Cloud computing offers businesses all kinds of new opportunities to collect Big Data. But the true value of Big Data collection – actually culling actionable information from it via analytics -- has not been as easy. While the elasticity of the cloud makes it ideal for Big Data analytics, its distributed nature simultaneously has made data analysis difficult to this point.

A March 2015 IDC study on Big Data and analytics found that the “majority of organizations will invest in data analytics as they expect to gain the most business value from its solutions.” The study also found that over the past year, the number of organizations with deployed/implemented data-driven projects increased by 125%.

But the more important question is, how many of those organizations will be able to successfully create value out of that data?

A recent Bain & Company study found that only 4% of companies said they had the right people, tools, data and intent to draw meaningful insights from data—and to act on them.

Benefits of Big Data Insight

There are numerous benefits of analyzing Big Data effectively, like empowering decision makers with information that can drive even better business decisions. In fact, the Bain study found that companies that are able to effectively cull insight from their data are twice as likely to be in the top quartile of financial performance within their industries, three times more likely to execute decisions as intended, and five times more likely to make decisions faster.

Companies such as Google, Amazon, Microsoft and IBM recognize companies need help in this area, but they have also faced architectural hurdles in helping companies support Big Data analysis in the cloud. Fortunately, these companies have been working hard to figure out ways to modify their cloud architectures and improve the agility of their cloud services in order to better support Big Data analysis in the cloud.

Forward Progress

Not only must the software tools cloud companies provide for cloud data analytics work well, the data analysis must be fast in order for companies to get the most value out of the data and enable the types of decisions that improve competitiveness.  

Last week, Google announced that two of its Big Data toolsets have moved out of beta and into full commercial release. Google Cloud Dataflow provides a framework for fusing different sources of data within one processing pipeline, while Google Cloud Pub/Sub helps companies manage data streams in real time. The two services have potential to allow Google cloud customers to glean much better information from their data much faster while also requiring less maintenance and oversight than in-house data processing systems.

Google said some of its customers are already using these tools for Big Data tasks such as financial fraud detection, inventory management and user interaction testing.

While Google is leading the way so far, it’s likely other big cloud companies will soon release tools that enable easier and faster Big Data analytics. Overall, it’s a positive sign to see that cloud providers recognize the need and are working to address it. Doing so may help increase the number of truly data-driven organizations we see in the future.