Making Data Better – Resolving Inefficiencies in Data Preparation

1
Making Data Better Resolving Inefficiencies in
Data Preparation
2
In order to ensure a data analyst in your
organization is provided with the right data, it
needs to have a credible source, valid and
verified, non-problematic metadata, and most
importantly consistent data quality of a high
level. It becomes hard to trust your data when
you constantly come across discrepancies that get
in the way of real data science tasks.
3
Steps to take towards increasing the efficiency
of your data
4
Organize Your Data
The best way to make data usable in shorter
periods of time with minimal redundancies and
discrepancies, is to organize your data. It might
sound easy enough, but as we mentioned above,
organizing the data you think you need is a
trial-and-error process that ultimately results
in loss of time and money, therefore building an
archive of all the data you could possibly want
and have is a mammoth task, especially when you
want to also be able to have some command over
said data.
5
Data Profiling and Cataloguing
There are plenty of different avenues of action
you can take towards organizing and arranging
your data, including data profiling and
cataloging. Nevertheless, data prepping has been
known to take up a crucial 80 of a data
scientists time, and the lack of good quality
data that can be trusted by every member of your
team acts as a hindrance towards enterprise data
management, especially when it comes to managing
employees and sharing one particular vision for
the company.
6
Appropriated Best Data
Enterprise data management is a well-known term
that encompasses the process of ensuring you have
the best possible data in order to maximize
workflow and streamline communication within your
organization, resulting in the improvised ability
to make key business decisions, both individually
and collaboratively.