The benefits of analytics are fairly straight-forward. According to a recent KPMG survey of C-level employees, nearly all (97%) are already using data and analytics in some area of their business. Almost as many – 81 percent – are using analytics to better understand customers, according to the survey, Going Beyond The Data, Turning Data From Insights To Value. Just as important: Analytics are helping them make faster decisions, according to 86 percent of C-level employees. And four out of five say analytics help them make more accurate decisions. We have truly arrived squarely in the insight economy.
However, it can still be difficult to translate data into action. Even though there are plenty of tools out there to help extract and analyze data from databases and warehouses, most – like Hadoop – require strong technical knowledge. Users such as data analysts and engineers are required to have programming skills. Unfortunately, even if they have those skills, it can take a while to build apps as well. Those who work with big data needed a quick, easy way to build algorithms and extract value from it in a speedy manner.
In June IBM made an announcement that changes all that. The company, in conjunction with open source Apache Foundation, committed to the advancement of Apache Spark, an in-memory compute engine that works with data – not a data store. The announcement is something that many are heralding as the most significant open source project of the decade since it enables highly iterative analysis on large volumes of data at scale as well as simplifying the development of apps built on that data.
Want to learn more? Click through to sign up for the August 25 webinar, Build Smarter Applications Fueled by Data with IBM and Apache(r) Spark™, to hear more about Spark and how it can change the way you look at analytics.
