It doesn’t matter where you turn, data is everywhere, and it’s coming at your business faster and in greater volume than ever before. There are all kinds of data, too, from Big Data to operational and streaming data. This data is serving to remake entire industries, institutions, and businesses. It doesn’t matter where you stand in relation to this data, because you need a comprehensive solution for dealing with it in order to remain successful.
Naturally, this data present a huge challenge to business, but that challenge is also a tremendous opportunity. What’s more is that there are always new streams of data. For example, governmental regulation has dramatically increased the amount of data that must be recorded and analyzed within the financial sector, and there is also data coming from things like RFID tags.
All this data falls under the umbrella term “Big Data”, and that’s certainly something that’s been in the news a lot. But, there is another form of data that deserves just as much attention, if not more attention: mainframe data. This data exists in the same amount and moves at the same velocity of Big Data, and it requires a comprehensive approach from businesses.
This kind of data encompasses many different things, ranging from data that’s designed to control finances and billing, to data that’s designed to control stock trading and airline tickets. This data is vitally important, just ask any bank. Bank mainframes are responsible for handling an incredible amount of transactions, and the data required to process those transactions must be available on the business side and the customer side on the fly.
All of this serves to illustrate the point that a comprehensive approach to mainframe data is essential for any Business Intelligence or analytics strategy. Such a comprehensive approach demands that mainframe data be moved closer to the analytics tools designed to deal with that data. It requires that non-relational and relational data be blended together seamlessly, so that all of the data can be accessed simply and securely. To accomplish this, the traditional methods of physically moving data for the purpose of analytics must be discontinued.
To accomplish this, there needs to be a comprehensive solution for integrating data from all sources, including mainframe sources. As this data is integrated together, the data must also be standardized so that it can be dealt with through analytics tools that are available to businesses, as well as tools that customers might be using to access this data. These modes of access must be capable of facilitating immediate, on-the-fly access.
The best way to do this is through virtualization. This method allows for data to be accessed as it is at any time, which in turn dramatically increases the efficacy of analytics strategies. Of course, mainframe data presents the biggest obstacle here. For the most part, businesses have relied on the Extract, Transform, and Load (ETL) method for dealing with mainframe data. This method is obsolete, as data dealt with in this manner cannot be current. And, data that isn’t current is of little use with today’s powerful analytics tools. Further, transforming data in this manner takes time, resulting in numerous problems such as latency and inconsistency.
Mainframe data virtualization software is the answer to the problems that ETL presents. This software is present on the mainframe server, and it utilizes specialty processors, such as the IBM System Z, to transform data in real time. This method obviates other problems, as well. For example, there is no concern with software licensing charges, and a mainframe’s TCO is reduced, keeping data where it should be.
All of this serves to place data right next to the analytics tools. Consistency is enforced, and latency is done away with, allowing businesses access to data as it exists at any given moment. Further, non-relational data can be quickly and easily accessed through SQL, and the data, as a whole, can be dealt with easily and efficiently through BI tools and analytics tools. Because of this, there’s no need for developers to familiarize themselves with unfamiliar mainframe environments.
There is only one goal for any CEO: to drive growth. To accomplish this, a CEO must have access to current data at a moment’s notice. Mainframe data virtualization makes such access a reality. Because of this, relationships are fostered between a business and its data, customers and their data, and between different business processes. The end result is that business growth is facilitated, because businesses are better able to identify new opportunities and threats, and they’re able to better meet their customers’ needs.
About the Author
Mike Miranda writes about enterprise software and covers products offered by software companies like Rocket Software.