Remove Cost-Benefit Remove IoT Remove Structured Data Remove Unstructured Data
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8 data strategy mistakes to avoid

CIO Business Intelligence

“Similar to disaster recovery, business continuity, and information security, data strategy needs to be well thought out and defined to inform the rest, while providing a foundation from which to build a strong business.” Overlooking these data resources is a big mistake. What are the goals for leveraging unstructured data?”

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Transforming Big Data into Actionable Intelligence

Sisense

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

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New Software Development Initiatives Lead To Second Stage Of Big Data

Smart Data Collective

For example, you can organize an employee table in a database in a structured manner to capture the employee’s details, job positions, salary, etc. Unstructured. Unstructured data lacks a specific format or structure. As a result, processing and analyzing unstructured data is super-difficult and time-consuming.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Within the context of a data mesh architecture, I will present industry settings / use cases where the particular architecture is relevant and highlight the business value that it delivers against business and technology areas. A Client Example.

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What is a Data Pipeline?

Jet Global

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. As data flows into the pipeline, it is processed in real-time or near-real-time.