  AI application developers typically begin with a dataset of interest and a
vision of the end analytic or insight they wish to gain from the data at hand.
Although these are two very important components of an AI workflow, one often
spends the first few weeks (sometimes months) in the phase we refer to as data
conditioning. This step typically includes tasks such as figuring out how to
prepare data for analytics, dealing with inconsistencies in the dataset, and
determining which algorithm (or set of algorithms) will be best suited for the
application. Larger, faster, and messier datasets such as those from Internet
of Things sensors, medical devices or autonomous vehicles only amplify these
issues. These challenges, often referred to as the three Vs (volume, velocity,
variety) of Big Data, require low-level tools for data management, preparation
and integration. In most applications, data can come from structured and/or
unstructured sources and often includes inconsistencies, formatting
differences, and a lack of ground-truth labels.
  In this report, we highlight a number of tools that can be used to simplify
data integration and preparation steps. Specifically, we focus on data
integration tools and techniques, a deep dive into an exemplar data integration
tool, and a deep-dive in the evolving field of knowledge graphs. Finally, we
provide readers with a list of practical steps and considerations that they can
use to simplify the data integration challenge. The goal of this report is to
provide readers with a view of state-of-the-art as well as practical tips that
can be used by data creators that make data integration more seamless.
