Examples of 'big data projects' in a sentence

Meaning of "big data projects"

This phrase refers to initiatives or undertakings that involve the processing, analysis, and management of large and complex datasets. Big data projects typically seek to extract valuable insights and patterns from massive amounts of data using various tools and techniques

How to use "big data projects" in a sentence

Basic
Advanced
big data projects
Not all projects are big data projects.
Big data projects in healthcare.
Pioneers in big data projects.
How to integrate robotic process automation in big data projects.
Earlier big data projects focused on storing target data sources.
Start by defining realistic goals for your big data projects.
But big data projects can also fail because of specific reasons.
The initiative has overseen a number of successful mobile big data projects.
Big data projects like this one should always be ethically scrutinised.
This leads to a dilemma for decision makers in charge of big data projects.
Big data projects can have a transformative affect on business operations and processes.
I started my career as a computer consultant specializing in process optimization and big data projects.
Most of the big data projects instigate with the need to answer business questions.
This uncertainty highlights the challenges in determining the value of big data projects.
Big Data projects focused on data analysis and public interest solutions.

See also

The data explosion has led to a significant increase of investment in big data projects.
Understand the context of Big Data projects and the analytical transformation of organisations and sectors.
This resulted in the proposal to develop an inventory of Big Data projects and resources.
Big Data projects of third parties.
An IT consulting firm responsible for leading and supporting integration and big data projects.
The success of Big Data projects seems strongly linked to governance.
Common mistakes that firms make in Big Data projects.
Discuss the Big Data projects and the main players involved.
Data collection and storage are thus necessary stages to any Big Data projects.
Around ten Big Data projects are planned for implementation in the near future.
Note that the main obstacle encountered in Big Data projects is data quality.
Conduct Big Data projects and manage innovation.
Many analysts will choose the sexy Big Data projects and skip the basics.
Big Data projects publication.
Understanding the importance of the Big Data projects.
Contribute to big data projects for industry-leading clients.
New data protection regulations and Big Data projects.
However, most big data projects depend on short-term use of substantial computing resources.
AntiPatterns or what you should not do in your Big Data projects.
Conceptual and technical guidance for big data projects - from the idea to the finished solution.
This will drive a healthier investment, and faster return in big data projects.
What are the examples of successful big data projects in banking and insurance?
Big data projects gaining steam, but not due to the CIO.
How to deliver ROI on big data projects in 6 months.
Also Read, How to integrate robotic process automation in big data projects.
Why are so many Big Data projects open source?
Value is hard to demonstrate, Only 27 % of CxO consider their Big Data projects as valuable.
Real-world Big Data projects that are already paying rewards.
That's the mistake many early stage Big Data projects face.
Typically, big data projects start with a specific use-case and data set.
Based upon my experience, most of the Big Data projects are complex and lengthy.
No longer will multi-channel retailers need expensive long-term multi-year Big Data projects.
Thus far, many examples of Big Data projects have been demonstrated.
Further, according to Gartner analyst Nick Heudecker, 85 % of big data projects fail.
However, more than 55 % of big data projects are doomed to fail.

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