6 don'ts when leading big data projects

6 don’ts when leading big data projects

6 don’ts when leading big data projects

Most organizations have a set of big data best practices they have formulated from their successful project work. An equally important list is the pitfalls that organizations should stay away from when it comes to big data and analytics. Here are six don'ts to keep in mind during your big data projects.

The most successful big data initiatives build a strong foundation for big data and analytics and use them. The best way to do this is by creating a constant path of new big data deliverables that incrementally and continuously improve the organization's ability to tackle strategies and operational issues with richer and better data.

Dashboard and spreadsheet-style data delivery that also give the end business user the ability to drill down into data and ask more questions work exceptionally well. A big reason why is users are already familiar with these types of data capture and manipulation tools.

The more at ease that users are with the tools they use to access and manipulate data, the more they will believe in and adopt big data and analytics.

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Security is one of the largest missing pieces in big data projects. These are some security questions to consider.

If you don't know the critical questions that areas of the end business want to solve with big data, you can't deliver the solutions. Engage heavily with end users about the nagging questions in the business, and collaborate with them as you strategize how to obtain and extract information from big data.

 



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