We welcome guest blogger Mike Miranda, who writes about enterprise software and covers products offered by software companies like Rocket Software.

Data is only growing and coming at businesses faster and faster, whether it’s Big Data, which is always in the headlines, or streaming data, legacy data, or something else. The impact of this data is hard to overestimate, as whole industries and institutions are being remade by it. This only underscores how important it is for every business to have a comprehensive plan for dealing with data, leveraging it to businesses’ advantage.

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Of course, doing that is as much of a challenge as it is an opportunity. And, data is coming from sources you might not even be thinking about. Your business could be accumulating data from Radio Frequency Identification (RFID) tags, or it could be accumulating data due to new governmental regulations, particularly if your business is in the financial sector.

There’s a name for all of this data: Big Data. And while it’s receiving a lot of focuses from businesses, there’s something else that needs to be taken into account: data remains on mainframe servers, such as system of record data. This data comes at businesses at the same volume and velocity as Big Data, and it demands the same level of analytics.

This mainframe data includes many different things, including everything from a company’s financial records to its inventory and tax records. This data and these systems are still vitally important to business. Take, for example, a bank. Such a business must process millions of transactions every minute, and the data required to do that must be immediately accessible both on the business side and on the customer-facing side.

The need for businesses that tame their mainframe data is clear, as it is essential for analytics and Business Intelligence purposes. In order to tame this data, it must be moved closer to analytics within a business’ system. Doing this requires that businesses find ways of blending non-relational data and relational data together, which itself requires new methods that do not involve the physical transferring of data.

Those who run businesses, as well as clients and customers, have been conditioned by the world that we live in to expect immediate access to their data. Making that kind of immediate access requires a great deal, including a method of dealing with mainframe data. Mainframe data and other forms of data must be integrated and standardized so that the data can be accessed by customers and analytics tool alike.

The best way to do this is to virtually integrate that data in one place, regardless of where that data originates. Doing this allows analytics tools to provide an up-to-date and comprehensive view of a business and where it stands in the market and with its customers at any given moment. Mainframe data presents the biggest challenge to accomplishing this. Typically, businesses have employed the Extract, Transform, and Load (ETL) method when attempting to do this, however this method is not very effective. The reason is simple: extracted data that is then transformed and loaded can no longer be current. This results in businesses losing the biggest advantage they get from such data, which is timeliness.

Simply, the ETL method is outdated in today’s business environment, and businesses should expect better. The better method that businesses need is mainframe data virtualization software. Using IBM System Z specialty possessors, mainframe data can be transformed on the fly. There are no software licensing software issues with these specialty processors, and because of this MIP capacity is not affected. Further, TCO on mainframes is reduced by a great deal, resulting in data production being largely unaffected.

Employing this method accomplishes the task of bringing analytics closer to mainframe data. Further, latency, as would be experienced with the ETL method, is no longer an issue. Because of this mainframe data can be accessed without needing to move it through SQL, and that data will work well with any analytics tools or BI tools that a business might be using. Also, it eliminates the need for developers to develop familiarity with unfamiliar mainframe environments.

Those who run businesses have one concern: growing those businesses and mitigating risks posed to those businesses. Doing this effectively is only possible if the data need to make informed decisions is readily available, current, and accurate. Mainframe data virtualization makes this possible, and helps to facilitate the relationships that need to exist between a business’ data, its decision makers and its customers. Ultimately, this allows a business to grow effectively, to identify upcoming risks or challenge, and to meet the needs of its customers as well as possible.

 

Interested in learning about other Analytics and Big Data tools and techniques? Click on our course links and explore more.
Jigsaw’s Data Science with SAS Course – click here.
Jigsaw’s Data Science with R Course – click here.
Jigsaw’s Big Data Course – click here.
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