Business  

While businesses collect more and more of customer data, the ability to act on that data, instantaneously, to meet a customer need is difficult with separate data warehouses that do not provide a comprehensive, multi-dimensional view, nor change fast enough, to optimize the customer experience. One solution is mainframe data virtualization, eliminating the need to move data and helping to point to actionable results.

Mainframe data virtualization brings instant insight into a comprehensive view of your customer, driving revenue through repeat purchases, higher customer satisfaction, and customer acquisition. Our recent webinar, entitled Unlocking Revenue Opportunities with Mainframe Data Virtualization, with Bryan Smith, VP of Research and Development, and CTO, and Forrester’s Noel Yuhanna, Principal Analyst, serving Enterprise Architecture professionals explored and detailed how customer facing organizations must leverage insight instantly to maximize revenue from customers. Noel Yuhanna particularly noted that in the age of the customer empowered buyers demand new levels of customer obsession.

The age of the customer is fueling a high velocity of customer interactions that can lead to greater revenue opportunities. These customer interactions are growing exponentially in enterprises thanks to new online and offline channels, and the need for multiple customer facing organizations, i.e. marketing, service, and sales, to ensure the best customer experience.

However, there is a central problem in all enterprises. While the amount of customer data grows exponentially, the ability to act on that data, instantaneously, to meet a customer need is prevented by siloed data warehouses that do not provide a comprehensive, multi-dimensional view, nor change fast enough, to optimize the customer experience. This multi-dimensional view is not only transactional data, but behavioral, including social and all forms of service interactions with the customer. The complexity of gaining customer insight is further impacted by not transforming the data credibly into meaningful insight through today’s business intelligence tools. Ultimately, data needs to be fully integrated, secure, reliable, and always available, or what we refer to as instant insight.

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In Unlocking Revenue Opportunities with Mainframe Data Virtualization, Bryan and Noel bring together new and traditional elements that drive and transform data into instant customer insight – mainframe transactional data, structured and unstructured data in the Cloud, mobile, and business intelligence. Mainframe data virtualization enables data structures that are designed independently, to be leveraged together, in real time, and without data movement. Now architects and business intelligence professionals can work together to create data architectures that scale and extend to meet changing customer and business demands.

The webinar provided a use case on wealth managers responding to questions from clients on portfolio performance. Reviewing the performance of a portfolio is complex, given multiple investments, the buying and selling of investments, and calculating returns based on investment changes and reinvested dividends. Without mainframe data virtualization, providing an accurate response to clients involved accessing mainframe transactional data from multiple data stores, and then loading that data through multiple steps into a data warehouse for processing to ultimately assess performance. Most importantly, in this case, the client or customer experience is improved by delivering instant insight into portfolio returns that enables the wealth manager to meet the demanding and ever changing needs of clients.

The solution of mainframe data virtualization eliminates the need to move data and instead enables the synthesizing of data at the point in which it is available, providing direct access to line of business managers to answer customer requests instantaneously. For IT organizations and business intelligence professionals, no programming is involved, changes are not required of business intelligence tools, nor is the movement of data required.

This post originally appeared on Rocket Software, and is re-published with permission.

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Big Data  

The best virtualization software out there can transform relational data into non-relational data without a problem. Virtualization transforms your mainframe into an actual virtualization platform. This is an unbelievably cost-competitive way for getting to the information you need whenever you need it.

When you think about the important processes your company relies on, you may or may not touch on the way your data is managed at some point. If you’re like a lot of people, you might actually take this vital process for granted. There’s nothing particularly exciting about data management, especially when you compare it to things like marketing and sales funnels. Yet there is a lot of potential here for improving how your company operates. That’s why you should definitely invest in data virtualization. No matter what line of work you’re in, this technology could make all the difference.

