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The data flywheel: A better way to think about your data strategy

CIO Business Intelligence

Every day, it helps countless organizations do everything from measure their ESG impact to create new streams of revenue, and consequently, companies without strong data cultures or concrete plans to build one are feeling the pressure. Some are our clients—and more of them are asking our help with their data strategy.

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Creating Data Value With a Decentralized Data Strategy

CIO Business Intelligence

For decades organizations chased the Holy Grail of a centralized data warehouse/lake strategy to support business intelligence and advanced analytics. billion connected Internet of Things (IoT) devices by 2025, generating almost 80 billion zettabytes of data at the edge. You have to automate it.

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It’s not your data. It’s how you use it. Unlock the power of data & build foundations of a data driven organisation

CIO Business Intelligence

To meet these demands many IT teams find themselves being systems integrators, having to find ways to access and manipulate large volumes of data for multiple business functions and use cases. Without a clear data strategy that’s aligned to their business requirements, being truly data-driven will be a challenge.

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Optimizing a Centralized Approach for the Modern Distributed Data Estate

CIO Business Intelligence

With the focus shifting to distributed data strategies, the traditional centralized approach can and should be reimagined and transformed to become a central pillar of the modern IT data estate. billion connected Internet of Things (IoT) devices by 2025, generating almost 80 billion zettabytes of data at the edge.

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Data Warehouse Teams Adapt to Be Data Driven

TDAN

When companies embark on a journey of becoming data-driven, usually, this goes hand in and with using new technologies and concepts such as AI and data lakes or Hadoop and IoT. Suddenly, the data warehouse team and their software are not the only ones anymore that turn data […].

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021! Discover why.