GreyMatterz | Data Curation

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Grey Matterz
DATA-DRIVEN, FUTURE-READY
DATA CURATION
In the words of a great author Maria Popova
"Curation is a form of pattern recognition -
pieces of information or insight which over time
amount to an implicit point of view."
Let's now dive more into learning about data
curation but first let's understand what data
curation is in a real sense' ?
In today's world data is available at different
platforms public or private in very large volumes
but to find useful and required information is
like finding a needle in a haystack and that's
where data curation comes in handy.
It is the process of finding meaningful
information through that huge pile of data
curation. In simpler words it is creating,
organizing and maintaining datasets in a way that
can be used by the users to find useful
information efficiently and effectively.
Now let's go through the key components of Data
curation that will help in enhancing the process.
Data cleansing
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.' Grey Matterz
DATA-DRIVEN, FUTURE-READY
Purpose - The main aim in data cleaning is
identifying and removing the inconsistencies,
errors or any other inaccuracies in the dataset
and make the data set more clean and consistent
throughout the whole dataset.
Process - While performing cleansing of data
first you need to start with removing the
duplicate or dummy data in the set and also check
for any data that doesn't match the pattern and
remove that data instantly. This needs to be done
so that while showing the insights or any
graphical picture of your data it will provide
the accurate results and will be more efficient
in predicting the other similar results related
to that dataset.
Data Organization
Purpose - Organizing the data according to
different use cases or any other filters like
based on geo location or in any other logical
based filters that can help the user to get the
required information in less time because of the
indexing and filtering.
Process - For organizing the data make sure to
check the type, source or relevance of the
information that the user could search for and
can vary according to different needs of users
from different locations or fields of service.
Data Annotation
Purpose - So annotation is like adding the
meaning and description to different data to make
it more easy for the user to understand the
dataset more easily even if the user is new to
development or data analytics he/she will easily
understand about the dataset.
Process - This involves setting or arranging the
data with appropriate titles or description so
that others who will use that particular data set
will understand and easily find the required data.
Meta Data Management
Purpose - The metadata provides essential
information about the dataset like the source,
ownership, versioning or the structure.
Process - This type of data is mostly generated
automatically like timestamps, file formats or
data lineage(where the actual data came from and
when was edited if so).
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.' Grey Matterz
DATA-DRIVEN, FUTURE-READY
But even after knowing this, data curation has
many benefits in the real world.
Let's know about some of the benefits of data
curation amongst many of them.
Enhanced Data Usability
Improved Accessibility - Data curation helps in
cleaning and arranging the data in such a way
that it can be easily accessed by the users and
increases the readability and understandability.
Consistency and relevance - Through data curation
one arranges the data in a more sorted and
filtered way such that the same kind of data is
grouped together and we don't have to go through
all the data at once instead find the accurate
filter and group according to our requirement.
2. Improved Decision Making
Accurate Insights - Effective curation helps in
getting accurate, reliable and comprehensive data
for analysis, helping in better insights.
Faster decision cycles - Having accurate data
with better insights helps in taking business
decisions more smoothly and effectively within
less time.
3. Regulatory Compliance
Data Governance - Well curated data often aligns
with all compliances worldwide so that there must
not be any issues with the rules or regulations
like GDPR, HIPAA, etc.
Reduced risk - With efficient curation of data
all data is well recorded and traceable and
different organizations can meet the compliance
obligations more easily.
4. Data Longevity
Sustainability - Well curated data always serves
the purposes in the long run and helps users or
the analysts for many years to follow.
Data reuse - As the data is valid for longer time
durations it can be used again and again and
reduces the time in analysis.
After learning all about data curation let's see
some of the real life examples where the
organizations are using data curation techniques
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Grey Matterz DATA-DRIVEN, FUTURE-READY
Healthcare NIH(National Institute of Health)
Finance JP Morgan Chase Academia Harvard
Database Retail Walmart Telecommunication ATT
Our Services
Data Cleaning and Transformation Ensuring your
data is clean, accurate and in a consistent
format. Metadata Management Implementing robust
metadata management practices to improve data
discoverability and usability. Data
Enrichment Enhancing your data with additional
context and attributes to increase its value and
utility.
Data Cataloging
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.' Grey Matterz
DATA-DRIVEN, FUTURE-READY
Creating comprehensive data catalogs to
facilitate easy data access and management.
Data Quality Monitoring
Continuously monitoring and improving data
quality to maintain high standards.
Conclusion
In this data heavy world where today the most
important wealth is data and that too having
factual and correct data. Data curation is no
longer a luxury but a necessity for every
business which needs to excel in this ever
growing and data consuming world. And with proper
structure and techniques this can help to shape
any business and provide a vast amount of
opportunities in the near future with AI and ML
on boom.
At last but not the least I would like to end
with another interesting quote by another great
author "Where data is smoke there business is
fire." By investing in right data curation
techniques and methods it can fuel the fire of
innovation in who knows how many and provide a
new way about how we look at the datasets.