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How AI and ML Can Transform Data Integration

Smart Data Collective

The data integration landscape is under a constant metamorphosis. In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. Why is Data Integration a Challenge for Enterprises?

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Data Integrity, the Basis for Reliable Insights

Sisense

However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. In this article, we’ll dig into the core aspects of data integrity, what processes ensure it, and how to deal with data that doesn’t meet your standards.

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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

Notification to Affected Parties: Once a problem is identified and the responsible party is notified, informing those impacted by the change is crucial. Implement a communication protocol that swiftly informs stakeholders, allowing them to brace for or address the potential impacts of the data change.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

In an era of business where every industry requires its stakeholders to be capable of quick pivots and sharp turns, siloed information that ultimately slows decision-making can be the ultimate vulnerability. In this way, data may just be the ultimate disruptor – a fact that the insurance industry knows all too well.

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

In today’s data-driven world, businesses are drowning in a sea of information. Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. This allows your teams to make informed decisions based on real data, not just intuition.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Similar to disaster recovery, business continuity, and information security, data strategy needs to be well thought out and defined to inform the rest, while providing a foundation from which to build a strong business.” Overlooking these data resources is a big mistake.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.