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Mainframe Data Virtualization is the Future

It is impossible to underestimate the importance of data in today’s business environment, data
that comes in a variety different forms, from Big Data to streaming data. This data moves
quickly and is always increasing, necessitating a comprehensive solution from businesses,
industries, institutions, and other entities. Such a solution is necessary, because dealing with
this data effectively can drive business success, by assisting businesses with leveraging new
trends, identifying threats, and even finding new opportunities.

Of course, a comprehensive solution to all of this data is a huge challenge. And, there’s always
more data that needs to be taken into account. Regulations are constantly expanding, requiring
businesses to keep track of more than ever before. And, there is even new technology like RFID
tags that need to be taken into account.

There is a name for all of this new data, and that name is “Big Data”. However, there’s another
form of data, one which has existed for much longer, that deserves just as much attention:
mainframe data. This data exists at the same volume as Big Data, and it moves at the same
speed. Because of this, comprehensive analytics tools are needed to address it.

Mainframe data can include a wide variety of things, all of them important for essential business
functions. This data could include things like tax and financial records, or it could include
something like reservations. Banks understand the importance of this kind of data, as their
mainframes are responsible for handling an incredible amount of transactions around the clock.
That data involved in those transactions must be readily available, current and reliable in order
for a bank to function.

Any business though, must have a comprehensive solution for mainframe data if its analytics
strategies (and Business Intelligence strategies) are to be effective. This comprehensive
solution must offer a way from mainframe data to be moved closer to analytics. Further, this
comprehensive solution must facilitate the easy blending of relational data and non-relational
data. Once combined, this data must then be capable of being accessed easily, meaning that
traditional methods of physically moving data must be eliminated.

This level of convenient, quick access isn’t optional; it’s a must. Both customers and the people
that make business decisions expect this level of access. Offering this level of access involves a
number of different challenges. How can data from many different sources, including mainframe
sources, be combined and integrated? How can such data be standardized, so that it is the
same for customers as it is on the business side of things?

Mainframe data virtualization is the answer to all of these questions. It provides a much more
efficient manner of dealing with mainframe data than methods that have been employed
heretofore. Extract, Transform and Load (ETL) methods have been largely employed in such
scenarios, but these methods cannot achieve the level of efficiency, consistency, and immediate
access that businesses need. Because the data must first be extracted before anything can be
done to it, the data that results from this method can never be timely, resulting in the

47 Cyber Warnings E-Magazine – March 2015 Edition
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