McKinsey has described Big Data as “the next frontier for innovation, competition and productivity.” IDC has characterized Big Data as one of the four pillars of the next dominant computing platform, along with cloud services, social networking, mobility, and the API Economy.
While the potential for Big Data is unquestionably exciting, in reality IT organizations will have to ensure that the underlying technology to achieve that potential is up to the challenge – providing the speed, capacity, reliability, security, scalability and intelligence to deliver actionable information that can drive business activity in real time.
When you take the time to think about Big Data and separate the reality from the hype you realize that there is no better way to ensure the performance required of Big Data than to run your business intelligence, big data analytics and Big Data applications on an IBM System z mainframe.
It is widely known that about 70% to 80% of the world’s business data already resides on System z mainframes. In order to do analytics outside of the mainframe environment, you have to offload this data to a third-party server. For a while this was the only way to do the type of analytics that businesses are striving to achieve in today’s environment. But enterprises have found the offloading process to be expensive, time consuming and a drain on performance.
Fortunately, organizations no longer have to go outside of the mainframe environment for business intelligence and analytics. The IBM DB2 Analytics Accelerator for z/OS based on Netezza and Power Technology is the ultimate data warehouse appliance designed for running complex analytics on very large data volumes.
This appliance attaches directly onto to the mainframe and easily integrates with DB2 on z/OS. It also easily integrates with preferred ETL and BI tools such as IBM InfoSphere DataStage and Cognos Business Intelligence, along with numerous other non-IBM solutions using industry-standard interfaces.
Here’s the critical point: Not only can this appliance run complex analytics on very large data volumes, it can do so with speed and performance that are orders of magnitude faster than competing solutions. Not to mention that it delivers this performance with mainframe-level security, quality of service, reliability, scalability, and increases the efficiency of DB2 on z/OS
The real value of Big Data is to take massive amounts of information from a wide range of sources – including data gleaned from social media and other environments outside of the organization – to gather it, access it and analyze it so that the organization can have actionable information in real time. Think of a customer buying an item and the organization being able to use a tidbit from a Facebook post to push an additional offer to that customer while she is still shopping.
For Big Data to achieve that kind of real-time potential, the underlying technology has to deliver an accurate reflection of the truth – as it exists in real time. Once the analytics system has to offload the data to another system, and then reload it back, you are potentially introducing delays and other potential problems that could make the data slightly outdated and not quite an accurate reflection of the truth. The movement of data outside the system also introduces security risks that could impact operations.
With a System z solution, however, those performance and security risks simply do not exist. Big Data is, of course, a lot of data and the System z can handle a significantly higher scale of data than any other solution available on the market. With the right tools in place, organizations now have the opportunity to keep analytics, business intelligence and Big Data all under one roof – with the highest security, QoS and availability of any solution in the world. If you’re really ready to see what Big Data can do for your organization, you really need to find out more about the analytics, business intelligence and Big Data solutions available to you on the IBM System z. Only then will you be able to separate the reality of Big Data analytics from the hype.
