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Data Integrity, the Basis for Reliable Insights

Sisense

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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Private cloud makes its comeback, thanks to AI

CIO Business Intelligence

Private cloud providers may be among the key beneficiaries of today’s generative AI gold rush as, once seemingly passé in favor of public cloud, CIOs are giving private clouds — either on-premises or hosted by a partner — a second look. billion in 2024, and more than double by 2027. billion in 2024 and grow to $66.4 We have no choice.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues.

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The power of remote engine execution for ETL/ELT data pipelines

IBM Big Data Hub

Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. There are several styles of data integration.

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CIO insights: What’s next for AI in the enterprise?

CIO Business Intelligence

IT leaders expect AI and ML to drive a host of benefits, led by increased productivity, improved collaboration, increased revenue and profits, and talent development and upskilling. Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT. There may be times when department-specific data needs and tools are required.

IT 125
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How to accelerate your data monetization strategy with data products and AI

IBM Big Data Hub

A value exchange system built on data products can drive business growth for your organization and gain competitive advantage. This growth could be internal cost effectiveness, stronger risk compliance, increasing the economic value of a partner ecosystem, or through new revenue streams.

Strategy 106