Understanding the Importance of Mainframe Data Virtualization

It’s coming faster than ever before, and it’s coming in greater volume than ever before. We’re talking about data, of course, which can take many forms, including streaming data, operational data, and even the dreaded Big Data. This data has been reshaping entire industries, remaking the ways in which institutions, businesses, and other entities go about their work. Having a comprehensive solution for meeting the challenges (and opportunities) that this data presents is vitally important for future business success.

New Technology

Transactional data isn’t the only kind of data that businesses have to contend with these days, either. In fact, machine-to-machine data is on the rise, thanks to new technologies such as RFID tags. Also, governmental regulations have made it incumbent for businesses to retain more data than ever before. Just ask any business that operates in the financial sector.

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Of course, unstructured data, which many refer to as “Big Data”, is what’s foremost on every businesses’ minds. However, there is another form of data that perhaps deserves more attention, and this data is called mainframe data. This kind of data relates to a number of different things, most often to the control of various things such as billing, finances, tax records, and even transactions. Businesses in the banking sector understand this well, as their mainframes are required to process millions of transactions in a timely and accurate fashion for their customers around the clock.

The Advantages

Any effective Business Intelligence or analytics strategy must be able to deal with this mainframe data in a timely, effective, and efficient manner. The only way to accomplish this is to employ a method of dealing with this data that moves it as close as possible to analytics. Further, both non-relational data and relational data must be blended together, so that all data can be accessed simply and cleanly. Because of this, old methods that relied upon physically moving data have become entirely obsolete.

A Little History

The reason that such old methods are obsolete is simple. Both customers and the people that make decisions for businesses have been conditioned to expect real-time access to data. Of course, meeting that expectation is not without its challenges, most importantly developing a way of bringing all data streams together. Further, the data in question must also be standardized and integrated together in order to facilitate easy access for decision makers and customers alike. It’s a big challenge.
In the past, businesses have been employing a method for accomplishing this called ETL, or Extract, Transform, and Load. While this method was sufficient for a time, it is no longer capable of keeping up with the demands the people have for data. This method relies on physically moving data so that it can be transformed into a form that’s usable by analytics and BI tools. This method of physically moving data presents a number of challenges, chief among them being timeliness – moving all of that data is time consuming. Aside from this, the physical movement of data introduces the possibility of inconsistencies developing, reducing the accuracy of the data, and in turn the effectiveness of analytics. All told, this increases the complexities and costs that businesses face.

The Solution

The solution to this problem is mainframe data virtualization, which works in a relatively simple manner. With this method, data is integrated and transformed by a specialty processor – the IBM System Z – rather than by a mainframes central processor. Because of this, the mainframe experiences a reduction in TCO, and the production of data is allowed to continue uninterrupted. Further, such a method eliminates charges associated with software licenses.
The benefits that such a method offers are numerous. For one, the latency that’s experienced with ETL methods is entirely eliminated. Also, the consistency and accuracy of data is strictly enforced, which facilities ease of access through both customer-facing and business-facing applications. Overall, this completely removes the need to have specialized developers capable of dealing with any given mainframes specific operating environment.

Conclusion

Through mainframe data virtualization, CEOs and business decision makers can be empowered to do what they do best: drive business growth and mitigate risk. This is possible because mainframe data virtualization provides real-time access to accurate business information, allowing for supremely informed decisions to be made. Ultimately, this will allow any business to successfully meet the challenges that face it in the marketplace, whether they be new potential opportunities or threats posed by competitors. Do you want to be ahead of the curve?

Mike Miranda writes about enterprise software and covers products offered by software companies like Rocket Software.

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