Why the Current Way You’re Handling Data Is on the Way Out

Don’t get us wrong. While the way you’re currently doing things with your data is far from ideal, it’s not like you’re alone. Most companies are still operating like they were a decade or so ago where their data is concerned. Like we mentioned, it’s not as though this type of thing gets the same attention or is treated with the same kind of enthusiasm as other aspects of business.

Still, let’s look at why the traditional method of accessing information in your mainframe just won’t cut it for much longer.

When you rely on data management via yesterday’s systems, data movement and replication has to go through point-to-point integration. The same goes for intermediary servers and connectors. This creates unnecessary hurdles where there are already enough in the first place.

Furthermore, the future isn’t bright for this type of approach. For one thing, it costs a lot of money to handle data in this way. That’s money that you could be using for a number of other activities. On top of that, a lot of your competitors may have already made the move to data virtualization or are in the process of doing so. This is going to hurt your ability to stay in competition with them.

When you consider how much data is being produced on a daily basis, though, it becomes even clearer that something has to change. In the next 10 years, for example, your company is going to produce all kinds of information. We’re not just talking about things like sales copy or posts on your social media pages. Think about the amount of information that comes with every transaction. Consider all the data that goes along with your sales funnel.

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This is what is referred to as Big Data. The sheer amount of information that is being created is truly hard to grasp and it’s only growing in size. You absolutely have to have software for dealing with this exponential growth. Obviously, you can expect this to add to your overhead as well.

Finally, your customers aren’t going to be impressed—or even understand—if you can’t get a hold of data you have in your own storage on demand. Whether it’s to answer a question they have about their account or facilitate a transaction, the point is the same: you have to be able to get that information or suffer losing credibility with your customers.

A Solution Is Here: Data Virtualization 

With this type of software, you can harvest data from various sources within your digital infrastructure. This is going to be key for most organizations, especially those that have been in business for 10 years or more and most likely have a fragmented storage facility at the moment.

You can overcome this type of problem with data virtualization, though. It’s a specific way of handling all the data your company produces and stores, including the type you may already have stored from years and years before even leveraging the platform.

With this type of software, you can harvest data from various sources within your digital infrastructure. This is going to be key for most organizations, especially those that have been in business for 10 years or more and most likely have a fragmented storage facility at the moment.
Best of all, you can get a hold of this data, spread out though it may be, from a single interface. Basically, you just implement a search and the software handles separating and abstracting the results you need.

Obviously, this provides you with a high degree of flexibility and adaptability. Whatever your interior systems may currently look like, whatever the type of data is that you’ve been storing, how you’ve been doing it or how you use it, you can make these platforms work in the best possible way for harvesting it.

No longer do you have to struggle with the inconvenience of physically moving the data you want. From now on, the metadata necessary for creating a virtual view of your company’s data sources will always be available. As a result, you get a faster and more agile method for accessing and combining data as necessary.

Sources you can now run through whenever you like include:

• Mainframe
• Cloud
• Distributed
• Big Data

Whether you have them now or know you will in the future, virtualization is the solution you need for conducting productive searches.

Why Virtualization Works

There are many other reasons to love what virtualization has to offer. The main thing it does that the traditional method can’t do, though, is transform your mainframe into an actual virtualization platform. This is an unbelievably cost-competitive way for getting to the information you need whenever you need it.

That’s to say nothing of the better security you’ll get too. Obviously, you don’t want to invest in a solution that can find your info, but makes it easier for prying eyes to do so as well.

Yet, virtualization is still the more affordable option when compared to the way most companies are still doing things. That’s even when you take into consideration the growth of Big Data we’re all about to see.

As we mentioned, formats don’t really matter anymore. The best virtualization software out there can transform relational data into non-relational data without a problem. Again, it doesn’t matter where your data is located either. By turning your mainframe into a platform that makes virtualization possible, you have an all-powerful, easy-to-use software at your fingertips.

Going forward, any company that wants access to its own information is going to need a reliable data virtualization platform for the job. Don’t be the company that still has dust on their methodology in the 21st century.

