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Real-World Data Governance: The Role of Data Governance in a Data Strategy

Precisely

To make a difference for your organization, your data strategy should address more than just raw data; it needs to lay out a roadmap for aligning the people, processes, and technology that can support a truly data-driven culture. Data governance plays a critical role in any effective data strategy.

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Why Data Integrity Is the Baseline for Innovation

Precisely

The success of all of those investments hinges on high levels of data integrity. Data infrastructure spending is expected to reach $200 billion in 2022, and 68% of Fortune 1000 businesses now have hired Chief Digital Officers, elevating data integrity to the level of the C-suite. What Is Data Integrity?

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Use Data Enrichment to Supercharge AI

Precisely

The answers lie in data integrity and the contextual richness of the data that fuels your AI. Businesses must navigate many legal and regulatory requirements, including data privacy laws, industry standards, security protocols, and data sovereignty requirements. Contextual data. User trust and credibility.

Raw Data 121
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Data Warehouse vs. Data Lake

Precisely

We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data. Precisely helps enterprises manage the integrity of their data.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

Data silos: Legacy architectures often result in data being stored and processed in siloed environments, which can limit collaboration and hinder the ability to generate comprehensive insights. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

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Data Testing Tools: Key Capabilities and 6 Tools You Should Know

Databand.ai

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. This is part of a series of articles about data quality.

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Tips to Build a Robust Data Lake Infrastructure

DareData

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data.