Analytics

How Mainframe Data Virtualization is bringing data and analytics closer together

29th Oct `15, 11:17 AM in Analytics

It probably goes without saying that data plays a huge role in today’s business world. We also know…

Mike Miranda
Mike Miranda Contributor
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It probably goes without saying that data plays a huge role in today’s business world. We also know that data is growing at an exponential rate, the likes of which we have never been seen before. Whether its legacy data, streaming data, or operational data, every day, there is more and more of it. Naturally, this massive amount of data is transforming the way business is done in a number of ways. While this can definitely be seen in a positive light, there’s no denying that all this data represents a number of real challenges too. Those companies able to meet them through legacy modernization and other options will live to tell about it. Those that don’t will soon find themselves in the shadows of their competitors.

Big Data

Though the tech industry certainly doesn’t need any more buzzwords any time soon, one term you should definitely know about is Big Data. This refers to all of the unstructured data that businesses produce. Think of things like machine-to-machine information or the type that comes with regulatory compliance. No matter what industry you’re working in, your company most likely produces some amount of big data. However, if you’re working in the financial sector or the tech world, you most likely have more than others.

Mainframe Data

Despite its name, Big Data isn’t the only giant in terms of information aggregation. Mainframe data is another huge contributor most companies probably see on a regular basis. Again, though, those in the financial industry probably see the biggest contributions of mainframe data. After all, just imagine what a mainframe has to organize and produce with practically every transaction. It’s not like there’s only one transaction a bank has to carry out either. This only places greater demands on the mainframes responsible for facilitating them.

The importance of Analytics

In the world of marketing, analytics have long been a necessary tool. Way before the digital age, analytics were much more cumbersome to organize, but they were no less important. Through analytics, marketing firms found out which campaigns were most effective and companies learned what version of a product their customers preferred.

Then came the Internet. Nowadays, one person outfits selling products and services from their own website can leverage all kinds of tools to figure out what the market truly thinks about what they have to offer. This information can allow even the smallest organization to throw their weight around in the most effective matter.

If analytics are just a way of interpreting data, though, then it would make sense to use this kind of reporting to better make sense of the information mentioned above. Big Data and mainframe data aren’t throwaway terms. They’re not meant to make that info sound any less important. It’s just that there is so much of it and, without the right tools, it would be impossible to make sense out of.

However, imagine if you could. What if bank employees could do a better job of assessing the data connected to all the transactions they see throughout the year? They may be able to anticipate trends and thus meet customer demands better. It could also allow them to offer better pricing, services and deals.

Obviously, the same could be said for just about any other type of company too. Thanks to legacy modernization practices, it’s never too late to begin either.

The problem of ready access

Analytics are great. We’ve established that. However, as it turns out, most people in the business world understand that. It’s not a matter of appreciating that having analytics on hand will improve a company’s operations; it’s figuring out a way to harness those analytics in an efficient manner. Ready access has traditionally been a huge challenge.

Keep in mind that you’re not just dealing with a single collection of data or even just a single kind of information. For one thing, even if you were, that data would have to be effectively realigned closer to analytics. On top of that, you would need non-relational data to blend with relational data seamlessly, without any issues.

In the past, this has involved data being physically moved before any kind of reporting could ever extract the analytics you desire. Unfortunately, this is no longer a practical approach. If you’ve been depending on it in the past, a legacy modernization campaign that includes doing away with it is probably a good idea.

The demand for convenience

Providing analytics is important. Doing it quickly is also helpful. However, there’s no doubt that the demand for convenience needs to be met too. People have very little patience nowadays. For example, consider that any web page that takes more than just four seconds to load will see visitors immediately leave. This reaction to even the smallest inconvenience carries over to the B2B realm too, so don’t think you’re safe if you’re only dealing with other companies.

Of course, just because people demand convenience, that doesn’t mean they’ll actually get it. The main challenge with analytics for the aforementioned type of data is that it must all be converted into the same format. After that integration occurs, the info must then be standardized. Finally, the data must appear the same way on both the customer and business side of things.

The solution

Fortunately, there’s a solution that can be carried out through legacy modernization or by those building a reporting program from scratch. It begins by ensuring that the software is indiscriminate when it comes to data sources. As long as you can accomplish this, the BI and analytic tools can effectively be used to run through the information and pull what you need.

This isn’t happening at the moment because of the number of companies out there that depend on the ETL (Extract, Transform, and Load) method. While ETL was definitely the way to go for a long time, the current demands made by companies that have lots of data to harvest mean that it’s no longer effective.

Like we mentioned earlier, moving data physically just isn’t practical these days and that’s the core method at the heart of ETL. This process is too slow to meet the demand for convenience. It can also reduce the overall consistency of the data in question, which introduces all kinds of extra costs and unnecessary complexities.

Enter Mainframe Data Virtualization

While it will mean doing some serious digging into an application during your legacy modernization process, mainframe data virtualization is the way to go if you want the best possible analytics for your company’s data. Obviously, building this component from the ground up will be demanding too.

The key factor that makes all the difference in mainframe data virtualization is that it puts your company’s data right next to the analytics program you’re using to analyze it. Doing this means having specialized processors available to operate with a mainframe’s own central processors.

By compiling and analyzing data this way, there are no charges to be considered as far as software licenses go. Also, data integration won’t have a negative effect on MIP’s capacity. This means the production of data on your company’s mainframe remains undisturbed while you benefit from a dramatic reduction in TCO.

Other issues you may be worried about, like consistency, latency and accuracy compared to ETL methods, are not going to come up in the least. In fact, problems you may have experienced with them when you used to rely on ETL are completely eliminated altogether.

BI and analytic tools can be used to easily access the information you need without having to access unfamiliar mainframes that would only inconvenience you.

The end product

As a result, your company can greatly expand their operation thanks to being able to effectively assess all the data constantly being created. In the past, it would have been a laborious process to do so. By the time you discovered what was going on, it would most likely be too late. On top of that, it could be said that it would’ve been unsafe to try using the data you harvested because its accuracy would have been in question.

Furthermore, these analytics aren’t just available to those IT experts capable of maneuvering an ETL protocol in order to get them. Your employees can have the benefit of using them as well. Day-to-day projects will be handled better going forward, but think about the next time you take on legacy modernization.

There are a number of factors that help improve legacy modernization protocols, but none compare to having solid information in your hands. Now, you can.

You could even hand important data over to your customers. B2B relationships will definitely improve thanks to mainframe virtualization and not just for marketing purposes. There’s no telling how much more you’ll be able to do once you can integrate all the info you have on various clients into one aggregate to be mined.

Mainframe data virtualization is the future of analytics. Fortunately, thanks to modernization, your legacy software can begin benefiting from it now, making it possible for your business to do more than ever before.

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