B2E Data Blog

Why Dirty Data is Killing Your Data Marketing Results

Jun 15, 2021 3:34:26 PM / by Keith Snow

By now, it’s likely your company data plays an important role in powering performance and
results. Good data shines a light on what drives core areas of business, such as how and where
you acquire customers, what your “ideal” customer looks like, which products and services
propel growth and with who, how customers engage with your brand … and the list goes on.

Companies need deep and insightful customer intelligence to be competitive today, but the
insights are only as good as the data. Quality counts! Poor data can be worse than no data if it
leads to bad decision-making that hampers your ability to attract customers, or worse yet,
jeopardizes the ones you already have.

Common Types of Dirty Data
More data isn’t better if it suffers from poor data hygiene. “Dirty data” is way to describe
databases that contain errors, outdated information, duplicate information or unstandardized
formatting.

Don’t take offense if it sounds like we’re “data name-calling.” Dirty data is the industry term for a
problem that affects organizations of all sizes across all industries. As businesses collect more
and more information, it becomes a greater challenge to store, transform, and maintain it in a
way that yields accurate strategic insights.

Here are some of the common dirty data problems your company database may suffer from.

  1. Duplicate data. With corporate data amassing through multiple systems and points of
    entry, it’s easy for duplicates to accumulate. Carbon copies are one form of duplication,
    but duplicates with non-matching records (such as one that’s up-to-date and one that’s
    not), can be very problematic. They prevent accurate customer understanding, wreck
    your personalization and waste marketing efforts.
  2. Unstandardized data. Another issue that arises from data aggregated through various
    portals are inconsistencies in the way the information is formatted. Even if you are
    collecting the right information accurately, it cannot be properly analyzed across the
    numerous non-standardized formatting variations. This hurts the validity of your analytics
    and makes it difficult to effectively create targeted customer segments.
  3. Inaccurate data. Inaccuracies can arise in a database for several reasons. Over time,
    simple spelling errors, populating data into the incorrect field, outdated information, and
    poor collection methods wreak havoc on the integrity of your data. The cornerstones of
    effective data strategy are accuracy, targeting, and personalization – all of which are
    nearly impossible with inaccurate information.
  4. Incomplete data. Though more data isn’t necessarily better, you need to accumulate
    enough data to yield deeper insights and generate highly tailored and relevant
    campaigns. A database with a lot of blanks can’t accomplish that. This is one of the most
    common dirty data issues that inhibits organizations from having a complete view of their
    customers and important trends.

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Marketers Don’t Have Time to Deal with Dirty Data
Organizational silos and decentralized data collection mean that dirty data problems can fly
under the radar for years. When they are discovered, it may not be a simple fix. A 2020 State of
Data Science research report showed that data scientists spend nearly half their time on data preparation and management tasks

When it comes time to put data to work in revenue-generating campaigns, most marketers want
to spring quickly into action and capitalize on valuable opportunities. They simply don’t have the
time, bandwidth, or expertise to deal with tedious and time-consuming dirty data problems that
are sure to diminish the anticipated results.

As a standard practice, B2E continuously runs data hygiene on customer data. This includes
practices such as address standardization and verification, deceased matching, mover
identification, email hygiene and verification. This means whenever you need to put your data to
work, you’ll have confidence in the underlying information.

There is a lot that can be done to turn bad data into good! Contact the data scientists at B2E to
discuss how we can help bring order and clarity to your information.

Tags: dirty data, data hygiene

Keith Snow

Written by Keith Snow

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