Page 205 - Building Big Data Applications

Chapter 11 Data discovery and connectivity 205

FIGURE 11.8 Frustrated customer; angry executive.

If we do not meet these compliance requirements, we will end up with fines and
issues that need to be managed, and the mishandling of data which is out of compliance
and can be breached or hacked easily means you will have frustrated consumers and
angry executives (Fig. 11.8).
How do we get over this log jam? There are several issues to be answered here

Catalog of all data
Catalog with systems info
Catalog of current data architecture
Catalog of new data architecture
Catalog of flexible design

This is where the next generation of technologies comes into play with artificial in-
telligence and machine learning built into data management architecture. Lots of
buzzwords? No, the real-life industry is gravitating toward this point as we need to
exceed human boundaries of performance and make it machine driven. Machines can
work 24 7 365 and do not need a break or a holiday. They can learn with Neural
Networks and perform tasks which are mundane including data profiling, data format-
ting, rules engine execution, and more. The delivery of services by these technologies has
broken several grounds for data management and even has solved the long-standing
desire to have a single platform to manage the data lifecycle from a catalog and meta-
data perspectives. There are several growth areas as the improvements are done with the
solutions and we will see further evolutions for a positive change in the world of data.
There is a lot of hype over artificial intelligence (AI) and machine learning (ML) today
than ever before. We have reached a tipping point in terms of the infrastructure, the data
processing and analytics ecosystems which is a driver for this hype, which is the next
reality that we will undertake across enterprises. Understanding how your company can
really make use of them can be a bewildering experience. The industry speak tends to
focus on the technical minutiae, making it difficult to see the relevance to your orga-
nization or to see your organization as an eligible candidate to adopt and apply AI. The
problem generally is not you or your organization, it is the level of the coverage and the
focus level of the industry itself. While looking at AI from the perspective of a data