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Big Data Implementation Best Practices | The Big Data Hub

Big Data Implementation Best Practices | The Big Data Hub | e-Xploration | Scoop.it
Top 10 best practices that implementation teams should follow to increase the chances of success with big data projects (10 Big Data Implementation Best Practices http://t.co/xd32urVMra #analytics...
luiy's insight:

1. Gather business requirements before gathering data. Begin big data implementations by first gathering, analyzing and understanding the business requirements; this is the first and most essential step in the big data analytics process. If you take away nothing else, remember this: Align big data projects with specific business goals.

2. “Implementing big data is a business decision not IT.” This is a wonderful quote that wraps up one of the most important best practices for implementing big data. Analytics solutions are most successful when approached from a business perspective and not from the IT/Engineering end. IT needs to get away from the model of “Build it and they will come” to “Solutions that fit defined business needs.”

3. Use Agile and Iterative Approach to Implementation. Typically, big data projects start with a specific use-case and data set. Over the course of implementations, we have observed that organization needs evolve as they understand the data – once they touch and feel and start harnessing its potential value. Use agile and iterative implementation techniques that deliver quick solutions based on current needs instead of a big bang application development. When it comes to the practicalities of big data analytics, the best practice is to start small by identifying specific, high-value opportunities, while not losing site of the big picture. We achieve these objectives with our big data framework: Think Big, Act Small.

4. Evaluate data requirements. Whether a business is ready for big data analytics or not, carrying out a full evaluation of data coming into a business and how it can best be used to the business’s advantage is advised. This process usually requires input from your business stakeholders. Together we analyze what data needs to be retained, managed and made accessible, and what data can be discarded.

5. Ease skills shortage with standards and governance. Since big data has so much potential, there’s a growing shortage of professionals who can manage and mine information. Short of offering huge signing bonuses, the best way to overcome potential skills issues is standardizing big data efforts within an IT governance program.

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Data Visualization: Communication & Creativity

Data Visualization: Communication & Creativity | e-Xploration | Scoop.it

Visual communication skills are alien to some in the research industry, but they needn’t be. Data visualisation can become part of the research process through smart hiring, skills training and expert partnerships.

Data visualisation should not be regarded as an end in itself; the real point to data visualisation - the value that it brings to research buyers and suppliers - is as an aid to storytelling. It’s about seeing the patterns in the data that flush out a story and then help you to start telling that story. Only by doing that can you move data off the spreadsheet and out into the real world of consumer behaviour and preferences.

The best analogy and the one used frequently, is with journalism. It’s no surprise either that many great examples of data visualisation come from the publishing and media sectors. Journalists face the same challenge that we do of sifting large amounts of often conflicting data to arrive at a truth or an insight...

Via Lauren Moss
Lauren Moss's curator insight, January 8, 2013 4:23 PM

An interesting look at the current role of data visualization and data journalism in the advancement of research, communication, and brand development.

Scott Turner's curator insight, January 9, 2013 8:31 AM

An interesting look at the current role of data visualization and data journalism in the advancement of research, communication, and brand development.