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How A Converged Architecture Improves Big Data Applications
May 26, 2016 News

“You can’t handle the truth.” We all remember Jack Nicholson’s iconic words in A Few Good Men as a seminal moment in pop culture. Yet, that statement has a lot of relevance for companies and their data architecture. When they look at their architecture, businesses should be asking themselves: “Can we handle the truth?”

Big data has created a new reality in which companies have more data at their disposal than ever before. However, just because you have more data doesn’t mean you’re any closer to discovering the real truth. If you don’t have the proper infrastructure in place to seamlessly integrate all your data, from all your sources, you risk not only producing data siloes, but also making decisions on partial truths. For businesses of all sizes, this can have a dramatic impact. For instance, a clothing chain might be able to review customer transactions, but not their browsing history, meaning they have only limited insight into customer behavior. Having customer info spread across multiple applications, none of which are speaking to each other, can greatly impair the effectiveness of your data analytics.

There is an alternative. A converged architecture changes basic assumptions about applications. Instead of using the current application paradigm in which batch, transactional, and streaming applications are all separated, with a converged architecture you can employ all or parts of your application patterns into one to get to a single version of truth. The availability of a converged architecture allows companies to foster creativity and spur innovation. Let’s take a look at how employing this architecture can improve your business.

The Trend Toward Converged Everything

The idea of convergence is highly related to the notion of creating a product. As I pointed out in “Big Data 2.0”, my view is that the primary way that big data and advanced analytics technology to penetrate the wider business world, that is the early and late majority segments, will be through products as opposed to general purpose platforms. The early and late majority segments just don’t have the engineering talent to cobble together a system that fits their needs perfectly and evolves to meet new needs from a general purpose tool. They need a product that fits their use case and can be configured to get the job done.

The idea of convergence is another way to talk about an advanced form of productization. Separate entities must exist before they can be converged. For example, SAP and Oracle created their massive business suites by converging lots of separate applications into a larger integrated package. The goal was to productize the integration between all of the TLA applications that were combined into the suites such as ERP, CRM, MRP, HRM, SCM, and so on. Of course, convergence is a lofty goal and many would say that these business suites didn’t really converge into that pretty of a product. But given that SAP and Oracle are two of the top three software companies in the world, perhaps convergence did succeed just a little bit.

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