As we transition into the 3rd Platform of IT, enterprises are storing unprecedented amounts of data and those numbers are only going to increase. Alongside these large data sets is the demand for real-time analytics that are low latency.
Most organizations are dealing with the question of how to get the most from their massive stores of big data. A large percentage of this data is unstructured data, which makes the task of extracting meaningful information all that more challenging.
Many companies are considering simplifying their data center with a converged infrastructure. A converged infrastructure increases reliability, decreases downtime and provides a reliable and repeatable infrastructure.
Although it was very conceptual to talk about big data a few years ago in the context of exploding data growth, the reason that the industry is still talking about big data today is because of the critical ways businesses are using it.
Launching a big data project can be a little like launching a rocket: in both cases, getting them off the ground is the hard part. Most enterprises simply don’t know where to start when it comes to big data.
In case you’ve been living under a stack of spreadsheets for the last few years, maybe you haven’t heard that SAP HANA is one of the most exciting data stories of the decade.
Big data is still the big buzzword among banks, healthcare companies and other data-intensive industries, and a lot of that buzz has been generated by SAP HANA, an in-memory data analytics solution that performs up to 10,000X faster than some current systems. You read that right: ten thousand times faster.
With the high-impact arrival of Big Data, many CIOs are already beginning to think about what the next “big” thing will be in IT. SAP is putting its bets, and its future, on Predictive Analytics.
When the concept of Big Data was first introduced, the industry collectively described this new phenomenon using the four V’s of Volume, Velocity, Variety and Veracity. For businesses, however, Big Data can be distilled down to two basic needs: they need nearly infinite data storage and instant results to analytic queries.
The value of big data is bigger than the size of the data or the speed of the analytics. The truth is, while it’s interesting to talk about petabytes of data and sub-second analytic responses, it’s all meaningless if you don’t trust the results.