Big data has changed how we manage, analyze, and leverage data across industries. One of the most notable areas where data analysis is making big changes is healthcare.

In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and looking for the best strategies to use these numbers.

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Everywhere you turn these days, “the cloud” is being talked about. It’s a hot topic, and as technologies continue to evolve at a rapid pace, the scope of the cloud continues to expand.

Yes, this ambiguous term seems to encompass almost everything about us. While “the cloud” is just a metaphor for the internet, cloud computing is what people are really talking about these days. It provides better data storage, data security, flexibility, improved organizational visibility, smoother processes, extra data intelligence, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing expenses.

It is clear that utilizing the cloud is a trend that continues to grow – and will long into the future. We have already predicted the importance and implementation of the cloud in companies like Alibaba, Amazon, Google and Microsoft in our BI trends article earlier this year. Now, we’re going to dig a little deeper.

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Effective decision-making processes in business are dependent upon high-quality information. That’s a fact in today’s competitive business environment that requires agile access to a data storage warehouse, organized in a manner that will improve business performance, and deliver fast, accurate, and relevant data insights. BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes.

In this post, we will explain the definition, connection, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that will visually explain the correlation of these terms, and the framework on which they operate. But first, let’s start with basic definitions.

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Big data plays a crucial role in online data analysis, organizational information, and intelligent reporting. In our fast-paced digital age, companies must adjust to the ambiguity of data and act accordingly. BI reporting is the answer.

Spreadsheets no longer provide adequate solutions for a serious organization looking to analyze and utilize every strand of relevant data gathered accurately.

So, that’s where business intelligence reporting comes into play – and, indeed, is proving pivotal in empowering organizations to collect data effectively and transform insight into action.

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Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.

You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. These applications are designed to benefit logistics and shipping companies alike.

Did you know? The big data market is expected to exceed $68 billion in value by 2025, a testament to its growing value and necessity across industries.

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