Data Quality Data Warehouse Modeling Operational Reporting 
CIO Business Intelligence
FEBRUARY 14, 2023
To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions.
Jet Global
MAY 6, 2024
However, if your team is accustomed to traditional methods they might hesitate to embrace SAP IBP’s AI-powered data anomaly detection for a few reasons. Firstly, there’s a potential fear of the unknown – relying on AI for such a critical task as data quality can feel like a leap of faith.
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Jet Global
SEPTEMBER 26, 2023
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.
Jet Global
MAY 8, 2024
The Dresner Customer Experience Model maps metrics like the sales and acquisition process, technical support, and consulting services, against general customer sentiment. The Vendor Credibility Model measures value for money against user confidence. Logi Symphony scored as a leader in both models and earned a perfect recommended score.
Jet Global
MAY 9, 2024
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.
Jet Global
AUGUST 24, 2023
Although many companies run their own on-premises servers to maintain IT infrastructure, nearly half of organizations already store data on the public cloud. The Harvard Business Review study finds that 88% of organizations that already have a hybrid model in place see themselves maintaining the same strategy into the future.
Jet Global
JULY 17, 2023
One of the major challenges in most business intelligence (BI) projects is data quality (or lack thereof). In fact, most project teams spend 60 to 80 percent of total project time cleaning their data—and this goes for both BI and predictive analytics. One can also create a classification model.
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