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DATA WRANGLING: WHAT IT IS & WHY IT’S IMPORTANT
* 19 Jan 2021
Tim Stobierski Author Contributors
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Businesses have long relied on professionals with data science and analytical
skills to understand and leverage information at their disposal. With the
proliferation of data, due to the development of smart devices and other
technological advancements, this need has accelerated.
It’s impossible to choose a single data science skill that’s most important for
business professionals. One thing that's certain, however, is that insights are
only as good as the data that informs them. This means it’s vital for
organizations to employ individuals who understand what clean data looks like
and how to shape raw data into usable forms. This is where data wrangling comes
into play.
Below is an overview of what data wrangling is, its key steps, and why it’s
crucial for business.
--------------------------------------------------------------------------------
Free E-Book: A Beginner's Guide to Data & Analytics
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WHAT IS DATA WRANGLING?
Data wrangling—also called data cleaning, data remediation, or data
munging—refers to a variety of processes designed to transform raw data into
more readily used formats. The exact methods differ from project to project
depending on the data you’re leveraging and the goal you’re trying to achieve.
Some examples of data wrangling include:
* Merging multiple data sources into a single dataset for analysis
* Identifying gaps in data (for example, empty cells in a spreadsheet) and
either filling or deleting them
* Deleting data that’s either unnecessary or irrelevant to the project you’re
working on
* Identifying extreme outliers in data and either explaining the discrepancies
or removing them so that analysis can take place
Data wrangling can be a manual or automated process. In scenarios where datasets
are exceptionally large, automated data cleaning becomes a necessity. In
organizations that employ a full data team, a data scientist or other team
member is typically responsible for data wrangling. In smaller organizations,
non-data professionals are often responsible for cleaning their data before
leveraging it.
DATA WRANGLING STEPS
Each data project requires a unique approach to ensure its final dataset is
reliable and accessible. That being said, several processes typically inform the
approach. These are commonly referred to as data wrangling steps or activities.
1. DISCOVERY
Discovery refers to the process of familiarizing yourself with data so you can
conceptualize how you might use it. You can liken it to looking in your
refrigerator before cooking a meal to see what ingredients you have at your
disposal.
During discovery, you may identify trends or patterns in the data, along with
obvious issues, such as missing or incomplete values that need to be addressed.
This is an important step, as it will inform every activity that comes
afterward.
2. STRUCTURING
Raw data is typically unusable in its raw state because it’s either incomplete
or misformatted for its intended application. Data structuring is the process of
taking raw data and transforming it to be more readily leveraged. The form your
data takes will depend on the analytical model you use to interpret it.
3. CLEANING
Data cleaning is the process of removing inherent errors in data that might
distort your analysis or render it less valuable. Cleaning can come in different
forms, including deleting empty cells or rows, removing outliers, and
standardizing inputs. The goal of data cleaning is to ensure there are no errors
(or as few as possible) that could influence your final analysis.
4. ENRICHING
Once you understand your existing data and have transformed it into a more
usable state, you must determine whether you have all of the data necessary for
the project at hand. If not, you may choose to enrich or augment your data by
incorporating values from other datasets. For this reason, it’s important to
understand what other data is available for use.
If you decide that enrichment is necessary, you need to repeat the steps above
for any new data.
5. VALIDATING
Data validation refers to the process of verifying that your data is both
consistent and of a high enough quality. During validation, you may discover
issues you need to resolve or conclude that your data is ready to be analyzed.
Validation is typically achieved through various automated processes and
requires programming.
6. PUBLISHING
Once your data has been validated, you can publish it. This involves making it
available to others within your organization for analysis. The format you use to
share the information—such as a written report or electronic file—will depend on
your data and the organization’s goals.
THE IMPORTANCE OF DATA WRANGLING
Any analyses a business performs will ultimately be constrained by the data that
informs them. If data is incomplete, unreliable, or faulty, then analyses will
be too—diminishing the value of any insights gleaned.
Data wrangling seeks to remove that risk by ensuring data is in a reliable state
before it’s analyzed and leveraged. This makes it a critical part of the
analytical process.
It’s important to note that data wrangling can be time-consuming and taxing on
resources, particularly when done manually. This is why many organizations
institute policies and best practices that help employees streamline the data
cleanup process—for example, requiring that data include certain information or
be in a specific format before it’s uploaded to a database.
For this reason, it’s vital to understand the steps of the data wrangling
process and the negative outcomes associated with incorrect or faulty data.
Are you interested in improving your data science and analytical skills? Learn
more about Data Science for Business and our other online analytics courses, and
discover how you can use data to generate insights and tackle business
decisions.
ABOUT THE AUTHOR
Tim Stobierski is a marketing specialist and contributing writer for Harvard
Business School Online.
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vary slightly from program to program, but all ask for some personal background
information. You can apply for and enroll in programs here. If you are new to
HBS Online, you will be required to set up an account before starting an
application for the program of your choice.
Our easy online application is free, and no special documentation is required.
All applicants must be at least 18 years of age, proficient in English, and
committed to learning and engaging with fellow participants throughout the
program.
After submitting your application, you should receive an email confirmation from
HBS Online. If you do not receive this email, please check your junk email
folders and double-check your account to make sure the application was
successfully submitted.
Updates to your application and enrollment status will be shown on your
Dashboard. We confirm enrollment eligibility within one week of your
application.
DOES HARVARD BUSINESS SCHOOL ONLINE OFFER AN ONLINE MBA?
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No, Harvard Business School Online offers business certificate programs.
WHAT ARE MY PAYMENT OPTIONS?
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We accept payments via credit card, Western Union, and (when available) bank
loan. Some candidates may qualify for scholarships or financial aid, which will
be credited against the Program Fee once eligibility is determined. Please refer
to the Payment & Financial Aid page for further information.
We also allow you to split your payment across 2 separate credit card
transactions or send a payment link email to another person on your behalf. If
splitting your payment into 2 transactions, a minimum payment of $350 is
required for the first transaction.
In all cases, net Program Fees must be paid in full (in US Dollars) to complete
registration.
WHAT ARE THE POLICIES FOR REFUNDS AND DEFERRALS?
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After enrolling in a program, you may request a withdrawal with refund (minus a
$100 nonrefundable enrollment fee) up until 24 hours after the start of your
program. Please review the Program Policies page for more details on refunds and
deferrals. If your employer has contracted with HBS Online for participation in
a program, or if you elect to enroll in the undergraduate credit option of the
Credential of Readiness (CORe) program, note that policies for these options may
differ.
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