Most firms estimate that they are only analyzing 12% of the data that they already have, leaving 88% of it on the cutting-room floor (Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012).
Repressive data silos and a lack of analytics capabilities are key reasons for this.
In addition, it’s often impossible to judge what data is valuable and what isn’t. In the age of big data, you have to capture and store it all. Data that might seem completely irrelevant to your business now, such as mobile GPS data, might be a gold mine in the future. The effort and cost of capturing and storing all data have often forced decisions on what to store and what to throw away.
Hadoop has made it possible for enterprises to capture, store, and analyze lots more data in a much more cost-effective way.
Traditional business intelligence (BI) tools can benefit from big data, but firms also want to use advanced visualization tools and predictive analytics to explore data in new ways and discover new patterns. A big data solution doesn’t just involve supporting large volumes of data; it also has to have the compute horsepower to perform the advanced statistical and machine learning algorithms that data scientists use.
Hadoop can do both and is designed to scale out from a single server to thousands of servers for optimized performance.
Figure 1: Forrester Wave™: Big Data Hadoop Solutions, Q1 ’14