By Mike Miranda

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Author Bio - Mike Miranda writes about enterprise software and covers products offered by software companies like Rocket Software.about topics such as Terminal Emulation, Legacy Modernization, Enterprise Search, Big Data and Enterprise Mobility.

 Big Data  

Traditional approaches to data integration that rely on moving data, are struggling to handle the extreme volume and diversity of Big Data. Data virtualization has emerged to address the need for real-time, universal access to data, regardless of format or location.

No person would ever discount the importance that data plays in today’s business environment. This data has grown exponentially over the years—whether it’s in the form of streaming data, legacy data, or even operational data—and it is moving faster than ever before. All told, this data is completely transforming the face of business, and it is no incumbent upon businesses to have a comprehensive solution for dealing with it in order for those businesses to ensure their long-term success.

Dealing with all of this data presents a number of technical challenges. But, it also presents a number of tremendous opportunities. Further, not all of the data that businesses have to contend with these days is transactional. Some of it is machine-to-machine based data, as is the case with RFID tags, and some of it has to do with regulatory compliance, as is the case with data in the financial sector.

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All of this unstructured data has come to be known by the name of Big Data. No doubt you’ve heard a lot about that. But, Big Data is not the end-all-be-all of data. In fact, there is another form of data that deserves close attention: mainframe data. This data is incredibly important for vital business functions, and it moves in the same volume and at the same speed as Big Data. Mainframe data includes a number of different things, depending upon the business in question. For example, banks must deal with an incredible amount of this type of data in order to effectively handle the many transactions of their customers on a day-to-day basis.

Having ready access to this data is incredibly important for every business’ analytics and business intelligence. However, facilitating such ready access requires a great deal, namely the realignment of data closer to analytics. Also, such ready access requires that non-relational data and relational data be seamlessly blended together. To accomplish this, traditional methods of accessing this data, which require data to be physically moved, must be done away with.

No one can doubt the importance of offering this level of convenient access to data. Both decision makers and customers have been conditioned by our modern society to expect access to data on the fly. However, just because this expectation has been conditioned doesn’t mean that meeting that expectation is easy. There are a number of obstacles that must be overcome in order to facilitate this. Most importantly, a solution must allow for data from multiple sources to be integrated together and standardized, and the data must be made consistent across the business side of things and the customer side of things.

The way to accomplish this is through a solution that virtually combines data indiscriminately of where that data comes from. If this can be accomplished, then the BI tools and analytics tools that a business employs can be used to their maximum effectiveness. However, the majority of businesses do not deploy such a solution. Rather, they rely upon the outdated ETL method, which stands for Extract, Transform and Load. While this method may have been sufficient in the past, it isn’t sufficient any longer. Such a method fails to meet the need of timely access to data, as the data must be physically moved for the process to work. Further, the process of physically moving the data reduces the consistency of the data, introducing a number of additional costs and complexities for businesses.

Mainframe data virtualization is the answer, as it places data next to the analytics software used to analyze that data. It accomplishes this by diverting the process of data integration to specialized processors – IBM System Z processors – that operate in tandem with a mainframe’s central processors. There are no software license charges that need to be considered with this method, and MIPs capacity is not affected by the process of data integration. Because of this, the production of data on the mainframe is undisturbed and TCO is dramatically reduced.

The problems with latency, consistency, and accurateness experienced with ETL methods are not experienced with mainframe data virtualization. In fact, those problems are entirely eliminated. Such a method allows data to be easily accessed through and dealt with through BI tools and analytics tools, and the problem of dealing with unfamiliar mainframe environments is eliminated.

All told, this empowers the decision makers of businesses to meet their goals of mitigating risk and driving expansion. Timely and accurate data is put right in their hands, empowering these decision makers to successfully meet the demands of their customers, identify threats in the market place, and even to attack new business opportunities. Mainframe data virtualization is the future, and it leaves everything else in the dust.

By Mike Miranda

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Author Bio - Mike Miranda is a writer and PR person for Rocket Software.