In the rapidly evolving SQL-on-Hadoop space, IBM’s Big SQL 3.0 moves the industry forward through its contributions in improving query performance and workload management, while maintaining compatibility with open source Hive and SQL standard. This blog gets under the hood to explain how Big SQL 3.
Research shows that enterprises only analyze 12 percent of their data. InfoSphere BigInsights for Hadoop empowers enterprises of all sizes to cost effectively manage and analyze big data so that they leverage all data and not just a sliver.
"The issue around identifying targeted analysis for anti-corruption is you just can't look at one data source," says Vince Walden, a partner at Ernst & Young with responsibility for fraud investigation and dispute services. "When you're looking for potentially improper payments that could be
If most organizations are using analytics to improve customer interactions, optimize supply chains and reduce financial risk then where does the advantage in today's marketplace come from? The IBV 2014 Analytics study will explore how organizations are creating a competitive advantage in today's
The recent controversy over the ethics of Facebook's attempts to influence moods through tweaks to its newsfeed algorithms is overblown. Essentially, Facebook data scientists conducted one of many real-world experiments that are standard operating procedure with them and with most online businesses
Video content analytics tools are humanity's unblinking eyes, capable of continuously filtering the world's media streams at scale. Video content analytics algorithms can parse the fine details within and between successive frames of specific streams, supporting pattern recognition, gesture
You don’t want to miss the next TDWI Solution Spotlight on “Maximizing the Value of Your Data through Analytics.” Claudia Imhoff, president and founder of Intelligent Solutions Inc., will share everything from modern data architectures and use cases to data scientists and “things to ponder” when
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on?
Data science is extremely important in today’s data-driven world, but is only effective if it can be efficiently executed in a production environment. Find out about an essential best practice to make your data science effective.
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The
Many make out the data scientist to be a Renaissance woman or man who can single-handedly elevate the organization’s analytics savvy. However, preparing students for corporate roles in data science means training them for many positions on a team. At Arizona State University, we work closely with
With big data financial and transactional data no longer in silos, we can now look at them together. Vince Walden, Ernst and Young partner, says that big data technologies allow them to look at data from all angles.
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.