We are excited to announce our sponsorship of a local cricket team, the Stormers. Our very own dbSeer team member, Nanda Kumar, is not only an avid player, but is team captain.
The team formed in the Fall of 2012 and plays two seasons per year, in the Spring and the Fall. The Stormers are part of the Loudoun County Cricket League (LCCL) http://cricclubs.com/vlccl as well as the Potomac Cricket League (PCL) http://cricclubs.com/PCL, both hard tennis ball cricket leagues near Leesburg, VA.
It’s fun for us to follow the Stormers as they play throughout the year and help to bring more fans locally to experience the sport of cricket, and we wish them luck on a successful season. You can check out the league web pages to find out current game schedules. Come on out and watch the Stormers play!
I recently read a piece published on March 30, 2016 by the siliconangle.com web site questioning whether traditional business intelligence (BI) has reached its peak. It raised a couple of interesting points. Due to the recent “flattening of consumption of business intelligence” and the consolidation of BI players (via mergers and acquisitions, for example), some analysts are proposing that this signals BI has reached its peak. In an interview by siliconangle.com with Ian Andrews, VP of Products at Pivotal, Inc., Mr. Andrews explained that this implies the industry is just shifting customers among vendors and that we may be “hitting the peak platform for data.” He argues, however, that this is not the case and that “we’re just at the beginning from a platform perspective.” He further states that consumption of data is where it’s now at, with delivering “information in context.”
I would agree with him that BI is shifting toward extracting the complexity out of analytics and pushing “information in context.” This means pushing out information to be consumed and used by any user in the organization, and embedding the relevant analytics into the daily applications that users use to complete daily workflow, so that business processes can be improved right when they need to be. We are shifting toward delivering the right information, right when users need it and right where they need it. Further, analytics are becoming critical to the more non-traditional stakeholders throughout the organization, switching from IT-led, system-of- record reporting to business-led analytics.
In my opinion, the recent self- service push in the analytics market has gone too far, and, while it has a role to play in the “information in context” model, it will not be the only way toward the “democratization” of analytics. Embedded BI and custom BI applications will be key components of “information in context.” The democratization of analytics will continue to be the focus in the organization, and BI has certainly not reached its peak. If anything, the opportunities for information in context are far and wide.
Click here for the full interview on siliconangle.com.
In March, I had the privilege of attending the premiere big data conference O’Reilly and Cloudera Strata + Hadoop World in San Jose, CA. I’ll describe some of the more interesting topics/sessions in more detail below. The key technology areas and trends that seemed to be the focus were around:
- Machine learning
- Streaming and real-time data processing
- Real-time Analytics
Hadoop Clusters – There were a few sessions/talks about the challenges of managing Hadoop clusters in Enterprise environments. I attended two talks about the topic by GE and British Telecom. GE’s talk focused on how to use a big data platform to change the enterprise culture to a data-driven culture, by opening up the data and creating data lakes where data is accessible by businesses. The BT talk was about successful design patterns for data hubs. Both talks highlighted the enterprise approach on data lakes or data hubs and the Hadoop challenge of job management within the cluster.
IoT – Intel hosted a session showcasing a data-streaming platform that helped Levi Strauss to find its items in a store. This solutions used RFID (IoT) on each item in the store and a machine learning algorithm (that learns over time) where each item should be located in the store. While the application of the technology was a bit simplistic, the platform itself was very impressive.
Machine Learning – Microsoft hosted a talk on machine learning, in which they showcased research on machine learning and neuroscience. Remarkably, they have developed an algorithm that is able to identify basic thoughts just by analyzing electrical signals released from the brain; in this case, the algorithm was able to identify if an individual was seeing a face or a building. They showed images in a few milliseconds to a patient, and the computer, with over 90% accuracy, could guess what picture the patient saw.
Real-time analytics – Something that came up on in a few sessions was the challenge of applying real-time analytics to massive data. There was one use case on credit card security fraud discussed by MapR. Combining streaming technology and machine learning, MapR impressively made under a 1-second decision to determine if a transaction is a fraud transaction.
Future of Data Analytics
Another key takeaway from the conference included an overview of the future of data analytics. The following diagram created by Amplab – UC Berkely is a great representation. In summary, it shows from the bottom level to the top:
- Virtualization/distributed file system at the lowest level
- Compression and encryption at the storage level
- Spark as the processing engine (Notice no alternative for spark!)
- Access – Still too many options (I think this is the issue that need to be resolved there is no clear way to access)