Data, in all of its various forms, has completely transformed our society. And, it’s not just one kind of data that’s responsible for this; it’s all kinds, whether it’s streaming data, Big data, legacy data, or other kinds. Having a comprehensive means of leveraging this data to your business’ best interests is vitally important in today’s world, as it is the only viable way to effectively identify new opportunities, mitigate risks, and to meet customer demands.
To be sure, the kinds of data that must be dealt with are always expanding. For example, machine-to-machine data has grown in importance, thanks to technological advances like RFID tags. In addition to this, government regulations have necessitated that more forms of data are kept by businesses, particularly those that operate within the financial services sector.
There is a name for all of the unstructured data that we’re talking about, and that name is “Big Data”. While this form of data has received an incredible amount of focus from businesses around the world, there’s another form that deserves just as much if not more focus: mainframe data. Those with technological acumen will be quick to acknowledge that mainframe data is essentially the original form of Big Data.
This mainframe data is vitally important, as it often is corned with vital business functions, for example the control of inventory and billing, or the control of tax records and transactions. Banks, in particular, understand how important mainframe data is, as their mainframes must accurately, reliably, and quickly conduct a tremendous number of transactions around the clock.
This all serves to illustrate how important it is to have a comprehensive solution for mainframe data, especially if you’re concerned with the effectiveness of your business intelligence and analytics strategies. In order for such strategies to be effective, data must be moved as closely as possible to analytics and BI tools. Further, all the different forms of a data that a business has access to must be blended together seamlessly and effectively.
Accomplishing this, however, means that old methods of physically moving data must be eliminated. There are other technical challenges, as well. For one, the data that is being dealt with must be integrated and standardized in such a way that ensures consistency across both business-facing and customer-facing applications. Also, the data must be positioned in such a way as to facilitate easy, quick and accurate access, regardless of who is accessing it.
For the most part, businesses have deployed the ETL method to deal with data in this manner. However, this method has proven to be woefully deficient for needs of today’s business environment. By physically moving data, the ETL method contributes to a high degree of latency when working with the data being transformed. Further, the process of transformation that such a method employs often introduces inconsistencies and fails to enforce strict standardization. Ultimately, however, the greatest flaw of this method is that it negates the timeliness of data. Because the data must be moved and transformed in separate processes, the resulting transformed data is instantly out of date when it is accessed through analytics and BI tools.
The way around this problem is mainframe data virtualization. This method employs specialty processors – IBM System Z processors – within a mainframe that take care of the data processing, integration, and transformation. Because of this, a mainframe’s central processors do not need to be employed for these tasks. This, in turn, leads to a reduction in a mainframe’s TCO, leaves data undisturbed, and eliminates the cost of software license charges. Naturally, the problems of inaccuracy, lack of timeliness and latency encountered with ETL methods are eliminated as well.
In the end, mainframe data virtualization allows for data to be moved as close to analytics as possible. This allows those that drive business to make the most informed decisions possible, decisions that are backed by current and accurate analytics that are derived from current and accurate data. In addition to this, ready access to such data can facilitate connections between the different components of a business, for example its people, process and systems, making it possible for all of these different components to work in tandem. In the end, this allows a business to accomplish its ultimate goals: meeting the demands of its customers, meeting potential challenges and competitors in the marketplace, and identifying new opportunities for growth and expansion.
Try deep learning using MATLAB