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What companies get wrong about data transformation

CIO Business Intelligence

Such strategic missteps may signal an ongoing issue at the C-level, with company leaders recognizing the importance of data and analytics but falling short on making the strategic changes and investments necessary for success. And that makes educating the C-suite on the importance of data transformation a key CIO remit today.

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Texas Rangers data transformation modernizes stadium operations

CIO Business Intelligence

She decided to bring Resultant in to assist, starting with the firm’s strategic data assessment (SDA) framework, which evaluates a client’s data challenges in terms of people and processes, data models and structures, data architecture and platforms, visual analytics and reporting, and advanced analytics.

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Agentic AI: Why this emerging technology will revolutionise multiple sectors

CIO Business Intelligence

These developments come as data shows that while the GenAI boom is real and optimism is high, not every organisation is generating tangible value so far. For now, 51% say this strategic alignment has not been fully achieved, according to NTT DATAs study. [3] NTT DATAs Global GenAI Report now. [1] 3] Preparation.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is a data discovery tool with essential functionalities: recommendations, data marketplace, and business content (i.e.,

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Automate alerting and reporting for AWS Glue job resource usage

AWS Big Data

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

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Ensuring Data Transformation Results with Great Expectations

Wayne Yaddow

Data quality rules are codified into structured Expectation Suites by Great Expectations instead of relying on ad-hoc scripts or manual checks. The framework ensures that your data transformations comply with rigorous specifications from the moment they are created through every iteration of your pipeline.

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Sirius About Snowflake Demo: How to Create a Reporting Dashboard

CDW Research Hub

Key features include: A scalable pipeline to store and process transaction data, supporting daily update updates to a reporting dashboard with high-performance analytics. Manageability and use for non-technical users, democratizing data enterprisewide. Stay tuned for the next video in our Sirius About Snowflake demo series.