Are Homemade Customer Analytics Applications Better?

Homemade apple pie beats the Entenmann’s variety, right? Well, only if there’s a good cook at the helm. The same principle holds true when building analytics applications. With an advanced analytics solution, IT teams can, for example, tailor their websites to a visitor in-real-time, delivering the right content at the right time. And your team may be perfectly capable of building an infrastructure and enterprise application.

But what happens when you have to build for a more specialized function, such as predictive behavioral analytics? More often than not, the necessary skill sets for building these kinds of applications fall outside the scope of your team. You’ll know at the time of deployment just how tall this order will be for your company’s IT coder. They will declare victory after they have created a number of scripts, yet still there is no product.

That’s why large organizations – including government agencies – get an advanced behavioral analytics solution to sit on top of their open-source big data stack. It’s that lattice top that every homemade apple pie needs. In this way, organizations have complete control over their data, proprietary models and implementation. There is no confidential or proprietary data that ever needs to be shared with an outside vendor. And with an open-source framework, your data scientists have the freedom to build models on top of it if they so wish.

So, the question is which applications should you build in-house and which you should buy. Enterprise analytics applications are designed for enterprise IT to build in-house, whereas customer analytics applications are acquired, as long as it is the right one – one that can sit on your data lake and is not a “black box” of proprietary technology.

Do you have a similar experience? Tweet at me or email me.