In
the data collection process encompassed in Big Data, erroneous, duplicate or
inaccurate data can be strained. That is dirty data. We delve into it
and how to solve it with data
wiping.
Dirty
data, translated into Spanish as dirty data, is a set of erroneous data that is
part of Big Data. They sneak in during the data collection process and
make processing difficult. For the conclusions drawn to be true, it is
important that an exhaustive data wiping process is carried out, in which all
unreliable information is discarded. We explain more about what dirty data is and
how to combat it with data wiping.
Dirty data explained
As we
explained to you in this article about Big Data , this technology
consists of the collection, analysis and processing of a massive amount of
structured, semi-structured and unstructured data. The main idea: convert all this
data into quality information that supports the
decision-making of a company.
To
guarantee the quality of this information it is necessary that the analysis and
processing be correct, but, as in everything, the raw material must also be of
quality. In this case, the raw material is the data, which must be truthful, correct
and reliable.
Therefore,
after the collection, it is essential to eliminate the trash, the data that is
not real, the lies, the duplications, the outdated, the errors, the inaccuracies
or inaccuracies, to do a wiping to guarantee that it works with raw material of
quality. All that needs to be suppressed is dirty data.
How dirty data comes up
Dirty data
can be the result of an intentional forgery, but also carelessness
or a lie by the
user. Imagine that you have a landing page as part of a digital campaign
of a company and that it includes a contact form with basic information, for
example, name, age, e-mail and telephone number.
Only with
these three fields can multiple problems arise, for example:
- A typo when
typing the phone number.
- A false email,
on purpose, as a way for the user to avoid commercial information that the
company may send them later.
- A form that,
due to one person's mistake, is filled out twice with the same information.
- A lie when
telling the age.
In fact,
studies indicate that 8% of users fill out a form online, as this IpMark
article states . This has an impact on all company strategies. If
the Data Wiping of the dirty data is
not correct, the decisions will be made around information that is not real,
ergo, they will be wrong and basically none of this will make any sense.
Examples of bad strategies based on
dirty data
As we
explained before, the fundamental use of Big Data is to improve the
decision making of a company. However, if the data is
false or erroneous, the information derived from its processing will also be
false. Investing in infrastructure and technology will do no good.
For
example, a company may use your information to improve its marketing campaigns. If the
definition of this audience is based on data from people who have not lied
about their age, the channels nor the messages of the marketing strategy will
be adequate.
This
affects not only the way of impacting that audience. Also to the knowledge
about what your specific needs are. For example, if a
company wants to better adapt its products or services to its target audience,
one of the keys is that it knows what age segment it belongs to. If that
information is wrong, the efforts made will be in vain or the potential for
results will not be exploited.
Dirty data and Data Wiping
Taking all
of this into account, business awareness about the importance of maintaining
accurate and up-to-date databases is growing. In this context, data wiping arises, a set
of tools and solutions that allow wiping dirty data in an automated way.
The
process consists of verifying a massive amount of data. It is about
doing an analysis that allows looking for duplicates, misprints, errors, etc.
that can be corrected automatically. Technologies included within
Artificial Intelligence enter this process, including Machine Learning.
In
addition, there are ways to reduce the chances of collecting erroneous data,
from the most basic, such as simplifying forms, to resorting to test questions,
identity verification systems and other developments that slow down data
extraction a bit but At the same time, they increase its reliability .
Benefits of Data Wiping
Wiping
dirty data through data wiping brings benefits both for companies that update
their databases and for potential clients of those companies. Thus, the
main advantages are:
- From the point of view of the company: a greater knowledge of the
market and of the target audiences allows developing more successful sales
strategies, with products, services, messages and channels that better
reach the target and, therefore, are more likely to conversion.
- From the user's point of view: if the company focuses its
campaigns, products and services on the customer, it will better satisfy
their needs, give a better response to their problems, and the customer
service and experience will be much more satisfactory for them.
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