Best Practices for Big Data Analytics
Like any other technology or process, there obviously are best practices that can be applied to the problems of Big Data. In most cases, best practices usually arise from years of testing and measuring results, giving them a solid foundation to build on. However, Big Data, as it is applied today, is relatively new, short circuiting the tried-and-true methodology used in the past to derive best practices. Nevertheless, best practices are presenting themselves at a fairly accelerated rate, which means that we can still learn from the mistakes and successes of others to define what works best and what doesn’t.
The evolutionary aspect of Big Data tends to affect best practices, so what may be best today may not necessarily be best tomorrow. That said, there are still some core proven techniques that can be applied to Big Data analytics and that should withstand the test of time. With new terms, new skill sets, new products, and new providers, the world of Big Data analytics can seem unfamiliar, but tried-and-true data management best practices do hold up well in this still emerging discipline.
As with any business intelligence (BI) and/or data warehouse initiative, it is critical to have a clear understanding of an organization’s data management requirements and a well-defined strategy before venturing too far down the Big Data analytics path. Big Data analytics is widely hyped, and companies in all sectors are being flooded with new ...