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The definition of a lake varies greatly depending on where a person is from. For example — in the southwest, what most parts of the country would consider a pond, is referred to as a lake. But if you’re from the upper-Midwest, it’s only considered a lake if you can’t see the other side of it.

Similarly, in the world of data analytics, the definition of a data lake can vary from business to business and implementation to implementation. For some companies, it’s just a bigger, more dynamic form of storage. But for those leveraging big data and advanced analytics, data lakes are a vital technology for building a centralized, intelligent, and flexible repository for all forms of data from multiple sources.

And given how data is transforming and challenging business today, the need for the power that data lakes can bring is more important than ever. Aberdeen research has found that the amount of data coming into organizations has increased by 25% every year over the last five years.

Along with this increase in volume of data, storage has become increasingly complex and diverse. Businesses today are challenged by the disconnected siloes of multiple storage sources, which lead to poor visibility, low performance and limited management capabilities.

When we’ve looked at the businesses that are leaders in data management, storage and analytics, we’ve found that these Best-in-Class organizations are taking key steps to overcome these challenges. And the number one thing that the Best-in-Class does is implement data lakes.

By deploying these unified and optimized storage repositories designed for efficiency and ease of management, Best-in-Class businesses gain significant benefits including:

  • Faster time to deploy new applications and services
  • Reduced downtime
  • Less management headaches

Not surprisingly, with data lakes in place, Best-in-Class organizations see improvements in their bottom line, with 71% reducing IT costs for storage and data management.

The importance of data lakes is only increasing, with newer implementations leveraging other emerging technologies such as machine learning and software-defined infrastructures. This means that data lakes will continue to improve and evolve, along with how we all define their capabilities and importance.