Data is everywhere, and it’s coming at your business faster and in greater volume than it ever has before. This data can take a number of different forms, from streaming data to legacy data. No doubt, this data is serving to change the way that you do business, as it has for countless other businesses, institutions and entities. In order for you to best leverage this data to your business’ maximum benefit, a comprehensive solution is necessary.
Developing such a comprehensive solution is obviously a huge challenge, but it’s also an incredible opportunity. Further, this data can be coming from a variety of new streams, for example from RFID tags, or through information required for regulatory compliance.
This kind of data is most often referred to as Big Data, and many businesses are concerned with that these days. However, there’s something else that deserves these businesses’ attention, and that thing is mainframe data. This data is just as great in volume as Big Data, and it also moves at high velocity. It encompasses a number of different things from tax records to airline ticket reservations, and dealing with it is vital for critical business functions. Banks understand this problem, as their mainframes have to handle millions of customer transactions every day, and all of that data must be dealt with on the fly, both on the business side of things and the customer side of things.
Having an effective analytics and Business Intelligence strategy entails having a comprehensive solution for mainframe data. Further, this comprehensive solution must offer a way for moving analytics closer to mainframe data, blending relational and non-relational data as it does. Such a solution must also allow for data to be accessed simply, and must not rely on methods that physically move data in order for analytics to be used.
Simply, this is the expectation that has been set up both for customers and the people that make decisions in business. However, while the expectation is great, the challenge is even greater, as there are a number of technical obstacles that must be overcome in order to facilitate such easy access to mainframe data. On the most basic level, the mainframe data must be integrated and standardized so that it can be accessed through analytics and through customer-facing tools.
There is only one comprehensive solution for this, and it is one wherein data is virtually combined regardless of where that data originates. This kind of solution is the only one capable of maximizing the effectiveness of analytics and BI tools. Traditionally, businesses have employed the ETL (Extract, Transform, and Load) method for dealing with mainframe data. But this solution is not capable of offering the level of timeliness, accuracy and consistency that’s needed. It’s simply a function of the way in which such a method must physically move data in order for that data to be dealt with. Ultimately, this method leads to a high degree of latency, inconsistency, and inaccurateness.
In today’s business environment, ETL simply doesn’t cut it. Instead, something more adaptable and versatile is needed, something like mainframe data virtualization. This system works incredibly well, as it does not occupy space on a mainframe’s central processors. Rather the data is handled on a specialty IBM System Z processor, which frees up MIPs capacity from having to deal with data integration. This serves to reduce mainframe TCO, as the production of data on the mainframe is allowed to continue uninterrupted.
Mainframe data virtualization allows for mainframe data to be brought closer to analytics. Because of this, the problem of latency is eliminated, as data can be accessed in real time. SQL queries can also be used to access non-relational data without the need to move that data. Because of all of these factors, BI tools and analytics tools work well with a business’ mainframe data, and developers no longer need to familiarize themselves with the inner workings of any particular mainframe.
The result? CEOs and those who make business decisions are empowered to mitigate risk and to facilitate the growth of their businesses. Data is available on the fly, and BI tools and analytics tools are closely related to the data they’re designed to access. Because of this, the connections made between business systems, customers, and processes are all strengthened, and this then empowers businesses to drive their success better than they were able to before.
By Mike Miranda writes about enterprise software and covers products offered by software companies like Rocket Software.
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