Not all Data Management Services Are Equal

The amount of time it takes to go from disconnected data to actionable insights is extreme. So, it is critical to harness business data for effectual decision-making.

 

Many organizations struggle with the accuracy of data. Bad or Dirty Data refers to information that can be erroneous, misleading, and without general formatting.

 

Unfortunately, no industry, organization, or department is immune to it. If not acknowledged and fixed early on, bad data can cause serious coercions.

These slower processes can lead to missed opportunities and lost revenue. 

Gartner research indicates that the “average financial impact of poor data quality on organizations is $9.7 million per year.”

Dirty data has many consequences.

 

Not only does it prevent marketing campaigns from performing, but bad data also costs sales reps their time and energy to fix. That’s additional lost value for every dollar spent.

 

For organizations that want to make the most out of their marketing campaigns, having good data is essential.

Here are a few of the big offenders:

Lost Revenue

Failing to be able to communicate with people can result in lost revenue. That money can add up over time, especially depending on how long it takes businesses to clean up and manage their databases.

Misinformed Decisions

Data-driven decision making is a smart way for companies to make large-scale choices. But it’s impossible without accurate information.

Risk to Brand's Reputation

Accurate data ensures a smooth and effective workflow, so no more bombarding customers due to duplicate contact data.

Lack of competitive differentiation

Poor marketing results along with time wasted following up on the wrong leads reduces sales and marketing productivity and increase marketing software costs, and can damage company reputation.

Bad Data Recap

Missing Data: Empty fields that should contain data.

 

Wrong or inaccurate data: Information that is not entered correctly or maintained.

 

Inappropriate data: Data that’s entered in the wrong field.

 

Non-conforming data: Data that is not normalized as per the system of records.

 

Duplicate data: A single Account, Contact, Lead, etc. that occupies more than one record in the database.

 

Poor data entry: Misspellings, typos, transpositions, and variations in spelling, naming or formatting.

Our friends over at ConnextDigital put together this awesome infographic. Check it out.

The Real Cost of Bad Data (And How to Cleanse It)

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