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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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Why Choose a Hybrid Data Cloud in Financial Services?

Cloudera

Then there are the more extensive discussions – scrutiny of the overarching, data strategy questions related to privacy, security, data governance /access and regulatory oversight. These are not straightforward decisions, especially when data breaches always hit the top of the news headlines.

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Insights from Gartner’s 2023 Data Security Hype Cycle – Data Security Posture Management (DSPM) Highlights

Laminar Security

The Shifting Data Security Landscape Cloud service providers (CSPs) have revolutionized data analytics and data pipelines, presenting data security teams with novel challenges. The Importance of DSPM As data proliferates across the cloud, the need to identify and address privacy and security risks becomes paramount.

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What Is Data Modeling? Data Modeling Best Practices for Data-Driven Organizations

erwin

Companies that want to advance artificial intelligence (AI) initiatives, for instance, won’t get very far without quality data and well-defined data models. With the right approach, data modeling promotes greater cohesion and success in organizations’ data strategies. But what is the right data modeling approach?

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The Benefits of Data Governance in Banks and Financial Institutions

Alation

Data governance means putting in place a continuous process to create and improve policies and standards around managing data to ensure that the information is usable, accessible, and protected. In banks, this means: Setting data format standards. Identifying structured and unstructured data that needs to be protected.

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Driving Success With a Modern Data Architecture and a Hybrid Approach in the Financial Services and Telco Industries

Cloudera

Similarly, data should be treated as a corporate asset with a dedicated long-term strategy that lets the organization store, manage, and utilize its data effectively. Most importantly, it helps organizations control costs and reduce risks, enforcing consistent security and governance across all enterprise data assets.”.