Page 209 - Building Big Data Applications

Chapter 11 Data discovery and connectivity 209

improve their experience along the way. Your task goes beyond just making a
team, as you will also have to monitor their progress moving forward.
Data Accessibility and Culture
While three fourths of all businesses want to be data driven, only around 29%
can agree that they are good at connecting their analytics and data to actively
generate insights. If the data you have is not ready for you to get actionable in-
sights, unite your organization around that analysis, and make business de-
cisions based on that.
Data accessibility and culture are necessary for your organization because
accessible data enables you to focus on business decisions, move on quickly
and build an informed culture where data helps you make better decisions and
take better actions.
End-to-End AI Lifecycle Management
End-to-end AI lifecycle management relates to the management of data from its
extraction to when it is presented in the form of actionable insight. The process
entails different stages like the acquisition, storage, dissemination, learning and
implementation of the data. By implementing end-to-end management, you can
ensure that your data is always in safe hands.
Most new technologies, when they first emerge, are treated as curiosities. It’s the
nature of the beast. People are intrigued by technology in general and so, when a new
technology comes out, fascination abounds.
But there is a problem with that fascination phenomenon, and there is an issue with
putting a new technology on a pedestal. Doing so encourages practitioners and cus-
tomers alike to romanticize the science of it. Every cool capability, every technical
nuance is part of the technology’s mystique. While this is cool at the beginning, it in-
hibits the maturation process as interest in a technology picks up.
In the world of data management, the utility of applied AI is at least double that of the
average scenario. That may be a strong statement but consider that data is growing in
volume at incredible velocity while its management is being regulated at an ever-
growing rate. As the requirements grow (and grow) and the data grows with it, man-
agement and governance of that data cannot be done manually. The task is too
gargantuan. But the substance of data management involves careful inspection and
remediation, so how can it be carried out in any fashion other than a manual one?
In fact, AI is built for exactly such a situation: intelligent, judicious examination, and
execution, on an automated basis, at scale. Embedded AI is therefore the thing to search
for in a data management product, including data governance, data prep, and data dis-
covery. Look for it in these products and check to see how “real” it is. Ask your vendor how
they use AI and what functionality is driven by it. The more you know about embedded AI
in a product you are considering, the better a purchasing decision you will make.
The AI world’s counterpart to a prepared meal is a product with intelligent technology
embedded, where machine learning is used behind the scenes to drive or enhance the