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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

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How to build a successful AI strategy

IBM Big Data Hub

Without an AI strategy, organizations risk missing out on the benefits AI can offer. An AI strategy helps organizations address the complex challenges associated with AI implementation and define its objectives. What is an AI strategy? A successful AI strategy should act as a roadmap for this plan.

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CDOs’ biggest problem? Getting colleagues to understand their role

CIO Business Intelligence

One possible definition of the CDO is the organization’s leader responsible for data governance and use, including data analysis , mining , and processing. In many cases, CDOs focus on business objectives, but in other cases, they have equal business and technology remits, according to the authors.

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). Five Steps to GDPR/CCPA Compliance. How erwin Can Help.

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5 recommendations to get your data strategy right

IBM Big Data Hub

The rise of data strategy. There’s a renewed interest in reflecting on what can and should be done with data, how to accomplish those goals and how to check for data strategy alignment with business objectives. The evolution of a multi-everything landscape, and what that means for data strategy.

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Eating the Elephant of Data Strategy

Paul Blogs on BI

I have a had a lot of conversations about data strategy this year. With both the rise in organizations looking to move their data to the cloud and the increasing awareness of the power of BI and generative AI, data strategy has become a top priority. This is where the infamous “How do you eat an elephant?”

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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. Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads.