Data helps the world move around, there is no argument about that. With increasing progress
and technology influencing our everyday lives, more information can be collected and
processed than before, making it easier than ever to access data.

However, upholding the quality of business data continues to be a significant challenge for
many firms, with a long list of factors leading corporations to suffer from their data’s efficacy.

We’ve outlined a list of seven common data quality problems in order to get a clearer
understanding of what you should not do while operating in data-driven environs:


The information used by most data scientists to develop, analyze, theorize, and forecast the
outcomes or results is always missing. The way data drips down to business analysts in big firms
from agencies, branches, sub-divisions, and finally, the teams that operate on the data-brings
information may or may not have straight access to the next user.

Poorly interpreted Data

Frequently, Data is incorrectly interpreted, creating considerable uncertainty about the
effective management approach. For instance, data that is categorized into the wrong section,
such as a business account being filed as a single individual's contact, can really screw it up in
the database and make it more difficult to comprehend and work through the entire thing.

Poor coordination

If you cannot scan the data quickly, you can find that it becomes substantially more difficult to
use. There are hundreds of ways that data can be interpreted via numerous operational
strategies and procedures.

Data Inconsistency

Inconsistency is a major sign that there is a data integrity concern when working with multiple
data sources. The same documents might appear in a database many times in certain

Duplicate Data is one of the main challenges for data-driven organizations and can reduce
revenue rapidly than any other data challenge.

Fragmented data

Most times, because data has not been properly integrated into the system or some files could
have been manipulated, there could be some missing variables in the remaining data. For instance, if an address does not have a zip code at all, the remaining details might be of little use, since it may be challenging to ascertain the geographical component of it.

Security of poor Data

Twenty percent of individuals declare that they will never consider doing business again with an
organization that refused to treat their data competently and healthily. There must still be
safeguards in place when dealing with consumer data to ensure that it will not be used for
theft, spam, and fraud, which would also assure the lack of potential renewal.

Poor Recovery of Data

People normally spend 30 percent of their time with data, just searching for the data they need.
Perhaps worse, in 40 percent of searches. Individuals can even locate the data that they were
searching for in the first instance.

Throughout these data challenges, the general thread to get your records in the best possible
state is that careful handling is the key. The perfect way to keep the data in order is by
incorporating a constructive data approach that will take care of all the common data quality
problems identified.


If you need help, iData is here! You can also check out the Getting Started with Data Quality
and Advancing Your Career in Data courses from the iData Quality Academy.

Author: Kate Strachnyi

Your Data Quality Primer: Everything you need to know

Welcome to your Data Quality Primer from the iData Quality Academy – in this useful guide you’ll find everything you need to know to improve your understanding of data quality, assorted into useful categories for you. You don’t have to read through it in order, you can jump right to the section you’re interested in!

Your Data Quality Primer: Everything you need to know

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