Tips for making your data lake thrive

Big data offers tremendous opportunities to outsmart your competition and obtain insights on your business. By transforming big data into actionable information, you can open your organization up to new opportunities by identifying additional markets and customer segments, and by capitalizing on product innovation.

One of the leading principles for enabling enterprise big data is to develop a data lake strategy using a data structure based on Apache Hadoop. With the right information management principles in place, data lakes can deliver the scale, flexibility, and cost-effectiveness you need to manage your big data.

But why have data lakes emerged as a viable IT architecture pattern to capture and provision big data? It really comes down to three things:

However, despite the promise of big data, significant challenges remain before big data can deliver any economic benefits. Most enterprises think of data lakes as cheap storage for bulky low-value data; consequently, raw data tends to get dumped as-is, leaving the data consumer to having to assess data quality and cleanse the data before it can bet turned into useful information.

To guarantee your data lakes can effectively manage the complexity, volume, and variety of your critical business data, it’s important to understand the primary data lake information management challenges and solutions:

1. Data access, exploration, and discovery: Finding useful information in a typical data lake can feel like finding a needle in a haystack. Fortunately, several data lake vendors offer specialized technical tools to help in this process. These tools provide business consumers with interactive, visual, click-based solutions powered by metadata to simplify information access, exploration, and discovery.

Start by investing in self-service solutions that enable business metadata definition with tagging, annotations, descriptions but be sure they feature search, visual data lake exploration, and assisted intelligence. Machine learning, text, and semantic analytics are other key criteria as they facilitate quick data access, exploration, and discovery.