. No big deal, right? Wrong. That "small data issue" could get you killed. Or worse, kill someone else. Or at least, cost your driver's license.
Take the complexity out of your most challenging data projects
For Black Tiger, technology must be the guarantor of freedom of action and thought.
