datapine
NOVEMBER 27, 2019
Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 1) Data Quality Management (DQM). We all gained access to the cloud.
CIO Business Intelligence
JUNE 30, 2023
All of this renewed attention on data and AI, however, brings greater potential risks for those companies that have less advanced data strategies. As these trends continue to evolve, building your data strategy around the principles of openness and governance assures trust in the data.
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CDW Research Hub
NOVEMBER 29, 2021
Your first thought about the Internet of Things (IoT) might be of a “smart” device or sensor. Collect, visualize and analyze data the sensors and devices gather. Layer 5: Data management. The value of data is at the heart of every IoT solution. Handle different hardware and software communication protocols.
Sisense
OCTOBER 15, 2020
The world is moving faster than ever, and companies processing large amounts of rapidly changing or growing data need to evolve to keep up — especially with the growth of Internet of Things (IoT) devices all around us. Every data professional knows that ensuring data quality is vital to producing usable query results.
erwin
JUNE 11, 2020
To keep pace with technology, businesses have been employing more tools and methods that incorporate modern technology like, Machine Learning, and the Internet of Things(IoT) to enhance the consumer experience. More businesses employing data intelligence will be incorporating blockchain to support its processes.
AWS Big Data
MARCH 7, 2023
ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.
datapine
AUGUST 29, 2022
In this at-times contrarian and unflinching book, Dr. Barry Devlin shows how modern BI often fails to deal with data from mobile, social media, and the Internet of Things in a meaningful way. Designed to be an accessible resource, this essential big data book does not include exhaustive coverage of all analytical techniques.
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