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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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Data architecture strategy for data quality

IBM Big Data Hub

Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. Practice proper data hygiene across interfaces. How to build a data architecture that improves data quality.

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It’s not your data. It’s how you use it. Unlock the power of data & build foundations of a data driven organisation

CIO Business Intelligence

Organisations have to contend with legacy data and increasing volumes of data spread across multiple silos. To meet these demands many IT teams find themselves being systems integrators, having to find ways to access and manipulate large volumes of data for multiple business functions and use cases.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Data within a data fabric is defined using metadata and may be stored in a data lake, a low-cost storage environment that houses large stores of structured, semi-structured and unstructured data for business analytics, machine learning and other broad applications. Security Data security is a high priority.

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What is Data Classification? Guidelines, Types, & Examples

Alation

Let’s discuss what data classification is, the processes for classifying data, data types, and the steps to follow for data classification: What is Data Classification? Either completed manually or using automation, the data classification process is based on the data’s context, content, and user discretion.