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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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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data. Many people are confused about these two, but the only similarity between them is the high-level principle of data storing.

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11 dark secrets of data management

CIO Business Intelligence

And then there is the rise of privacy concerns around so much data being collected in the first place. Following are some of the dark secrets that make data management such a challenge for so many enterprises. Unstructured data is difficult to analyze. Even structured data is often unstructured.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

When these systems connect with external groups — customers, subscribers, shareholders, stakeholders — even more data is generated, collected, and exchanged. The result, as Sisense CEO Amir Orad wrote , is that every company is now a data company. This is quantitative data. or “how often?”

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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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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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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

This analytics engine will process both structured and unstructured data. “We We are constantly collecting data from all kinds of different sources — whether it is a library of documents, analytics reports, pictures, or even videos,” says Chris.