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3 Ways to Replace Distrust of Your SAP Data With Confidence

Jet Global

Data Cleansing Imperative: The same report revealed that organizations recognized the importance of data quality, with 71% expressing concerns about data quality issues. This underscores the need for robust data cleansing solutions.

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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Improve Retention by Bridging the SAP-to-Excel Gap

Jet Global

Addressing these challenges often requires investing in data integration solutions or third-party data integration tools.

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Save Time and Stress with Dynamics Data Merging from Atlas

Jet Global

Its easy-to-configure, pre-built templates get you up and running fast without having to understand complex Dynamics data structures. Free your team to explore data and create or modify reports on their own with no hard coding or programming skills required.

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Top 10 Reasons to Acquire a Product Information Management Solution (PIM or PXM)

Jet Global

A Centralized Hub for Data Data silos are the number one inhibitor to commerce success regardless of your business model. Through effective workflow, data quality, and governance tools, a PIM ensures that disparate content is transformed into a company-wide strategic asset.

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Use Angles for SAP to Measure, Analyze and Improve Your OTIF 

Jet Global

Discover how SAP data quality can hurt your OTIF. If you deliver the right products on time, offering a regular price and good quality, you will have happy customers,” Richard den Ouden, co-founder of Angles of SAP. Live demo tailored to your business requirements. Interested in Financial Reporting.

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The New Era of Transparency: Navigating CSRD with insightsoftware

Jet Global

Moving data across siloed systems is time-consuming and prone to errors, hurting data quality and reliability. Manual processes and juggling multiple tools won’t cut it under the ever-changing CSRD regulations. Inconsistent formats and standards across different tools further hinder comparison and aggregation.